2.3.1. Batch ETL (AWS Glue, EMR, Athena)
First Principle: Batch ETL services fundamentally enable large-scale, scheduled transformations of data for ML, ensuring data cleanliness, consistency, and proper structuring for efficient model training.
For cleaning, combining, and preparing large volumes of data that do not require real-time processing, batch ETL services are the workhorses in an ML pipeline.
Key AWS Services for Batch ETL in ML:
- AWS Glue: (Serverless data integration service.)
- What it is: A serverless data integration service that makes it easy to discover, prepare, and combine data for analytics and machine learning.
- Components:
- Glue Data Catalog: A centralized metadata repository for all your data assets, automatically populated by crawlers. Integrates with Athena, Redshift Spectrum, and EMR.
- Glue ETL Jobs: Serverless Spark or Python shell jobs for data transformation. You provide scripts, Glue handles provisioning and scaling.
- Glue Studio: A visual interface for creating, running, and monitoring Glue ETL jobs.
- Use Cases: General data transformation, data cleansing, schema inference, data lake population.
- Amazon EMR (Elastic MapReduce): (Managed Hadoop framework.)
- What it is: A managed cluster platform that simplifies running big data frameworks like Apache Spark, Hadoop, Hive, Presto, and more.
- Use Cases: Highly customized big data processing, when you need fine-grained control over cluster configuration, complex distributed jobs, or want to use specific open-source frameworks not fully supported by Glue.
- Amazon Athena: (Interactive query service.)
- What it is: A serverless interactive query service that makes it easy to analyze data directly in Amazon S3 using standard SQL.
- Use Cases: Ad-hoc querying of data in S3 (often defined in Glue Data Catalog) for EDA or simple transformations before loading into other services. Not suitable for complex, iterative transformations.
Scenario: You have a large volume of raw JSON log files in Amazon S3 that need to be transformed daily into a clean, structured Parquet format. This transformed data will then be used for training a monthly ML model.
Reflection Question: How do batch ETL services like AWS Glue (for serverless ETL jobs) and Amazon EMR (for managed Hadoop clusters) fundamentally enable large-scale, scheduled transformations of data for ML, ensuring data cleanliness, consistency, and proper structuring for efficient model training?