Configuring Transformation Stage

In the Transformation stage, the legacy code and the business logic are transformed to the preferred target. Double-click the Transformation stage to access the configuration page.

In this Topic:

Overview
Transform
Output

Overview

In this section, you can customize the Transformation stage’s name and give a suitable description as required. By default, Transformation is provided as the Name field. Provide a relevant name and description that helps you to understand the purpose and scope of the Transformation stage.

Transform

The transformation stage ensures seamless transformation and operationalization of large-scale legacy workloads to the modern cloud platform.

LeapLogic’s intelligent grammar engine automatically converts legacy EDW workloads, including core business logic into target-native equivalents. Once transformed, the target compatible packaged code can be orchestrated and executed as production-ready jobs in the target environment.

You can configure the transformation stage as a standalone pipeline by defining the source, target, input, and output types required for workload transformation. Alternatively, you include it an integrated pipeline (Migration stage and Transformation stage). In this case, the outputs of the DML scripts, source type, and the metadata details of the Migration stage are automatically mapped to the Transformation stage, minimizing manual configuration effort.
The transformation stage also provides a notebook-based inline editor, enabling you to review and fine-tune queries for optimization. LeapLogic enables workload transformation across:

By automating workload conversion and optimization, the transformation stage minimizes manual effort and accelerates the journey to a modern data platform.

The table below lists the EDW sources along with the input types and supported targets for workload transformation.

Source	Input Type	Target
Teradata	SQL/BTQ/Procedure	Hive
		Spark
		Amazon Redshift
		Azure Synapse
		Databricks Lakehouse
		Databricks Notebook
		Google BigQuery
		Snowflake
		AWS Glue
	KSH	Spark
		Google BigQuery
		Amazon Redshift
		Hive
	MLOAD	Databricks Notebook
	TPT	AWS Glue Jobs Spark Databricks
	Triggers	Databricks Notebook
	Analytics queries	Kyvos semantic cube
Netezza	SQL/Procedure	Spark
		Amazon Redshift
		Azure Synapse
		Databricks Lakehouse
		Databricks Notebook
		Snowflake
		SQL Scripting (Databricks)
		Hive
	KSH	Hive
	KSH	Spark
Oracle	SQL/Procedure	Spark
		Amazon Redshift
		Azure Synapse
		Databricks Lakehouse
		Databricks Notebook
		GCP PostgreSQL
		Google BigQuery
		Snowflake
		Hive
		Amazon Aurora
		SQL Scripting (Databricks)
SQL Server	SQL/Procedure	Spark
		Amazon Redshift
		Aurora PostgresSQL
		Databricks Lakehouse
		Databricks Notebook
		Snowflake
		SQL Scripting (Databricks)
		Hive
Vertica	SQL/Procedure	Spark
	SQL/Procedure	Hive
	KSH/BASH/SH	Spark
	SQL/Procedure	Databricks Lakehouse
DB2	SQL/Procedure	AWS Glue Job (Delta and Iceberg)
DB2	SQL/Procedure	Amazon Redshift
Sybase	SQL	Databricks Lakehouse
Dremio	SQL	Athena
Greenplum	SQL/Procedure	Spark
Greenplum	SQL/Procedure	Hive
Snowflake	DML/DDL	Databricks Notebook
Snowflake	JavaScript Procedures	Databricks Notebook
Generic ANSI SQL	SQL/Procedure	Amazon Redshift
		Databricks Lakehouse
		Snowflake
		Spark
PostgreSQL	SQL/Procedure	Databricks Notebook
Redshift	SQL/Procedure	Databricks Notebook
Google BigQuery	SQL/Procedure	Amazon Redshift
EDW Unity Catalog		Databricks Lakehouse

To view the Transformation Stage report, visit Transformation Report.

Output

The output of this transformation is HQL/Spark SQL, Java/Python/Scala code, or cloud compatible scripts such as SnowSQL. A detailed report is generated as output and required queries can be edited through an online notebook-based code editor.

You can configure the output of this transformation for navigation to a further stage. By default, the output configuration is set to Stop if the transformation is not 100%, or that can be configured to Continue, Error, and Pause as required.

Next:

Configuring Validation Stage