About the organization
A leading player in the general insurance industry, this organization serves both individual and corporate clients with a diverse portfolio of insurance products. Committed to innovation and customer-centric solutions, they continuously upgrade their technology stack to enhance operational efficiency and service quality.
Business Challenges
The organization sought to improve the efficiency of its ETL (Extract, Transform, Load) pipelines, particularly in processing complex JSON documents and optimizing Databricks compute costs. Key areas of concern for the organization were:
- High Compute Costs and Slow ETL runtimes –Extensive compute resource consumption led to prolonged runtimes and inflated operational costs in Databricks.
- Complex Data Parsing – Extracting insights from intricate JSON data structures posed a significant challenge for reporting and analysis.
- Hardcoded ETLs & Schema Management Issues – Hardcoded ETLs and static schema definitions limited flexibility, making adaptation and maintenance cumbersome.
To scale efficiently while reducing costs, the organization required a cutting-edge solution that streamlined data transformation without compromising performance.
Why DataNimbus?
DataNimbus stood out for its expertise in ETL optimization, Databricks performance tuning, and its ability to tackle complex data transformation challenges. Unlike conventional approaches, DataNimbus introduced a fresh perspective leveraging automation and dynamic schema management to eliminate inefficiencies. The organization’s trust in DataNimbus was by its proven track record in solving similar challenges, ensuring a scalable and future-proof implementation.
Solution Implementation
DataNimbus deployed a structured, phased approach, starting with infrastructure provisioning in close collaboration with the organization’s teams.
Solution Implementation Flow within Datanimbus Designer natively on top of Databricks
Key innovations included:
- JSON Transformer for Complex NoSQL Parsing: Designed to efficiently process over 1 million JSON records with 20,000+ nested tags, significantly reducing transformation time.
- Automated Schema Management: Enabled dynamic schema detection and storage, eliminating manual interventions and simplifying ETL adaptability.
- Optimized ETL Workflow: Implemented an advanced ETL process in Databricks, reducing runtimes while maintaining data integrity and performance.
- Infrastructure Provisioning & Training: Equipped the client’s ETL team with best practices and hands-on training to ensure long-term sustainability.
By focusing on automation, flexibility, and compute efficiency, DataNimbus delivered a solution that addressed immediate challenges and positioned the organization for future scalability.
Impact and Value Creation
The implementation of DataNimbus’s solution delivered significant improvements in efficiency, cost savings, and performance:
- 85% Reduction in Data Infrastructure Cost: Optimized resource consumption and best practices on workflows, freeing up budget for strategic initiatives.
- 3x Faster JSON Processing: Parsing and Delta Table creation times dropped from 1.5–2 hours to under 30 minutes for each workflow redesigned.
- Enhanced ETL Agility: Dynamic schema management reduced manual dependencies, improving scalability and adaptability for the future.
Future Roadmap
Building on the success of this engagement, the organization is set to enhance its ETL ecosystem by incorporating automation and AI-driven insights. By leveraging DataNimbus Designer, they aim to enhance data governance, integrate reusable components, and optimize operational efficiency
Why Consider DataNimbus?
For organizations seeking to modernize their data infrastructure, optimize cloud costs, and achieve unparalleled efficiency in ETL workflows, DataNimbus offers a proven, high-impact solution that delivers real results. Let’s connect to discuss your requirement.
