Overview
Capabilities
Challenges
- Fragmented Data Landscape: Disparate data residing in multiple databases (Greenplum, Oracle, file repositories) hindered consolidation and analytics.
- Heterogeneous Data Formats: Handling structured, semi-structured, and fixed-width file types required a flexible and automated ingestion framework.
- Data Quality & Governance: Lack of consistent cleansing, de-duplication, and versioning processes reduced reliability for enterprise reporting.
- Limited Scalability: Existing architecture couldn’t meet growing analytical demands across operational, financial, and customer data domains.
- Delayed Insights: Siloed data pipelines led to latency in reporting and slowed down strategic decision-making.
Solution
Coforge executed a comprehensive Data Lakehouse transformation leveraging AWS Databricks to unify, enrich, and operationalize enterprise data.
Key initiatives included:
- Design and development of the Data Lakehouse and EDW on AWS Databricks platform
- Data ingestion from heterogeneous sources like Greenplum, Oracle, and various file types (CSV, JSON, fixed-width)
- Delta and Hive table creation using Spark and Spark-SQL
- Designed 4 data layers: Bronze, Silver, Silver-derived, and Gold
- De-duplication, cleansing, and SCD implementation at Silver and Silver-derived levels
- Implementation of all required business use cases
- Visualization and dashboard creation
- Coforge roles include end-to-end solution implementation: setting up AWS Databricks environment, staging, ingesting, enriching, curating, and publishing data
The Impact
- Data Lakehouse and EDW for all enterprise DSS needs
- Enterprise-wide cleansed, de-duplicated, and analytics-ready data
- Managed data with well-maintained history
- Robust data quality and governance processes for assurance
- Use-case-driven actionable roadmap for incremental benefits
- Scalable analytical tool workbench for impactful, robust business decision-making
Related reads.
