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The Datameer Interface

Watch this short demo to see how to navigate through the Datameer Interface.

Datameer UI

Let’s take a quick tour of the Datameer User Interface.

Explore and Transform Datasets
  • Here on the home page, you can see the ability to create a new project, alongside the ability to perform a keyword search for any Snowflake datasets, schemes, or transformation projects created and cataloged in Datameer.
  • Searching for content is a great place to start. Here in the Datasets tab, we can search any Snowflake tables and views.
Explore and Transform Datasets
  • Selecting a dataset allows you to browse the column metadata. Here we can see the names, data types, and any descriptions added to the dataset.
  • I can also preview what the data looks like by selecting the data preview tab. Once you have the data set you want, you select the add to project button
  • Inside the project, the workbench is where we can transform our Snowflake data. In the middle of the screen, we can see how the data is being transformed and a preview of how the data is being transformed.
  • To transform your data, select the green plus button, or select the new transformation or new SQL editor buttons at the top.
  • Let's start with the SQL editor. If you are a data engineer, you will be used to writing SQL code to query and transform data using this approach. We can execute Snowflake statements, call native Snowflake functions and see a preview of the data being transformed. Once applied, you can see your SQL in the workbench as a transformation step.
  • To add more data sets to your workbench, browse your Snowflake data and schemes on the left-hand side and select that add to project button.
  • You upload CSV files to the workbench if your data isn't already in Snowflake.
  • To join multiple datasets together, I could use the SQL editor or use the no code join recipe. I can select the data source I'd like to join, and Datameer will recommend column names to join against. With the ability to change the join mode or behavior.
  • I can also perform other no-code recipes, such as pivot and aggregating my data. Here I can select the fields or column names to be aggregated. I can add measurable value to calculate. And I can apply aggregate functions, such as sum, avg, min, or max. And optionally, you can include the row count.
  • You can see how your aggregate view looks before you hit the apply button to add this transformation as another recipe.

Create Snowflake Datasets Fast.

Speedy. Scalable. Quality Data.