What AWS services are commonly used in data engineering (e.g., S3, Redshift, Glue, EMR)?

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As of 2025, AWS offers a rich ecosystem of services tailored to data engineering workflows. The most commonly used AWS services for data engineering include:

1. Amazon S3 (Simple Storage Service)
S3 is the backbone of data lakes. It's a scalable, durable object storage service used to store raw, processed, and curated data. It integrates seamlessly with other AWS analytics and machine learning services.

2. AWS Glue
Glue is a serverless data integration service used for ETL (Extract, Transform, Load). It automates data discovery, schema inference, and job orchestration. Glue Studio offers a visual interface, while Glue Jobs support both PySpark and Python Shell scripts.

3. Amazon Redshift
A fully managed data warehouse optimized for analytical queries. It allows data engineers to run complex SQL queries on large datasets efficiently and supports Redshift Spectrum to query data directly in S3 without loading it.

4. Amazon EMR (Elastic MapReduce)
EMR provides a managed Hadoop framework that supports big data processing engines such as Apache Spark, Hive, and Presto. It's ideal for processing vast datasets at scale.

5. AWS Lambda
Used for serverless, event-driven transformations or data triggers. Commonly used to preprocess or validate incoming data before further processing.

6. AWS Data Pipeline
Though less used now in favor of Glue Workflows or Step Functions, Data Pipeline is still used in legacy systems for orchestrating data movement and transformation.

7. Amazon Kinesis
A real-time data streaming service used to capture and analyze streaming data. It works well for ingesting IoT, logs, or clickstream data.

8. AWS Step Functions
Orchestrates workflows across multiple AWS services. Commonly used to manage complex data pipelines involving Glue, Lambda, and Redshift.

These services together enable scalable, cost-effective, and flexible data engineering solutions in the cloud.

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