If data is stored in S3 and processed to another place, how do downstream systems consume it?

Downstream consumers typically ingest data from S3 through one of several patterns. First, event‑driven ingestion uses S3 event notifications to trigger Lambda or Step Functions that copy or transform the data into a target system. Second, scheduled batch jobs run on EMR, Glue, or Airflow, reading the S3 objects, performing transformations, and writing results to a data warehouse or a message queue. Third, serverless query services like Athena or Presto can read the S3 files directly, allowing analysts to run SQL without moving data. For real‑time pipelines, data can be streamed from S3 to Kinesis Data Firehose or Kafka, which downstream services consume. Finally, data cataloging tools (Glue Data Catalog, AWS Lake Formation) expose the schema to downstream applications, enabling schema‑aware consumption. The choice depends on latency requirements, data volume, and the target system’s ingestion capabilities.