Business Need:
A leading enterprise in the Healthcare/FinTech sector was operating a 100+ node on-premise Hadoop cluster to support their big data workloads, including customer analytics, network performance tracking, and operational reporting. The legacy setup was expensive to maintain, lacked elasticity, and created bottlenecks in data processing and insight delivery. The company needed a modern, cloud-native platform that could improve performance, reduce infrastructure overhead, and accelerate time-to-insight for advanced analytics and AI use cases.
Solution:
Data Ninjas designed and implemented a comprehensive migration strategy to move the client’s Hadoop ecosystem to Azure Databricks. The solution included:
Assessment and cataloging of all Hive tables, Spark jobs, and workflows
Automated conversion and optimization of Spark workloads for Databricks
Migration of data from HDFS to Azure Data Lake Storage Gen2 with Delta Lake support
Results:
3x faster execution of data pipelines and analytics workflows
Improved reliability, scalability, and SLA compliance across data operations
Enhanced data governance and lineage tracking via Unity Catalog
Increased agility for data scientists to experiment and operationalize models
