Converting legacy PL/SQL code to modern ETL (Extract, Transform, Load) processes involves transitioning procedural code into a streamlined and scalable ETL workflow. ETL is a crucial data integration process that encompasses extracting data from various sources, applying transformations, and loading it into a target data warehouse or database for analysis and reporting.
Understanding the Process
DataTerrain employs a systematic approach to this transformation, beginning with a comprehensive review of the existing PL/SQL code. This entails understanding the underlying business logic, data sources, destinations, as well as identifying transformations and data mappings present in the legacy code.
Choosing the Right Modern ETL Tool
DataTerrain assists clients in selecting the most suitable modern ETL tool that aligns with their organizational requirements. Among the popular choices are Informatica and AWS Glue, both of which provide powerful ETL capabilities.
Implementation and Data Extraction
The implementation phase kicks off by setting up connections to data sources (databases, files, APIs) and the target data warehouse or database where the data will be loaded. ETL tools are then employed to extract data from the source systems. Parallel extraction strategies, including incremental extraction to capture only changed data, enhance the process efficiency.
Data Transformation and Enrichment
DataTerrain ensures seamless transition by meticulously applying necessary data transformations prescribed by the legacy PL/SQL code. This can encompass data cleansing, enrichment, aggregation, and execution of business logic calculations.
Loading and Error Handling
Transformed data is subsequently loaded into the designated target data warehouse or database. DataTerrain configures the ETL tool to accommodate various loading strategies, such as bulk or row-by-row loading, based on data volume and performance needs. The implementation of error-handling mechanisms helps manage issues during the ETL process, with detailed error logging and alerts for data quality anomalies.
Automation and Optimization
Automation plays a pivotal role in establishing a repeatable and consistent ETL workflow. Optimizing the ETL process for performance entails considering data volume, hardware resources, and data distribution. Rigorous testing is conducted to validate the ETL workflow, ensuring data accuracy by comparing results against legacy PL/SQL outputs.
Deployment and Monitoring
Once thoroughly tested and validated, DataTerrain facilitates the deployment of the ETL process into the production environment. The setup of robust monitoring and logging mechanisms allows for tracking ETL job execution and performance.
Informatica stands as a leader in the ETL domain, offering a suite of data integration and quality tools. Informatica PowerCenter serves as a flagship ETL tool, enabling users to design, develop, and manage data integration workflows. Additional components like Informatica Data Quality (IDQ) and Informatica MDM enhance data accuracy, consistency, and integrity.
About AWS Glue
AWS Glue, a fully managed ETL service from Amazon Web Services, simplifies data integration and transformation. Glue’s serverless development environment, coupled with its automatic scaling and support for large-scale data processing, makes it an attractive choice for ETL tasks in the AWS cloud environment.
The conversion of legacy PL/SQL to modern ETL is a substantial undertaking that requires a profound understanding of ETL principles and the chosen ETL tool. Diligent planning, meticulous testing, and leveraging the expertise of DataTerrain ensure a seamless transition to the new ETL workflow, setting the stage for data-driven success. Connect with DataTerrain to explore practical scenarios and engagement options for achieving this transformation.