COST-EFFICIENT DATA LAKE DESIGN: BALANCING DATABRICKS AND REDSHIFT IN FINANCIAL SYSTEMS.

Abstract

Financial institutions handle massive amounts of data daily, from transaction records to market trends, and need systems that are both fast and affordable to stay competitive. Designing a data lake that balances performance with cost is a big challenge, especially when dealing with tasks like catching fraud in real time or preparing detailed regulatory reports. This paper explores how to build a cost-efficient data lake for financial systems by combining two powerful tools: Databricks, a platform great for processing huge datasets and running machine learning, and Amazon Redshift, a data warehouse optimized for structured queries and reporting. By testing these tools in real-world financial scenarios, we aim to find the best way to use them together to save money while keeping performance high.

Our approach compares Databricks and Redshift based on how fast they process data, how much they cost, and how well they handle financial tasks. We looked at two key use cases: real-time fraud detection, where speed is critical to spot suspicious transactions instantly, and batch processing for regulatory reports, where accuracy and compatibility with business intelligence tools matter most. We tested these scenarios using a hybrid data lake setup, where Databricks handles data ingestion, transformation, and machine learning, and Redshift takes care of structured analytics and reporting. Performance was measured by how quickly queries ran and how well each tool scaled with large datasets, while costs were tracked by analyzing compute, storage, and operational expenses, including new serverless options for both platforms.

The findings show that Databricks excels in real-time analytics, processing up to 1 million transactions per second with 200 ms latency, ideal for fraud detection. Redshift outperforms in complex SQL queries for reports, with 12-second execution times and seamless Tableau integration for compliance dashboards. The hybrid model, using Databricks for data preparation and machine learning alongside Redshift for analytics, reduced costs by 30% versus single-tool setups while improving query speeds by 25%. For instance, it leveraged Redshift’s serverless features to cut reporting expenses and Databricks’ Delta Lake for optimized storage.

This research is significant for financial institutions looking to modernize their data systems without breaking the bank. It offers practical guidance on when to use Databricks or Redshift based on workload needs and recommends best practices like using open table formats for better integration. By adopting this hybrid approach, banks and FinTech companies can process data faster, meet regulatory demands, and invest savings into innovation, ultimately improving customer trust and operational agility.