ETL and ELT

Extract data from sources, transform it and load it into a warehouse, or load first and transform inside the warehouse. These pipelines run in batches or continuously, outside the user request path.

Where it shows up in real systems

  • Data warehouse

    Built for large analytical queries that take seconds, not for serving users. Placing it in a request path destroys latency; feed it asynchronously through a pipeline instead.

    In production: Snowflake, Google BigQuery, Amazon Redshift, ClickHouse, Databricks SQL

  • Batch processing cluster

    Spark-style jobs split data into partitions processed by many executors. Throughput is enormous but results arrive hours later; feed it from a queue, a log or object storage, never from the user request path.

    In production: Apache Spark (Databricks, EMR), Google Dataflow, AWS Glue, Hadoop MapReduce

See it break, then fix it

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