Configuring On-premises to Cloud Transformation

In the Transformation stage, the legacy codes and the business logic are transformed to the preferred target.

To configure the Transformation stage, follow the below steps:

In Source Type, select the required source data store, such as Netezza, Oracle, SQL Server, Teradata, Vertica, and more.

In Input Type, select the type of input script, such as SQL/BTQ/Procedure or KSH.
In Target Type, select the preferred target type, such as Spark, Snowflake, Amazon Redshift, and so on.
In Output Type, select the output type format for the generated artifacts.

In Input Artifacts, upload the files that you need to transform to the target source.

Click Data Configuration to configure the data.

Note:

If the selected target is Databricks Lakehouse, enable Unity Catalog to transform the constraints such as primary key, foreign key, etc., present in the source data to the Databricks-native equivalent. Otherwise, the default Hive meta store will be used, and Hive or Spark tables will be generated without any constraints. In Databricks, there are two types of catalogs:

Hive Meta Store: Hive meta store serves as a central repository to create, store, and manage large datasets or tables. However, it does not support constraints like primary key, foreign key, etc.
Unity Catalog: Unity Catalog in Databricks serves as a centralized metadata repository with advanced metadata management features that support constraints including primary key, foreign key, etc. It serves as a comprehensive repository for storing metadata information within Databricks.

In Validation Type, select the validation type such as None or Cluster.

In Source Configuration, select the source configuration as Live or Offline.
If the selected Source Configuration is:
1. Live-Upload the data source and select the Default Database.
2. Offline-Upload the DDL files. It supports .sql and .zip file formats.

In Target Configuration, select the target configuration as Live.
Upload the target data source.

Note:

To increase the accuracy, it is recommended to include metadata in the Transformation stage.

In File Format, select the storage type as ORC, Parquet, Text File, or Avro
In Mapping as per Changed Data Model, upload files for mapping between source and target tables.

Notes:

If the selected target is Databricks Lakehouse or Databricks Notebook, then you will get a provision to upload the schema optimization recommendations (CSV) report from the assessment output to incorporate optimizations in the converted statements.
If the selected source is Teradata and the target is Kyvos, in the Table-Cube mapping field upload the mapping file. It is used to map between table and cube where table is replaced with <Folder Name> and table alias is replaced with <Cube Name>. The input to the Table-Cube mapping field is the output CSV file generated from the assessment, where you need to add <Folder Name> and <Cube Name> corresponding to the queries manually.

In Triggers, upload the trigger statements for a more accurate conversion.
Click Save to save the Transformation stage.
An alert pop-up message appears. This message prompts you to refer your respective assessment to determine the anticipated quota deduction required when converting your scripts to target. Then click Ok.

Click to execute the integrated or standalone pipeline. Clicking (Execute) navigates you to the pipeline listing page which shows your pipeline status as Running state. It changes its state to Success when it is completed successfully.

Click on your pipeline card to see reports.

To view the On-premises to Cloud Transformation, visit On-premises to Cloud Transformation Report.

Note:

When you license LeapLogic, it comes with a certain quota limit, for example unit, script, and block. The system alarms the user when the limit exceeds and highlights quota exhaustion in the pipeline reports.