The Various Facets of AWS ETL Services

The Extract, Transform, Load (ETL) service of AWS helps to define data movement and transformations across different AWS ETL services. This is true for on-premises resources also. With the AWS ETL services, you can define the processes to create your Data Pipeline to hold nodes that contain data and business logic such as EMR or SQL queries.


The following are some of the areas that are handled by AWS ETL services and the AWS Data Pipeline.

  • Scheduling of jobs, execution, and retry logic.
  • Monitoring dependencies between data sources, business logic, and past processing steps. It is to ensure that unless all the dependencies are met, the logic does not run.
  • Informing of any necessary failure notifications.
  • As and when required by the job, creating and managing to compute resources.
  • Given here are some of the popular use cases of the AWS ETL services. 
  • Copying RDS or DynamoDB tables to S3, transforming data structure, running analytics using SQL queries, and loading it to Redshift. 
  • Analyzing unstructured data like clickstream logs using Hive or Pig on EMR, combining it with structured data from RDS, and uploading it to Redshift for easy querying. 
  • Loading log files. Examples include AWS billing logs, AWS CloudTrail, Amazon CloudFront, and Amazon CloudWatch logs from Amazon S3 to Redshift.
  • Easily copy data from an on-premises data store, like a MySQL database, and move it to an AWS data store like S3. This makes it available to a variety of AWS ETL services such as Amazon EMR, Amazon Redshift, and Amazon RDS.
  • Backing up the Dynamo DB table to S3 periodically for disaster recovery purposes.

These are a few of the AWS ETL services. 

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