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Fernandez"},"flexible_column_cont":{"id":"flexible-column-container-7830db4512","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-1b93a68663",":itemsOrder":["contentfragment","flexible_column_cont"],":items":{"contentfragment":{"id":"contentfragment-9c3604c883","description":"This is a guide to get familiar with Snowflake's support for unstructured data","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis Quickstart is designed to help you understand the capabilities included in Snowflake’s support for unstructured data. Sign up for a free 30-day trial of Snowflake and follow along with this lab exercise. After completing this lab, you’ll be ready to start storing and managing your own unstructured data in Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake account\u003C/li\u003E\u003Cli\u003EBasic knowledge of SQL, database concepts, and objects\u003C/li\u003E\u003Cli\u003ERecommended to first complete \u003Ca href=\"/en/developers/guides/getting-started-with-snowsql/\"\u003EGetting Started with SnowSQL\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to access and store unstructured data\u003C/li\u003E\u003Cli\u003EHow to govern unstructured data\u003C/li\u003E\u003Cli\u003EHow to search for unstructured data using directory tables\u003C/li\u003E\u003Cli\u003EHow to securely share unstructured data\u003C/li\u003E\u003Cli\u003EHow to process unstructured data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA stage for storing and accessing files in Snowflake\u003C/li\u003E\u003Cli\u003EA user-defined function using Snowflake's engine to process files\u003C/li\u003E\u003Cli\u003EA secure view to share in the Snowflake Marketplace\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ENotice and Terms of Use\u003C/h2\u003E\n","\u003Cp\u003EThe data provided for this lab is an extract from the Enron email database made available by Carnegie Mellon University (\u003Ca href=\"https://www.cs.cmu.edu/~enron/\"\u003Ehttps://www.cs.cmu.edu/~enron/\u003C/a\u003E).\u003C/p\u003E\n","\u003Cp\u003EUse of the data provided is limited to this quickstart in connection with the Snowflake service and is subject to any additional terms and conditions on the Carnegie Mellon site.\u003C/p\u003E\n","\u003Cp\u003EBy accessing this data, you acknowledge and agree to the limits and terms related to the use of the Enron email data.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare your lab environment\u003C/h2\u003E\n","\u003Cp\u003EIf you haven't already, register for a \u003Ca href=\"https://trial.snowflake.com/\"\u003ESnowflake free 30-day trial\u003C/a\u003E.\nThe Snowflake edition (Standard, Enterprise, Business Critical, e.g.), cloud provider (AWS, Azure, e.g.), and Region (US East, EU, e.g.) do not matter for this lab. We suggest you select the region which is physically closest to you and the Enterprise Edition, our most popular offering. After registering, you will receive an email with an activation link and your Snowflake account URL.\u003C/p\u003E\n","\u003Ch3\u003ENavigating to Snowsight\u003C/h3\u003E\n","\u003Cp\u003EFor this lab, you will use the latest Snowflake web interface, Snowsight.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELog into your Snowflake trial account\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003ESnowsight\u003C/strong\u003E Worksheets tab. The new web interface opens in a separate tab or window.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EWorksheets\u003C/strong\u003E in the left-hand navigation bar. The \u003Cstrong\u003EReady to Start Using Worksheets and Dashboards\u003C/strong\u003E dialog opens.\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003EEnable Worksheets and Dashboards button\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/3_1.png\" alt=\"Enable Worksheets and Dashboards\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStore & Access Unstructured Data\u003C/h2\u003E\n","\u003Cp\u003ELet’s start by preparing to load the unstructured data into Snowflake. Snowflake supports two types of stages for storing data files used for loading and unloading:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-overview.html#internal-stages\"\u003EInternal stages\u003C/a\u003E store the files internally within Snowflake.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-overview.html#external-stages\"\u003EExternal stages\u003C/a\u003E store the files in an external location (i.e. S3 bucket) that is referenced by the stage. An external stage specifies location and credential information, if required, for the bucket.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECreate a Database, Schema, and Warehouse\u003C/h3\u003E\n","\u003Cp\u003EBefore creating any stages, let’s create a database and a schema that will be used for loading the unstructured data. We will use the UI within the Worksheets tab to run the DDL that creates the database and schema. Copy the commands below into your trial environment, and execute each individually.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role sysadmin;\n\ncreate or replace database emaildb comment = 'Enron Email Corpus Database';\ncreate or replace schema raw;\ncreate or replace warehouse quickstart;\n\nuse database emaildb;\nuse schema raw;\nuse warehouse quickstart;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAccess Unstructured Data Stored in an S3 Bucket\u003C/h3\u003E\n","\u003Cp\u003EThe data we will be using in this lab is stored in an S3 bucket.\u003C/p\u003E\n","\u003Ch4\u003ECreate an External Stage\u003C/h4\u003E\n","\u003Cp\u003EYou are working with unstructured PDFs that have already been staged in a public, external S3 bucket. Before you can use this data, you first need to create a Stage that specifies the location of our external bucket.\u003C/p\u003E\n","\u003Cp\u003E<aside class="warning">\nFor this lab we are using an AWS S3 bucket in us-east-1. To minimize data egress/transfer costs in the future, you should select a staging location from the same cloud provider and region as your Snowflake environment.\n</aside>\u003C/p\u003E\n","\u003Cp\u003EGrant the \u003Ccode\u003EPUBLIC\u003C/code\u003E schema access to the database, schema, and warehouse just created (This will be relevant in the next section).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role sysadmin;\ngrant usage on database emaildb to public;\ngrant usage on schema emaildb.raw to public;\ngrant usage on warehouse quickstart to public;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFrom the same worksheet you’ve been using, run this command to create an external stage called \u003Ccode\u003Eemail_stage\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse schema emaildb.raw;\n-- Create an external stage where files are stored.\ncreate or replace stage email_stage\n url = "s3://sfquickstarts/Getting Started Unstructured Data/Emails/mailbox/"\n directory = (enable = true auto_refresh = true);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can run this command to see a list of the files in your external stage.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Els @email_stage;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/4_1.png\" alt=\"List External Stage\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe can see that the stage contains various mailboxes from Enron users containing some files (which are text files) of various sizes. We can get a summary of the file corpus in the query results on the right-hand side. For the purpose of this quickstart, just a sample of 226 files out of 517,551 files has been extracted.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/4_2.png\" alt=\"External Stage Query Details\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe size of the files\u003C/strong\u003E: We can see that the file size ranges from 666 bytes to 1023 bytes, with the majority of the files closer to 1023 bytes. If we click on the size metric, we can get more detailed information. The total corpus size is 208,414 bytes, with an average size of 922 bytes. If we hover over the histogram, we can filter results based on file size.\u003C/p\u003E\n","\u003Ch3\u003EStore Unstructured Data in an Internal Stage\u003C/h3\u003E\n","\u003Cp\u003EAlternatively, you can store data directly in Snowflake with internal stages. Now, we want to create an internal stage and upload the same files while maintaining the directory structure of the various individual mailboxes on an internal stage.\u003C/p\u003E\n","\u003Ch4\u003ECreate an Internal Stage\u003C/h4\u003E\n","\u003Cp\u003ERun this command to create an internal stage called \u003Ccode\u003Eemail_stage_internal\u003C/code\u003E as follows.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse schema emaildb.raw;\ncreate or replace stage email_stage_internal\ndirectory = (enable = TRUE)\nencryption = (type = 'SNOWFLAKE_SSE');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E<aside class="warning">\nNote that we have to specify a server-side encryption on the internal stage. When using the default client-side encryption, the files will be returned encrypted and not readable when accessed through URLs (in Section 6).\n</aside>\u003C/p\u003E\n","\u003Ch4\u003EDownload Data and Scripts\u003C/h4\u003E\n","\u003Cp\u003EWe need to first download the following files to the local workstation by clicking on the hyperlinks below. The subsequent steps require \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql.html\"\u003ESnowSQL CLI\u003C/a\u003E installed on the local workstation where the lab is ran:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E[upload.snf](<https://sfquickstarts.s3.amazonaws.com/Getting Started Unstructured Data/Files/upload.snf>): SnowSQL Script which will upload the files to the internal stage just created.\u003C/li\u003E\u003Cli\u003E[mailbox.tar.gz](<https://sfquickstarts.s3.amazonaws.com/Getting Started Unstructured Data/Files/mailbox.tar.gz>): The actual email data.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EOnce downloaded, untar the contents of the files on your local workstation and note the full path including the mailbox parent directory of the tar archive. In the example below, this path is \u003Ccode\u003E/Users/znh/Downloads/quickstart/\u003C/code\u003E. The file can be untarred as follows.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd /Users/znh/Downloads/quickstart/\ntar xzvf mailbox.tar.gz\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EUpload Files using SnowSQL\u003C/h4\u003E\n","\u003Cp\u003EBefore opening terminal, find out your account identifier which for the trial account will be \u003Ccode\u003E<account-locator>.<region-id>.<cloud>\u003C/code\u003E. These fields can be retrieved from the URL of your Snowflake account.\u003C/p\u003E\n","\u003Cp\u003EFor example, the URL to access the trial account is \u003Ccode\u003Ehttps://xx74264.ca-central-1.aws.snowflakecomputing.com/\u003C/code\u003E. These are the values for the account identifier:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAccount Locator: \u003Ccode\u003Exx74264\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ERegion ID: \u003Ccode\u003Eca-central-1\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECloud: \u003Ccode\u003Eaws\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThere may be additional segments if you are using your own account part of an organization. You can find those from the URL of your Snowflake account. Please check the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#account-identifiers\"\u003ESnowflake Documentation\u003C/a\u003E for additional details on this topic.\u003C/p\u003E\n","\u003Cp\u003ENow open a terminal on your workstation and run the following SnowSQL command. You will be prompted for the password for the Snowflake user passed as a parameter.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Esnowsql -a <account-identifier> \\\n-u <user-id> -d emaildb -r sysadmin -s raw \\\n-D srcpath=<source-path> \\\n-D stagename=@email_stage_internal \\\n-o variable_substitution=true -f upload.snf\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EUsing the examples above for the path and the account identifier, and the userid \u003Ccode\u003Emyuser\u003C/code\u003E, the command would be:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Esnowsql -a xx74264.ca-central-1.aws \\\n-u myuser -d emaildb -r sysadmin -s raw \\\n-D srcpath=/Users/znh/Downloads/quickstart/mailbox \\\n-D stagename=@email_stage_internal \\\n-o variable_substitution=true -f /Users/znh/Downloads/quickstart/upload.snf\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe upload may take a few seconds depending on the speed of your internet connection. If the upload is successful, you should see data being uploaded in each subfolder, and you may see an output like the following on your terminal.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/4_4.png\" alt=\"SnowSQL PUT Files to Internal Stage\"\u003E\u003C/p\u003E\n","\u003Cp\u003EVerify if the files have been uploaded successfully by entering the following command on your Snowflake worksheet.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Els @email_stage_internal;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should now see an identical list of files uploaded to the internal stage. Make sure you see 226 files uploaded\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/4_5.png\" alt=\"List Internal Stage\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGovern Unstructured Data Access\u003C/h2\u003E\n","\u003Cp\u003EJust like structured and semi-structured data, access permissions to unstructured data in Snowflake can be governed using role-based access control (RBAC).\u003C/p\u003E\n","\u003Ch3\u003ECreate Role and Grant Access\u003C/h3\u003E\n","\u003Cp\u003ELet’s create a role that will provide read access to the external stage we’ve already created that contains PDFs.\u003C/p\u003E\n","\u003Cp\u003ELet’s first create the analyst role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role accountadmin;\ncreate or replace role analyst;\ngrant role analyst to role sysadmin;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen, switch back to \u003Ccode\u003Esysadmin\u003C/code\u003E role, and \u003Ccode\u003Egrant\u003C/code\u003E the role \u003Ccode\u003Eanalyst\u003C/code\u003E the rights to use the database \u003Ccode\u003Eemaildb\u003C/code\u003E, and the schema \u003Ccode\u003Eraw\u003C/code\u003E we just created earlier, as well as the ability to \u003Ccode\u003Eread\u003C/code\u003E from the stage.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Egrant usage on database emaildb to role analyst;\ngrant usage on schema emaildb.raw to role analyst;\ngrant usage on warehouse quickstart to role analyst;\ngrant read on stage email_stage_internal to role analyst;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo make sure this works as expected, make sure secondary roles are disabled for \u003Ccode\u003Eanalyst\u003C/code\u003E role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role analyst;\nselect current_secondary_roles();\nuse secondary roles none;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can verify the \u003Ccode\u003Eanalyst\u003C/code\u003E role only has access to read by listing the files in the internal stage, then trying to remove files from the external stage. When trying to remove, this should result in an error message.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role analyst;\n\n-- List files from the stage. This should execute successfully.\nls @email_stage_internal;\n\n-- Try to remove files from the stage. This should return an error.\nrm @email_stage_internal;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/5_1.png\" alt=\"Remove Internal Stage Analyst\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the subsequent sections, we will see a more fine-grained access control of the different unstructured files stored in Snowflake using scoped URLs.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECatalog Unstructured Data using Directory Tables\u003C/h2\u003E\n","\u003Cp\u003EOne of the main pain points in managing large repositories of unstructured data is the ability to access metadata easily on the numerous files, as well as retrieve files per some metadata attributes (last modified, file size, file patterns).\u003C/p\u003E\n","\u003Cp\u003EDirectory Tables are built-in tables in Snowflake that provide an up-to-date, tabular file catalog for external and internal stages. Directory Tables make it easy to search for and query files using SQL.\u003C/p\u003E\n","\u003Cp\u003EWe will first reset the session parameters to the correct role, virtual warehouse, database and schema:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role sysadmin;\nuse warehouse quickstart;\nuse schema emaildb.raw;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPrior to accessing the directory, it needs to be refreshed first for the files previously uploaded using the following command.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ealter stage email_stage_internal refresh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the following command to access the directory table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect *\nfrom directory(@email_stage_internal);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis will provide some detailed metadata information about the files stored in the stage including the \u003Ccode\u003ERELATIVE_PATH\u003C/code\u003E, the \u003Ccode\u003ELAST MODIFIED\u003C/code\u003E timestamp, the \u003Ccode\u003ESIZE\u003C/code\u003E, the \u003Ccode\u003EETAG\u003C/code\u003E as well as the \u003Ccode\u003EFILE URL\u003C/code\u003E (more information on this in the next section).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/6_1.png\" alt=\"Directory Table\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESearching Directory Tables\u003C/h3\u003E\n","\u003Cp\u003EWe could now query these files using some SQL commands. For example, let’s assume we want to identify all the emails from the mailbox belonging to the user \u003Ccode\u003Enemec-g\u003C/code\u003E. We could easily do this with the following SQL query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect *\nfrom directory(@email_stage_internal) \nwhere RELATIVE_PATH like '%nemec-g%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/6_2.png\" alt=\"Directory Table filtered path\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis query returns the 13 emails belonging to that user. Now let’s try to identify the 5 largest email text in the dataset. We can do that with the following query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect *\nfrom directory(@email_stage_internal)\norder by size desc\nlimit 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/6_3.png\" alt=\"Directory Table filtered size\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAutomatic Refresh\u003C/h3\u003E\n","\u003Cp\u003ESay you want the directory table to refresh whenever a file is added to your S3 bucket. This can be accomplished by using event notifications in S3. When a new file is added to a bucket, S3 will send a notification to Snowflake, and a Stream can refresh the directory table.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart, we won’t setup notifications in S3, but the command below is what you would use to create a stream on the directory table for a stage. More detailed documentation for automatically refreshing directory tables can be found \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-dirtables-auto.html\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create a table stream on directory table\ncreate stream documents_stream on directory(<stage_name>);\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EURLs for Secure Access\u003C/h2\u003E\n","\u003Cp\u003EIn the previous sections, we have seen how to store unstructured data in Snowflake, as well as access metadata about the unstructured files, and build queries to retrieve files based on metadata filters.\u003C/p\u003E\n","\u003Cp\u003EIn this section, we will look into how Snowflake offers access to the unstructured data through various types of URLs, as well as provide a more granular governance over unstructured data than at the stage level, as reviewed previously in Section 2.\u003C/p\u003E\n","\u003Cp\u003EThere are three different types of URLs that you can use to access unstructured data:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/build_scoped_file_url.html\"\u003E\u003Cstrong\u003EScoped URL\u003C/strong\u003E\u003C/a\u003E: A scoped file URL can be generated for a user to give the user short-lived, scoped access to the file without giving privileges on the stage.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/build_stage_file_url.html\"\u003E\u003Cstrong\u003EFile URL\u003C/strong\u003E\u003C/a\u003E: A file URL requires a user to be authenticated with Snowflake and requires the user to have read privileges on the stage.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/get_presigned_url.html\"\u003E\u003Cstrong\u003EPre-signed URL\u003C/strong\u003E\u003C/a\u003E: As the name suggests, pre-signed URLs are already authenticated. Users can simply download the files using pre-signed URLs.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe URL format for files is https://<account>.snowflakecomputing.com/api/files/<db_name>/<schema_name>/<stage_name>/<file_path>.\u003C/p\u003E\n","\u003Ch3\u003EScoped URL\u003C/h3\u003E\n","\u003Cp\u003EScoped URLs are encoded URLs that permit temporary access to a staged file without granting privileges to the stage. The URL expires when the persisted query result period ends (i.e. the results cache expires), which is currently 24 hours.\u003C/p\u003E\n","\u003Cp\u003EWe can generate a scoped URL of any file using the function \u003Ccode\u003Ebuild_scoped_url()\u003C/code\u003E. For example, let’s create a view which provides the scoped URL for files in the stage \u003Ccode\u003Eemail_stage_internal\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace view email_scoped_url_v\nas\nselect\n\trelative_path\n\t, build_scoped_file_url(@email_stage_internal,relative_path) as scoped_url\nfrom directory(@email_stage_internal);\n\nselect * from email_scoped_url_v limit 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/7_1.png\" alt=\"Scoped URL\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs explained previously, this URL will be valid for 24 hours. Snowsight retrieves the file only for the user who generated the scoped URL.\u003C/p\u003E\n","\u003Cp\u003EScoped URLs enable access to the files via a view that retrieves scoped URLs. Only roles that have privileges on the view can access the files. The scoped URL contents are all encrypted and doesn't give any information about the bucket, database or schema.\u003C/p\u003E\n","\u003Ch4\u003ESecure Access with RBAC and Scoped URL\u003C/h4\u003E\n","\u003Cp\u003ELet’s assume a scenario where the \u003Ccode\u003Eanalyst\u003C/code\u003E role needs access to all the files from the inbox of \u003Ccode\u003ENEMEC-G\u003C/code\u003E. We can build a dynamic view which will filter the output based on the role of the view user.\u003C/p\u003E\n","\u003Cp\u003EFirst let’s build an assignment table where various roles (or even users) can be assigned a given mailbox for further analysis.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace table assignment (mailbox string, role string, filter string);\n\ninsert into assignment values ('NEMEC-G','ANALYST','%nemec-g%');\ninsert into assignment values ('*','SYSADMIN','%');\n\nselect * from assignment;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs we can see from the query output above, the assignment table controls a 1-to-1 role-to-mailbox access mapping.\u003C/p\u003E\n","\u003Cp\u003EWe can now build a SQL view which will join with the assignment table to dynamically filter rows based on the role of the user executing the view and grant access to the view to the \u003Ccode\u003Eanalyst\u003C/code\u003E role:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace secure view analyst_file_access_v as\n    select\n\t    relative_path\n\t\t, build_scoped_file_url(@email_stage_internal,relative_path) as scoped_url\n    from directory(@email_stage_internal)\n\t    inner join assignment\n    where relative_path like filter\n  \t    and role = current_role();\n\ngrant select on analyst_file_access_v to role analyst;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet’s switch to role \u003Ccode\u003Eanalyst\u003C/code\u003E and query the view.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role analyst;\nuse warehouse quickstart;\nuse schema raw;\nselect * from analyst_file_access_v;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/7_2.png\" alt=\"Secure View Scoped URL\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick the \u003Ccode\u003Escoped_url\u003C/code\u003E corresponding to the file \u003Ccode\u003E1222.\u003C/code\u003E to your workstation and review the file locally.\u003C/p\u003E\n","\u003Cp\u003EIf we switch the role to \u003Ccode\u003Esysadmin\u003C/code\u003E and run the same query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role sysadmin;\nuse warehouse quickstart;\nuse schema raw;\nselect * from analyst_file_access_v;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis returns scoped URLs to access all the files.\u003C/p\u003E\n","\u003Cp\u003EAs we just experimented, scoped URLs are ideal for use in custom applications, providing unstructured data to other accounts via a share, or for downloading and ad-hoc analysis of unstructured data via Snowsight.\u003C/p\u003E\n","\u003Ch3\u003EFile URL\u003C/h3\u003E\n","\u003Cp\u003EA file URL is a permanent URL that identifies the database, schema, stage, and file path to a set of files, as opposed to the previous scoped URL where all this information is encrypted. A role that has sufficient privileges on the stage can access the files. It does not contain any authentication token. Authentication needs to be done when connecting through REST API. However, it works from our current authenticated Snowsight session.\u003C/p\u003E\n","\u003Cp\u003EThe following query will provide the file urls for the mailbox \u003Ccode\u003Earnold-j\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect\n    relative_path\n    , build_stage_file_url(@email_stage_internal,relative_path) as stage_file_url\nfrom directory(@email_stage_internal) \nwhere relative_path like '%arnold-j%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/7_3.png\" alt=\"File URL\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on any of the stage file URLs and download the file to your workstation.\u003C/p\u003E\n","\u003Ch3\u003EPre-signed URL\u003C/h3\u003E\n","\u003Cp\u003EPre-signed URLs are used to download or access files, via a web browser for example, without authenticating into Snowflake or passing an authorization token. These URLs are ideal for business intelligence applications or reporting tools that need to display the unstructured file contents.\u003C/p\u003E\n","\u003Cp\u003EPre-signed URLs are open but temporary. The expiration time for the access token is configurable when generating the URL. Any user or application can directly access or download the files until the expiration time is reached.\u003C/p\u003E\n","\u003Cp\u003EThe following query will generate the pre-signed URLs for the mailbox \u003Ccode\u003Ebeck-s\u003C/code\u003E. In the \u003Ccode\u003Eget_presigned_url()\u003C/code\u003E system function, we pass the parameter \u003Ccode\u003E300\u003C/code\u003E which represents the expiration time for the token in seconds (which is 300s, so 5 minutes in this case).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect\n    relative_path\n    , get_presigned_url(@email_stage_internal,relative_path,300) as presigned_url\nfrom directory(@email_stage_internal) \nwhere relative_path like '%beck-s%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClick on any cell value in the \u003Ccode\u003EPRESIGNED_URL\u003C/code\u003E column. This should give the actual full URL on the right hand side in the grey cell. (Click to Copy)\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/7_4.png\" alt=\"Pre-signed URL\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOpen a new tab in your web browser, and paste the copied URL. This should download the file on your workstation. As you can see, the URL is valid even when executed outside from the Snowsight UI.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPerform Natural Language Processing\u003C/h2\u003E\n","\u003Cp\u003EWe have so far reviewed how to store unstructured data files, retrieve them, provide granular access to the files through various URLs and through secure views. In this section, we want to extract additional attributes from the files. The entities extracted are going to be person names mentioned in the emails, as well as locations. The goal is to have these additional attributes used to enrich the file-level metadata for analytics.\u003C/p\u003E\n","\u003Cp\u003EUsing Snowpark runtimes and libraries, you can securely deploy and process Python, Java and Scala code to build pipelines, ML models, and applications in Snowflake. You can process unstructured files in Java (generally available), Python (public preview), and Scala (public preview) natively in Snowflake using Snowpark. In the following sections, you'll see how entity extraction can be done with Snowpark for both Java and Python.\u003C/p\u003E\n","\u003Ch3\u003EPython\u003C/h3\u003E\n","\u003Cp\u003EFor convenience, a number of popular open source third-party Python packages that are built and provided by Anaconda are made available to use out of the box inside Snowflake via the \u003Ca href=\"https://repo.anaconda.com/pkgs/snowflake\"\u003EAnaconda Snowflake channel\u003C/a\u003E. For any third-party packages not yet included, you can use stages to import. In this example, we'll use the punkt, averaged_perceptron_tagger, maxent_ne_chunker, and words language models from the \u003Ca href=\"https://www.nltk.org/\"\u003Enltk\u003C/a\u003E package, which have been uploaded and available on a Snowflake s3 public bucket.\u003C/p\u003E\n","\u003Ch4\u003ECreating a Python UDF\u003C/h4\u003E\n","\u003Cp\u003ECreating the UDF involves a few steps in Snowflake.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate the external stage mapping to the S3 bucket URI where the jar file is currently available. From the Snowflake worksheet, enter the following command:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role sysadmin;\nuse schema emaildb.raw;\n\ncreate or replace stage nltk_imports\nurl = "s3://sfquickstarts/Getting Started Unstructured Data/Files/"\ndirectory = (enable = true);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFrom the Snowflake worksheet, you can run the following command to confirm the nltk zip file is listed in the external stage.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Els @nltk_imports/nltk_data;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_8.png\" alt=\"List nltk imports external stage\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EWe can now create the UDF in Snowflake as the following.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace function python_parseText(file_path string)\n    returns variant\n    language python\n    runtime_version=3.8\n    imports = ('@nltk_imports/nltk_data.zip')\n    packages = ('snowflake-snowpark-python','nltk')\n    handler = 'udf_compute_named_entity'\nAS\n$$\ndef load_files(): \n    """Helper function that loads zipped files from stage and adds them\n    to the nltk data path"""\n    import os\n    import zipfile\n    import nltk\n    import sys\n    \n    #import directory, to be able to make references to files in the stage\n    IMPORT_DIRECTORY_NAME = "snowflake_import_directory"\n    import_dir = sys._xoptions[IMPORT_DIRECTORY_NAME]\n    \n    #paths to extract to, this is where the nltk library will search for the required files\n    nltk_dir = "/tmp/nltk_data"\n        \n    with zipfile.ZipFile(os.path.join(import_dir, "nltk_data.zip"), 'r') as zip_ref: \n        zip_ref.extractall(nltk_dir)\n    \n    #append extracted path to the nltk library search path for usage in UDF\n    nltk.data.path.append(nltk_dir)\n\ndef udf_compute_named_entity(file_path:str) -> dict:\n    from snowflake.snowpark.files import SnowflakeFile\n    import email\n    import json\n    import nltk\n    from nltk.tokenize import word_tokenize\n    from nltk.tag import pos_tag\n    from nltk.chunk import ne_chunk\n\n    def parse_email_file(file_path):\n        with SnowflakeFile.open(file_path, 'r') as f:\n            msg = email.message_from_file(f)\n            payload = ""\n            if msg.is_multipart():\n                for _payload in msg.get_payload():\n                    # if payload.is_multipart(): ...\n                    payload += _payload.get_payload()\n            else:\n                payload= msg.get_payload()\n            return payload\n    def perform_named_entity_extraction(text):\n        tokens = word_tokenize(text)\n        tagged_tokens = pos_tag(tokens)\n        named_entities = ne_chunk(tagged_tokens)\n        persons = []\n        locations = []\n        for entity in named_entities:\n            if hasattr(entity, 'label'):\n                if (entity.label() == 'PERSON'):\n                    persons.append(' '.join([token for token, pos in entity]))\n                elif (entity.label() == 'GPE'):\n                    locations.append(' '.join([token for token, pos in  entity]))\n        return {"PERSONS":persons,"LOCATIONS":locations}        \n    email_body = parse_email_file(file_path)\n    #load the files from stage for usage\n    load_files()\n    return perform_named_entity_extraction(email_body)\n\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EInvoking the Python UDF\u003C/h4\u003E\n","\u003Cp\u003EThe UDF can be invoked on any text file containing readable english. From the email corpus stored on the internal stage, we can invoke the UDF as follows.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect python_parseText(build_scoped_file_url(@email_stage_internal,'/sanders-r/inbox/60.')) \nas entities_extraction;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_9.png\" alt=\"Python UDF Results\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe output is serialized as a valid JSON format by the UDF. It contains 2 arrays, one for each named entities extraction:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "LOCATIONS": [\n    "Toronto"\n  ],\n  "PERSONS": [\n    "Vince Carter",\n    "Hakeem"\n  ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESince we are using pre-built models which haven’t been trained on this particular corpus, the entity extraction may not always be accurate. However, the models perform overall quite well for illustration purposes for this quickstart.\u003C/p\u003E\n","\u003Ch3\u003EJava\u003C/h3\u003E\n","\u003Cp\u003EAlternatively to Python, the same entity extraction can be accomplished with Java. The Java code for the UDF has already been written and provided below. This code uses the open source \u003Ca href=\"https://opennlp.apache.org/\"\u003EApache OpenNLP\u003C/a\u003E library to perform natural language processing on English text in this occurrence.\u003C/p\u003E\n","\u003Cp\u003EThe Java code leverages pre-built \u003Ca href=\"http://opennlp.sourceforge.net/models-1.5/\"\u003Emachine learning models\u003C/a\u003E to perform named entity extraction for persons and locations. These models are packaged manually post-build in a Fat JAR.\u003C/p\u003E\n","\u003Cp\u003EAt a high level, the code does the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EParse the text file contents using an Apache Tika text parser.\u003C/li\u003E\u003Cli\u003ETokenize the parsed contents using a model.\u003C/li\u003E\u003Cli\u003EFrom the tokens, perform a named entity extraction for persons and locations using pre-built ML models.\u003C/li\u003E\u003Cli\u003ESerialize the results in a JSON string returned as an output.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-java\"\u003Epublic String ParseText(String filePath) throws IOException, SAXException, TikaException {\n\t\t\t\n// Configure gson\t      \nGsonBuilder gsonBuilder = new GsonBuilder()\nGson gson = gsonBuilder.create();\n\t\t\n//detecting the file type\nBodyContentHandler handler = new BodyContentHandler();\nMetadata metadata = new Metadata();\nParseContext pcontext=new ParseContext();\n\t     \nSnowflakeFile file = SnowflakeFile.newInstance(filePath);\nInputStream ins = file.getInputStream();\n\t      \n//Text document parser\nTXTParser  TexTParser = new TXTParser();\nTexTParser.parse(ins, handler, metadata, pcontext);\n\t      \nString Contents = handler.toString();\n\t      \nString[] AllPersonEntities = null;\nString[] AllLocationEntities = null;\nString JsonResult = null;\n\t      \nNamedEntities NE = new NamedEntities(AllPersonEntities, AllLocationEntities);\n\t\t\t\n try {\n         for(int i=0;i<sentences.length;i++){\n\t          String Tokens[] = new NamedEntityExtraction().ParseTokens(Contents);\n\t          String PersonEntities[] = new NamedEntityExtraction().findName(Tokens);\n\t          String LocationEntities[] = new NamedEntityExtraction().findLocation(Tokens);\n\t           \n\t          AllPersonEntities = ArrayUtils.addAll(AllPersonEntities, PersonEntities);\n\t          AllLocationEntities = ArrayUtils.addAll(AllLocationEntities, LocationEntities);\n\t           \n\t          NE.setPersons(AllPersonEntities);\n\t          NE.setLocations(AllLocationEntities);\n\t           \n\t          JsonResult = gson.toJson(NE).toString();\t\n               \t\t\n\t\t\n\t\t\t} catch (IOException e) {\n\t            e.printStackTrace();\n\t        }\n\t\t     return(JsonResult);\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA few elements relevant for this code:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENotice the main class is \u003Cstrong\u003ENamedEntityExtraction\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EThe method \u003Cstrong\u003EParseText\u003C/strong\u003E will be invoked by the UDF in the next section.\u003C/li\u003E\u003Cli\u003EThe file path is passed as a parameter. It can be a URL to the file, or the path on the stage.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ECreating a Java UDF\u003C/h4\u003E\n","\u003Cp\u003EThe precompiled jar file including all the dependencies has been uploaded and available on a Snowflake s3 public bucket. Creating the UDF involves a few steps in Snowflake.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ECreate the external stage mapping to the S3 bucket URI where the jar file is currently available. From the Snowflake worksheet, enter the following command:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role sysadmin;\nuse schema emaildb.raw;\n\ncreate or replace stage jars_stage_external\nurl = "s3://sfquickstarts/Common JARs/"\ndirectory = (enable = true auto_refresh = true);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFrom the Snowflake worksheet, you can run the following command to confirm the jar file is listed in the external stage.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Els @jars_stage_external;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_1.png\" alt=\"List jar external stage\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EWe can now create the UDF in Snowflake as the following.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace function  java_parseText(file string)\nreturns string\nlanguage java\nimports = ('@jars_stage_external/EmailNLPv3-3.0.jar')\nhandler = 'NamedEntityExtraction.ParseText'\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EInvoking the Java UDF\u003C/h3\u003E\n","\u003Cp\u003EThe UDF can be invoked on any text file containing readable english. From the email corpus stored on the internal stage, we can invoke the UDF as follows.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect java_parseText(build_scoped_file_url('@email_stage_internal','/sanders-r/inbox/60.')) \nas entities_extraction;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_2.png\" alt=\"Java UDF Results\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe output is serialized as a valid JSON format by the UDF. It contains 2 arrays, one for each named entities extraction:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n"Persons":["David","Richard","Rob","Mark","Richard","Vince Carter"],\n"Locations":["Toronto","Toronto"]\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EExtracting and Storing Named Entities\u003C/h3\u003E\n","\u003Cp\u003EWe want to store the named entities as additional attributes for analysts to be able to select and retrieve the files of interest in their analysis, as well as perform some analytics on the attributes found. For the purpose of this quickstart, we'll use the Java UDF to complete this task. However, the same could be done using the Python UDF.\u003C/p\u003E\n","\u003Cp\u003EWe first want to scale-up the default warehouse size to run the Java UDF at scale across all cores available on all nodes on a 2XL warehouse (64 nodes). This can be done easily and quickly because of Snowflake's instant elasticity:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ealter warehouse quickstart set warehouse_size = xxlarge;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow let's run the following query to store the named entities into a table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace table email_named_entities_base as\nselect\n    relative_path\n    , upper(replace(get(split(relative_path, '/'), 0), '\\"', ''))\tas mailbox\n    , java_parseText(build_scoped_file_url('@email_stage_internal/', relative_path)) \t\tas named_entities\nfrom (\n    select relative_path\n    from directory(@email_stage_internal)\n    group by relative_path\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter running this query, we can set the \u003Ccode\u003Ewarehouse\u003C/code\u003E size back to a smaller size.\u003C/p\u003E\n","\u003Cp\u003E<aside class="warning">\nTHIS STEP IS VERY IMPORTANT NOT TO EXHAUST YOUR TRIAL CREDIT\n</aside>\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ealter warehouse quickstart set warehouse_size = xsmall;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify with the following command that the warehouse is back to an \u003Ccode\u003Exsmall\u003C/code\u003E size.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eshow warehouses;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe output should show the \u003Ccode\u003EQUICKSTART\u003C/code\u003E size as being \u003Ccode\u003EXSMALL\u003C/code\u003E. Now let's query the base table containing the named entities.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect\n    *\nfrom email_named_entities_base\nlimit 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor each email, we now have created a \u003Ccode\u003EMAILBOX\u003C/code\u003E attribute, as well as JSON string containing the named entities present in the file.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_3.png\" alt=\"Named Entities Base Table\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EExploring the Mailbox Corpus\u003C/h3\u003E\n","\u003Cp\u003EWe have now extracted the named entities the analysts are interested in seeing to do some analytics on this email corpus. We can use Snowflake native capabilities to easily store and query semi-structured data, in this case JSON.\u003C/p\u003E\n","\u003Cp\u003EWe will first create a view to parse the \u003Ccode\u003ENAMED_ENTITIES\u003C/code\u003E JSON string and extract separately the persons and the locations entities, as well as count the number of entities identified in each email:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ecreate or replace view email_info_v\nas\nwith named_entities\nas (\n    select\n\t    relative_path\n\t    , mailbox\n\t    , parse_json(named_entities) as named_entities\n    from email_named_entities_base\n)\nselect\n    relative_path\n    , mailbox\n    , named_entities:Persons::variant\t\t\t\tas persons\n    , array_size(persons)\t\t\t\t\t\t\tas num_person_entities\n    , named_entities:Locations::variant\t\t\t\tas locations\n    , array_size(locations)\t\t\t\t\t\t\tas num_location_entities\n    , array_size(persons) + array_size(locations)\tas total_entities\n    , build_scoped_file_url(@email_stage_internal, relative_path)\tas scoped_email_url\nfrom named_entities;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can query the view and examine the output. Notice that we now have JSON arrays in separate columns for name and location entities, count of entities, and a scoped URL to access the file if we need to further examine its contents.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * from email_info_v limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_3_2.png\" alt=\"Examine view output\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe can now query the view to retrieve various entity metrics. For example, the following query identifies the top 5 emails in terms of total number of entities.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect\n\trelative_path\n\t, mailbox\n\t, total_entities\n\t, num_person_entities\n\t, num_location_entities\n\t, persons\n\t, locations\n\t, scoped_email_url\nfrom email_info_v\norder by total_entities desc\nlimit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_4.png\" alt=\"Number of entities view\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe following query aggregates statistics on the number of entities identified per mailbox and returns the top 5 mailbox by total number of entities identified in the mailbox.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect\n\tmailbox\n\t, count(relative_path)\t\t\t\tas num_emails\n\t, sum(total_entities)\t\t\t\tas sum_entities\n\t, round(avg(total_entities))\t\tas avg_entities\n\t, sum(num_person_entities)\t\t\tas sum_persons\n\t, round(avg(num_person_entities))\tas avg_persons\n\t, sum(num_location_entities)\t\tas sum_locations\n\t, round(avg(num_location_entities))\tas avg_locations\nfrom email_info_v\ngroup by mailbox\norder by sum_entities desc\nlimit 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe know that the most prolific mailbox is \u003Ccode\u003EWHITE-S\u003C/code\u003E for identified entities in this email corpus.\u003C/p\u003E\n","\u003Ch3\u003EPerforming Analytics on the Mailbox\u003C/h3\u003E\n","\u003Cp\u003ELet’s assume an analyst wants to identify all email correspondence where the name of 'Willman' (chosen completely randomly in this example) is mentioned. With Snowflake, you can easily flatten arrays to run this type of query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect\n\trelative_path\n\t, mailbox\n\t, pers.value::string as person_entity\nfrom\n\temail_info_v\n\t, lateral flatten(input => Persons) pers;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_5.png\" alt=\"Flattened JSON array\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe can use the previous query as a CTE to retrieve all emails mentioning a specific person, including a scoped URL to access the actual email file.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith persons_flattened as (\n    select\n    \trelative_path\n\t\t, mailbox\n\t\t, pers.value::string as person_entity\n    from\n    \temail_info_v\n\t\t, lateral flatten(input => Persons) pers\n)\nselect\n    relative_path\n    , mailbox\n    , person_entity\n    , build_scoped_file_url(@email_stage_internal,relative_path) as scoped_email_url\nfrom persons_flattened\nwhere person_entity like '%Willmann%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_6.png\" alt=\"Mentions of Willmann\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on any \u003Ccode\u003EMAY-L\u003C/code\u003E mailbox email to download and review the email. This will allow you to learn a little bit more about that person's role and responsibilities in the Enron organization.\u003C/p\u003E\n","\u003Cp\u003ELet’s assume we want to identify the top 5 locations mentioned in the email corpus.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ewith locations_flattened as (\n    select\n    \trelative_path\n\t    , mailbox\n\t    , loc.value::string\tas location_entity\n    from\n\t    email_info_v\n\t    , lateral flatten(input => Locations) loc\n)\nselect\n    location_entity\n    , count(location_entity) as num_occurrences\nfrom locations_flattened\ngroup by location_entity\norder by num_occurrences desc\nlimit 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can use Snowsight to produce a visualization. Click on chart and set the settings as shown below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_7.png\" alt=\"Number of location occurrences bar chart\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs you can see, we can perform aggregations, and analytics on unstructured text data after extracting entities and information of interest. At this point, one could run more advanced data science use cases or visualizations using the numerous options available in Snowflake.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EShare Unstructured Data\u003C/h2\u003E\n","\u003Cp\u003EIn this example, we want to share all the email corpus mentioning 'Willman' with another party using a Snowflake reader account.\u003C/p\u003E\n","\u003Ch3\u003ECreating a Reader Account\u003C/h3\u003E\n","\u003Cp\u003EThe first step is to create a reader account as follows. Note that you will need to provide a secure password of your choice.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role accountadmin;\ncreate managed account if not exists emaildb_reader\nadmin_name='admin', admin_password='<password>',\ntype=reader, COMMENT='Emaildb Reader Account';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis command should return the account name, and the URL to access the account. Please copy and paste the output of the command as it provides you the login URL information for the account, as well as the user and password you chose\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E{\n"accountName":"<account_name>",\n"loginUrl":"https://<account>.snowflakecomputing.com"\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can run the following command to retrieve the managed account information at anytime.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eshow managed accounts;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can connect to the previous reader account using the URL, and the userid/password credentials you passed as parameters.\u003C/p\u003E\n","\u003Ch3\u003ECreating a Secure View to Share\u003C/h3\u003E\n","\u003Cp\u003ELet’s now create the secure view based on the query ran in the previous section that will be shared with the reader account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role sysadmin;\nuse schema emaildb.raw;\n\ncreate or replace secure view email_corpus_willman_v as\nwith persons_flattened as (\n    select\n        relative_path\n        , mailbox\n        , pers.value::string as person_entity\n    from\n\t\temail_info_v\n        , lateral flatten(input => Persons) pers\n)\nselect\n    relative_path\n    , mailbox\n    , person_entity\n    , build_scoped_file_url(@email_stage_internal,relative_path) as email_url\nfrom persons_flattened\nwhere person_entity like '%Willmann%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Share\u003C/h3\u003E\n","\u003Cp\u003EWe can now create the share as follows. You will need to provide the reader account name created earlier:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create the share object\nuse role accountadmin;\ncreate or replace share email_corpus \ncomment='Share in scope email corpus information';\n\n--what are we sharing?\ngrant usage on database emaildb to share email_corpus;\ngrant usage on schema emaildb.raw to share email_corpus;\ngrant select on view emaildb.raw.email_corpus_willman_v to share email_corpus;\n\n-- whom are we sharing with?\nalter share email_corpus add accounts = <reader-account-locator>;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can review the share we have just created. The following command provides all the shares in the account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- check the share\nshow shares like 'email_corpus';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can get more details about the share, and the scope of the objects shared using the following command.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- review share\ndescribe share email_corpus;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/9_1.png\" alt=\"Number of location occurrences bar chart\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis command shows us that the view \u003Ccode\u003EEMAIL_CORPUS_WILLMAN_V\u003C/code\u003E is shared from the database \u003Ccode\u003EEMAILDB\u003C/code\u003E and schema \u003Ccode\u003ERAW\u003C/code\u003E in the current account.\u003C/p\u003E\n","\u003Ch3\u003EAccessing Shared Data\u003C/h3\u003E\n","\u003Cp\u003ESwitch to the web browser tab where you opened the session with the reader account created in step 6.1 or open a new session on the reader account. Now, click on the blue Snowsight UI button at the top and authenticate again.\u003C/p\u003E\n","\u003Cp\u003EAfter logging in, as this is a new account, click on the worksheet button at the top and create a new virtual warehouse.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role sysadmin;\n\t\ncreate or replace warehouse compute_wh \nwarehouse_size=xsmall\nauto_suspend=1\nauto_resume=true\ninitially_suspended=true;\n\ngrant usage on warehouse compute_wh to public;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFirst, let's switch back to the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role. Click on the \u003Cstrong\u003EHome\u003C/strong\u003E button in the top-left. Then in the top-left, click on \u003Cstrong\u003EADMIN\u003C/strong\u003E, then hover over \u003Cstrong\u003ESwitch Role\u003C/strong\u003E, and click on \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/9_2.png\" alt=\"Switch to ACCOUNTADMIN role\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow let's view the shared data. In the pane on the left, click on on \u003Cstrong\u003EData\u003C/strong\u003E, then \u003Cstrong\u003EPrivate Sharing\u003C/strong\u003E. You will see the \u003Ccode\u003EEMAIL_CORPUS\u003C/code\u003E database listed under \u003Cstrong\u003EReady to Get\u003C/strong\u003E. Select it and give the database name \u003Ccode\u003EEMAIL_CORPUS\u003C/code\u003E and make it available to \u003Ccode\u003EPUBLIC\u003C/code\u003E, then click the \u003Cstrong\u003EGet Data\u003C/strong\u003E button.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/9_3.png\" alt=\"Get Data dialogue box\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EDatabases\u003C/strong\u003E, then click on the \u003Cstrong\u003ERefresh\u003C/strong\u003E button (round arrow button on the right side above the database list). You will now see the database \u003Ccode\u003EEMAIL_CORPUS\u003C/code\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/9_4.png\" alt=\"EMAIL_CORPUS shared database\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect the worksheet created in this reader account. Add the following commands to set the worksheet session parameters and review the view objects available.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role sysadmin;\nuse schema <account-locator>_email_corpus.raw;\nuse warehouse compute_wh;\n\nshow views;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you expand the database hierarchy on the left side of the window, you will see that the share appears from the consumer side as a database \u003Ccode\u003EEMAIL_CORPUS\u003C/code\u003E, with a schema \u003Ccode\u003ERAW\u003C/code\u003E and a single view object \u003Ccode\u003EEMAIL_CORPUS_WILLMAN_V\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EWe can now query the shared data. The query below will display only the emails related to 'Willmann'.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * from email_corpus_willman_v;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFrom the results, notice that all the URLs are encrypted, not revealing any information of the location where the shared data came from. Click on any \u003Ccode\u003EEMAIL_URL\u003C/code\u003E and get access to the actual email text downloaded to your workstation. Download and review the email. Make sure it is valid.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/9_5.png\" alt=\"Access shared data files\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou have now completed this demonstration of how unstructured data can be securely shared in the Snowflake Data Cloud using Snowflake Data Sharing capabilities.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You used Snowflake to perform natural language processing on email files.\u003C/p\u003E\n","\u003Ch3\u003EWhat we've covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccessing external data with an \u003Cstrong\u003EExternal Stage\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EStoring unstructured data with an \u003Cstrong\u003EInternal Stage\u003C/strong\u003E and \u003Cstrong\u003ESnowSQL\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EGoverning unstructured data with \u003Cstrong\u003ERole-Based Access Control\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ECatalog unstructured data with \u003Cstrong\u003EDirectory Tables\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESecurely access unstructured data with \u003Cstrong\u003EScoped, File, and Pre-signed URLs\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EProcessing unstructured data with \u003Cstrong\u003ESnowpark for Python and Java\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESharing unstructured data in the \u003Cstrong\u003EData Cloud\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/unstructured.html\"\u003EUnstructured Data Docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/udf/java/udf-java.html\"\u003EJava UDF Docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/index.html\"\u003ESnowpark Docs\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"title":"Getting Started with Unstructured Data",":itemsOrder":[],":items":{},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nThis Quickstart is designed to help you understand the capabilities included in Snowflake’s support for unstructured data. Sign up for a free 30-day trial of Snowflake and follow along with this lab exercise. After completing this lab, you’ll be ready to start storing and managing your own unstructured data in Snowflake. \n\n### Prerequisites\n- Snowflake account\n- Basic knowledge of SQL, database concepts, and objects\n- Recommended to first complete [Getting Started with SnowSQL](/en/developers/guides/getting-started-with-snowsql/)\n\n### What You’ll Learn \n- How to access and store unstructured data\n- How to govern unstructured data\n- How to search for unstructured data using directory tables\n- How to securely share unstructured data\n- How to process unstructured data\n\n### What You'll Build\n- A stage for storing and accessing files in Snowflake\n- A user-defined function using Snowflake's engine to process files\n- A secure view to share in the Snowflake Marketplace\n\n\u003C!-- ------------------------ --\u003E\n## Notice and Terms of Use\n\nThe data provided for this lab is an extract from the Enron email database made available by Carnegie Mellon University ([https://www.cs.cmu.edu/~enron/](https://www.cs.cmu.edu/~enron/)).\n\nUse of the data provided is limited to this quickstart in connection with the Snowflake service and is subject to any additional terms and conditions on the Carnegie Mellon site.\n\nBy accessing this data, you acknowledge and agree to the limits and terms related to the use of the Enron email data.\n\n\u003C!-- ------------------------ --\u003E\n## Prepare your lab environment\n\nIf you haven't already, register for a [Snowflake free 30-day trial](https://trial.snowflake.com/).\nThe Snowflake edition (Standard, Enterprise, Business Critical, e.g.), cloud provider (AWS, Azure, e.g.), and Region (US East, EU, e.g.) do not matter for this lab. We suggest you select the region which is physically closest to you and the Enterprise Edition, our most popular offering. After registering, you will receive an email with an activation link and your Snowflake account URL.\n\n\n### Navigating to Snowsight\nFor this lab, you will use the latest Snowflake web interface, Snowsight.\n1. Log into your Snowflake trial account\n2. Click on __Snowsight__ Worksheets tab. The new web interface opens in a separate tab or window.\n3. Click __Worksheets__ in the left-hand navigation bar. The __Ready to Start Using Worksheets and Dashboards__ dialog opens.\n4. Click the __Enable Worksheets and Dashboards button__.\n\n![Enable Worksheets and Dashboards](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/3_1.png)\n\n\u003C!-- ------------------------ --\u003E\n## Store & Access Unstructured Data\n\nLet’s start by preparing to load the unstructured data into Snowflake. Snowflake supports two types of stages for storing data files used for loading and unloading:\n- [Internal stages](https://docs.snowflake.com/en/user-guide/data-load-overview.html#internal-stages) store the files internally within Snowflake.\n- [External stages](https://docs.snowflake.com/en/user-guide/data-load-overview.html#external-stages) store the files in an external location (i.e. S3 bucket) that is referenced by the stage. An external stage specifies location and credential information, if required, for the bucket.\n\n### Create a Database, Schema, and Warehouse\nBefore creating any stages, let’s create a database and a schema that will be used for loading the unstructured data. We will use the UI within the Worksheets tab to run the DDL that creates the database and schema. Copy the commands below into your trial environment, and execute each individually.\n\n```sql\nuse role sysadmin;\n\ncreate or replace database emaildb comment = 'Enron Email Corpus Database';\ncreate or replace schema raw;\ncreate or replace warehouse quickstart;\n\nuse database emaildb;\nuse schema raw;\nuse warehouse quickstart;\n```\n\n### Access Unstructured Data Stored in an S3 Bucket\nThe data we will be using in this lab is stored in an S3 bucket.\n\n#### Create an External Stage\nYou are working with unstructured PDFs that have already been staged in a public, external S3 bucket. Before you can use this data, you first need to create a Stage that specifies the location of our external bucket.\n\n\u003Caside class=\"warning\"\u003E\n  For this lab we are using an AWS S3 bucket in us-east-1. To minimize data egress/transfer costs in the future, you should select a staging location from the same cloud provider and region as your Snowflake environment.\n\u003C/aside\u003E\n\nGrant the `PUBLIC` schema access to the database, schema, and warehouse just created (This will be relevant in the next section).\n\n```sql\nuse role sysadmin;\ngrant usage on database emaildb to public;\ngrant usage on schema emaildb.raw to public;\ngrant usage on warehouse quickstart to public;\n```\n\nFrom the same worksheet you’ve been using, run this command to create an external stage called `email_stage`.\n\n```sql\nuse schema emaildb.raw;\n-- Create an external stage where files are stored.\ncreate or replace stage email_stage\n url = \"s3://sfquickstarts/Getting Started Unstructured Data/Emails/mailbox/\"\n directory = (enable = true auto_refresh = true);\n```\n\nYou can run this command to see a list of the files in your external stage.\n\n```sql\nls @email_stage;\n```\n![List External Stage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/4_1.png)\n\nWe can see that the stage contains various mailboxes from Enron users containing some files (which are text files) of various sizes. We can get a summary of the file corpus in the query results on the right-hand side. For the purpose of this quickstart, just a sample of 226 files out of 517,551 files has been extracted.\n\n![External Stage Query Details](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/4_2.png)\n\n__The size of the files__: We can see that the file size ranges from 666 bytes to 1023 bytes, with the majority of the files closer to 1023 bytes. If we click on the size metric, we can get more detailed information. The total corpus size is 208,414 bytes, with an average size of 922 bytes. If we hover over the histogram, we can filter results based on file size.\n\n### Store Unstructured Data in an Internal Stage\nAlternatively, you can store data directly in Snowflake with internal stages. Now, we want to create an internal stage and upload the same files while maintaining the directory structure of the various individual mailboxes on an internal stage.\n\n#### Create an Internal Stage\nRun this command to create an internal stage called `email_stage_internal` as follows.\n\n```sql\nuse schema emaildb.raw;\ncreate or replace stage email_stage_internal\ndirectory = (enable = TRUE)\nencryption = (type = 'SNOWFLAKE_SSE');\n```\n\n\u003Caside class=\"warning\"\u003E\n    Note that we have to specify a server-side encryption on the internal stage. When using the default client-side encryption, the files will be returned encrypted and not readable when accessed through URLs (in Section 6).\n\u003C/aside\u003E\n\n#### Download Data and Scripts\nWe need to first download the following files to the local workstation by clicking on the hyperlinks below. The subsequent steps require [SnowSQL CLI](https://docs.snowflake.com/en/user-guide/snowsql.html) installed on the local workstation where the lab is ran:\n\n- [upload.snf](\u003Chttps://sfquickstarts.s3.amazonaws.com/Getting Started Unstructured Data/Files/upload.snf\u003E): SnowSQL Script which will upload the files to the internal stage just created.\n- [mailbox.tar.gz](\u003Chttps://sfquickstarts.s3.amazonaws.com/Getting Started Unstructured Data/Files/mailbox.tar.gz\u003E): The actual email data.\n\nOnce downloaded, untar the contents of the files on your local workstation and note the full path including the mailbox parent directory of the tar archive. In the example below, this path is `/Users/znh/Downloads/quickstart/`. The file can be untarred as follows.\n\n```bash\ncd /Users/znh/Downloads/quickstart/\ntar xzvf mailbox.tar.gz\n```\n\n#### Upload Files using SnowSQL\nBefore opening terminal, find out your account identifier which for the trial account will be `\u003Caccount-locator\u003E.\u003Cregion-id\u003E.\u003Ccloud\u003E`. These fields can be retrieved from the URL of your Snowflake account.\n\nFor example, the URL to access the trial account is `https://xx74264.ca-central-1.aws.snowflakecomputing.com/`. These are the values for the account identifier:\n\n- Account Locator: `xx74264`\n- Region ID: `ca-central-1`\n- Cloud: `aws`\n\nThere may be additional segments if you are using your own account part of an organization. You can find those from the URL of your Snowflake account. Please check the [Snowflake Documentation](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#account-identifiers) for additional details on this topic.\n\nNow open a terminal on your workstation and run the following SnowSQL command. You will be prompted for the password for the Snowflake user passed as a parameter.\n\n```\nsnowsql -a \u003Caccount-identifier\u003E \\\n-u \u003Cuser-id\u003E -d emaildb -r sysadmin -s raw \\\n-D srcpath=\u003Csource-path\u003E \\\n-D stagename=@email_stage_internal \\\n-o variable_substitution=true -f upload.snf\n```\n\nUsing the examples above for the path and the account identifier, and the userid `myuser`, the command would be:\n\n```\nsnowsql -a xx74264.ca-central-1.aws \\\n-u myuser -d emaildb -r sysadmin -s raw \\\n-D srcpath=/Users/znh/Downloads/quickstart/mailbox \\\n-D stagename=@email_stage_internal \\\n-o variable_substitution=true -f /Users/znh/Downloads/quickstart/upload.snf\n```\n\nThe upload may take a few seconds depending on the speed of your internet connection. If the upload is successful, you should see data being uploaded in each subfolder, and you may see an output like the following on your terminal.\n\n![SnowSQL PUT Files to Internal Stage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/4_4.png)\n\nVerify if the files have been uploaded successfully by entering the following command on your Snowflake worksheet.\n\n```sql\nls @email_stage_internal;\n```\n\nYou should now see an identical list of files uploaded to the internal stage. Make sure you see 226 files uploaded\n\n![List Internal Stage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/4_5.png)\n\n\u003C!-- ------------------------ --\u003E\n## Govern Unstructured Data Access\n\nJust like structured and semi-structured data, access permissions to unstructured data in Snowflake can be governed using role-based access control (RBAC).\n\n### Create Role and Grant Access\nLet’s create a role that will provide read access to the external stage we’ve already created that contains PDFs.\n\nLet’s first create the analyst role.\n\n```sql\nuse role accountadmin;\ncreate or replace role analyst;\ngrant role analyst to role sysadmin;\n```\n\nThen, switch back to `sysadmin` role, and `grant` the role `analyst` the rights to use the database `emaildb`, and the schema `raw` we just created earlier, as well as the ability to `read` from the stage.\n\n```sql\ngrant usage on database emaildb to role analyst;\ngrant usage on schema emaildb.raw to role analyst;\ngrant usage on warehouse quickstart to role analyst;\ngrant read on stage email_stage_internal to role analyst;\n```\n\nTo make sure this works as expected, make sure secondary roles are disabled for `analyst` role.\n```sql\nuse role analyst;\nselect current_secondary_roles();\nuse secondary roles none;\n```\n\nYou can verify the `analyst` role only has access to read by listing the files in the internal stage, then trying to remove files from the external stage. When trying to remove, this should result in an error message. \n\n```sql\nuse role analyst;\n\n-- List files from the stage. This should execute successfully.\nls @email_stage_internal;\n\n-- Try to remove files from the stage. This should return an error.\nrm @email_stage_internal;\n```\n\n![Remove Internal Stage Analyst](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/5_1.png)\n\nIn the subsequent sections, we will see a more fine-grained access control of the different unstructured files stored in Snowflake using scoped URLs.\n\n\u003C!-- ------------------------ --\u003E\n## Catalog Unstructured Data using Directory Tables\n\nOne of the main pain points in managing large repositories of unstructured data is the ability to access metadata easily on the numerous files, as well as retrieve files per some metadata attributes (last modified, file size, file patterns).\n\nDirectory Tables are built-in tables in Snowflake that provide an up-to-date, tabular file catalog for external and internal stages. Directory Tables make it easy to search for and query files using SQL.\n\nWe will first reset the session parameters to the correct role, virtual warehouse, database and schema:\n\n```sql\nuse role sysadmin;\nuse warehouse quickstart;\nuse schema emaildb.raw;\n```\n\nPrior to accessing the directory, it needs to be refreshed first for the files previously uploaded using the following command.\n\n```sql\t\nalter stage email_stage_internal refresh;\n```\n\nRun the following command to access the directory table.\n\n```sql\nselect *\nfrom directory(@email_stage_internal);\n```\n\nThis will provide some detailed metadata information about the files stored in the stage including the `RELATIVE_PATH`, the `LAST MODIFIED` timestamp, the `SIZE`, the `ETAG` as well as the `FILE URL` (more information on this in the next section).\n\n![Directory Table](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/6_1.png)\n\n\n### Searching Directory Tables\nWe could now query these files using some SQL commands. For example, let’s assume we want to identify all the emails from the mailbox belonging to the user `nemec-g`. We could easily do this with the following SQL query.\n\n```sql\nselect *\nfrom directory(@email_stage_internal) \nwhere RELATIVE_PATH like '%nemec-g%';\n```\n![Directory Table filtered path](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/6_2.png)\n\nThis query returns the 13 emails belonging to that user. Now let’s try to identify the 5 largest email text in the dataset. We can do that with the following query.\n\n```sql\nselect *\nfrom directory(@email_stage_internal)\norder by size desc\nlimit 5;\n```\n\n![Directory Table filtered size](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/6_3.png)\n\n### Automatic Refresh\nSay you want the directory table to refresh whenever a file is added to your S3 bucket. This can be accomplished by using event notifications in S3. When a new file is added to a bucket, S3 will send a notification to Snowflake, and a Stream can refresh the directory table.\n\nIn this quickstart, we won’t setup notifications in S3, but the command below is what you would use to create a stream on the directory table for a stage. More detailed documentation for automatically refreshing directory tables can be found [here](https://docs.snowflake.com/en/user-guide/data-load-dirtables-auto.html).\n\n```sql\n-- Create a table stream on directory table\ncreate stream documents_stream on directory(\u003Cstage_name\u003E);\n```\n\n\u003C!-- ------------------------ --\u003E\n## URLs for Secure Access\n\nIn the previous sections, we have seen how to store unstructured data in Snowflake, as well as access metadata about the unstructured files, and build queries to retrieve files based on metadata filters. \n\nIn this section, we will look into how Snowflake offers access to the unstructured data through various types of URLs, as well as provide a more granular governance over unstructured data than at the stage level, as reviewed previously in Section 2.\n\nThere are three different types of URLs that you can use to access unstructured data:\n- [__Scoped URL__](https://docs.snowflake.com/en/sql-reference/functions/build_scoped_file_url.html): A scoped file URL can be generated for a user to give the user short-lived, scoped access to the file without giving privileges on the stage.\n- [__File URL__](https://docs.snowflake.com/en/sql-reference/functions/build_stage_file_url.html): A file URL requires a user to be authenticated with Snowflake and requires the user to have read privileges on the stage.\n- [__Pre-signed URL__](https://docs.snowflake.com/en/sql-reference/functions/get_presigned_url.html): As the name suggests, pre-signed URLs are already authenticated. Users can simply download the files using pre-signed URLs.\n\n\nThe URL format for files is https://\u003Caccount\u003E.snowflakecomputing.com/api/files/\u003Cdb_name\u003E/\u003Cschema_name\u003E/\u003Cstage_name\u003E/\u003Cfile_path\u003E. \n\n### Scoped URL\nScoped URLs are encoded URLs that permit temporary access to a staged file without granting privileges to the stage. The URL expires when the persisted query result period ends (i.e. the results cache expires), which is currently 24 hours.\n\nWe can generate a scoped URL of any file using the function `build_scoped_url()`. For example, let’s create a view which provides the scoped URL for files in the stage `email_stage_internal`.\n\n```sql\ncreate or replace view email_scoped_url_v\nas\nselect\n\trelative_path\n\t, build_scoped_file_url(@email_stage_internal,relative_path) as scoped_url\nfrom directory(@email_stage_internal);\n\nselect * from email_scoped_url_v limit 5;\n```\n![Scoped URL](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/7_1.png)\n\nAs explained previously, this URL will be valid for 24 hours. Snowsight retrieves the file only for the user who generated the scoped URL.\n\nScoped URLs enable access to the files via a view that retrieves scoped URLs. Only roles that have privileges on the view can access the files. The scoped URL contents are all encrypted and doesn't give any information about the bucket, database or schema.\n\n#### Secure Access with RBAC and Scoped URL\nLet’s assume a scenario where the `analyst` role needs access to all the files from the inbox of `NEMEC-G`. We can build a dynamic view which will filter the output based on the role of the view user.\n\nFirst let’s build an assignment table where various roles (or even users) can be assigned a given mailbox for further analysis.\n\n```sql\ncreate or replace table assignment (mailbox string, role string, filter string);\n\ninsert into assignment values ('NEMEC-G','ANALYST','%nemec-g%');\ninsert into assignment values ('*','SYSADMIN','%');\n\nselect * from assignment;\n```\n\nAs we can see from the query output above, the assignment table controls a 1-to-1 role-to-mailbox access mapping.\n\nWe can now build a SQL view which will join with the assignment table to dynamically filter rows based on the role of the user executing the view and grant access to the view to the `analyst` role:\n\n```sql\ncreate or replace secure view analyst_file_access_v as\n    select\n\t    relative_path\n\t\t, build_scoped_file_url(@email_stage_internal,relative_path) as scoped_url\n    from directory(@email_stage_internal)\n\t    inner join assignment\n    where relative_path like filter\n  \t    and role = current_role();\n\ngrant select on analyst_file_access_v to role analyst;\n```\n\nLet’s switch to role `analyst` and query the view.\n\n```sql\nuse role analyst;\nuse warehouse quickstart;\nuse schema raw;\nselect * from analyst_file_access_v;\n```\n![Secure View Scoped URL](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/7_2.png)\n\nClick the `scoped_url` corresponding to the file `1222.` to your workstation and review the file locally.\n\nIf we switch the role to `sysadmin` and run the same query:\n\n```sql\nuse role sysadmin;\nuse warehouse quickstart;\nuse schema raw;\nselect * from analyst_file_access_v;\n```\n\nThis returns scoped URLs to access all the files.\n\nAs we just experimented, scoped URLs are ideal for use in custom applications, providing unstructured data to other accounts via a share, or for downloading and ad-hoc analysis of unstructured data via Snowsight.\n\n\n### File URL\nA file URL is a permanent URL that identifies the database, schema, stage, and file path to a set of files, as opposed to the previous scoped URL where all this information is encrypted. A role that has sufficient privileges on the stage can access the files. It does not contain any authentication token. Authentication needs to be done when connecting through REST API. However, it works from our current authenticated Snowsight session.\n\nThe following query will provide the file urls for the mailbox `arnold-j`.\n\n```sql\nselect\n    relative_path\n    , build_stage_file_url(@email_stage_internal,relative_path) as stage_file_url\nfrom directory(@email_stage_internal) \nwhere relative_path like '%arnold-j%';\n```\n![File URL](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/7_3.png)\n\nClick on any of the stage file URLs and download the file to your workstation.\n\n### Pre-signed URL\nPre-signed URLs are used to download or access files, via a web browser for example, without authenticating into Snowflake or passing an authorization token. These URLs are ideal for business intelligence applications or reporting tools that need to display the unstructured file contents.\n\nPre-signed URLs are open but temporary. The expiration time for the access token is configurable when generating the URL. Any user or application can directly access or download the files until the expiration time is reached. \n\nThe following query will generate the pre-signed URLs for the mailbox `beck-s`. In the `get_presigned_url()` system function, we pass the parameter `300` which represents the expiration time for the token in seconds (which is 300s, so 5 minutes in this case).\n```sql\nselect\n    relative_path\n    , get_presigned_url(@email_stage_internal,relative_path,300) as presigned_url\nfrom directory(@email_stage_internal) \nwhere relative_path like '%beck-s%';\n```\nClick on any cell value in the `PRESIGNED_URL` column. This should give the actual full URL on the right hand side in the grey cell. (Click to Copy)\n\n![Pre-signed URL](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/7_4.png)\n\nOpen a new tab in your web browser, and paste the copied URL. This should download the file on your workstation. As you can see, the URL is valid even when executed outside from the Snowsight UI.\n\n\u003C!-- ------------------------ --\u003E\n## Perform Natural Language Processing\n\nWe have so far reviewed how to store unstructured data files, retrieve them, provide granular access to the files through various URLs and through secure views. In this section, we want to extract additional attributes from the files. The entities extracted are going to be person names mentioned in the emails, as well as locations. The goal is to have these additional attributes used to enrich the file-level metadata for analytics.\n\nUsing Snowpark runtimes and libraries, you can securely deploy and process Python, Java and Scala code to build pipelines, ML models, and applications in Snowflake. You can process unstructured files in Java (generally available), Python (public preview), and Scala (public preview) natively in Snowflake using Snowpark. In the following sections, you'll see how entity extraction can be done with Snowpark for both Java and Python.\n\n### Python\nFor convenience, a number of popular open source third-party Python packages that are built and provided by Anaconda are made available to use out of the box inside Snowflake via the [Anaconda Snowflake channel](https://repo.anaconda.com/pkgs/snowflake). For any third-party packages not yet included, you can use stages to import. In this example, we'll use the punkt, averaged_perceptron_tagger, maxent_ne_chunker, and words language models from the [nltk](https://www.nltk.org/) package, which have been uploaded and available on a Snowflake s3 public bucket.\n\n#### Creating a Python UDF\nCreating the UDF involves a few steps in Snowflake.\n\n1. Create the external stage mapping to the S3 bucket URI where the jar file is currently available. From the Snowflake worksheet, enter the following command:\n\n\n```sql\nuse role sysadmin;\nuse schema emaildb.raw;\n\ncreate or replace stage nltk_imports\nurl = \"s3://sfquickstarts/Getting Started Unstructured Data/Files/\"\ndirectory = (enable = true);\n```\n\n\nFrom the Snowflake worksheet, you can run the following command to confirm the nltk zip file is listed in the external stage.\n```sql\nls @nltk_imports/nltk_data;\n```\n\n![List nltk imports external stage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_8.png)\n\n2. We can now create the UDF in Snowflake as the following.\n```sql\ncreate or replace function python_parseText(file_path string)\n    returns variant\n    language python\n    runtime_version=3.8\n    imports = ('@nltk_imports/nltk_data.zip')\n    packages = ('snowflake-snowpark-python','nltk')\n    handler = 'udf_compute_named_entity'\nAS\n$$\ndef load_files(): \n    \"\"\"Helper function that loads zipped files from stage and adds them\n    to the nltk data path\"\"\"\n    import os\n    import zipfile\n    import nltk\n    import sys\n    \n    #import directory, to be able to make references to files in the stage\n    IMPORT_DIRECTORY_NAME = \"snowflake_import_directory\"\n    import_dir = sys._xoptions[IMPORT_DIRECTORY_NAME]\n    \n    #paths to extract to, this is where the nltk library will search for the required files\n    nltk_dir = \"/tmp/nltk_data\"\n        \n    with zipfile.ZipFile(os.path.join(import_dir, \"nltk_data.zip\"), 'r') as zip_ref: \n        zip_ref.extractall(nltk_dir)\n    \n    #append extracted path to the nltk library search path for usage in UDF\n    nltk.data.path.append(nltk_dir)\n\ndef udf_compute_named_entity(file_path:str) -\u003E dict:\n    from snowflake.snowpark.files import SnowflakeFile\n    import email\n    import json\n    import nltk\n    from nltk.tokenize import word_tokenize\n    from nltk.tag import pos_tag\n    from nltk.chunk import ne_chunk\n\n    def parse_email_file(file_path):\n        with SnowflakeFile.open(file_path, 'r') as f:\n            msg = email.message_from_file(f)\n            payload = \"\"\n            if msg.is_multipart():\n                for _payload in msg.get_payload():\n                    # if payload.is_multipart(): ...\n                    payload += _payload.get_payload()\n            else:\n                payload= msg.get_payload()\n            return payload\n    def perform_named_entity_extraction(text):\n        tokens = word_tokenize(text)\n        tagged_tokens = pos_tag(tokens)\n        named_entities = ne_chunk(tagged_tokens)\n        persons = []\n        locations = []\n        for entity in named_entities:\n            if hasattr(entity, 'label'):\n                if (entity.label() == 'PERSON'):\n                    persons.append(' '.join([token for token, pos in entity]))\n                elif (entity.label() == 'GPE'):\n                    locations.append(' '.join([token for token, pos in  entity]))\n        return {\"PERSONS\":persons,\"LOCATIONS\":locations}        \n    email_body = parse_email_file(file_path)\n    #load the files from stage for usage\n    load_files()\n    return perform_named_entity_extraction(email_body)\n\n$$;\n```\n\n#### Invoking the Python UDF\n\nThe UDF can be invoked on any text file containing readable english. From the email corpus stored on the internal stage, we can invoke the UDF as follows.\n\n```sql\nselect python_parseText(build_scoped_file_url(@email_stage_internal,'/sanders-r/inbox/60.')) \nas entities_extraction;\n```\n\n![Python UDF Results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_9.png)\n\nThe output is serialized as a valid JSON format by the UDF. It contains 2 arrays, one for each named entities extraction:\n\n```json\n{\n  \"LOCATIONS\": [\n    \"Toronto\"\n  ],\n  \"PERSONS\": [\n    \"Vince Carter\",\n    \"Hakeem\"\n  ]\n}\n```\n\nSince we are using pre-built models which haven’t been trained on this particular corpus, the entity extraction may not always be accurate. However, the models perform overall quite well for illustration purposes for this quickstart.\n\n### Java\nAlternatively to Python, the same entity extraction can be accomplished with Java. The Java code for the UDF has already been written and provided below. This code uses the open source [Apache OpenNLP](https://opennlp.apache.org/) library to perform natural language processing on English text in this occurrence.\n\nThe Java code leverages pre-built [machine learning models](http://opennlp.sourceforge.net/models-1.5/) to perform named entity extraction for persons and locations. These models are packaged manually post-build in a Fat JAR.\n\nAt a high level, the code does the following:\n\n- Parse the text file contents using an Apache Tika text parser. \n- Tokenize the parsed contents using a model.\n- From the tokens, perform a named entity extraction for persons and locations using pre-built ML models.\n- Serialize the results in a JSON string returned as an output.\n \n```java\npublic String ParseText(String filePath) throws IOException, SAXException, TikaException {\n\t\t\t\n// Configure gson\t      \nGsonBuilder gsonBuilder = new GsonBuilder()\nGson gson = gsonBuilder.create();\n\t\t\n//detecting the file type\nBodyContentHandler handler = new BodyContentHandler();\nMetadata metadata = new Metadata();\nParseContext pcontext=new ParseContext();\n\t     \nSnowflakeFile file = SnowflakeFile.newInstance(filePath);\nInputStream ins = file.getInputStream();\n\t      \n//Text document parser\nTXTParser  TexTParser = new TXTParser();\nTexTParser.parse(ins, handler, metadata, pcontext);\n\t      \nString Contents = handler.toString();\n\t      \nString[] AllPersonEntities = null;\nString[] AllLocationEntities = null;\nString JsonResult = null;\n\t      \nNamedEntities NE = new NamedEntities(AllPersonEntities, AllLocationEntities);\n\t\t\t\n try {\n         for(int i=0;i\u003Csentences.length;i++){\n\t          String Tokens[] = new NamedEntityExtraction().ParseTokens(Contents);\n\t          String PersonEntities[] = new NamedEntityExtraction().findName(Tokens);\n\t          String LocationEntities[] = new NamedEntityExtraction().findLocation(Tokens);\n\t           \n\t          AllPersonEntities = ArrayUtils.addAll(AllPersonEntities, PersonEntities);\n\t          AllLocationEntities = ArrayUtils.addAll(AllLocationEntities, LocationEntities);\n\t           \n\t          NE.setPersons(AllPersonEntities);\n\t          NE.setLocations(AllLocationEntities);\n\t           \n\t          JsonResult = gson.toJson(NE).toString();\t\n               \t\t\n\t\t\n\t\t\t} catch (IOException e) {\n\t            e.printStackTrace();\n\t        }\n\t\t     return(JsonResult);\n}\n```\n\nA few elements relevant for this code:\n\n- Notice the main class is __NamedEntityExtraction__.\n- The method __ParseText__ will be invoked by the UDF in the next section.\n- The file path is passed as a parameter. It can be a URL to the file, or the path on the stage.\n\n#### Creating a Java UDF\nThe precompiled jar file including all the dependencies has been uploaded and available on a Snowflake s3 public bucket. Creating the UDF involves a few steps in Snowflake.\n\n1. Create the external stage mapping to the S3 bucket URI where the jar file is currently available. From the Snowflake worksheet, enter the following command:\n\n\n```sql\nuse role sysadmin;\nuse schema emaildb.raw;\n\ncreate or replace stage jars_stage_external\nurl = \"s3://sfquickstarts/Common JARs/\"\ndirectory = (enable = true auto_refresh = true);\n```\n\n\nFrom the Snowflake worksheet, you can run the following command to confirm the jar file is listed in the external stage.\n```sql\nls @jars_stage_external;\n```\n\n![List jar external stage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_1.png)\n\n2. We can now create the UDF in Snowflake as the following.\n```sql\ncreate or replace function  java_parseText(file string)\nreturns string\nlanguage java\nimports = ('@jars_stage_external/EmailNLPv3-3.0.jar')\nhandler = 'NamedEntityExtraction.ParseText'\n;\n```\n\n### Invoking the Java UDF\nThe UDF can be invoked on any text file containing readable english. From the email corpus stored on the internal stage, we can invoke the UDF as follows.\n\n```sql\nselect java_parseText(build_scoped_file_url('@email_stage_internal','/sanders-r/inbox/60.')) \nas entities_extraction;\n```\n\n![Java UDF Results](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_2.png)\n\nThe output is serialized as a valid JSON format by the UDF. It contains 2 arrays, one for each named entities extraction:\n\n```json\n{\n\"Persons\":[\"David\",\"Richard\",\"Rob\",\"Mark\",\"Richard\",\"Vince Carter\"],\n\"Locations\":[\"Toronto\",\"Toronto\"]\n}\n```\n\n### Extracting and Storing Named Entities\nWe want to store the named entities as additional attributes for analysts to be able to select and retrieve the files of interest in their analysis, as well as perform some analytics on the attributes found. For the purpose of this quickstart, we'll use the Java UDF to complete this task. However, the same could be done using the Python UDF.\n\nWe first want to scale-up the default warehouse size to run the Java UDF at scale across all cores available on all nodes on a 2XL warehouse (64 nodes). This can be done easily and quickly because of Snowflake's instant elasticity:\n\n```sql\nalter warehouse quickstart set warehouse_size = xxlarge;\n```\n\nNow let's run the following query to store the named entities into a table.\n\n```sql\ncreate or replace table email_named_entities_base as\nselect\n    relative_path\n    , upper(replace(get(split(relative_path, '/'), 0), '\\\"', ''))\tas mailbox\n    , java_parseText(build_scoped_file_url('@email_stage_internal/', relative_path)) \t\tas named_entities\nfrom (\n    select relative_path\n    from directory(@email_stage_internal)\n    group by relative_path\n);\n```\n\nAfter running this query, we can set the `warehouse` size back to a smaller size.\n\n\u003Caside class=\"warning\"\u003E\n    THIS STEP IS VERY IMPORTANT NOT TO EXHAUST YOUR TRIAL CREDIT\n\u003C/aside\u003E\n\n```sql\nalter warehouse quickstart set warehouse_size = xsmall;\n```\n\nVerify with the following command that the warehouse is back to an `xsmall` size.\n\n```sql\nshow warehouses;\n```\n\nThe output should show the `QUICKSTART` size as being `XSMALL`. Now let's query the base table containing the named entities.\n\n```sql\nselect\n    *\nfrom email_named_entities_base\nlimit 5;\n```\n\nFor each email, we now have created a `MAILBOX` attribute, as well as JSON string containing the named entities present in the file.\n\n![Named Entities Base Table](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_3.png)\n\n### Exploring the Mailbox Corpus\nWe have now extracted the named entities the analysts are interested in seeing to do some analytics on this email corpus. We can use Snowflake native capabilities to easily store and query semi-structured data, in this case JSON.\n\nWe will first create a view to parse the `NAMED_ENTITIES` JSON string and extract separately the persons and the locations entities, as well as count the number of entities identified in each email:\n\n```sql\ncreate or replace view email_info_v\nas\nwith named_entities\nas (\n    select\n\t    relative_path\n\t    , mailbox\n\t    , parse_json(named_entities) as named_entities\n    from email_named_entities_base\n)\nselect\n    relative_path\n    , mailbox\n    , named_entities:Persons::variant\t\t\t\tas persons\n    , array_size(persons)\t\t\t\t\t\t\tas num_person_entities\n    , named_entities:Locations::variant\t\t\t\tas locations\n    , array_size(locations)\t\t\t\t\t\t\tas num_location_entities\n    , array_size(persons) + array_size(locations)\tas total_entities\n    , build_scoped_file_url(@email_stage_internal, relative_path)\tas scoped_email_url\nfrom named_entities;\n```\nWe can query the view and examine the output. Notice that we now have JSON arrays in separate columns for name and location entities, count of entities, and a scoped URL to access the file if we need to further examine its contents.\n\n```sql\nselect * from email_info_v limit 10;\n```\n![Examine view output](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_3_2.png)\n\nWe can now query the view to retrieve various entity metrics. For example, the following query identifies the top 5 emails in terms of total number of entities.\n\n```sql\nselect\n\trelative_path\n\t, mailbox\n\t, total_entities\n\t, num_person_entities\n\t, num_location_entities\n\t, persons\n\t, locations\n\t, scoped_email_url\nfrom email_info_v\norder by total_entities desc\nlimit 10;\n```\n![Number of entities view](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_4.png)\n\nThe following query aggregates statistics on the number of entities identified per mailbox and returns the top 5 mailbox by total number of entities identified in the mailbox.\n\n```sql\nselect\n\tmailbox\n\t, count(relative_path)\t\t\t\tas num_emails\n\t, sum(total_entities)\t\t\t\tas sum_entities\n\t, round(avg(total_entities))\t\tas avg_entities\n\t, sum(num_person_entities)\t\t\tas sum_persons\n\t, round(avg(num_person_entities))\tas avg_persons\n\t, sum(num_location_entities)\t\tas sum_locations\n\t, round(avg(num_location_entities))\tas avg_locations\nfrom email_info_v\ngroup by mailbox\norder by sum_entities desc\nlimit 5;\n```\n\nWe know that the most prolific mailbox is `WHITE-S` for identified entities in this email corpus.\n\n### Performing Analytics on the Mailbox\nLet’s assume an analyst wants to identify all email correspondence where the name of 'Willman' (chosen completely randomly in this example) is mentioned. With Snowflake, you can easily flatten arrays to run this type of query.\n\n```sql\nselect\n\trelative_path\n\t, mailbox\n\t, pers.value::string as person_entity\nfrom\n\temail_info_v\n\t, lateral flatten(input =\u003E Persons) pers;\n```\n![Flattened JSON array](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_5.png)\n\nWe can use the previous query as a CTE to retrieve all emails mentioning a specific person, including a scoped URL to access the actual email file.\n\n```sql\nwith persons_flattened as (\n    select\n    \trelative_path\n\t\t, mailbox\n\t\t, pers.value::string as person_entity\n    from\n    \temail_info_v\n\t\t, lateral flatten(input =\u003E Persons) pers\n)\nselect\n    relative_path\n    , mailbox\n    , person_entity\n    , build_scoped_file_url(@email_stage_internal,relative_path) as scoped_email_url\nfrom persons_flattened\nwhere person_entity like '%Willmann%';\n```\n![Mentions of Willmann](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_6.png)\n\nClick on any `MAY-L` mailbox email to download and review the email. This will allow you to learn a little bit more about that person's role and responsibilities in the Enron organization.\n\nLet’s assume we want to identify the top 5 locations mentioned in the email corpus.\n\n```sql\nwith locations_flattened as (\n    select\n    \trelative_path\n\t    , mailbox\n\t    , loc.value::string\tas location_entity\n    from\n\t    email_info_v\n\t    , lateral flatten(input =\u003E Locations) loc\n)\nselect\n    location_entity\n    , count(location_entity) as num_occurrences\nfrom locations_flattened\ngroup by location_entity\norder by num_occurrences desc\nlimit 5;\n```\n\nWe can use Snowsight to produce a visualization. Click on chart and set the settings as shown below.\n\n![Number of location occurrences bar chart](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/8_7.png)\n\nAs you can see, we can perform aggregations, and analytics on unstructured text data after extracting entities and information of interest. At this point, one could run more advanced data science use cases or visualizations using the numerous options available in Snowflake.\n\n\n\u003C!-- ------------------------ --\u003E\n## Share Unstructured Data\n\nIn this example, we want to share all the email corpus mentioning 'Willman' with another party using a Snowflake reader account.\n\n### Creating a Reader Account\nThe first step is to create a reader account as follows. Note that you will need to provide a secure password of your choice.\n\n```sql\nuse role accountadmin;\ncreate managed account if not exists emaildb_reader\nadmin_name='admin', admin_password='\u003Cpassword\u003E',\ntype=reader, COMMENT='Emaildb Reader Account';\n```\n\nThis command should return the account name, and the URL to access the account. Please copy and paste the output of the command as it provides you the login URL information for the account, as well as the user and password you chose\n\n```\n{\n\"accountName\":\"\u003Caccount_name\u003E\",\n\"loginUrl\":\"https://\u003Caccount\u003E.snowflakecomputing.com\"\n}\n```\n\nYou can run the following command to retrieve the managed account information at anytime.\n\n```sql\nshow managed accounts;\n```\n\nYou can connect to the previous reader account using the URL, and the userid/password credentials you passed as parameters.\n\n### Creating a Secure View to Share\nLet’s now create the secure view based on the query ran in the previous section that will be shared with the reader account.\n\n```sql\nuse role sysadmin;\nuse schema emaildb.raw;\n\ncreate or replace secure view email_corpus_willman_v as\nwith persons_flattened as (\n    select\n        relative_path\n        , mailbox\n        , pers.value::string as person_entity\n    from\n\t\temail_info_v\n        , lateral flatten(input =\u003E Persons) pers\n)\nselect\n    relative_path\n    , mailbox\n    , person_entity\n    , build_scoped_file_url(@email_stage_internal,relative_path) as email_url\nfrom persons_flattened\nwhere person_entity like '%Willmann%';\n```\n\n### Create the Share\n\nWe can now create the share as follows. You will need to provide the reader account name created earlier:\n\n```sql\n-- Create the share object\nuse role accountadmin;\ncreate or replace share email_corpus \ncomment='Share in scope email corpus information';\n\n--what are we sharing?\ngrant usage on database emaildb to share email_corpus;\ngrant usage on schema emaildb.raw to share email_corpus;\ngrant select on view emaildb.raw.email_corpus_willman_v to share email_corpus;\n\n-- whom are we sharing with?\nalter share email_corpus add accounts = \u003Creader-account-locator\u003E;\n```\n\nWe can review the share we have just created. The following command provides all the shares in the account.\n\n```sql\n-- check the share\nshow shares like 'email_corpus';\n```\n\nWe can get more details about the share, and the scope of the objects shared using the following command.\n\n```sql\n-- review share\ndescribe share email_corpus;\n```\n![Number of location occurrences bar chart](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/9_1.png)\n\nThis command shows us that the view `EMAIL_CORPUS_WILLMAN_V` is shared from the database `EMAILDB` and schema `RAW` in the current account.\n\n### Accessing Shared Data\nSwitch to the web browser tab where you opened the session with the reader account created in step 6.1 or open a new session on the reader account. Now, click on the blue Snowsight UI button at the top and authenticate again.\n\nAfter logging in, as this is a new account, click on the worksheet button at the top and create a new virtual warehouse.\n\n```sql\nuse role sysadmin;\n\t\ncreate or replace warehouse compute_wh \nwarehouse_size=xsmall\nauto_suspend=1\nauto_resume=true\ninitially_suspended=true;\n\ngrant usage on warehouse compute_wh to public;\n```\n\nFirst, let's switch back to the `ACCOUNTADMIN` role. Click on the __Home__ button in the top-left. Then in the top-left, click on __ADMIN__, then hover over __Switch Role__, and click on __ACCOUNTADMIN__.\n![Switch to ACCOUNTADMIN role](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/9_2.png)\n\nNow let's view the shared data. In the pane on the left, click on on __Data__, then __Private Sharing__. You will see the `EMAIL_CORPUS` database listed under __Ready to Get__. Select it and give the database name `EMAIL_CORPUS` and make it available to `PUBLIC`, then click the __Get Data__ button.\n![Get Data dialogue box](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/9_3.png)\n\nClick on __Databases__, then click on the __Refresh__ button (round arrow button on the right side above the database list). You will now see the database `EMAIL_CORPUS`.\n![EMAIL_CORPUS shared database](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/9_4.png)\n\nSelect the worksheet created in this reader account. Add the following commands to set the worksheet session parameters and review the view objects available.\n\n```sql\nuse role sysadmin;\nuse schema \u003Caccount-locator\u003E_email_corpus.raw;\nuse warehouse compute_wh;\n\nshow views;\n```\n\nIf you expand the database hierarchy on the left side of the window, you will see that the share appears from the consumer side as a database `EMAIL_CORPUS`, with a schema `RAW` and a single view object `EMAIL_CORPUS_WILLMAN_V`.\n\nWe can now query the shared data. The query below will display only the emails related to 'Willmann'.\n\n```sql\nselect * from email_corpus_willman_v;\n```\n\nFrom the results, notice that all the URLs are encrypted, not revealing any information of the location where the shared data came from. Click on any `EMAIL_URL` and get access to the actual email text downloaded to your workstation. Download and review the email. Make sure it is valid.\n![Access shared data files](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-unstructured-data/9_5.png)\n\nYou have now completed this demonstration of how unstructured data can be securely shared in the Snowflake Data Cloud using Snowflake Data Sharing capabilities.\n\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion\n\nCongratulations! You used Snowflake to perform natural language processing on email files. \n\n### What we've covered\n- Accessing external data with an __External Stage__\n- Storing unstructured data with an __Internal Stage__ and __SnowSQL__\n- Governing unstructured data with __Role-Based Access Control__\n- Catalog unstructured data with __Directory Tables__\n- Securely access unstructured data with __Scoped, File, and Pre-signed URLs__\n- Processing unstructured data with __Snowpark for Python and Java__\n- Sharing unstructured data in the __Data Cloud__\n\n### Related Resources\n- [Unstructured Data Docs](https://docs.snowflake.com/en/user-guide/unstructured.html)\n- [Java UDF Docs](https://docs.snowflake.com/en/developer-guide/udf/java/udf-java.html)\n- [Snowpark Docs](https://docs.snowflake.com/en/developer-guide/snowpark/index.html)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-c2a80d4bd3","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-b3b27acbf1",":itemsOrder":["quickstart_last_modi","text"],":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-61a9749b3b","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-21ef4f431c","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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and Terms of Use\u003C/h2\u003E","\u003Ch2\u003EPrepare your lab environment\u003C/h2\u003E","\u003Ch2\u003EStore & Access Unstructured Data\u003C/h2\u003E","\u003Ch2\u003EGovern Unstructured Data Access\u003C/h2\u003E","\u003Ch2\u003ECatalog Unstructured Data using Directory Tables\u003C/h2\u003E","\u003Ch2\u003EURLs for Secure Access\u003C/h2\u003E","\u003Ch2\u003EPerform Natural Language Processing\u003C/h2\u003E","\u003Ch2\u003EShare Unstructured 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