Cloud Migration and Cost Optimization.
Data warehousing, storage, and analytics are expensive, particularly in cloud-based platforms like Databricks, which operates on a pay-as-you-go model. While Databricks Unity Catalog offers time-saving features such as governance, compliance, granular access control, and metadata management, having a poor setup, lack of standardization, and inefficient Spark data pipelines can significantly drive up costs and hinder scalability. Organizations often struggle with managing high-cost workflows and optimizing data processing efficiency.
Data Migration to Datalakes.
- Optimizing Spark data pipelines with techniques like Predicate Pushdown, Partition Pruning, and Z-Ordering to improve efficiency.
- Categorizing workflows to allocate dedicated resources for critical tasks while shifting non-critical ones to cost-effective spot instances.
- Leveraging Spot Instances to cut costs by bidding on lower-cost, short-term computing power.
Reducing Costs by 90%.
- Completed workflow SLA time reduced from 2 hours to 10 minutes.
- Completed the project in 8 months.
