Automated Retail Lakehouse with Scheduled Ingestion Workflows

Added Jun 2025 3 design docs

Retail data engineering is less about exotic algorithms and more about dependable automation: nightly sales files must load the moment they arrive, streaming events must flow continuously, and when something breaks, the right person must know before the morning business review. Building that reliability layer is what separates a demo pipeline from an operational one. The intern develops an orchestrated lakehouse for retail data on Databricks. Batch ingestion is automated with file arrival triggers that pick up CSV drops as they land, Kafka supplies real-time streaming events, and DLT pipelines transform everything through a Medallion Architecture with data quality rules applied at each promotion from Bronze to Silver to Gold using Spark, Python, and SQL over Delta Lake. The distinctive focus is workflow management: the intern configures job scheduling, builds parameterized notebooks so the same logic serves multiple stores and date ranges, wires up failure and success notifications, and assembles Databricks SQL dashboards over the curated layer for sales and inventory visibility. The project teaches production habits, idempotent runs, alerting, parameterization, that most coursework skips entirely, and demonstrates the intern can be trusted with pipelines that run unattended every night in a real retail operation.

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