Data Lake Solution Accelerator For Low Latency Data Processing
Business Problem
- The requirement was to develop a data platform to collect, organize, process and provide insights into business, operational aspects and enable development of customer value added, data driven product features and dashboards.
- ​Raw data was stored with no oversight of the contents​
- The platform needs to have defined mechanisms to catalog, and secure data. Without these elements, data cannot be found, or trusted resulting in a “data swamp“.​
Solution
- ​Real time streaming data from source systems (batch load scripts are also in place).​
- Connectors developed for Oracle DB (PeopleSoft, Banner) and Workday.​
- Uses Oracle DB Streams feature to identify changes from Redo Logs and stream to Kafka​
- Data access is controlled with views set up in Apache Hive which is connected to data lake.​
- ETLS run in loop and identify changed files (via Hive) and update Report Mart. Sample ET L scripts and reports developed for HR Diversity data. PostgreSQL acts as Report Mart.​
- Data changes form source system are reflected in the reports within two minutes.​
Outcome
- Easier and quicker to populate as no transformation is involved ​
- Allows to import any amount of data that can come in real-time​
- Allows organizations to generate different types of insights including reporting on historical data​
- Ability to store all types of structured and unstructured data​
- Elimination of data silos​
- Democratized access to data via a single, unified view of data​
