Data Shaping with Pipelines

Lookups from other SingleStore tables (in addition to the destination table(s))
Normalizing data
Denormalizing data
Adding computed columns
Filtering data (excluding specific columns or records)
Mapping data values from the data source to new values
Splitting records from the data source into multiple destination tables
Adding surrogate keys

Data modifications made during shaping are not written back to the data source, unless done explicitly in a transform (SingleStore Self-Managed only).

Ways to specify data shaping logic:

In a CREATE PIPELINE statement.
In a stored procedure that is called from the pipeline.

Methods for Data Shaping with Pipelines

The details of each data shaping method are explained in the following table.

Data Shaping Method	Amount of Customization Logic Allowed	Ease of Use	Comments	Examples
In a `CREATE PIPELINE` statement	Low	Easiest	Pros: Generally, runs the fastest of the three data shaping methods; transactional guarantees. Cons: Limited to SQL WHERE CLAUSE to perform filtering and SQL expressions to perform other shaping logic; can only load data into one target table; cannot perform lookups in other tables; a single row in the data source cannot generate multiple rows in the target table.	`CREATE PIPELINE` uses the `LOAD DATA` clause to perform data shaping. See examples in the LOAD DATA topic.
Pipeline Stored Procedure	Medium	More Difficult	Pros: Transactional guarantees; cons of specifying data shaping logic directly in your `CREATE PIPELINE` statement don’t apply. Cons: Limited to the capabilities of the PSQL language; cannot use third-party libraries.	See examples in CREATE PIPELINE ... INTO PROCEDURE

Data Shaping with Pipelines

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Methods for Data Shaping with Pipelines

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