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and modeling.","paragraphs":["\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EDatameer provides users with data discovery & transformation capabilities to visually explore and transform their data to identify the key insights for improving data driven decisions. Users can transform their data using a variety of no-code, low-code and SQL transformation recipes and then quickly publish this data back into Snowflake to support the development of BI dashboards, reports and data science driven insights in your organization. All this can be delivered without the need for IT assistance or formal training and without leaving a single user interface.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/architecture.png\" alt=\"Architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn this training lab you will learn how to model and transform your data, create highly valuable datasets, and publish the data into Snowflake. No coding skills are necessary using our no-code tools or writing code using our SQL toolset. Or even use both! We will provide you with a test Datameer & Snowflake instance for this hands-on lab.\u003C/p\u003E\n","\u003Ch3\u003ELab Overview\u003C/h3\u003E\n","\u003Cp\u003EThis entry-level lab introduces you to the user interface and basic capabilities of Datameer. When done with the lab you should be ready to create your own data transformations against your own Snowflake data.\u003C/p\u003E\n","\u003Ch3\u003ETarget Audience\u003C/h3\u003E\n","\u003Cp\u003EAnyone who will be exploring & transforming data in Snowflake. Includes technical roles like members of an Analytics & BI team, data engineers to non-technical business users in the various lines of business. No SQL or technical skills are needed to do this lab.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll learn\u003C/h3\u003E\n","\u003Cp\u003EIn this workshop lab, you will experience the following capabilities in Datameer:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESearch and discover your Snowflake inventory\u003C/li\u003E\u003Cli\u003EPrepare and curate your data sets\u003C/li\u003E\u003Cli\u003EExplore and analyze your data sets\u003C/li\u003E\u003Cli\u003EShare and deploy your data sets\u003C/li\u003E\u003Cli\u003EDocument and catalog your new datasets\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EBusiness Use Case\u003C/h3\u003E\n","\u003Cp\u003EFor the purposes of this workshop, you play the role of a loans analyst. Throughout this lab we will be working with some current and historical loans data from our fictitious loans company. We will begin with looking at our current loans data only, but throughout the course of the lab will incorporate some geographical information to transform & understand our loan portfolio by state/region being able to spot business opportunities in these regions.\u003C/p\u003E\n","\u003Ch2\u003EPrepare Your Lab Environment\u003C/h2\u003E\n","\u003Cp\u003EClick the link below to get to the trial setup:\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.datameer.com/trial\"\u003EDatameer Trial\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/trial_setup_one.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe first time you login to Datameer you can choose to be taken directly to a Project, supported by an onboarding checklist which guides you though essential Datameer features. We will begin exploring and transforming data in the next module.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/project_one_loans_selcted.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBut before, let's have a quick look at the Datameer Workbench UI:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/UI_description.png\" alt=\"Login\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EData Browser\u003C/strong\u003E - provides all available Snowflake schemas and datasets, and the 'File Upload' feature as well as browsing your data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFlow Area\u003C/strong\u003E - visually represents your whole transformation pipeline, including data sources, transformations as well as deployed data sets\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData Grid\u003C/strong\u003E - provides a data preview, allows you to explore columns of a dataset, calculates column metrics, and provides SQL query for further usage; individual explorations can be executed to visualize your data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInspector\u003C/strong\u003E - presents all information such as 'Source', 'Description' or 'Owner' about the currently selected dataset/ Project or schema that is selected\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EData Exploration\u003C/h2\u003E\n","\u003Cp\u003ELet’s add our first data source by clicking the \u003Cstrong\u003E+\u003C/strong\u003E button next to the \u003Ccode\u003ELOANS\u003C/code\u003E table from the Data Browser. This will add the table to our Workbench for discovery and transformation.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/project_one_loans_selcted.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe can explore this dataset in three different ways:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPreview\u003C/strong\u003E - Under the ´Preview´ tab you can see the actual data values and total number of records.  Datameer also utilizes Snowflake’s native sampling when transforming large tables. This will be indicated in the ´Preview´ tab with the ‘Sampled’ label.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EColumns\u003C/strong\u003E - This tab contains the column names, data types, and descriptions.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProfile\u003C/strong\u003E - You can view column metrics within this tab, by clicking on the ´Calculate´ button for the desired column. Click on the  \u003Cstrong\u003ECalculate\u003C/strong\u003E button for the \u003Ccode\u003EGRADE\u003C/code\u003E column, and you can see the number of values, empty values, uniques, nulls, and the distribution of values.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/loans_project.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet’s now add another dataset to this Project - this time by adding the \u003Ccode\u003EZIPCODE\u003C/code\u003E dataset by selecting the \u003Cstrong\u003E+\u003C/strong\u003E button next to the table. You should see both added datasets in your Flow Area.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/loans_zipcode.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Ch2\u003ENo-Code Transformations\u003C/h2\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003ELOANS\u003C/strong\u003E node and select the \u003Cstrong\u003E+\u003C/strong\u003E button to bring up the list of no-code transformations.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/transform_one.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet’s apply a filter by doing the following:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003EFilter\u003C/strong\u003E in the popup context menu.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ELOAN_STATUS\u003C/strong\u003E as the column to filter by.\u003C/li\u003E\u003Cli\u003ELeave \u003Cstrong\u003EEquals\u003C/strong\u003E as the operator.\u003C/li\u003E\u003Cli\u003EEnter \u003Cstrong\u003ECurrent\u003C/strong\u003E (case sensitive) as the value to search by.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EApply\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-datameer/filter.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003EHere we can see a Filter condition in the UI with a Recipe step on the right hand side. You may see another purple hint appear on the screen informing you that you have created your first Receipe.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/filter_loan_status_result.png\" alt=\"Filter loan status\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet’s apply another transformation. The ´Extract´ operation allows us to extract a certain number of characters from one field to a new column name. In our example we want to extract the first 3 digits from our \u003Ccode\u003EZIP_CODE\u003C/code\u003E column and populate this in a new column name called \u003Ccode\u003EZIP\u003C/code\u003E.  (Please note we use an INDEX value starting at 0 and extract 3 characters from this position to get the first 3 characters from this column.)\u003C/p\u003E\n","\u003Cp\u003EHere are the steps for an Extract:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick the \u003Cstrong\u003E+\u003C/strong\u003E button next to the ´Filter´ node and select \u003Cstrong\u003EExtract\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EZIP_CODE\u003C/strong\u003E for the source column.\u003C/li\u003E\u003Cli\u003EEnter \u003Cstrong\u003EZIP\u003C/strong\u003E for the new column name.\u003C/li\u003E\u003Cli\u003EEnter \u003Cstrong\u003E0\u003C/strong\u003E for a start index and \u003Cstrong\u003E3\u003C/strong\u003E for length.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EApply\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-datameer/extract.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou should now see the following flow:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/flow_one.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Ch2\u003ELow-Code Transformations\u003C/h2\u003E\n","\u003Cp\u003EThe Formula Builder can be used to enter low-code formulas on a columnar basis. All functions can be found on the right hand side, grouped together by different categories (STRING, NUMERIC, DATE & TIME, CONVERSION, CONTEXT & CONDITIONAL) - selecting a category allows you to browse the Snowflake functions and obtain help on how to use these against your data. Inside the Formula Builder we can select a field to transform using a variety of functions or we can select the \u003Cstrong\u003E+\u003C/strong\u003E button to add a new column.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/formula_builder.png\" alt=\"Formula Builder\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELet’s apply a formula to concatenate the first 4 characters from the \u003Ccode\u003ELOAD_ID\u003C/code\u003E column with the \u003Ccode\u003EMEMBER_ID\u003C/code\u003E column and put a \u003Ccode\u003E-\u003C/code\u003E separator between the two values:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003E+\u003C/strong\u003E button next to the Extract node, and select the \u003Cstrong\u003EFormula Builder\u003C/strong\u003E option.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the \u003Cstrong\u003ELOAN_ID\u003C/strong\u003E column.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EEnter the following formula:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\tCONCAT(LEFT(Source.LOAN_ID,4),'-',MEMBER_ID) \n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EApply Formula\u003C/strong\u003E to validate the formula, and then \u003Cstrong\u003EApply\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou will see the following screen after entering the formula described in Step 3 above.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/formula.png\" alt=\"Formula Editor\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EJoining Data Together\u003C/h2\u003E\n","\u003Cp\u003EThe third Recipe step is complete and now we want to join our \u003Ccode\u003ELOANS\u003C/code\u003E data with our \u003Ccode\u003EZIPCODE\u003C/code\u003E (POSTCODE) dataset. Let’s apply a join:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick the \u003Cstrong\u003E+\u003C/strong\u003E button next to the ´Formula node and select \u003Cstrong\u003EJoin\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003EZipCode\u003C/strong\u003E dataset in the ´Source´ dropdown.\u003C/li\u003E\u003Cli\u003ELeave Join Mode as it is as ´Outer Left´.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EGet Suggestions\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003EZip-Zip\u003C/strong\u003E suggestion.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EApply\u003C/strong\u003E Button.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EJoin configuration:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/join_config.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe resultant flow:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/join_flow.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EExploring the Data\u003C/h2\u003E\n","\u003Cp\u003EDatameer provides functionality to perform exploration of datasets through dimensions and measures without having to export to a BI tool. For example, to calculate the average interest rate by loan grade and purpose, perform the following steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick the \u003Cstrong\u003EJoin\u003C/strong\u003E node.\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003EExplore\u003C/strong\u003E tab in the ´Data Grid´ and click on \u003Cstrong\u003EExplore\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EAggregate\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EChoose \u003Cstrong\u003EGRADE\u003C/strong\u003E for the dimension column.\u003C/li\u003E\u003Cli\u003EAdd a second dimension \u003Cstrong\u003EPURPOSE\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EFor Measure, choose \u003Cstrong\u003EINT_RATE\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EFor Function, select \u003Cstrong\u003EAVERAGE\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EOK\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-datameer/explore_one.png\" alt=\"Configuration of aggregated Exploration\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will then see the aggregated results, which are always calculated based on the full data set. Additionally users can filter and sort the explorations. The explorations are tied to the nodes, and can be retrieved by clicking on the saved \u003Cstrong\u003EExplore\u003C/strong\u003E tab. Currently the explorations are saved in the browser's cache, with future plans to persist them as nodes, as well as to provide visual exploration. You can also visualize the results by clicking on the bar chart icon.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/explore_viz.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EAggregating Data\u003C/h2\u003E\n","\u003Cp\u003EUsers can create persistent aggregation and pivot nodes. For example, we can create an aggregation of the interest rate by the state:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick the \u003Cstrong\u003E+\u003C/strong\u003E button next to the ´Join´ node and select \u003Cstrong\u003EAggregate\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EADDR_STATE\u003C/strong\u003E for the ´Group By´ field.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EINT_RATE\u003C/strong\u003E for the ´Measure´.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EAverage\u003C/strong\u003E for the ´Aggregation Function´.\u003C/li\u003E\u003Cli\u003EClick the \u003Cstrong\u003EApply\u003C/strong\u003E button.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EAggregate configuration:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/agg_conf.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe updated pipeline flow:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/agg_flow.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EDeploying Assets\u003C/h2\u003E\n","\u003Cp\u003EWe now want to deploy our newly transformed joined and aggregated data back into Snowflake:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESelect the \u003Cstrong\u003EAGGREGATION\u003C/strong\u003E node and click \u003Cstrong\u003EDEPLOY TO SNOWFLAKE\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003ETable\u003C/strong\u003E option.\u003C/li\u003E\u003Cli\u003EName the table \u003Cstrong\u003ELOAN_AGGREGATION\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003EPUBLIC\u003C/strong\u003E schema under \u003Cstrong\u003EPRIVATE_DB_<DB_NAME>\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EDeploy Data\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-datameer/deploy_config.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYour transformations are now deployed to Snowflake as a table!\u003C/p\u003E\n","\u003Ch2\u003EUploading Files\u003C/h2\u003E\n","\u003Cp\u003EUsers can upload files through Datameer which are then materialized as Snowflake tables. You can download a sample CSV file \u003Ca href=\"https://datameer.box.com/s/0wfvv4gue31ng2311c81i6fq8lzivfzk\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ETo upload a file, perform the following:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003EUpload CSV\u003C/strong\u003E in the ´Data Browser´.\u003C/li\u003E\u003Cli\u003EDrag \u003Cstrong\u003Eloans.csv\u003C/strong\u003E into the upload window (or browse to the file).\u003C/li\u003E\u003Cli\u003EName the new table \u003Cstrong\u003ELOAN_GRADES\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003EPUBLIC\u003C/strong\u003E schema under \u003Cstrong\u003EPRIVATE_DB_<DB_NAME>\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EUpload\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-datameer/upload_config.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe file is now materialized as a table.\u003C/p\u003E\n","\u003Ch2\u003ESQL Transformations\u003C/h2\u003E\n","\u003Cp\u003EUsers can also add SQL nodes as part of the overall transformation process. SQL nodes can be combined with no- or low-code nodes. We can try the following to insert a SQL node into our pipeline:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EClick the \u003Cstrong\u003ELOANS\u003C/strong\u003E node.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ENew SQL Editor\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EEnter the following SQL:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003E\t        SELECT \n\t\t        LOAN_ID, MEMBER_ID, LOAN_AMNT, LEFT(TERM, 2)   \n\t\t    FROM LOANS \n\t\t    WHERE  \n\t\t\t    LOAN_AMNT < 10000 AND LOAN_STATUS = 'Current'  \n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EApply\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EYou can now see the SQL node in your Flow Area:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/flow.png\" alt=\"Login\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EConclusion and Next Steps\u003C/h2\u003E\n","\u003Ch3\u003ESummary\u003C/h3\u003E\n","\u003Cp\u003EThis training was designed to be an introduction in how to use Datameer for data transformation geared towards a business user or analyst. Through this lab we have walked through how the Datameer Project Workbench enables us to quickly explore & transform Snowflake data in a self-service manner using a no-code, low-code and SQL based approach. Because Datameer is SaaS and pushes all queries to Snowflake for execution, Datameer can combine its user interface and the unlimited power of Snowflake to give users a quick & easy way to transform your data, but with no limit on the amount of data you wish to analyze. Unlock the value of all your data and users!\u003C/p\u003E\n","\u003Ch3\u003EStill have Questions?\u003C/h3\u003E\n","\u003Cp\u003EWe're happy to help with whatever questions you have! Reach out to us using our support address at support@datameer.com\u003C/p\u003E\n","\u003Ch3\u003EOther Resources\u003C/h3\u003E\n","\u003Cp\u003EDocumentation: \t\t         \u003Ca href=\"https://documentation.datameer.com/datameer/\"\u003Ehttps://documentation.datameer.com/datameer/\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EKnowledge Base:\t\t         \u003Ca href=\"https://help.datameer.com/hc/en-us\"\u003Ehttps://help.datameer.com/hc/en-us\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EDatameer Blog: \t\t         \u003Ca href=\"https://www.datameer.com/blog/\"\u003Ehttps://www.datameer.com/blog/\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EResources/Case Studies:         \u003Ca href=\"https://www.datameer.com/resources/\"\u003Ehttps://www.datameer.com/resources/\u003C/a\u003E\u003C/p\u003E"],"title":"Getting Started with Datameer",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"title":"Quickstart Article Body","dataType":"string","value":"## Overview\n\n\nDatameer provides users with data discovery & transformation capabilities to visually explore and transform their data to identify the key insights for improving data driven decisions. Users can transform their data using a variety of no-code, low-code and SQL transformation recipes and then quickly publish this data back into Snowflake to support the development of BI dashboards, reports and data science driven insights in your organization. All this can be delivered without the need for IT assistance or formal training and without leaving a single user interface.  \n\n\n![Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/architecture.png)\n\n\n\n\nIn this training lab you will learn how to model and transform your data, create highly valuable datasets, and publish the data into Snowflake. No coding skills are necessary using our no-code tools or writing code using our SQL toolset. Or even use both! We will provide you with a test Datameer & Snowflake instance for this hands-on lab.\n\n### Lab Overview\n\nThis entry-level lab introduces you to the user interface and basic capabilities of Datameer. When done with the lab you should be ready to create your own data transformations against your own Snowflake data.\n\n### Target Audience\n\nAnyone who will be exploring & transforming data in Snowflake. Includes technical roles like members of an Analytics & BI team, data engineers to non-technical business users in the various lines of business. No SQL or technical skills are needed to do this lab.\n\n### What You'll learn\n\nIn this workshop lab, you will experience the following capabilities in Datameer: \n\n* Search and discover your Snowflake inventory\n* Prepare and curate your data sets\n* Explore and analyze your data sets \n* Share and deploy your data sets \n* Document and catalog your new datasets\n\n\n### Business Use Case\n\nFor the purposes of this workshop, you play the role of a loans analyst. Throughout this lab we will be working with some current and historical loans data from our fictitious loans company. We will begin with looking at our current loans data only, but throughout the course of the lab will incorporate some geographical information to transform & understand our loan portfolio by state/region being able to spot business opportunities in these regions.\n\n\n## Prepare Your Lab Environment\n\n\nClick the link below to get to the trial setup:\n\n[Datameer Trial](https://www.datameer.com/trial)\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/trial_setup_one.png)\n\nThe first time you login to Datameer you can choose to be taken directly to a Project, supported by an onboarding checklist which guides you though essential Datameer features. We will begin exploring and transforming data in the next module.\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/project_one_loans_selcted.png)\n\nBut before, let's have a quick look at the Datameer Workbench UI:\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/UI_description.png)\n\n* **Data Browser** - provides all available Snowflake schemas and datasets, and the 'File Upload' feature as well as browsing your data\n* **Flow Area** - visually represents your whole transformation pipeline, including data sources, transformations as well as deployed data sets\n* **Data Grid** - provides a data preview, allows you to explore columns of a dataset, calculates column metrics, and provides SQL query for further usage; individual explorations can be executed to visualize your data\n* **Inspector** - presents all information such as 'Source', 'Description' or 'Owner' about the currently selected dataset/ Project or schema that is selected\n\n\n## Data Exploration\n\n\nLet’s add our first data source by clicking the **+** button next to the `LOANS` table from the Data Browser. This will add the table to our Workbench for discovery and transformation. \n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/project_one_loans_selcted.png)\n\nWe can explore this dataset in three different ways:\n\n* **Preview** - Under the ´Preview´ tab you can see the actual data values and total number of records.  Datameer also utilizes Snowflake’s native sampling when transforming large tables. This will be indicated in the ´Preview´ tab with the ‘Sampled’ label.\n* **Columns** - This tab contains the column names, data types, and descriptions.\n* **Profile** - You can view column metrics within this tab, by clicking on the ´Calculate´ button for the desired column. Click on the  **Calculate** button for the `GRADE` column, and you can see the number of values, empty values, uniques, nulls, and the distribution of values. \n\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/loans_project.png)\n\n\nLet’s now add another dataset to this Project - this time by adding the `ZIPCODE` dataset by selecting the **+** button next to the table. You should see both added datasets in your Flow Area.\n\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/loans_zipcode.png)\n\n\n\n## No-Code Transformations\n\n\nClick on the **LOANS** node and select the **+** button to bring up the list of no-code transformations.\n\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/transform_one.png)\n\n\nLet’s apply a filter by doing the following:\n\n1. Click **Filter** in the popup context menu.\n2. Select **LOAN_STATUS** as the column to filter by.\n3. Leave **Equals** as the operator.\n4. Enter **Current** (case sensitive) as the value to search by.\n5. Click **Apply**.\n\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/filter.png)\n\n\nHere we can see a Filter condition in the UI with a Recipe step on the right hand side. You may see another purple hint appear on the screen informing you that you have created your first Receipe.\n\n![Filter loan status](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/filter_loan_status_result.png)\n\nLet’s apply another transformation. The ´Extract´ operation allows us to extract a certain number of characters from one field to a new column name. In our example we want to extract the first 3 digits from our `ZIP_CODE` column and populate this in a new column name called `ZIP`.  (Please note we use an INDEX value starting at 0 and extract 3 characters from this position to get the first 3 characters from this column.)  \n\nHere are the steps for an Extract:\n\n1. Click the **+** button next to the ´Filter´ node and select **Extract**.\n2. Select **ZIP_CODE** for the source column.\n3. Enter **ZIP** for the new column name.\n4. Enter **0** for a start index and **3** for length.\n5. Click **Apply**. \n\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/extract.png)\n\n\nYou should now see the following flow:\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/flow_one.png)\n\n\n## Low-Code Transformations \n\n\nThe Formula Builder can be used to enter low-code formulas on a columnar basis. All functions can be found on the right hand side, grouped together by different categories (STRING, NUMERIC, DATE & TIME, CONVERSION, CONTEXT & CONDITIONAL) - selecting a category allows you to browse the Snowflake functions and obtain help on how to use these against your data. Inside the Formula Builder we can select a field to transform using a variety of functions or we can select the **+** button to add a new column. \n\n![Formula Builder](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/formula_builder.png)\n\nLet’s apply a formula to concatenate the first 4 characters from the `LOAD_ID` column with the `MEMBER_ID` column and put a `-` separator between the two values:\n\n1. Click on the **+** button next to the Extract node, and select the **Formula Builder** option.\n2. Select the **LOAN_ID** column.\n3. Enter the following formula:\n\n\t```sql  \n\tCONCAT(LEFT(Source.LOAN_ID,4),'-',MEMBER_ID) \n\t```  \n\n4. Click **Apply Formula** to validate the formula, and then **Apply**.\n\n\nYou will see the following screen after entering the formula described in Step 3 above.\n\n![Formula Editor](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/formula.png)\n\n\n## Joining Data Together\n\nThe third Recipe step is complete and now we want to join our `LOANS` data with our `ZIPCODE` (POSTCODE) dataset. Let’s apply a join:\n\n1. Click the **+** button next to the ´Formula node and select **Join**.\n2. Select the **ZipCode** dataset in the ´Source´ dropdown.\n3. Leave Join Mode as it is as ´Outer Left´.\n4. Click **Get Suggestions**.\n5. Select the **Zip-Zip** suggestion.\n6. Click **Apply** Button.\n\nJoin configuration:\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/join_config.png)\n\nThe resultant flow:\n\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/join_flow.png)\n\n## Exploring the Data\n\n\nDatameer provides functionality to perform exploration of datasets through dimensions and measures without having to export to a BI tool. For example, to calculate the average interest rate by loan grade and purpose, perform the following steps:\n\n1. Click the **Join** node.\n2. Select the **Explore** tab in the ´Data Grid´ and click on **Explore**.\n3. Click **Aggregate**.\n4. Choose **GRADE** for the dimension column.\n5. Add a second dimension **PURPOSE**.\n6. For Measure, choose **INT_RATE**.\n7. For Function, select **AVERAGE**.\n8. Click **OK**.\n\n\n\n![Configuration of aggregated Exploration](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/explore_one.png)\n\n\nYou will then see the aggregated results, which are always calculated based on the full data set. Additionally users can filter and sort the explorations. The explorations are tied to the nodes, and can be retrieved by clicking on the saved **Explore** tab. Currently the explorations are saved in the browser's cache, with future plans to persist them as nodes, as well as to provide visual exploration. You can also visualize the results by clicking on the bar chart icon.\n\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/explore_viz.png)\n\n\n\n## Aggregating Data\n\n\nUsers can create persistent aggregation and pivot nodes. For example, we can create an aggregation of the interest rate by the state:  \n\n1. Click the **+** button next to the ´Join´ node and select **Aggregate**.\n2. Select **ADDR_STATE** for the ´Group By´ field.\n3. Select **INT_RATE** for the ´Measure´.\n4. Select **Average** for the ´Aggregation Function´.\n5. Click the **Apply** button.\n\nAggregate configuration:\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/agg_conf.png)\n\nThe updated pipeline flow:\n\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/agg_flow.png)\n\n\n## Deploying Assets\n\n\nWe now want to deploy our newly transformed joined and aggregated data back into Snowflake:\n\n1. Select the **AGGREGATION** node and click **DEPLOY TO SNOWFLAKE**.\n2. Select the **Table** option.\n3. Name the table **LOAN_AGGREGATION**.\n4. Select the **PUBLIC** schema under **PRIVATE\\_DB\\_\\\u003CDB_NAME\\\u003E**.\n5. Select **Deploy Data**.\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/deploy_config.png)\n\nYour transformations are now deployed to Snowflake as a table!\n\n\n## Uploading Files\n\n\nUsers can upload files through Datameer which are then materialized as Snowflake tables. You can download a sample CSV file [here](https://datameer.box.com/s/0wfvv4gue31ng2311c81i6fq8lzivfzk).\n\nTo upload a file, perform the following:\n\n1. Click **Upload CSV** in the ´Data Browser´.\n2. Drag **loans.csv** into the upload window (or browse to the file).\n3. Name the new table **LOAN_GRADES**.\n4. Select the **PUBLIC** schema under **PRIVATE\\_DB\\_\\\u003CDB_NAME\\\u003E**.\n5. Click **Upload**.\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/upload_config.png)\n\nThe file is now materialized as a table.\n\n## SQL Transformations\n\n\nUsers can also add SQL nodes as part of the overall transformation process. SQL nodes can be combined with no- or low-code nodes. We can try the following to insert a SQL node into our pipeline:\n\n\n\n1. Click the **LOANS** node.\n2. Click **New SQL Editor**.\n3. Enter the following SQL:\n\n\t```SQL  \n\t        SELECT \n\t\t        LOAN_ID, MEMBER_ID, LOAN_AMNT, LEFT(TERM, 2)   \n\t\t    FROM LOANS \n\t\t    WHERE  \n\t\t\t    LOAN_AMNT \u003C 10000 AND LOAN_STATUS = 'Current'  \n\t```  \n\n4. Click **Apply**.\n\nYou can now see the SQL node in your Flow Area:\n\n\n![Login](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-datameer/flow.png)\n\n\n## Conclusion and Next Steps\n\n\n### Summary\n\nThis training was designed to be an introduction in how to use Datameer for data transformation geared towards a business user or analyst. Through this lab we have walked through how the Datameer Project Workbench enables us to quickly explore & transform Snowflake data in a self-service manner using a no-code, low-code and SQL based approach. Because Datameer is SaaS and pushes all queries to Snowflake for execution, Datameer can combine its user interface and the unlimited power of Snowflake to give users a quick & easy way to transform your data, but with no limit on the amount of data you wish to analyze. Unlock the value of all your data and users!\n\n### Still have Questions?\n\nWe're happy to help with whatever questions you have! Reach out to us using our support address at support@datameer.com\n\n### Other Resources\n\nDocumentation: \t\t         [https://documentation.datameer.com/datameer/](https://documentation.datameer.com/datameer/) \n\nKnowledge Base:\t\t         [https://help.datameer.com/hc/en-us](https://help.datameer.com/hc/en-us)\n\nDatameer Blog: \t\t         [https://www.datameer.com/blog/](https://www.datameer.com/blog/) \n\nResources/Case Studies:         [https://www.datameer.com/resources/](https://www.datameer.com/resources/) \n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-b91d595f04","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-fd4a6971a6",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-dad623a10b","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-b9f429d3c4","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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