References → All Data Studio Recipes
Recipes are foundational tools to performing data quality checks, cleansing, analysis, blending and much more.
Recipe Index
Content Transformation
Change Type - Change the data type of column(s).
Filter - Remove records from a dataset based on a condition.
Select - Select which columns to keep or remove from a dataset.
Sort - Sort data within a dataset.
Unpivot - Transpose your dataset based into columns and values.
Formula - Add customer logic to create a new calculated field.
Sample - Select a subset of records within your dataset.
Aggregation - Aggregate youdata set and set granularity through 'group by' logic.
Rename - Rename column labels in your dataset.
Split - Split the dataset into two datasets.
Structure Transformation
Join - Join two datasets based on a set of join logic.
Union - Union two datasets together.
Data Quality and Validation
Fuzzy Join - Cleanse data through providing a lookup table.
Data Quality - Write a set of conditions and flag any violations in your dataset.
Advanced Querying
Python - Inject custom pySpark into your data flow.
SQL - Inject custom SQL into your data flow.
Deploy and Eject Operations
Save MV - Save your data flow output to a Materialized View.