Reducing Data Silos with Modern Data Architecture on Databricks
Objective
Assess and optimize a client’s Azure Databricks environment to reduce data silos, eliminate inefficiencies, lower compute costs, and strengthen scalability within a modern Lakehouse architecture.`
Challenge
The client had implemented a Lakehouse architecture on Azure Databricks, but performance and cost inefficiencies were limiting its effectiveness. Data pipelines were reprocessing full datasets instead of incremental updates, governance practices were not fully optimized, and resource utilization lacked visibility. These issues increased compute spend and reduced overall system efficiency.
Solution
NLP Logix conducted an eight-week structured assessment of the Databricks environment that included:
- Comprehensive architecture and pipeline review
- Evaluation of Unity Catalog configuration and governance controls
- Identification of redundant processing and compute inefficiencies
- Cost and performance benchmarking
- Prioritized remediation roadmap
- Executive-level summary and technical best practices guide
The engagement delivered both strategic recommendations and tactical steps to modernize the environment.
Results
- Identified redundant full-data processing driving unnecessary compute costs
- Reduced inefficient workload execution patterns
- Improved data governance and catalog utilization
- Increased visibility into performance and cost drivers
- Delivered a clear action plan to support scalable, cost-efficient growth
- Strengthened the foundation for advanced analytics and AI initiatives
Tech Stack
- Microsoft Azure
- Azure Databricks
- Databricks Unity Catalog
- Lakehouse Architecture