85% cost reduction: A Case Study on how DataNimbus Designer accelerates workflows and reduces costs
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.............For More Information...........Read More
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