1Data Engineering

• 1.1Data Collection and Ingestion

  The starting point of ML—understand data sources, streaming ingestion (Kinesis), batch ingestion, and the S3 data lake. Land training data in S3 first.

• 1.2Data Transformation and Feature Preparation

  Shape data into usable form—understand Glue ETL, the data catalog, Data Wrangler, feature engineering, and the Feature Store. Good features drive model quality.

• 1.3Storage and Data Formats

  Store efficiently—understand columnar (Parquet/ORC), partitioning, S3 storage classes, and SageMaker input modes. Format and layout drive cost and speed.