Data integration for Hive DB with Cloudera for efficient data processing

For a large Asian bank

CLIENT & PROBLEM STATEMENT

  • A Large Asian bank needing data engineering for machine learning (ML) models.
  • Require integration of Hive DB with Cloudera for efficient data processing and ML model creation.

APPROACH

  • G-Square extracted 12 million credit card records spanning 6 years from a Hive database and transferred them to Cloudera for ML processing using PySpark.
  • The team developed and optimized ETL processes to ensure smooth data extraction and transformation. .
  • R and Python were used for continuous data fetching to support ongoing ML model development.
  • The resulting models were seamlessly integrated back into Hive for further analysis and business use.

SOLUTION & OUTPUT

  • The optimized ETL processes and ML models provided real-time insights, enhancing fraud detection, customer behaviour analysis, and credit risk assessment..
  • The integration into Hive allowed for streamlined access to actionable insights, improving overall decision-making and operational efficiency.
Are you ready ?

Get a free 30-min consultation with our experts. And also get an analysis report with some actionable insights post that for only $20
book a slot now