An illustration of a serverless ETL migration with AWS Glue.

Serverless ETL Migration with AWS Glue

Discover, prepare, and combine data for analytics, machine learning, and application development with ease.

An illustration of a serverless ETL migration with AWS Glue.
Overview

ETL on AWS Glue

Organizations’ data is becoming increasingly complex: it may be diverse, from multiple novel sources, growing rapidly, and/or used by many applications and people with varied skill sets. On-premises ETL solutions are simply not suited to handle the intricacy of workloads today.

AWS Glue is a fully managed data integration service that makes it simple and cost-effective to categorize, clean and enrich your organization’s data, and move it reliably between data stores.

Why Embrace Serverless ETL with AWS Glue?

AWS Glue provides a simple way to prepare data for analytics, machine learning, and application development. Where traditional on-premises ETL solutions lack scalability, affordability and flexibility, AWS Glue provides:

High Integration Capabilities

AWS Glue connects to 30+ data sources, processes petabytes of data in real-time, and includes batch and event-driven modes.

Serverless ETL

There is no infrastructure to set up, manage, or maintain. Simply allocate needed compute power and run your Glue jobs.

Ease of Use (Flexible UI)

AWS Glue provides development environments catered to different skillsets: visual ETL development for Data Engineers, notebook-style development for Data Scientists, and no-code development for Data Analysts.

Cost-Effectiveness

Only pay for the resources consumed while your jobs are running. The all-in-one pricing model includes infrastructure and is 55% cheaper than other cloud data integration options.

No Technology Lock-In

Develop data integration pipelines in open source SparkSQL, PySpark and Scala.

Powerful Automation

Efficient and scalable data integration with automated ETL jobs that can be triggered based on a schedule or event.
Hot data lake storage with AWS data lake implementaiton.
Success Story

Hot Data Storage through AWS Data Lake Implementation

Our client needed a single data platform solution to integrate customer data so it could be accessed immediately when needed for use cases or analysis.

Adastra implemented a data lake in Amazon S3 and created an enterprise data model on top of it, running on Redshift. We created data marts, using stored procedures and AWS Glue jobs, allowing us to overlay the enterprise data model on top of the data lake, mimicking the client’s on-prem solution, in the cloud.

100+

jobs handled in real time

6x

faster reporting

Less

IT infrastructure costs

READ SUCCESS STORY

Common AWS Glue Use Cases

Building data lakes and lake houses for scalable data analysis
Processing petabytes of data both in batch and real-time using Apache Spark
Migrating from expensive traditional ETL solutions to gain flexibility and reduce costs
Preparing data for machine learning
Cataloging data assets to make them available to AWS analytics services

Adastra measures its success by the success of the organizations they provide support to and that is proved by their actions and work ethics. I was really happy with Adastra’s capabilities of not only delivering the right solution to our needs but also helping us with planning how the Cloud Analytics Platform can evolve in the near future in order to better support our growth and success.

Chris Smith

VP Digital Health, Skylight Health Group

Featured Resources

Embrace Serverless ETL with AWS Glue Now