Are you trying to derive deeper insights from your Amazon DynamoDB by moving the data into a larger Database like Amazon S3? Well, you have landed on the right article. Now, it has become easier to replicate data from DynamoDB to S3 using AWS Glue.

Connecting DynamoDB with S3 allows you to export NoSQL data for analysis, archival, and more. In just two easy steps, you can configure an AWS Glue crawler to populate metadata about your DynamoDB tables and then create an AWS Glue job to efficiently transfer data between DynamoDB and S3 on a scheduled basis.

This article will tell you how you can connect your DynamoDB to S3 using AWS Glue along with their advantages and disadvantages in the further sections. Read along to seamlessly connect DynamoDB to S3.

Prerequisites

You will have a much easier time understanding the steps to connect DynamoDB to S3 using AWS Glue if you have:

  • An active AWS account.
  • Working knowledge of Databases.
  • A clear idea regarding the type of data to be transferred.

Steps to Connect DynamoDB to S3 using AWS Glue

This section details the steps to move data from DynamoDB to S3 using AWS Glue. This method would need you to deploy precious engineering resources to invest time and effort to understand both S3 and DynamoDB. They would then need to piece the infrastructure together bit by bit. This is a fairly time-consuming process.

Now, let us export data from DynamoDB to S3 using AWS glue. It is done in two major steps:

Step 1: Create a Crawler

The first step in connecting DynamoDB to S3 using AWS Glue is to create a crawler. You can follow the below-mentioned steps to create a crawler.

  • Create a Database DynamoDB.
DynamoDB to S3 using AWS Glue: Creating AWS Glue Crawler | Hevo Data
Image Source: Self
  • Pick a table from the Table drop-down list.
  • Let the table info get created through the crawler. Set up crawler details in the window below. Provide a crawler name, such as dynamodb_crawler.
  • Add database name and DynamoDB table name.
DynamoDB to S3 using AWS Glue: Adding Crawler | Hevo Data
Image Source: Self
  • Provide the necessary IAM role to the crawler such that it can access the DynamoDB table. Here, the created IAM role is AWSGlueServiceRole-DynamoDB.
  • You can schedule the crawler. For this illustration, it is running on-demand as the activity is one-time.
DynamoDB to S3 using AWS Glue: Configuring Crawler's Output | Hevo Data
Image Source: Self
  • Review the crawler information.
DynamoDB to S3 using AWS Glue: Reviewing Crawler Information | Hevo Data
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  • Run the crawler.
DynamoDB to S3 using AWS Glue: Running Crawler | Hevo Data
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  • Check the catalog details once the crawler is executed successfully.
DynamoDB to S3 using AWS Glue: Catalog Details | Hevo Data
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Step 2: Exporting Data from DynamoDB to S3 using AWS Glue

Since the crawler is generated, let us create a job to copy data from the DynamoDB table to S3. Here the job name given is dynamodb_s3_gluejob. In AWS Glue, you can use either Python or Scala as an ETL language. For the scope of this article, let us use Python

DynamoDB to S3 using AWS Glue: Job Properties | Hevo Data
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  • Pick your data source.
DynamoDB to S3 using AWS Glue: Picking the Data Source | Hevo Data
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  • Pick your data target.
DynamoDB to S3 using AWS Glue: Picking the Target Data | Hevo Data
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  • Once completed, Glue will create a readymade mapping for you.
DynamoDB to S3 using AWS Glue: Mapping the data | Hevo Data
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  • Once you review your mapping, it will automatically generate python code/job for you.
DynamoDB to S3 using AWS Glue: Review your Mappings | Hevo Data
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  • Execute the Python job.
DynamoDB to S3 using AWS Glue: Python Code Execution | Hevo Data
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  • Once the job completes successfully, it will generate logs for you to review.
DynamoDB to S3 using AWS Glue: Logs | Hevo Data
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  • Go and check the files in the bucket. Download the files.
DynamoDB to S3 using AWS Glue: Checking Files in the Bucket | Hevo Data
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  • Review the contents of the file.
DynamoDB to S3 using AWS Glue: Reviewing the Contents of File | Hevo Data
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Advantages of Connecting DynamoDB to S3 using AWS Glue

Some of the advantages of connecting DynamoDB to S3 using AWS Glue include:

  1. This approach is fully serverless and you do not have to worry about provisioning and maintaining your resources
  2. You can run your customized Python and Scala code to run the ETL
  3. You can push your event notification to Cloudwatch
  4. You can trigger the Lambda function for success or failure notification
  5. You can manage your job dependencies using AWS Glue
  6. AWS Glue is the perfect choice if you want to create a data catalog and push your data to the Redshift spectrum

Disadvantages of Connecting DynamoDB to S3 using AWS Glue

Some of the disadvantages of connecting DynamoDB to S3 using AWS Glue include:

  1. AWS Glue is batch-oriented and does not support streaming data. In case your DynamoDB table is populated at a higher rate. AWS Glue may not be the right option
  2. AWS Glue service is still in an early stage and not mature enough for complex logic
  3. AWS Glue still has a lot of limitations on the number of crawlers, number of jobs, etc.

Refer to AWS documentation to know more about the limitations.

Hevo Data, on the other hand, comes with a flawless architecture and top-class features that help in moving data from multiple sources to a Data Warehouse of your choice without writing a single line of code. It offers excellent Data Ingestion and Data Replication services. Compared to AWS Glue‘s support for limited sources.

Hevo supports 150+ ready-to-use integrations across databases, SaaS Applications, cloud storage, SDKs, and streaming services with a flexible and transparent pricing plan. With just a five-minute setup, you can replicate data from any of your Sources to a database or data warehouse Destination of your choice.

Conclusion

AWS Glue can be used for data integration when you do not want to worry about your resources and do not need to take control over your resources i.e., EC2 instances, EMR cluster, etc. Thus, connecting DynamoDB to S3 using AWS Glue can help you to replicate data with ease. Now, the manual approach of connecting DynamoDB to S3 using AWS Glue will add complex overheads in terms of time, and resources. Such a solution will require skilled engineers and regular data updates. Furthermore, you will have to build an in-house solution from scratch if you wish to transfer your data from DynamoDB or S3 to a Data Warehouse for analysis.

Hevo Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. Hevo caters to 150+ Sources & BI tools (including 40+ free sources) and can seamlessly transfer your S3 and DynamoDB data to the Data Warehouse of your choice in real-time. Hevo’s Data Pipeline enriches your data and manages the transfer process in a fully automated and secure manner without having to write any code. It will make your life easier and make data migration hassle-free.

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Share your experience of setting up DynamoDB to S3 Integration in the comments section below!

Ankur Shrivastava
Freelance Technical Content Writer, Hevo Data

Ankur loves writing about data science, ML, and AI and creates content tailored for data teams to help them solve intricate business problems.

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