What AWS services are most commonly used in data engineering?

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In data engineering, AWS offers a wide range of services to handle data collection, storage, processing, and analysis. Here are the most commonly used AWS services in the data engineering landscape:

1. Amazon S3 (Simple Storage Service): A core service for storing large volumes of structured and unstructured data. It’s often used as a data lake for staging raw and processed data.

2. AWS Glue: A serverless data integration service that helps in cleaning, transforming, and preparing data for analytics. It includes a data catalog, ETL engine, and support for job automation.

3. Amazon Redshift: A fully managed, petabyte-scale data warehouse that supports fast SQL queries for analytics and reporting. It integrates well with BI tools and is commonly used for structured data analysis.

4. Amazon Kinesis: A suite of services (Kinesis Data Streams, Firehose, Analytics) for real-time data ingestion, processing, and streaming analytics. Ideal for time-sensitive applications like monitoring and IoT.

5. AWS Lambda: A serverless compute service often used for event-driven data processing. It's ideal for lightweight transformations and orchestrating workflows without managing infrastructure.

6. Amazon EMR (Elastic MapReduce): A managed big data platform for processing large datasets using open-source tools like Apache Spark, Hadoop, Hive, and Presto. It’s suitable for complex, distributed data processing tasks.

7. Amazon RDS and Aurora: Managed relational databases for storing structured data. Often used as source or sink systems in ETL pipelines.

8. AWS Data Pipeline: A service for orchestrating data workflows. While somewhat older, it’s still used in some setups to schedule and manage data movement between AWS services.

These services enable data engineers to build scalable, secure, and efficient data pipelines for batch and real-time processing, supporting everything from ingestion to transformation and storage.

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