AWS Glue: Moving Data from DynamoDB to S3

In the realm of cloud - based data management, Amazon Web Services (AWS) offers a plethora of services that empower developers to handle data efficiently. Two such services are Amazon DynamoDB and Amazon S3. DynamoDB is a fully managed NoSQL database service, while S3 is an object storage service. AWS Glue serves as a critical link that can be used to transfer data from DynamoDB to S3. This blog post will explore how to use AWS Glue for this data transfer, covering core concepts, usage scenarios, common practices, and best practices.

Table of Contents#

  1. Introduction
  2. Table of Contents
  3. Core Concepts
  4. Typical Usage Scenarios
  5. Common Practices
  6. Best Practices
  7. Conclusion
  8. FAQ
  9. References

Core Concepts#

Amazon DynamoDB#

Amazon DynamoDB is a fully managed NoSQL database service provided by AWS. It offers high - performance, scalable storage for applications that require fast and predictable response times. DynamoDB can handle a large number of requests per second, and it automatically scales up or down based on the incoming traffic. Data in DynamoDB is stored in tables, and each table consists of items with attributes. Each item is uniquely identified by a primary key, which can be a simple partition key or a combination of a partition key and a sort key.

Amazon S3#

Amazon S3 is an object storage service that provides industry - leading scalability, data availability, security, and performance. It allows you to store and retrieve any amount of data at any time from anywhere on the web. S3 stores data as objects within buckets, and each object is identified by a unique key. S3 is often used for data archiving, backup, and as a data lake for analytics and machine learning workloads.

AWS Glue#

AWS Glue is a fully managed extract, transform, and load (ETL) service. It simplifies the process of preparing and loading data for analytics. AWS Glue can discover data, catalog it, and then transform and move it between different data stores. It uses a serverless architecture, which means you don't have to manage any infrastructure. AWS Glue has components like crawlers, jobs, and a data catalog to help with data discovery, transformation, and movement.

Typical Usage Scenarios#

  1. Data Archiving: DynamoDB is great for real - time access to data, but for long - term storage, S3 is a more cost - effective option. Moving old data from DynamoDB to S3 can free up space in DynamoDB and reduce costs while still keeping the data accessible for future reference.
  2. Data Analytics: S3 is a popular choice for building data lakes. By moving data from DynamoDB to S3, you can use various analytics tools like Amazon Athena, Amazon Redshift, or Amazon EMR to perform complex queries and analysis on the data.
  3. Backup and Disaster Recovery: Storing a copy of DynamoDB data in S3 provides an additional layer of protection. In case of any issues with DynamoDB, the data in S3 can be used to restore the database.

Common Practices#

Creating an AWS Glue Crawler#

  1. Define the Crawler: First, log in to the AWS Glue console. Navigate to the crawlers section and create a new crawler. When defining the crawler, select DynamoDB as the data source. You need to specify the DynamoDB table that you want to crawl.
  2. Set Permissions: The crawler needs appropriate IAM permissions to access the DynamoDB table. You should create an IAM role with permissions to read from the DynamoDB table and write to the AWS Glue Data Catalog.
  3. Run the Crawler: After configuring the crawler, run it. The crawler will scan the DynamoDB table, extract the schema information, and populate the AWS Glue Data Catalog with metadata about the table.

Building an AWS Glue ETL Job#

  1. Create the ETL Job: In the AWS Glue console, create a new ETL job. Select the DynamoDB table (from the Data Catalog) as the source and S3 as the target.
  2. Transform Data (Optional): You can use AWS Glue's built - in transformation functions to modify the data before writing it to S3. For example, you can perform data cleaning, aggregations, or type conversions.
  3. Configure the Output: Specify the S3 bucket and the format in which you want to store the data in S3, such as CSV, Parquet, or JSON.
  4. Run the ETL Job: Once the job is configured, start the ETL job. AWS Glue will extract the data from DynamoDB, perform any necessary transformations, and load it into the specified S3 bucket.

Best Practices#

  1. Data Partitioning: When writing data to S3, partition the data based on relevant attributes such as date or category. This can significantly improve query performance when using analytics tools on the data stored in S3.
  2. Monitoring and Logging: Use AWS CloudWatch to monitor the performance of your AWS Glue jobs. Set up alarms for job failures or long - running jobs. Also, enable logging to track the progress and any errors during the ETL process.
  3. Cost Optimization: DynamoDB has read and write capacity units, and AWS Glue incurs costs based on the resources used. Optimize your DynamoDB capacity settings and schedule your AWS Glue jobs during off - peak hours to reduce costs.

Conclusion#

AWS Glue provides a seamless way to transfer data from DynamoDB to S3, enabling various use cases such as data archiving, analytics, and backup. By understanding the core concepts, typical usage scenarios, common practices, and best practices, software engineers can effectively leverage these AWS services to manage their data more efficiently. Whether it's for long - term storage or in - depth data analysis, the combination of DynamoDB, S3, and AWS Glue offers a powerful solution for modern data management.

FAQ#

Can I transfer only a subset of data from DynamoDB to S3 using AWS Glue?#

Yes, you can. You can use AWS Glue's transformation capabilities to filter the data from DynamoDB before writing it to S3. For example, you can use conditional statements in your ETL job to select only the items that meet certain criteria.

How long does it take to transfer data from DynamoDB to S3 using AWS Glue?#

The transfer time depends on various factors such as the size of the DynamoDB table, the available read capacity units in DynamoDB, and the network bandwidth. Larger tables and lower read capacity units will generally result in longer transfer times.

Is it possible to run AWS Glue jobs on a schedule?#

Yes, AWS Glue jobs can be scheduled using the AWS Glue console or AWS CLI. You can set up a cron - like schedule to run your ETL jobs at specific intervals.

References#

  1. Amazon Web Services, Inc. Amazon DynamoDB Developer Guide. https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Introduction.html
  2. Amazon Web Services, Inc. Amazon S3 User Guide. https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html
  3. Amazon Web Services, Inc. AWS Glue Developer Guide. https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html
2025-10