Batch Data Lake Pipeline From MySQL to Delta Lake on S3

Added Jun 2025 3 design docs

Finance and retail companies keep their operational truth in relational databases, but running heavy analytics directly against production MySQL slows the business systems everyone depends on. The standard answer is a data lake: replicate operational data into cheap, scalable storage where analysts can explore it freely without ever touching production. The intern builds that pipeline end to end. Sqoop performs efficient bulk extraction from MySQL into the Hadoop ecosystem, where raw records land in Hive tables that preserve source schemas. Spark jobs written in Python then clean, join, and enrich the data, curating it into Delta Lake tables on S3 that add ACID transactions, schema evolution, and time travel to plain object storage. Presto completes the stack, giving analysts fast ad-hoc SQL over the curated layer, and the intern documents each zone of the lake, from raw to cleansed to curated, exactly as a production lakehouse team would. By the end the intern can explain and defend a modern batch architecture: ingestion tooling choices, multi-zone lake design, table format trade-offs, and how a distributed query engine ties the platform together for finance and retail analytics.

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