Mastering Data Ingestion and Storage for Machine Learning

Data ingestion and storage represent the core components of the Collection Phase in the Machine Learning lifecycle. This document explores how to move data from source systems into AWS and persist it in a way that ensures availability, durability, and performance for ML workflows.

Learning Objectives

After studying this guide, you should be able to:

• Distinguish between batch and real-time data ingestion methods.
• Select appropriate AWS storage services (S3, EBS, EFS, FSx) based on performance and cost tradeoffs.
• Identify key data formats (Parquet, JSON, CSV, RecordIO) and their use cases in ML.
• Recognize the data engineering lifecycle: Generation → Ingestion → Storage → Processing.

Key Terms & Glossary

• Ingestion: The process of collecting and consolidating data from diverse sources into a centralized AWS environment.
• Durability: The ability of a storage system to keep data intact over time without loss (e.g., S3's 11 nines of durability).
• Availability: The guarantee that data is accessible when needed by an ML model or training job.
• Object Storage: Data stored as discrete units (objects) with metadata, ideal for unstructured data and massive scale (Amazon S3).
• Block Storage: Data stored in fixed-size blocks, typically used as