Data Engineering Modernization

Strong Data foundation caters to trustable data decisions.

• ETL/ELT Modernization – Migrate from legacy data integration tools (Informatica Power Center, IBM DataStage) to IICS or Cloud Native Integration Suite (Azure Data Factory / AWS Glue)

• SAP Data Integration with SAP BW– Using SAP BO Data services and Sap Datasphere

• Oracle Financial Data Integration – Informatica / Cloud Native integration Solution (ADF / AWS Glue)

• Data Migration – Enterprise systems to Target platform

• Data Lake Transformation (OnPrem to Cloud, Cloud to Cloud)

• Real-time Data Pipelines – Integrate Data stream from different sources to target platform.

Business Outcome

• Faster access to trusted enterprise data

• Reduced reporting delays

• Improved data quality

Case Study - Financial DataLake on Apache Iceberg / AWS

Data Platform is built on AWS with multiple enterprise data feeds thru PySpark Pipeline loads data into DataLake and three tiered data processing and transformation is done to ensure raw source data is transformed to curated data addressing business KPI needs for Reporting and AI Analytics Consumption

Data sources Integration feeds data from variety of financial / operational systems thru PySpark /Spark Orchestration

Analytical Data Processing: Apache Iceberg

Multi-stage data transformation: Medallion architecture enables organizing logical data layers in a Lakehouse

Cloud compatibility: Built on Open-source framework /tools with portability across AWS/Azure or Google Data Clouds.