Project Name
How Apache Spark Integration Enabled Efficient Data Mapping & Management
Our client works on managing the massive amounts of data from different sources that help to scale up the 10,000 records per minute. However, with each JSON file that contains 30–40 data entities, it becomes quite complicated for them to manage the data that possesses multiple challenges. They are looking for a robust solution to manage this data influx.
- Limited Adaptability with Minimal Code Changes: Facing challenges in adapting to variations with little to no code modifications.
- Complex JSON Parsing and Mapping: Issues in parsing nested hierarchical JSON and mapping it instantly to Teradata tables.
- Handling High-Volume Data Flow: Struggling to accommodate the increasing year-on-year data influx efficiently.
Ksolves team provided a robust approach to their client that includes:
- Instant JSON to Database Mapping: Prepared mapping files to seamlessly map JSON hierarchical keys to database tables, including column names and types for data forecasting.
- Scalable Apache Spark Implementation: Utilized Apache Spark with multi-node clusters on Kubernetes, integrating the Kubernetes operator for future scalability.
- Optimized Data Organization: Structured data by date and time to prevent unnecessary reprocessing and improve efficiency.
- Flexible Mapping File Modifications: Implemented an Apache Spark system that enables easy modification of mapping files in CSV text format for swift updates.
- Efficient Data Management: Introduced separate mapping files for each data type to effectively manage diverse data sources.
With the innovative problem-solving Apache Spark implementation approach, the Ksolves team has addressed the client’s challenges in managing the massive amount of data. By leveraging Apache Spark and implementing a meta-data-driven approach, we delivered a comprehensive approach to instantly handling the data variations.
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