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Serverless Analytics in AWS

AWS serverless services allow you to build data lakes and data pipelines that process and analyze petabytes of data.

We live in a data-driven age, where data analytics has become key to the success of modern enterprises because it enables smarter decision making and faster detection of patterns and trends. Building the data pipeline and analytics infrastructure however proved to be a challenging feat for most companies. It requires ingesting, storing, processing, and consuming very large amounts of data which traditionally requires complex and large scale distributed systems.

Failing to implement a successful data analytics solution can turn out to be very costly for organizations both financially and in terms of time to insights. This is why serverless analytics today is such a compelling option. By using AWS serverless services as building blocks, you can now easily and rapidly build data lakes and data pipelines that process and analyze petabytes of data without needing to manage any infrastructure components.

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AWS currently offers the richest portfolio of serverless options and just announced one more in public preview (that likely will be generally available by the time you’re reading this):

  • AWS S3
  • AWS Glue and Glue Databrew
  • AWS Lake Formation
  • AWS Athena
  • AWS Kinesis
  • AWS Quicksight
  • AWS MSK Serverless
  • AWS EMR Serverless

In public preview:

  • AWS Redshift Serverless

Benefits of Serverless Analytics

  • Zero infrastructure to manage which is as close to zero administration overhead as you can get
  • Automatically scales resources with usage
  • Availability and fault tolerance built-in
  • Higher efficiency so you never pay for idle resources
  • Faster time to set up and hit the ground running and easier experimentation