Amazon S3 for ML Data Lakes

Amazon S3 underpins virtually every ML workload on AWS. Whether you are ingesting raw CSV files, running columnar queries through Amazon Athena, or streaming training data into SageMaker using pipe mode, S3 is the storage layer that connects each stage of the pipeline. For the AWS Certified Machine Learning Engineer–Associate exam, you need to understand that while S3 stores data, the choice of file format, storage class, and access control mechanism directly shapes pipeline throughput, cost efficiency, and security posture.

A data lakeA centralized repository that stores structured, semi-structured, and unstructured data at any scale without requiring a predefined schema. built on S3 serves as the single source of truth for ML pipelines that span ingestion, transformation, training, and inference. Each of these stages imposes different data access patterns. Ingestion favors append-friendly, row-oriented writes. Transformation and feature engineering benefit from columnar reads that minimize I/O. Training jobs demand high-throughput, sequential streaming. Inference pipelines may pull individual records or small batches.

S3 integrates natively with AWS Glue for cataloging and ETL, Amazon Athena for serverless SQL queries, and SageMaker for training and hosting. A raw CSV file landing in an S3 prefix can trigger an AWS Glue crawler, which populates the Glue Data Catalog and enables Athena to run SQL-based feature selection without provisioning infrastructure. That same data, once converted to Parquet and partitioned, feeds directly into a SageMaker training job. The decisions you make about format, tiering, and permissions propagate through every downstream service.

Let’s walk through file format selection, storage cost optimization ...