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.
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- 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.
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- 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.
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- Review the crawler information.
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- Check the catalog details once the crawler is executed successfully.
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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
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- Once completed, Glue will create a readymade mapping for you.
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- Once you review your mapping, it will automatically generate python code/job for you.
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- Once the job completes successfully, it will generate logs for you to review.
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- Go and check the files in the bucket. Download the files.
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- Review the contents of the file.
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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:
- This approach is fully serverless and you do not have to worry about provisioning and maintaining your resources
- You can run your customized Python and Scala code to run the ETL
- You can push your event notification to Cloudwatch
- You can trigger the Lambda function for success or failure notification
- You can manage your job dependencies using AWS Glue
- 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:
- 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
- AWS Glue service is still in an early stage and not mature enough for complex logic
- 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.
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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 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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