A retail organization operating across multiple sales channels required a structured and scalable data platform to manage and analyze large volumes of operational data. The client provided domain-specific datasets, including customer, orders, products, payments, and logistics data.

The primary objective was to transform this raw and distributed data into a reliable, scalable, and analytics-ready platform that supports accurate reporting and enables data-driven business decision-making.

• Inconsistent schemas, duplicate records, and unstructured data resulted in poor data quality and unreliable reporting.
• Direct ingestion of data from GitHub was not supported due to file system limitations, requiring a custom ingestion approach.
• Managing raw file access and handling repository structure added complexity to the ingestion layer.
• Ensuring consistent data availability and maintaining data integrity during ingestion and processing required additional control mechanisms.
• Lack of orchestration in existing workflows required the implementation of a structured and automated pipeline.

• To address these challenges, a scalable and production-ready data pipeline was implemented using Databricks, enabling efficient data ingestion, processing, and analytics across multiple business domains.
• The solution integrates GitHub as the source system and leverages Unity Catalog for secure and organized data storage.
• Data is ingested from the client-provided GitHub repository into Databricks using a controlled ingestion process.
• The ingested data is stored in Unity Catalog Volumes to ensure proper organization and accessibility.
• The data is then processed through multiple transformation layers to ensure quality, consistency, and usability.
• The solution enabled a unified and reliable data foundation, supporting accurate reporting and improved business decision-making.

• The implemented solution significantly improved data quality by eliminating inconsistencies and standardizing schemas across datasets.
• The structured Medallion Architecture enabled scalable data processing and simplified pipeline maintenance.
• The introduction of SCD Type 2 allowed the client to track historical changes effectively, enhancing analytical capabilities.
• The integration of GitHub with Databricks provided a streamlined workflow for managing both data and code, improving version control and reproducibility.
• Overall, the pipeline enabled faster data and more efficient processing, reliable reporting, and a strong foundation for future enhancements such as real-time processing and advanced analytics.
• The solution established a scalable and robust data platform, enabling the client to leverage data as a strategic asset.

• Databricks
• Delta Lake
• Apache Spark (PySpark)
• Unity Catalog
• Databricks Jobs
• GitHub
• Databricks Repos