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------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis guide is designed to help you understand the capabilities included in Snowflake’s support for Apache Iceberg. Iceberg Tables, now generally available, bring Snowflake’s easy platform management and great performance to data stored externally in the open source Apache Iceberg format.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIceberg Tables are now generally available.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA simple, open data lakehouse with Snowflake, Iceberg, and your cloud of choice\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create a Snowflake-managed Iceberg Table\u003C/li\u003E\u003Cli\u003EHow to apply governance policies on an Iceberg Table\u003C/li\u003E\u003Cli\u003EHow Snowpark can be used for Iceberg Table pipelines\u003C/li\u003E\u003Cli\u003EHow to share an Iceberg Table\u003C/li\u003E\u003Cli\u003EHow to access a Snowflake-managed Iceberg Table from Spark and DuckDB\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites or What You Will Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account. A \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003Efree trial\u003C/a\u003E will suffice. \u003Ca href=\"https://docs.snowflake.com/en/user-guide/intro-editions#standard-edition\"\u003EStandard Edition\u003C/a\u003E will work for most of this lab, but if you’d like to try governance features covered in section 4, you will need \u003Ca href=\"https://docs.snowflake.com/en/user-guide/intro-editions#enterprise-edition\"\u003EEnterprise\u003C/a\u003E or \u003Ca href=\"https://docs.snowflake.com/en/user-guide/intro-editions#business-critical-edition\"\u003EBusiness Critical Edition\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EA storage bucket with the same cloud provider in the same region that hosts your Snowflake account above. Direct credential access required as storage integrations are not supported for External Volumes.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Your Environment\u003C/h2\u003E\n","\u003Ch3\u003EInstall Conda, Spark, DuckDB, Jupyter\u003C/h3\u003E\n","\u003Cp\u003EIn this quickstart, you can use Conda to easily create a development environment and download necessary packages. This is only needed if you choose to follow the last section for using Spark to read Snowflake-managed Iceberg Tables. This is not required to create or use Iceberg Tables on Snowflake. Here are instructions for installing Conda:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html\"\u003EMac\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html\"\u003EWindows\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html\"\u003ELinux\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EEither download \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-iceberg-tables/blob/main/environment.yml\"\u003Ethis file\u003C/a\u003E, or create a file named environment.yml with the following contents.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Ename: iceberg-lab\nchannels:\n  - conda-forge\ndependencies:\n  - findspark=2.0.1\n  - jupyter\n  - openjdk=11\n  - pyspark=3.5.5\n  - python-duckdb\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo create the environment needed, run the following in your shell.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econda env create -f environment.yml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESetup Snowflake\u003C/h3\u003E\n","\u003Cp\u003EIn a Worksheet, create a database, schema, warehouse, role, and user called \u003Ccode\u003EICEBERG_LAB\u003C/code\u003E in your Snowflake account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE WAREHOUSE iceberg_lab;\nCREATE ROLE iceberg_lab;\nCREATE DATABASE iceberg_lab;\nCREATE SCHEMA iceberg_lab;\nGRANT ALL ON DATABASE iceberg_lab TO ROLE iceberg_lab WITH GRANT OPTION;\nGRANT ALL ON SCHEMA iceberg_lab.iceberg_lab TO ROLE iceberg_lab WITH GRANT OPTION;\nGRANT ALL ON WAREHOUSE iceberg_lab TO ROLE iceberg_lab WITH GRANT OPTION;\n\nCREATE USER iceberg_lab\n    PASSWORD='<your desired password>',\n    LOGIN_NAME='ICEBERG_LAB',\n    MUST_CHANGE_PASSWORD=FALSE,\n    DISABLED=FALSE,\n    DEFAULT_WAREHOUSE='ICEBERG_LAB',\n    DEFAULT_NAMESPACE='ICEBERG_LAB.ICEBERG_LAB',\n    DEFAULT_ROLE='ICEBERG_LAB';\n\nGRANT ROLE iceberg_lab TO USER iceberg_lab;\nSET USERNAME=CURRENT_USER();\nGRANT ROLE iceberg_lab TO USER IDENTIFIER($USERNAME);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis quickstart guide can be run from Snowflake Worksheets or Notebook. In this example, we will upload an existing notebook (.ipynb) into a Snowflake account. To load the demo notebook into your account, follow these steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn a browser tab for GitHub, download \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-iceberg-tables/blob/main/snowflake_notebook.ipynb\"\u003Ethis notebook\u003C/a\u003E by clicking \u003Cstrong\u003EDownload raw file\u003C/strong\u003E from the top-right.\u003C/li\u003E\u003Cli\u003EIn a browser tab for Snowflake, navigate to \u003Cstrong\u003EProject » Notebooks\u003C/strong\u003E from the left menu bar.\u003C/li\u003E\u003Cli\u003EClick the dropdown arrow next to \u003Cstrong\u003E+ Notebook\u003C/strong\u003E in the top-right, then click \u003Cstrong\u003EImport .ipynb file\u003C/strong\u003E, and select the .ipynb file you've downloaded and click \u003Cstrong\u003EOpen\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003ECreate Notebook\u003C/strong\u003E dialog will show up. Select \u003Ccode\u003EICEBERG_LAB\u003C/code\u003E database, schema, and warehouse, and click \u003Cstrong\u003ECreate\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-iceberg-tables/2-1_create-notebook.png\" alt=\"Create Notebook\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/2-2_setup-notebook.png\" alt=\"Setup Notebook\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate an Iceberg Table\u003C/h2\u003E\n","\u003Ch3\u003ECreate an External Volume\u003C/h3\u003E\n","\u003Cp\u003EBefore you create an Iceberg table, you must have an external volume. An external volume is a Snowflake object that stores information about your cloud storage locations and identity and access management (IAM) entities (for example, IAM roles). Snowflake uses an external volume to establish a connection with your cloud storage in order to access Iceberg metadata and Parquet data.\u003C/p\u003E\n","\u003Cp\u003ETo create an external volume, complete the instructions for your cloud storage service:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume-s3\"\u003EAccessing Amazon S3 using external volumes\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume-gcs\"\u003EAccessing Google Cloud Storage using external volumes\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume-azure\"\u003EAccessing Microsoft Azure Storage using external volumes\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EPrior to creating the external volume, be sure to use the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE accountadmin;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EName the external volume you create \u003Ccode\u003Eiceberg_lab_vol\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EAfter the external volume is created, use the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role to grant usage to the \u003Ccode\u003EICEBERG_LAB\u003C/code\u003E role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EGRANT ALL ON EXTERNAL VOLUME iceberg_lab_vol TO ROLE iceberg_lab WITH GRANT OPTION;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate a Snowflake-managed Iceberg Table\u003C/h3\u003E\n","\u003Cp\u003ESnowflake supports multiple Iceberg catalog options including Iceberg REST, Snowflake, AWS Glue. In this quickstart, we use Snowflake as the catalog to allow read and write operations to tables. More information about integrating catalogs can be found \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-catalog-integration\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ECreate an Iceberg Table referencing the external volume you just created. You can specify \u003Ccode\u003EBASE_LOCATION\u003C/code\u003E to instruct Snowflake where to write table data and metadata, or leave empty to write data and metadata to the root location specified in the external volume definition.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE iceberg_lab;\nUSE DATABASE iceberg_lab;\nUSE SCHEMA iceberg_lab;\nCREATE OR REPLACE ICEBERG TABLE customer_iceberg (\n    c_custkey INTEGER,\n    c_name STRING,\n    c_address STRING,\n    c_nationkey INTEGER,\n    c_phone STRING,\n    c_acctbal INTEGER,\n    c_mktsegment STRING,\n    c_comment STRING\n)  \n    CATALOG='SNOWFLAKE'\n    EXTERNAL_VOLUME='iceberg_lab_vol'\n    BASE_LOCATION='iceberg_lab/iceberg_lab/customer_iceberg';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELoad Data\u003C/h3\u003E\n","\u003Cp\u003EThere are \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-load\"\u003Emultiple ways to load new data\u003C/a\u003E into Snowflake-managed Iceberg Tables including INSERT, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-iceberg\"\u003Estreaming\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/kafka-connector-iceberg\"\u003EKafka Connector\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/copy-into-table\"\u003ECOPY INTO\u003C/a\u003E, and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-auto\"\u003ESnowpipe\u003C/a\u003E including options. COPY INTO and Snowpipe provide \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/copy-into-table#label-copy-into-table-usage-notes-iceberg-parquet\"\u003Eoptions\u003C/a\u003E to register compatible Parquet files into Iceberg tables instead of a full scan or transformation.\u003C/p\u003E\n","\u003Cp\u003EFor this quickstart, we will INSERT data from the sample tables in your Snowflake account to an Iceberg Table. This will write Parquet files and Iceberg metadata to your external volume.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO customer_iceberg\n  SELECT * FROM snowflake_sample_data.tpch_sf1.customer;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you check your cloud storage bucket, you should now see files that Snowflake has written as part of table creation. While Snowflake writes these files automatically, you can also use a \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/system_get_iceberg_table_information\"\u003Efunction\u003C/a\u003E to generate table metadata files that capture any data manipulation language (DML) changes that have been made since the last time Iceberg metadata was generated.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/3-3_storage.png\" alt=\"Storage\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EQuery and Time Travel\u003C/h3\u003E\n","\u003Cp\u003EIceberg Tables are treated much like other tables in Snowflake. For example, you can read different table types in a single query. This query is joining an Iceberg Table with a traditional Snowflake Table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    *\nFROM customer_iceberg c\nINNER JOIN snowflake_sample_data.tpch_sf1.nation n\n    ON c.c_nationkey = n.n_nationkey;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can also leverage Snowflake's built-in LLM functions to easily leverage AI in your queries as demonstrated \u003Ca href=\"/en/developers/guides/cortex-ai-sentiment-iceberg/\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EBenefits of the additional metadata that table formats like Iceberg and Snowflake’s provide are, for example, time travel.\u003C/p\u003E\n","\u003Cp\u003ELet’s first make a simple update to the table. Then, you can see that the row count has increased compared to the previous version of the table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESET query_id = LAST_QUERY_ID();\nINSERT INTO customer_iceberg\n    SELECT\n        *\n    FROM snowflake_sample_data.tpch_sf1.customer\n    LIMIT 5;\n\n\nSELECT\n    count(*) AS after_row_count,\n    before_row_count\nFROM customer_iceberg\nJOIN (\n        SELECT count(*) AS before_row_count\n        FROM customer_iceberg AT(STATEMENT=> $query_id)\n    )\n    ON 1=1\nGROUP BY 2;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/3-4_time-travel.png\" alt=\"Time Travel\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGovernance on Iceberg Tables\u003C/h2\u003E\n","\u003Cp\u003EGovernance and access controls work on Iceberg Tables just like internal tables. As described in the overview section, all of these features require Enterprise or Business Critical Edition of Snowflake.\u003C/p\u003E\n","\u003Ch3\u003ERow-level Security\u003C/h3\u003E\n","\u003Cp\u003ESuppose you need to control row-level access to an Iceberg Table for users having different roles. In this example, let's have a role that can see the US customers and one that only sees the non-US customers.\u003C/p\u003E\n","\u003Cp\u003EThis can be done with a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-row-using\"\u003Erow access policy\u003C/a\u003E on the Iceberg Table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE accountadmin;\nCREATE OR REPLACE ROLE tpch_us;\nSET USERNAME=CURRENT_USER();\nGRANT ROLE tpch_us TO USER IDENTIFIER($USERNAME);\nCREATE OR REPLACE ROLE tpch_intl;\nGRANT ROLE tpch_intl TO USER IDENTIFIER($USERNAME);\n\nUSE ROLE iceberg_lab;\nUSE DATABASE iceberg_lab;\nUSE SCHEMA iceberg_lab;\n\nCREATE ROW ACCESS POLICY rap_nation\nAS (nation_key number) RETURNS BOOLEAN ->\n  ('TPCH_US' = current_role() and nation_key = 24) OR\n  ('TPCH_INTL' = current_role() and nation_key != 24)\n;\n\nALTER ICEBERG TABLE customer_iceberg\nADD ROW ACCESS POLICY rap_nation ON (c_nationkey);\n\nGRANT ALL ON DATABASE iceberg_lab TO ROLE tpch_intl;\nGRANT ALL ON SCHEMA iceberg_lab.iceberg_lab TO ROLE tpch_intl;\nGRANT ALL ON ICEBERG TABLE iceberg_lab.iceberg_lab.customer_iceberg TO ROLE tpch_intl;\nGRANT ALL ON DATABASE iceberg_lab TO ROLE tpch_us;\nGRANT ALL ON SCHEMA iceberg_lab.iceberg_lab TO ROLE tpch_us;\nGRANT ALL ON ICEBERG TABLE iceberg_lab.iceberg_lab.customer_iceberg TO ROLE tpch_us;\nGRANT USAGE ON EXTERNAL VOLUME iceberg_lab_vol TO ROLE tpch_intl;\nGRANT USAGE ON EXTERNAL VOLUME iceberg_lab_vol TO ROLE tpch_us;\nGRANT USAGE ON WAREHOUSE iceberg_lab TO ROLE tpch_us;\nGRANT USAGE ON WAREHOUSE iceberg_lab TO ROLE tpch_intl;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThere are two separate roles to grant to Snowflake users, which allow them to see a subset of customers, either international or us.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE tpch_intl;\nUSE WAREHOUSE iceberg_lab;\nSELECT\n    count(*)\nFROM iceberg_lab.iceberg_lab.customer_iceberg;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE tpch_us;\nUSE WAREHOUSE iceberg_lab;\nSELECT\n    count(*)\nFROM iceberg_lab.iceberg_lab.customer_iceberg;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/4-1_rap.png\" alt=\"RAP\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EColumn-level Security\u003C/h3\u003E\n","\u003Cp\u003EWe want the team of analysts to be able to query the customer table but not see their name(c_name), address (c_address), or phone number(c_phone). To do so, we need to grant them access to all the rows but mask those fields.\u003C/p\u003E\n","\u003Cp\u003EWe can do that with a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/security-column-ddm-use\"\u003Emasking policy\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE accountadmin;\nCREATE OR REPLACE ROLE tpch_analyst;\nSET USERNAME=CURRENT_USER();\nGRANT ROLE tpch_analyst TO USER IDENTIFIER($USERNAME);\n\nUSE ROLE iceberg_lab;\nALTER ROW ACCESS POLICY rap_nation\nSET body ->\n  ('TPCH_US' = current_role() and nation_key = 24) or\n  ('TPCH_INTL' = current_role() and nation_key != 24) or\n  ('TPCH_ANALYST' = current_role()) or \n  ('ICEBERG_LAB' = current_role())\n;\n\nGRANT ALL ON DATABASE iceberg_lab TO ROLE tpch_analyst;\nGRANT ALL ON SCHEMA iceberg_lab.iceberg_lab TO ROLE tpch_analyst;\nGRANT ALL ON TABLE iceberg_lab.iceberg_lab.customer_iceberg TO ROLE tpch_analyst;\nGRANT USAGE ON WAREHOUSE iceberg_lab TO ROLE tpch_analyst;\nGRANT USAGE ON EXTERNAL VOLUME iceberg_lab_vol TO ROLE tpch_analyst;\nUSE ROLE iceberg_lab;\n\nCREATE OR REPLACE MASKING POLICY pii_mask AS (val string) RETURNS string ->\n    CASE\n        WHEN 'TPCH_ANALYST' = current_role() THEN '*********'\n        ELSE val\n    END;\n\nALTER ICEBERG TABLE customer_iceberg MODIFY COLUMN c_name SET MASKING POLICY pii_mask;\nALTER ICEBERG TABLE customer_iceberg MODIFY COLUMN c_address SET MASKING POLICY pii_mask;\nALTER ICEBERG TABLE customer_iceberg MODIFY COLUMN c_phone SET MASKING POLICY pii_mask;\n\nUSE ROLE tpch_analyst;\nSELECT\n    *\nFROM customer_iceberg;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/4-2_masking.png\" alt=\"Masking\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOther governance features can be applied to Iceberg Tables, including \u003Ca href=\"https://docs.snowflake.com/en/user-guide/object-tagging\"\u003Eobject tagging\u003C/a\u003E, and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tag-based-masking-policies\"\u003Etag-based masking\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EMonitor Governance in Snowsight\u003C/h3\u003E\n","\u003Cp\u003EAs a data administrator, you can use the built-in Dashboard and Tagged Objects interfaces to monitor and report on the usage of policies and tags with tables, views, and columns. This includes policies and tags applied to Iceberg Tables.\u003C/p\u003E\n","\u003Cp\u003EUsing the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role, or an account role that is granted the \u003Ccode\u003EGOVERNANCE_VIEWER\u003C/code\u003E and \u003Ccode\u003EOBJECT_VIEWER\u003C/code\u003E database roles, click \u003Cstrong\u003EData » Governance\u003C/strong\u003E to navigate to these interfaces. You can see the policies applied to the Iceberg Table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/4-3_governance-ui.png\" alt=\"Governance UI\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAnd if you notice certain tables are missing tags or policies, you can modify, create, and apply them directly from the interface.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/4-3_ui-rap.png\" alt=\"UI Create Tag RAP\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/4-3_ui-mask.png\" alt=\"UI Create Tag Mask\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETransform Iceberg Tables\u003C/h2\u003E\n","\u003Cp\u003ERaw data in Iceberg tables may require further cleaning, transformation, and aggregation for downstream consumption. Snowflake supports multiple options for building and orchestrating pipelines including:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/index\"\u003ESnowpark\u003C/a\u003E: Build and run pipelines with Python, including \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.DataFrameWriter.save_as_table\"\u003Esupport for Iceberg\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-intro\"\u003EDynamic Tables\u003C/a\u003E: An automated way to transform data, including \u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-create-iceberg\"\u003Esupport for Iceberg\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-pipelines-intro\"\u003EStreams & Tasks\u003C/a\u003E: Incorporate CDC and custom orchestration on top of Iceberg tables.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDynamic Tables\u003C/h3\u003E\n","\u003Cp\u003ECreating and orchestrating a transformation pipeline can be as simple as a SQL query of the desired results, target refresh lag, and let Snowflake automatically handle CDC, incremental processing, and when to start refreshes based on target lag.\u003C/p\u003E\n","\u003Cp\u003EDynamic Tables can be stored in Iceberg format. Create a Dynamic Iceberg Table as shown below.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE iceberg_lab;\nUSE DATABASE iceberg_lab;\nUSE SCHEMA iceberg_lab;\n\nCREATE OR REPLACE ICEBERG TABLE orders_iceberg \n    CATALOG = 'SNOWFLAKE'\n    EXTERNAL_VOLUME = 'iceberg_lab_vol'\n    BASE_LOCATION = 'iceberg_lab/iceberg_lab/orders_iceberg'\n    AS\n    SELECT * FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS;\n\nCREATE OR REPLACE ICEBERG TABLE nation_iceberg \n    CATALOG = 'SNOWFLAKE'\n    EXTERNAL_VOLUME = 'iceberg_lab_vol'\n    BASE_LOCATION = 'iceberg_lab/iceberg_lab/nation_iceberg'\n    AS\n    SELECT * FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.NATION;\n\nCREATE OR REPLACE DYNAMIC ICEBERG TABLE nation_orders_iceberg\n    TARGET_LAG = '1 minute'\n    WAREHOUSE = ICEBERG_LAB\n    CATALOG = 'SNOWFLAKE'\n    EXTERNAL_VOLUME = 'iceberg_lab_vol'\n    BASE_LOCATION = 'iceberg_lab/iceberg_lab/nation_orders_iceberg'\n    AS\n    SELECT\n        n.n_regionkey AS regionkey,\n        n.n_nationkey AS nationkey,\n        n.n_name AS nation,\n        c.c_custkey AS custkey,\n        COUNT(o.o_orderkey) AS order_count,\n        SUM(o.o_totalprice) AS total_price\n    FROM ICEBERG_LAB.ICEBERG_LAB.ORDERS_ICEBERG o\n    JOIN ICEBERG_LAB.ICEBERG_LAB.CUSTOMER_ICEBERG c\n        ON o.o_custkey = c.c_custkey\n    JOIN ICEBERG_LAB.ICEBERG_LAB.NATION_ICEBERG n\n        ON c.c_nationkey = n.n_nationkey\n    GROUP BY\n        n.n_regionkey,\n        n.n_nationkey,\n        n.n_name,\n        c.c_custkey\n    ;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo monitor refresh history and examine upstream/downstream dependencies in a graph view, navigate to \u003Cstrong\u003EMonitoring » Dynamic Tables\u003C/strong\u003E, and click on \u003Cstrong\u003EKeep Session Running\u003C/strong\u003E. Then click on \u003Ccode\u003ENATION_ORDERS_ICEBERG\u003C/code\u003E from the list.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/5-1_dit.png\" alt=\"Dynamic Table\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ESnowpark\u003C/h3\u003E\n","\u003Cp\u003ESnowpark allows you to interact with Iceberg Tables using \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark/python/working-with-dataframes\"\u003EDataFrames\u003C/a\u003E that are lazily executed and can be used for data transformation and machine learning use cases. Let’s try this by first navigating back to your Snowflake notebook under \u003Cstrong\u003EProjects » Notebooks » snowflake_notebook\u003C/strong\u003E , and in the list of cells on the right click on \u003Ccode\u003Epy_snowpark\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ESnowflake Notebooks make it easy to switch between running SQL and Python. Run this cell which uses Python to write a DataFrame as an Iceberg table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark.context import get_active_session\nfrom snowflake.snowpark.functions import col, rank\nfrom snowflake.snowpark.window import Window\n\nsession = get_active_session()\n\ndb = "iceberg_lab"\nschema = "iceberg_lab"\n\n# Load the input table\ndf = session.table("ICEBERG_LAB.ICEBERG_LAB.NATION_ORDERS_ICEBERG")\n\n# Define a window partitioned by nation, ordered by total_price descending\nnation_window = Window.partition_by("nation").order_by(col("total_price").desc())\n\n# Rank customers within each nation\ndf_ranked = df.with_column("nation_rank", rank().over(nation_window))\n\n# Flag top 3 customers per nation as VIPs\ndf_vips = df_ranked.with_column("is_vip", (col("nation_rank") <= 3))\n\n# Show the results\ndf_vips = df_vips.select("nationkey", "custkey", "total_price", "nation_rank", "is_vip")\n\noutput_table = "customer_vips_iceberg"\n\niceberg_config = {\n    "external_volume": "iceberg_lab_vol",\n    "catalog": "snowflake",\n    "base_location": f"{db}/{schema}/{output_table}",\n    "storage_serialization_policy": "COMPATIBLE",\n}\n\ndf_vips.show()\ndf_vips.write.mode("overwrite").save_as_table(f"{output_table}", iceberg_config=iceberg_config)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/5-2_snowpark.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWith this Iceberg table created based on other Iceberg tables, you can view the full lineage by clicking on the \u003Ccode\u003EICEBERG_LAB.ICEBERG_LAB.CUSTOMER_VIP_ICEBERG\u003C/code\u003E table in the \u003Cstrong\u003EDatabases\u003C/strong\u003E tab, then click on the \u003Cstrong\u003ELineage\u003C/strong\u003E tab.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/5-3_lineage.png\" alt=\"Lineage\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor a deeper dive on Snowpark for data engineering pipelines, try \u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003Ethis quickstart\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESharing Iceberg Tables\u003C/h2\u003E\n","\u003Cp\u003EIceberg Tables can be securely shared with consumers either through their own Snowflake account or a provisioned Snowflake Reader account. The consumer can be an external entity or a different internal business unit that is required to have its own unique Snowflake account.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ECross-cloud and cross-region sharing of Iceberg Tables is not currently supported. The provider’s external volume, Snowflake account, and consumer’s Snowflake account must all be in the same cloud region.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWith data sharing, including Iceberg Tables:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThere is only one copy of the data that’s stored in the data provider's cloud storage account.\u003C/li\u003E\u003Cli\u003EThe latest snapshot available in the producer account is always live, real-time, and immediately available to consumers.\u003C/li\u003E\u003Cli\u003EProviders can establish revocable, fine-grained access to shares.\u003C/li\u003E\u003Cli\u003EData sharing is simple and safe, especially compared to older data sharing methods, which were often manual and insecure, such as transferring large .csv files across the internet.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESuppose you have a partner who wants to analyze the data in your ICEBERG_LAB database on a near real-time basis. This partner also has their own Snowflake account in the same region as our account. Data sharing is an easy, secure solution to allow them to access this information.\u003C/p\u003E\n","\u003Ch3\u003ECreating a Reader Account\u003C/h3\u003E\n","\u003Cp\u003EFor the purposes of this lab, we’ll share data with a provisioned reader account. Return to your SQL worksheet, and grant the ICEBERG_LAB role the ability to create a reader account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE accountadmin;\nGRANT CREATE ACCOUNT ON ACCOUNT TO ROLE iceberg_lab;\nUSE ROLE ICEBERG_LAB;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExit your SQL worksheet and navigate to \u003Cstrong\u003EPrivate Sharing\u003C/strong\u003E, then click the tab \u003Cstrong\u003EReader Accounts\u003C/strong\u003E near the top of your window, then click \u003Cstrong\u003E+ New\u003C/strong\u003E. Use ICEBERG_LAB_READER as the Account Name, READER_ADMIN as the User Name, and provide a password. Then click \u003Cstrong\u003ECreate Account\u003C/strong\u003E. You’ll see the reader account now listed.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/6-1_create-reader.png\" alt=\"Create Reader Account\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate a Secure View\u003C/h3\u003E\n","\u003Cp\u003ENow create a secure view which is what will eventually be shared with the ICEBERG_LAB_READER account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE iceberg_lab;\nCREATE OR REPLACE SECURE VIEW nation_orders_v AS\nSELECT\n    nation,\n    SUM(order_count) as order_count,\n    SUM(total_price) as total_price\nFROM nation_orders_iceberg\nGROUP BY nation;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate an Outbound Share\u003C/h3\u003E\n","\u003Cp\u003EBy default, ACCOUNTADMIN is the only role that can create shares. From your SQL worksheet, grant the ICEBERG_LAB role to create a share, then use this role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE accountadmin;\nGRANT CREATE SHARE ON ACCOUNT TO ROLE iceberg_lab;\nUSE ROLE iceberg_lab;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExit the SQL worksheet and navigate to \u003Cstrong\u003EData » Private Sharing\u003C/strong\u003E, then click on the \u003Cstrong\u003EShared by My Account\u003C/strong\u003E tab near the top of your window, then click the \u003Cstrong\u003EShare\u003C/strong\u003E button in the top-right corner and select \u003Cstrong\u003ECreate a Direct Share\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/6-2_create-new-share.png\" alt=\"Select Share Data\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003E+ Select Data\u003C/strong\u003E and navigate to the ICEBERG_LAB database and schema. Select the NATION_ORDERS_V view you created in the schema and click the \u003Cstrong\u003EDone\u003C/strong\u003E button. Edit the default name to a more descriptive value that will help identify the share in the future (e.g. ICEBERG_LAB_NATION_ORDERS_SHARED_DATA). You can also add a comment.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/6-2_create-share.png\" alt=\"Create Share\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAccessing Shared Data\u003C/h3\u003E\n","\u003Cp\u003EIn a separate browser tab, login to the reader account previously created. After logging in, as this is a new account, create a new SQL worksheet.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE accountadmin;\n\nCREATE OR REPLACE WAREHOUSE iceberg_lab_reader \n    WAREHOUSE_SIZE = XSMALL\n    AUTO_SUSPEND = 1\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow let's view the shared data. Exit the worksheet, and in the pane on the left, click on on \u003Cstrong\u003EData » Private Sharing\u003C/strong\u003E. You will see ICEBERG_LAB_NATION_ORDERS_SHARED_DATA listed under Direct Shares. Select \u003Cstrong\u003EGet Data\u003C/strong\u003E and name it READER_ICEBERG_LAB_NATION_ORDERS_SHARED_DATA. Make it available to the PUBLIC role, then click \u003Cstrong\u003EGet Data\u003C/strong\u003E, then click \u003Cstrong\u003EView Database\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EYou can now query the shared data, which is a view on top of an Iceberg table. Run the queries below in a SQL worksheet to create a warehouse and see which countries had the most orders.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT *\nFROM reader_iceberg_lab_nation_orders_shared_data.iceberg_lab.nation_orders_v\nORDER BY order_count DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/6-3_query-share.png\" alt=\"Query Share\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs changes are made to the Iceberg Table from the producer’s account, those changes are available nearly instantly in the reader account. No copying or transferring of data required! The single copy of data is stored in your cloud storage.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERead with Other Engines\u003C/h2\u003E\n","\u003Cp\u003ESuppose other teams use use other engines such as Apache Spark or DuckDB to read the Snowflake-managed Iceberg tables. They can directly access data and metadata in object storage, all without using any Snowflake warehouses.\u003C/p\u003E\n","\u003Ch3\u003ERead with Apache Spark\u003C/h3\u003E\n","\u003Cp\u003EFrom your terminal, run the following commands to activate the virtual environment you created in the setup, and open Jupyter notebooks.\u003C/p\u003E\n","\u003Cp\u003EVerify that you are running Java 11 from the output:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sh\"\u003Econda activate iceberg-lab\nexport PATH=/opt/anaconda3/envs/iceberg-lab/lib/jvm/bin:$PATH\njava --version\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EStart Jupyter:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sh\"\u003Ejupyter notebook\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDownload the notebook \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-iceberg-tables/blob/main/iceberg_lab.ipynb\"\u003Eiceberg_lab.ipynb provided here\u003C/a\u003E, then open from Jupyter. Update and run the cells that are applicable to the cloud in which your Snowflake account is located.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/7_jupyter-notebook.png\" alt=\"PySpark\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ERead with DuckDB\u003C/h3\u003E\n","\u003Cp\u003ETo complete this step you will need to have DuckDB installed. \u003Ccode\u003Epython-duckdb\u003C/code\u003E was included in the virtual environment created at the beginning of this quickstart. Otherwise if needed, \u003Ca href=\"https://duckdb.org/#quickinstall\"\u003Einstall DuckDB\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EDownload the notebook \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-getting-started-with-iceberg-tables/blob/main/duckdb.ipynb\"\u003Educkdb.ipynb provided here\u003C/a\u003E, then open from Jupyter. Update and run the cells that are applicable to the cloud in which your Snowflake account is located.\u003C/p\u003E\n","\u003Cp\u003EImport DuckDB and Pandas, and create an in-memory DuckDB database.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport duckdb\nimport pandas as pd\n\nconn = duckdb.connect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGet your Iceberg metadata location by running the following query in Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT PARSE_JSON(SYSTEM$GET_ICEBERG_TABLE_INFORMATION('CUSTOMER_ICEBERG'))['metadataLocation']::varchar;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will need to have credentials to access the blob storage where the metadata and data are located.\u003C/p\u003E\n","\u003Cp\u003EExample creation of a secret for Amazon S3 access:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eaws_access_key_id = '<your AWS access key ID>'\naws_secret_access_key = '<your AWS secret access key>'\naws_s3_region = '<your s3 region>'\n\nconn.sql(f"""\n    CREATE OR REPLACE SECRET s3_credentials (\n        TYPE s3,\n        KEY_ID '{aws_access_key_id}',\n        SECRET '{aws_secret_access_key}',\n        REGION '{aws_s3_region}'\n        );\n""")\nCREATE SECRET (\n    TYPE GCS,\n    KEY_ID '<YOUR_HMAC_KEY>',\n    SECRET '<YOUR_HMAC_SECRET>'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExample creation of a secret for Google Cloud Storage access:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Egcs_hmac_key_id = '<your HMAC key ID>'\ngcs_hmac_secret = '<your HMAC secret>'\nconn.sql(f"""\n    CREATE OR REPLACE SECRET gcs_credentials (\n        TYPE gcs,\n        KEY_ID '{gcs_hmac_key_id}',\n        SECRET '{gcs_hmac_secret}'\n        );\n""")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can now query the table directly from DuckDB.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Edf = conn.sql(f"""\n    SELECT *\n    FROM iceberg_scan('{snapshot_path}');\n""").df()\n\ndf.head()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/7_duckdb.png\" alt=\"DuckDB\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow teams can use data stored in Snowflake using both Snowflake as well as DuckDB (as well as other tools supporing Iceberg).\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003ETo delete all of the objects created in this guide, you can drop the user, role, database, and warehouse.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE iceberg_lab;\nDROP SHARE iceberg_lab_nation_orders_shared_data;\nDROP DATABASE iceberg_lab;\nUSE ROLE accountadmin;\nDROP EXTERNAL VOLUME iceberg_lab_vol;\nDROP USER iceberg_lab;\nDROP ROLE iceberg_lab;\nDROP ROLE tpch_us;\nDROP ROLE tpch_intl;\nDROP ROLE tpch_analyst;\nDROP WAREHOUSE iceberg_lab;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully created an open data lakehouse on Snowflake with Iceberg.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create a Snowflake-managed Iceberg Table\u003C/li\u003E\u003Cli\u003EHow to apply governance policies on an Iceberg Table\u003C/li\u003E\u003Cli\u003EHow Snowpark can be used for Iceberg Table pipelines\u003C/li\u003E\u003Cli\u003EHow to share an Iceberg Table\u003C/li\u003E\u003Cli\u003EHow to access a Snowflake-managed Iceberg Table from Apache Spark and DuckDB\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/tables-iceberg\"\u003ESnowflake Documentation for Iceberg Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://iceberg.apache.org/\"\u003EApache Iceberg Documentation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Create a Snowflake-managed open data lakehouse with Apache Iceberg Tables, governance policies, and Spark integration.","title":"Getting Started with Iceberg Tables","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nThis guide is designed to help you understand the capabilities included in Snowflake’s support for Apache Iceberg. Iceberg Tables, now generally available, bring Snowflake’s easy platform management and great performance to data stored externally in the open source Apache Iceberg format.\n\n\u003E \n\u003E \n\u003E  Iceberg Tables are now generally available.\n\n### What You Will Build \n- A simple, open data lakehouse with Snowflake, Iceberg, and your cloud of choice\n\n### What You Will Learn \n- How to create a Snowflake-managed Iceberg Table\n- How to apply governance policies on an Iceberg Table\n- How Snowpark can be used for Iceberg Table pipelines\n- How to share an Iceberg Table\n- How to access a Snowflake-managed Iceberg Table from Spark and DuckDB\n\n### Prerequisites or What You Will Need \n- A Snowflake account. A [free trial](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) will suffice. [Standard Edition](https://docs.snowflake.com/en/user-guide/intro-editions#standard-edition) will work for most of this lab, but if you’d like to try governance features covered in section 4, you will need [Enterprise](https://docs.snowflake.com/en/user-guide/intro-editions#enterprise-edition) or [Business Critical Edition](https://docs.snowflake.com/en/user-guide/intro-editions#business-critical-edition).\n- A storage bucket with the same cloud provider in the same region that hosts your Snowflake account above. Direct credential access required as storage integrations are not supported for External Volumes.\n\n\u003C!-- ------------------------ --\u003E\n## Setup Your Environment\n\n### Install Conda, Spark, DuckDB, Jupyter\n\nIn this quickstart, you can use Conda to easily create a development environment and download necessary packages. This is only needed if you choose to follow the last section for using Spark to read Snowflake-managed Iceberg Tables. This is not required to create or use Iceberg Tables on Snowflake. Here are instructions for installing Conda:\n- [Mac](https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html)\n- [Windows](https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html)\n- [Linux](https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html)\n\nEither download [this file](https://github.com/Snowflake-Labs/sfguide-getting-started-with-iceberg-tables/blob/main/environment.yml), or create a file named environment.yml with the following contents.\n\n```yaml\nname: iceberg-lab\nchannels:\n  - conda-forge\ndependencies:\n  - findspark=2.0.1\n  - jupyter\n  - openjdk=11\n  - pyspark=3.5.5\n  - python-duckdb\n```\n\nTo create the environment needed, run the following in your shell.\n\n```\nconda env create -f environment.yml\n```\n\n\n### Setup Snowflake\n\nIn a Worksheet, create a database, schema, warehouse, role, and user called `ICEBERG_LAB` in your Snowflake account.\n\n```sql\nCREATE WAREHOUSE iceberg_lab;\nCREATE ROLE iceberg_lab;\nCREATE DATABASE iceberg_lab;\nCREATE SCHEMA iceberg_lab;\nGRANT ALL ON DATABASE iceberg_lab TO ROLE iceberg_lab WITH GRANT OPTION;\nGRANT ALL ON SCHEMA iceberg_lab.iceberg_lab TO ROLE iceberg_lab WITH GRANT OPTION;\nGRANT ALL ON WAREHOUSE iceberg_lab TO ROLE iceberg_lab WITH GRANT OPTION;\n\nCREATE USER iceberg_lab\n    PASSWORD='\u003Cyour desired password\u003E',\n    LOGIN_NAME='ICEBERG_LAB',\n    MUST_CHANGE_PASSWORD=FALSE,\n    DISABLED=FALSE,\n    DEFAULT_WAREHOUSE='ICEBERG_LAB',\n    DEFAULT_NAMESPACE='ICEBERG_LAB.ICEBERG_LAB',\n    DEFAULT_ROLE='ICEBERG_LAB';\n\nGRANT ROLE iceberg_lab TO USER iceberg_lab;\nSET USERNAME=CURRENT_USER();\nGRANT ROLE iceberg_lab TO USER IDENTIFIER($USERNAME);\n```\n\nThis quickstart guide can be run from Snowflake Worksheets or Notebook. In this example, we will upload an existing notebook (.ipynb) into a Snowflake account. To load the demo notebook into your account, follow these steps:\n1. In a browser tab for GitHub, download [this notebook](https://github.com/Snowflake-Labs/sfguide-getting-started-with-iceberg-tables/blob/main/snowflake_notebook.ipynb) by clicking **Download raw file** from the top-right.\n2. In a browser tab for Snowflake, navigate to **Project » Notebooks** from the left menu bar.\n3. Click the dropdown arrow next to **+ Notebook** in the top-right, then click **Import .ipynb file**, and select the .ipynb file you've downloaded and click **Open**.\n4. A **Create Notebook** dialog will show up. Select `ICEBERG_LAB` database, schema, and warehouse, and click **Create**.\n\n![Create Notebook](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/2-1_create-notebook.png)\n![Setup Notebook](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/2-2_setup-notebook.png)\n\n\u003C!-- ------------------------ --\u003E\n## Create an Iceberg Table\n\n### Create an External Volume\n\nBefore you create an Iceberg table, you must have an external volume. An external volume is a Snowflake object that stores information about your cloud storage locations and identity and access management (IAM) entities (for example, IAM roles). Snowflake uses an external volume to establish a connection with your cloud storage in order to access Iceberg metadata and Parquet data.\n\nTo create an external volume, complete the instructions for your cloud storage service:\n- [Accessing Amazon S3 using external volumes](https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume-s3)\n- [Accessing Google Cloud Storage using external volumes](https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume-gcs)\n- [Accessing Microsoft Azure Storage using external volumes](https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume-azure)\n\nPrior to creating the external volume, be sure to use the `ACCOUNTADMIN` role.\n\n```sql\nUSE ROLE accountadmin;\n```\n\nName the external volume you create `iceberg_lab_vol`.\n\nAfter the external volume is created, use the `ACCOUNTADMIN` role to grant usage to the `ICEBERG_LAB` role.\n\n```sql\nGRANT ALL ON EXTERNAL VOLUME iceberg_lab_vol TO ROLE iceberg_lab WITH GRANT OPTION;\n```\n\n### Create a Snowflake-managed Iceberg Table\n\nSnowflake supports multiple Iceberg catalog options including Iceberg REST, Snowflake, AWS Glue. In this quickstart, we use Snowflake as the catalog to allow read and write operations to tables. More information about integrating catalogs can be found [here](https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-catalog-integration).\n\nCreate an Iceberg Table referencing the external volume you just created. You can specify `BASE_LOCATION` to instruct Snowflake where to write table data and metadata, or leave empty to write data and metadata to the root location specified in the external volume definition.\n\n```sql\nUSE ROLE iceberg_lab;\nUSE DATABASE iceberg_lab;\nUSE SCHEMA iceberg_lab;\nCREATE OR REPLACE ICEBERG TABLE customer_iceberg (\n    c_custkey INTEGER,\n    c_name STRING,\n    c_address STRING,\n    c_nationkey INTEGER,\n    c_phone STRING,\n    c_acctbal INTEGER,\n    c_mktsegment STRING,\n    c_comment STRING\n)  \n    CATALOG='SNOWFLAKE'\n    EXTERNAL_VOLUME='iceberg_lab_vol'\n    BASE_LOCATION='iceberg_lab/iceberg_lab/customer_iceberg';\n```\n\n### Load Data\n\nThere are [multiple ways to load new data](https://docs.snowflake.com/en/user-guide/tables-iceberg-load) into Snowflake-managed Iceberg Tables including INSERT, [streaming](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-iceberg), [Kafka Connector](https://docs.snowflake.com/en/user-guide/kafka-connector-iceberg), [COPY INTO](https://docs.snowflake.com/en/sql-reference/sql/copy-into-table), and [Snowpipe](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-auto) including options. COPY INTO and Snowpipe provide [options](https://docs.snowflake.com/en/sql-reference/sql/copy-into-table#label-copy-into-table-usage-notes-iceberg-parquet) to register compatible Parquet files into Iceberg tables instead of a full scan or transformation.\n\nFor this quickstart, we will INSERT data from the sample tables in your Snowflake account to an Iceberg Table. This will write Parquet files and Iceberg metadata to your external volume.\n\n```sql\nINSERT INTO customer_iceberg\n  SELECT * FROM snowflake_sample_data.tpch_sf1.customer;\n```\n\nIf you check your cloud storage bucket, you should now see files that Snowflake has written as part of table creation. While Snowflake writes these files automatically, you can also use a [function](https://docs.snowflake.com/en/sql-reference/functions/system_get_iceberg_table_information) to generate table metadata files that capture any data manipulation language (DML) changes that have been made since the last time Iceberg metadata was generated.\n\n![Storage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/3-3_storage.png)\n\n### Query and Time Travel\n\nIceberg Tables are treated much like other tables in Snowflake. For example, you can read different table types in a single query. This query is joining an Iceberg Table with a traditional Snowflake Table.\n\n```sql\nSELECT\n    *\nFROM customer_iceberg c\nINNER JOIN snowflake_sample_data.tpch_sf1.nation n\n    ON c.c_nationkey = n.n_nationkey;\n```\n\nYou can also leverage Snowflake's built-in LLM functions to easily leverage AI in your queries as demonstrated [here](/en/developers/guides/cortex-ai-sentiment-iceberg/).\n\nBenefits of the additional metadata that table formats like Iceberg and Snowflake’s provide are, for example, time travel.\n\nLet’s first make a simple update to the table. Then, you can see that the row count has increased compared to the previous version of the table.\n\n```sql\nSET query_id = LAST_QUERY_ID();\nINSERT INTO customer_iceberg\n    SELECT\n        *\n    FROM snowflake_sample_data.tpch_sf1.customer\n    LIMIT 5;\n\n\nSELECT\n    count(*) AS after_row_count,\n    before_row_count\nFROM customer_iceberg\nJOIN (\n        SELECT count(*) AS before_row_count\n        FROM customer_iceberg AT(STATEMENT=\u003E $query_id)\n    )\n    ON 1=1\nGROUP BY 2;\n```\n\n![Time Travel](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/3-4_time-travel.png)\n\n\u003C!-- ------------------------ --\u003E\n## Governance on Iceberg Tables\n\nGovernance and access controls work on Iceberg Tables just like internal tables. As described in the overview section, all of these features require Enterprise or Business Critical Edition of Snowflake.\n\n### Row-level Security\n\nSuppose you need to control row-level access to an Iceberg Table for users having different roles. In this example, let's have a role that can see the US customers and one that only sees the non-US customers.\n\nThis can be done with a [row access policy](https://docs.snowflake.com/en/user-guide/security-row-using) on the Iceberg Table.\n\n```sql\nUSE ROLE accountadmin;\nCREATE OR REPLACE ROLE tpch_us;\nSET USERNAME=CURRENT_USER();\nGRANT ROLE tpch_us TO USER IDENTIFIER($USERNAME);\nCREATE OR REPLACE ROLE tpch_intl;\nGRANT ROLE tpch_intl TO USER IDENTIFIER($USERNAME);\n\nUSE ROLE iceberg_lab;\nUSE DATABASE iceberg_lab;\nUSE SCHEMA iceberg_lab;\n\nCREATE ROW ACCESS POLICY rap_nation\nAS (nation_key number) RETURNS BOOLEAN -\u003E\n  ('TPCH_US' = current_role() and nation_key = 24) OR\n  ('TPCH_INTL' = current_role() and nation_key != 24)\n;\n\nALTER ICEBERG TABLE customer_iceberg\nADD ROW ACCESS POLICY rap_nation ON (c_nationkey);\n\nGRANT ALL ON DATABASE iceberg_lab TO ROLE tpch_intl;\nGRANT ALL ON SCHEMA iceberg_lab.iceberg_lab TO ROLE tpch_intl;\nGRANT ALL ON ICEBERG TABLE iceberg_lab.iceberg_lab.customer_iceberg TO ROLE tpch_intl;\nGRANT ALL ON DATABASE iceberg_lab TO ROLE tpch_us;\nGRANT ALL ON SCHEMA iceberg_lab.iceberg_lab TO ROLE tpch_us;\nGRANT ALL ON ICEBERG TABLE iceberg_lab.iceberg_lab.customer_iceberg TO ROLE tpch_us;\nGRANT USAGE ON EXTERNAL VOLUME iceberg_lab_vol TO ROLE tpch_intl;\nGRANT USAGE ON EXTERNAL VOLUME iceberg_lab_vol TO ROLE tpch_us;\nGRANT USAGE ON WAREHOUSE iceberg_lab TO ROLE tpch_us;\nGRANT USAGE ON WAREHOUSE iceberg_lab TO ROLE tpch_intl;\n```\n\nThere are two separate roles to grant to Snowflake users, which allow them to see a subset of customers, either international or us.\n\n```sql\nUSE ROLE tpch_intl;\nUSE WAREHOUSE iceberg_lab;\nSELECT\n    count(*)\nFROM iceberg_lab.iceberg_lab.customer_iceberg;\n```\n\n```sql\nUSE ROLE tpch_us;\nUSE WAREHOUSE iceberg_lab;\nSELECT\n    count(*)\nFROM iceberg_lab.iceberg_lab.customer_iceberg;\n```\n\n![RAP](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/4-1_rap.png)\n\n\n### Column-level Security\n\nWe want the team of analysts to be able to query the customer table but not see their name(c_name), address (c_address), or phone number(c_phone). To do so, we need to grant them access to all the rows but mask those fields. \n\nWe can do that with a [masking policy](https://docs.snowflake.com/en/user-guide/security-column-ddm-use).\n\n```sql\nUSE ROLE accountadmin;\nCREATE OR REPLACE ROLE tpch_analyst;\nSET USERNAME=CURRENT_USER();\nGRANT ROLE tpch_analyst TO USER IDENTIFIER($USERNAME);\n\nUSE ROLE iceberg_lab;\nALTER ROW ACCESS POLICY rap_nation\nSET body -\u003E\n  ('TPCH_US' = current_role() and nation_key = 24) or\n  ('TPCH_INTL' = current_role() and nation_key != 24) or\n  ('TPCH_ANALYST' = current_role()) or \n  ('ICEBERG_LAB' = current_role())\n;\n\nGRANT ALL ON DATABASE iceberg_lab TO ROLE tpch_analyst;\nGRANT ALL ON SCHEMA iceberg_lab.iceberg_lab TO ROLE tpch_analyst;\nGRANT ALL ON TABLE iceberg_lab.iceberg_lab.customer_iceberg TO ROLE tpch_analyst;\nGRANT USAGE ON WAREHOUSE iceberg_lab TO ROLE tpch_analyst;\nGRANT USAGE ON EXTERNAL VOLUME iceberg_lab_vol TO ROLE tpch_analyst;\nUSE ROLE iceberg_lab;\n\nCREATE OR REPLACE MASKING POLICY pii_mask AS (val string) RETURNS string -\u003E\n    CASE\n        WHEN 'TPCH_ANALYST' = current_role() THEN '*********'\n        ELSE val\n    END;\n\nALTER ICEBERG TABLE customer_iceberg MODIFY COLUMN c_name SET MASKING POLICY pii_mask;\nALTER ICEBERG TABLE customer_iceberg MODIFY COLUMN c_address SET MASKING POLICY pii_mask;\nALTER ICEBERG TABLE customer_iceberg MODIFY COLUMN c_phone SET MASKING POLICY pii_mask;\n\nUSE ROLE tpch_analyst;\nSELECT\n    *\nFROM customer_iceberg;\n```\n\n![Masking](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/4-2_masking.png)\n\nOther governance features can be applied to Iceberg Tables, including [object tagging](https://docs.snowflake.com/en/user-guide/object-tagging), and [tag-based masking](https://docs.snowflake.com/en/user-guide/tag-based-masking-policies).\n\n### Monitor Governance in Snowsight\n\nAs a data administrator, you can use the built-in Dashboard and Tagged Objects interfaces to monitor and report on the usage of policies and tags with tables, views, and columns. This includes policies and tags applied to Iceberg Tables.\n\nUsing the `ACCOUNTADMIN` role, or an account role that is granted the `GOVERNANCE_VIEWER` and `OBJECT_VIEWER` database roles, click **Data » Governance** to navigate to these interfaces. You can see the policies applied to the Iceberg Table.\n\n![Governance UI](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/4-3_governance-ui.png)\n\nAnd if you notice certain tables are missing tags or policies, you can modify, create, and apply them directly from the interface.\n\n![UI Create Tag RAP](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/4-3_ui-rap.png)\n\n![UI Create Tag Mask](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/4-3_ui-mask.png)\n\n\u003C!-- ------------------------ --\u003E\n## Transform Iceberg Tables\n\nRaw data in Iceberg tables may require further cleaning, transformation, and aggregation for downstream consumption. Snowflake supports multiple options for building and orchestrating pipelines including:\n- [Snowpark](https://docs.snowflake.com/en/developer-guide/snowpark/index): Build and run pipelines with Python, including [support for Iceberg](https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.DataFrameWriter.save_as_table).\n- [Dynamic Tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-intro): An automated way to transform data, including [support for Iceberg](https://docs.snowflake.com/en/user-guide/dynamic-tables-create-iceberg).\n- [Streams & Tasks](https://docs.snowflake.com/en/user-guide/data-pipelines-intro): Incorporate CDC and custom orchestration on top of Iceberg tables.\n\n### Dynamic Tables\nCreating and orchestrating a transformation pipeline can be as simple as a SQL query of the desired results, target refresh lag, and let Snowflake automatically handle CDC, incremental processing, and when to start refreshes based on target lag.\n\nDynamic Tables can be stored in Iceberg format. Create a Dynamic Iceberg Table as shown below.\n\n```sql\nUSE ROLE iceberg_lab;\nUSE DATABASE iceberg_lab;\nUSE SCHEMA iceberg_lab;\n\nCREATE OR REPLACE ICEBERG TABLE orders_iceberg \n    CATALOG = 'SNOWFLAKE'\n    EXTERNAL_VOLUME = 'iceberg_lab_vol'\n    BASE_LOCATION = 'iceberg_lab/iceberg_lab/orders_iceberg'\n    AS\n    SELECT * FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.ORDERS;\n\nCREATE OR REPLACE ICEBERG TABLE nation_iceberg \n    CATALOG = 'SNOWFLAKE'\n    EXTERNAL_VOLUME = 'iceberg_lab_vol'\n    BASE_LOCATION = 'iceberg_lab/iceberg_lab/nation_iceberg'\n    AS\n    SELECT * FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.NATION;\n\nCREATE OR REPLACE DYNAMIC ICEBERG TABLE nation_orders_iceberg\n    TARGET_LAG = '1 minute'\n    WAREHOUSE = ICEBERG_LAB\n    CATALOG = 'SNOWFLAKE'\n    EXTERNAL_VOLUME = 'iceberg_lab_vol'\n    BASE_LOCATION = 'iceberg_lab/iceberg_lab/nation_orders_iceberg'\n    AS\n    SELECT\n        n.n_regionkey AS regionkey,\n        n.n_nationkey AS nationkey,\n        n.n_name AS nation,\n        c.c_custkey AS custkey,\n        COUNT(o.o_orderkey) AS order_count,\n        SUM(o.o_totalprice) AS total_price\n    FROM ICEBERG_LAB.ICEBERG_LAB.ORDERS_ICEBERG o\n    JOIN ICEBERG_LAB.ICEBERG_LAB.CUSTOMER_ICEBERG c\n        ON o.o_custkey = c.c_custkey\n    JOIN ICEBERG_LAB.ICEBERG_LAB.NATION_ICEBERG n\n        ON c.c_nationkey = n.n_nationkey\n    GROUP BY\n        n.n_regionkey,\n        n.n_nationkey,\n        n.n_name,\n        c.c_custkey\n    ;\n```\n\nTo monitor refresh history and examine upstream/downstream dependencies in a graph view, navigate to **Monitoring » Dynamic Tables**, and click on **Keep Session Running**. Then click on `NATION_ORDERS_ICEBERG` from the list.\n\n![Dynamic Table](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/5-1_dit.png)\n\n### Snowpark\nSnowpark allows you to interact with Iceberg Tables using [DataFrames](https://docs.snowflake.com/en/developer-guide/snowpark/python/working-with-dataframes) that are lazily executed and can be used for data transformation and machine learning use cases. Let’s try this by first navigating back to your Snowflake notebook under **Projects » Notebooks » snowflake_notebook** , and in the list of cells on the right click on `py_snowpark`.\n\nSnowflake Notebooks make it easy to switch between running SQL and Python. Run this cell which uses Python to write a DataFrame as an Iceberg table.\n\n```python\nfrom snowflake.snowpark.context import get_active_session\nfrom snowflake.snowpark.functions import col, rank\nfrom snowflake.snowpark.window import Window\n\nsession = get_active_session()\n\ndb = \"iceberg_lab\"\nschema = \"iceberg_lab\"\n\n# Load the input table\ndf = session.table(\"ICEBERG_LAB.ICEBERG_LAB.NATION_ORDERS_ICEBERG\")\n\n# Define a window partitioned by nation, ordered by total_price descending\nnation_window = Window.partition_by(\"nation\").order_by(col(\"total_price\").desc())\n\n# Rank customers within each nation\ndf_ranked = df.with_column(\"nation_rank\", rank().over(nation_window))\n\n# Flag top 3 customers per nation as VIPs\ndf_vips = df_ranked.with_column(\"is_vip\", (col(\"nation_rank\") \u003C= 3))\n\n# Show the results\ndf_vips = df_vips.select(\"nationkey\", \"custkey\", \"total_price\", \"nation_rank\", \"is_vip\")\n\noutput_table = \"customer_vips_iceberg\"\n\niceberg_config = {\n    \"external_volume\": \"iceberg_lab_vol\",\n    \"catalog\": \"snowflake\",\n    \"base_location\": f\"{db}/{schema}/{output_table}\",\n    \"storage_serialization_policy\": \"COMPATIBLE\",\n}\n\ndf_vips.show()\ndf_vips.write.mode(\"overwrite\").save_as_table(f\"{output_table}\", iceberg_config=iceberg_config)\n```\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/5-2_snowpark.png)\n\nWith this Iceberg table created based on other Iceberg tables, you can view the full lineage by clicking on the `ICEBERG_LAB.ICEBERG_LAB.CUSTOMER_VIP_ICEBERG` table in the **Databases** tab, then click on the **Lineage** tab.\n\n![Lineage](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/5-3_lineage.png)\n\nFor a deeper dive on Snowpark for data engineering pipelines, try [this quickstart](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/).\n\n\u003C!-- ------------------------ --\u003E\n## Sharing Iceberg Tables\n\nIceberg Tables can be securely shared with consumers either through their own Snowflake account or a provisioned Snowflake Reader account. The consumer can be an external entity or a different internal business unit that is required to have its own unique Snowflake account.\n\n\u003E \n\u003E \n\u003E  Cross-cloud and cross-region sharing of Iceberg Tables is not currently supported. The provider’s external volume, Snowflake account, and consumer’s Snowflake account must all be in the same cloud region.\n\nWith data sharing, including Iceberg Tables:\n- There is only one copy of the data that’s stored in the data provider's cloud storage account.\n- The latest snapshot available in the producer account is always live, real-time, and immediately available to consumers.\n- Providers can establish revocable, fine-grained access to shares.\n- Data sharing is simple and safe, especially compared to older data sharing methods, which were often manual and insecure, such as transferring large .csv files across the internet.\n\nSuppose you have a partner who wants to analyze the data in your ICEBERG_LAB database on a near real-time basis. This partner also has their own Snowflake account in the same region as our account. Data sharing is an easy, secure solution to allow them to access this information.\n\n### Creating a Reader Account\n\nFor the purposes of this lab, we’ll share data with a provisioned reader account. Return to your SQL worksheet, and grant the ICEBERG_LAB role the ability to create a reader account.\n\n```sql\nUSE ROLE accountadmin;\nGRANT CREATE ACCOUNT ON ACCOUNT TO ROLE iceberg_lab;\nUSE ROLE ICEBERG_LAB;\n```\n\nExit your SQL worksheet and navigate to **Private Sharing**, then click the tab **Reader Accounts** near the top of your window, then click **+ New**. Use ICEBERG_LAB_READER as the Account Name, READER_ADMIN as the User Name, and provide a password. Then click **Create Account**. You’ll see the reader account now listed.\n\n![Create Reader Account](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/6-1_create-reader.png)\n\n### Create a Secure View\n\nNow create a secure view which is what will eventually be shared with the ICEBERG_LAB_READER account.\n\n```sql\nUSE ROLE iceberg_lab;\nCREATE OR REPLACE SECURE VIEW nation_orders_v AS\nSELECT\n    nation,\n    SUM(order_count) as order_count,\n    SUM(total_price) as total_price\nFROM nation_orders_iceberg\nGROUP BY nation;\n```\n\n### Create an Outbound Share\n\nBy default, ACCOUNTADMIN is the only role that can create shares. From your SQL worksheet, grant the ICEBERG_LAB role to create a share, then use this role.\n\n```sql\nUSE ROLE accountadmin;\nGRANT CREATE SHARE ON ACCOUNT TO ROLE iceberg_lab;\nUSE ROLE iceberg_lab;\n```\n\nExit the SQL worksheet and navigate to **Data » Private Sharing**, then click on the **Shared by My Account** tab near the top of your window, then click the **Share** button in the top-right corner and select **Create a Direct Share**.\n\n![Select Share Data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/6-2_create-new-share.png)\n\nClick **+ Select Data** and navigate to the ICEBERG_LAB database and schema. Select the NATION_ORDERS_V view you created in the schema and click the **Done** button. Edit the default name to a more descriptive value that will help identify the share in the future (e.g. ICEBERG_LAB_NATION_ORDERS_SHARED_DATA). You can also add a comment.\n\n![Create Share](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/6-2_create-share.png)\n\n### Accessing Shared Data\n\nIn a separate browser tab, login to the reader account previously created. After logging in, as this is a new account, create a new SQL worksheet.\n\n```sql\nUSE ROLE accountadmin;\n\nCREATE OR REPLACE WAREHOUSE iceberg_lab_reader \n    WAREHOUSE_SIZE = XSMALL\n    AUTO_SUSPEND = 1\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE;\n```\n\nNow let's view the shared data. Exit the worksheet, and in the pane on the left, click on on **Data » Private Sharing**. You will see ICEBERG_LAB_NATION_ORDERS_SHARED_DATA listed under Direct Shares. Select **Get Data** and name it READER_ICEBERG_LAB_NATION_ORDERS_SHARED_DATA. Make it available to the PUBLIC role, then click **Get Data**, then click **View Database**.\n\nYou can now query the shared data, which is a view on top of an Iceberg table. Run the queries below in a SQL worksheet to create a warehouse and see which countries had the most orders.\n\n```sql\nSELECT *\nFROM reader_iceberg_lab_nation_orders_shared_data.iceberg_lab.nation_orders_v\nORDER BY order_count DESC;\n```\n\n![Query Share](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/6-3_query-share.png)\n\nAs changes are made to the Iceberg Table from the producer’s account, those changes are available nearly instantly in the reader account. No copying or transferring of data required! The single copy of data is stored in your cloud storage.\n\n\u003C!-- ------------------------ --\u003E\n## Read with Other Engines\n\nSuppose other teams use use other engines such as Apache Spark or DuckDB to read the Snowflake-managed Iceberg tables. They can directly access data and metadata in object storage, all without using any Snowflake warehouses.\n\n### Read with Apache Spark\nFrom your terminal, run the following commands to activate the virtual environment you created in the setup, and open Jupyter notebooks.\n\nVerify that you are running Java 11 from the output:\n\n```sh\nconda activate iceberg-lab\nexport PATH=/opt/anaconda3/envs/iceberg-lab/lib/jvm/bin:$PATH\njava --version\n```\n\nStart Jupyter:\n\n```sh\njupyter notebook\n```\n\nDownload the notebook [iceberg_lab.ipynb provided here](https://github.com/Snowflake-Labs/sfguide-getting-started-with-iceberg-tables/blob/main/iceberg_lab.ipynb), then open from Jupyter. Update and run the cells that are applicable to the cloud in which your Snowflake account is located.\n\n![PySpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/7_jupyter-notebook.png)\n\n### Read with DuckDB\nTo complete this step you will need to have DuckDB installed. `python-duckdb` was included in the virtual environment created at the beginning of this quickstart. Otherwise if needed, [install DuckDB](https://duckdb.org/#quickinstall).\n\nDownload the notebook [duckdb.ipynb provided here](https://github.com/Snowflake-Labs/sfguide-getting-started-with-iceberg-tables/blob/main/duckdb.ipynb), then open from Jupyter. Update and run the cells that are applicable to the cloud in which your Snowflake account is located.\n\nImport DuckDB and Pandas, and create an in-memory DuckDB database.\n```python\nimport duckdb\nimport pandas as pd\n\nconn = duckdb.connect()\n```\n\nGet your Iceberg metadata location by running the following query in Snowflake.\n\n```sql\nSELECT PARSE_JSON(SYSTEM$GET_ICEBERG_TABLE_INFORMATION('CUSTOMER_ICEBERG'))['metadataLocation']::varchar;\n```\n\nYou will need to have credentials to access the blob storage where the metadata and data are located.\n\nExample creation of a secret for Amazon S3 access:\n\n```python\naws_access_key_id = '\u003Cyour AWS access key ID\u003E'\naws_secret_access_key = '\u003Cyour AWS secret access key\u003E'\naws_s3_region = '\u003Cyour s3 region\u003E'\n\nconn.sql(f\"\"\"\n    CREATE OR REPLACE SECRET s3_credentials (\n        TYPE s3,\n        KEY_ID '{aws_access_key_id}',\n        SECRET '{aws_secret_access_key}',\n        REGION '{aws_s3_region}'\n        );\n\"\"\")\nCREATE SECRET (\n    TYPE GCS,\n    KEY_ID '\u003CYOUR_HMAC_KEY\u003E',\n    SECRET '\u003CYOUR_HMAC_SECRET\u003E'\n);\n```\n\nExample creation of a secret for Google Cloud Storage access:\n\n```python\ngcs_hmac_key_id = '\u003Cyour HMAC key ID\u003E'\ngcs_hmac_secret = '\u003Cyour HMAC secret\u003E'\nconn.sql(f\"\"\"\n    CREATE OR REPLACE SECRET gcs_credentials (\n        TYPE gcs,\n        KEY_ID '{gcs_hmac_key_id}',\n        SECRET '{gcs_hmac_secret}'\n        );\n\"\"\")\n```\n\nYou can now query the table directly from DuckDB.\n\n```sql\ndf = conn.sql(f\"\"\"\n    SELECT *\n    FROM iceberg_scan('{snapshot_path}');\n\"\"\").df()\n\ndf.head()\n```\n\n![DuckDB](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-iceberg-tables/7_duckdb.png)\n\nNow teams can use data stored in Snowflake using both Snowflake as well as DuckDB (as well as other tools supporing Iceberg). \n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\nTo delete all of the objects created in this guide, you can drop the user, role, database, and warehouse.\n\n```sql\nUSE ROLE iceberg_lab;\nDROP SHARE iceberg_lab_nation_orders_shared_data;\nDROP DATABASE iceberg_lab;\nUSE ROLE accountadmin;\nDROP EXTERNAL VOLUME iceberg_lab_vol;\nDROP USER iceberg_lab;\nDROP ROLE iceberg_lab;\nDROP ROLE tpch_us;\nDROP ROLE tpch_intl;\nDROP ROLE tpch_analyst;\nDROP WAREHOUSE iceberg_lab;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion\n\nCongratulations! You've successfully created an open data lakehouse on Snowflake with Iceberg.\n\n### What You Learned\n- How to create a Snowflake-managed Iceberg Table\n- How to apply governance policies on an Iceberg Table\n- How Snowpark can be used for Iceberg Table pipelines\n- How to share an Iceberg Table\n- How to access a Snowflake-managed Iceberg Table from Apache Spark and DuckDB\n\n### Related Resources\n- [Snowflake Documentation for Iceberg Tables](https://docs.snowflake.com/en/user-guide/tables-iceberg)\n- [Apache Iceberg Documentation](https://iceberg.apache.org/)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-77081d587c","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-ddaf95ff61",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-6e7b73dc42","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-5d0465087f","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. It may be out of date with current Snowflake 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