Data integration, warehousing, and analytics with Azure Databricks
Our client, a subscription-based meat delivery services company, was seeking to optimize their data processes that integrated multiple data sources and power analytics and AI modeling to extract customer behavioral patterns and help reduce customer churn. They had 26 data sources like financial data, and marketing data and had an initial Azure data lake, and used Azure data factory for ETL processes.
The client had 26 data sources consisting of their e-commerce platform and ERP and with rising competition, providing personalized customer experiences was pertinent for growth. But they were facing challenges in ensuring data quality and reporting.
The client was using Azure data lake and Azure data factory for data storage and processing. The major issue with the current process was there was no data observability or visibility to the data pipelines. There were no systems in place to track metrics, metadata, data lineage, and logs.
We proposed a solution using the Databricks platform for data integration, warehousing, and analytics with Azure data lake for storage.
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