Data Process Transformation with DataOps

Case Study

Challenges

An online subscription-based meat delivery company faced challenges in optimizing its data processes to understand customer behavior and reduce churn. The main issues were a lack of data observability and visibility in their data pipelines. There were no systems to track metrics, metadata management, data lineage, and logs, making it difficult for data engineers to monitor actions and ensure data quality. This lack of oversight led to errors and poor data quality management, increasing the risk of mistakes in decision-making and complicating monitoring and troubleshooting

Our Solution

We proposed using Databricks for data integration, warehousing, and data analytics with Azure data lake for storage.
Integrated data architecture with Databricks for warehousing and connected 26 data sources using FiveTran to build ML models for customer engagement insights.
Implemented Unity Catalog for data governance, automated data lineage tracking, and conducted impact analysis and root cause analysis of errors.
Developed an alerting system with Azure logic app to halt dependent jobs if issues arise or files are delayed.
Streamlined data flow through the medallion architecture, reducing manual efforts to fix data duplication and reporting errors.
Monitored and maintained infrastructure and ML models weekly to ensure accuracy, reliability, and prevent data/model drift, deciding on model retraining or retirement.

Benefits

7X better performance with Databricks due to auto-scaling, parallel processing, and memory optimization, speeding up data-driven solutions.
Ensured the quality of data accessed by various business users across the organization.
31% reduction in manual efforts required to fix data errors.
20% reduction in overall cloud computing costs.