3.2. Ingest and Transform Batch Data

💡 First Principle: Batch processing moves large volumes of data at scheduled intervals—like a daily mail delivery rather than instant messaging. The key decisions are choosing the right storage engine for your query patterns and the right transformation tool for your complexity and scale. Mismatches here create performance problems that no amount of optimization can fix.

What breaks without proper batch architecture? Storing structured relational data as raw files forces manual schema management. Using a lakehouse when T-SQL analysts need a warehouse creates constant friction. Choosing Dataflow Gen2 for petabyte-scale processing exhausts memory and times out.