BIG DATA ANALYTICS - DATA ENGINEERING

Chittaranjan Pradhana understands the challenges that financial service firms face when integrating diverse data sources to support client reporting, audit, regulatory compliance, anti-money laundering, and surveillance. In particular, Oracle-based data warehouses often create bottlenecks that hinder scalability, making it difficult to process large datasets and onboard new sources. These limitations not only compromise the efficiency of analytics reports but also increase licensing costs. Additionally, strict compliance mandates requiring secure data storage for a minimum of seven years put further strain on existing resources.

To address these challenges, Chittaranjan advocates for a modern data platform tailored for Private Banking Analytics. The platform must prioritize scalability to support horizontal scaling for large datasets and ease the onboarding of new data sources. Performance is another key consideration, ensuring fast query responses for both batch and real-time processing. Cost efficiency can be achieved by adopting pay-per-use cloud solutions, while flexibility is needed to accommodate diverse data types and workflows. Above all, the platform must maintain high standards of security and compliance, safeguarding sensitive financial data and adhering to regulatory requirements.

Chittaranjan emphasizes the importance of data engineering in solving these big data challenges. By designing, building, and maintaining systems that manage and process large volumes of structured, semi-structured, and unstructured data, data engineers enable seamless data flow. Through the creation of pipelines and systems, they facilitate the smooth transfer of data from diverse sources into repositories like data warehouses or data lakes, thus ensuring that financial firms can meet their data management, analytics, and compliance needs efficiently.