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Case Study · backend

Lakeflow Databricks Data Pipeline

Scalable cloud data engineering pipeline on Databricks executing automated data ingestion, transformation, and medallion storage.

Track

backend

Technologies

Python, Apache Spark, Databricks, Delta Lake

Primary Impact

Automated ETL ingestion pipelines

RepositoryView on GitHub ↗

Context & Challenge

Processing massive raw logs into analytics-ready structured tables requires robust schema validation and ACID guarantees.

Process & Implementation

Constructed a multi-stage medallion data architecture on Databricks utilizing Delta Lake and Apache Spark; incorporated automated schema validation and anomaly filtering.

Verified Outcomes & Key Facts

  • Automated ETL ingestion pipelines
  • Delta Lake ACID transaction guarantees
  • Automated data quality verification
  • Scalable batch & stream architecture