Amazon Glue

Simple, scalable, and serverless data integration

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Overview

Amazon Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. Amazon Glue provides all of the capabilities needed for data integration so that you can start analyzing your data and putting it to use in minutes instead of months.

Data integration is the process of preparing and combining data for analytics, machine learning, and application development. It involves multiple tasks, such as discovering and extracting data from various sources; enriching, cleaning, normalizing, and combining data; and loading and organizing data in databases, data warehouses, and data lakes. These tasks are often handled by different types of users that each use different products.

Amazon Glue provides both visual and code-based interfaces to make data integration easier. Users can easily find and access data using the Amazon Glue Data Catalog. Data engineers and ETL (extract, transform, and load) developers can create and run ETL workflows. Data analysts and data scientists can use Amazon Glue DataBrew to visually enrich, clean, and normalize data without writing code.

Benefits

Faster Data Integration

Different groups across your organization can use Amazon Glue to work together on data integration tasks, including extraction, cleaning, normalization, combining, loading, and running scalable ETL workflows. This way, you reduce the time it takes to analyze your data and put it to use from months to minutes.

No Servers to Manage

Amazon Glue runs in a serverless environment. There is no infrastructure to manage, and Amazon Glue provisions, configures, and scales the resources required to run your data integration jobs. You pay only for the resources your jobs use while running.

Automate Your Data Integration at Scale

Amazon Glue automates much of the effort required for data integration. Amazon Glue crawls your data sources, identifies data formats, and suggests schemas to store your data. It automatically generates the code to run your data transformations and loading processes. You can use Amazon Glue to easily run and manage thousands of ETL jobs or to combine and replicate data across multiple data stores using SQL.

How It Works

Amazon Glue can run your ETL jobs as new data arrives. For example, you can use an Amazon Lambda function to trigger your ETL jobs to run as soon as new data becomes available in Amazon S3. You can also register this new dataset in the Amazon Glue Data Catalog as part of your ETL jobs.

A use case diagram illustrating an ETL data pipeline using Amazon Glue. The diagram shows data flowing from Amazon S3 into Amazon Glue Data Catalog, then using Amazon Lambda to trigger Glue ETL jobs. The ETL jobs transform and load the data into target stores such as Amazon Redshift or Amazon S3, and logs or notifications are sent to Amazon CloudWatch.

You can use the Amazon Glue Data Catalog to quickly discover and search across multiple Amazon data sets without moving the data. Once the data is cataloged, it is immediately available for search and query using Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.

A flow diagram illustrating the use case for Amazon Glue Data Catalog and ETL. The diagram shows data sources like Amazon Redshift, Amazon S3, Amazon RDS, and databases running on Amazon EC2, storing metadata into the Glue Data Catalog. Amazon Glue Data Catalog serves as a central metadata repository, accessed by Amazon Athena, Amazon Redshift, and Amazon EMR for ETL and analytics, and is integrated with Amazon Glue ETL and Amazon QuickSight for report generation.

Amazon Glue DataBrew enables you to explore and experiment with data directly from your data lake, data warehouses, and databases, including Amazon S3, Amazon Redshift, Amazon Lake Formation, Amazon Aurora, and Amazon RDS. You can choose from over 250 prebuilt transformations in Amazon Glue DataBrew to automate data preparation tasks, such as filtering anomalies, standardizing formats, and correcting invalid values. After the data is prepared, you can immediately use it for analytics and machine learning. Learn more about Amazon Glue DataBrew here.

Workflow diagram illustrating the Amazon Glue DataBrew data preparation process, including connection to data sources, over 250 built-in transformations, evaluation of data quality, automation at scale, publishing to Amazon S3, and achieving faster insights for analytics and machine learning.

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