Moving data from Amazon DynamoDB to S3 is one of the efficient ways to derive deeper insights from your data. If you are trying to move data into a larger database. Well, you have landed on the right article. Now, it has become easier to replicate data from DynamoDB to S3.

This article will give you a brief overview of Amazon DynamoDB and Amazon S3. You will also get to know how you can set up your DynamoDB to S3 integration using 4 easy steps. Moreover, the limitations of the method will also be discussed. Read along to know more about connecting DynamoDB to S3 in the further sections.


You will have a much easier time understanding the ways for setting up the DynamoDB to S3 integration if you have gone through the following aspects:

  • An active AWS account.
  • Working knowledge of the ETL process.

What is Amazon DynamoDB?

DynamoDB to S3 - Amazon DynamoDB Logo | Hevo Data
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Amazon DynamoDB is a document and key-value Database with a millisecond response time. It is a fully managed, multi-active, multi-region, persistent Database for internet-scale applications with built-in security, in-memory cache, backup, and restore. It can handle up to 10 trillion requests per day and 20 million requests per second.

Some of the top companies like Airbnb, Toyota, Samsung, Lyft, and Capital One rely on DynamoDB’s performance and scalability.

Simplify Data Integration With Hevo’s No Code Data Pipeline

Hevo Data, an Automated No-code Data Pipeline, helps you directly transfer data from Amazon DynamoDBS3, and 150+ other sources (50+ free sources) to Business Intelligence tools, Data Warehouses, or a destination of your choice in a completely hassle-free & automated manner.

Hevo’s fully managed pipeline uses DynamoDB’s data streams to support Change Data Capture (CDC) for its tables and ingests new information via Amazon DynamoDB Streams & Amazon Kinesis Data Streams. Hevo also enables you to load data from files in an S3 bucket into your Destination database or Data Warehouse seamlessly. Moreover, S3 stores its files after compressing them into a Gzip format. Hevo’s Data pipeline automatically unzips any Gzipped files on ingestion and also performs file re-ingestion in case there is any data update.

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With Hevo in place, you can automate the Data Integration process which will help in enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure and flexible manner with zero data loss. Hevo’s consistent & reliable solution to manage data in real-time allows you to focus more on Data Analysis, instead of Data Consolidation. 

What is Amazon S3?

DynamoDB to S3 - Amazon S3 Logo | Hevo Data
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Amazon S3 is a fully managed object storage service used for a variety of purposes like data hosting, backup and archiving, data warehousing, and much more. Through an easy-to-use control panel interface, it provides comprehensive access controls to suit any kind of organizational and commercial compliance requirements.

S3 provides high availability by distributing data across multiple servers. This strategy, of course, comes with a propagation delay, however, S3 only guarantees eventual consistency. Also, in the case of Amazon S3, the API will always return either new or old data and will never provide a damaged answer.

What is AWS Data Pipeline?

AWS Data Pipeline Logo | Hevo Data
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AWS Data Pipeline is a Data Integration solution provided by Amazon. With AWS Data Pipeline, you just need to define your source and destination and AWS Data Pipeline takes care of your data movement. This will avoid your development and maintenance efforts. With the help of a Data Pipeline, you can apply pre-condition/post-condition checks, set up an alarm, schedule the pipeline, etc. This article will only focus on data transfer through the AWS Data Pipeline alone.

Limitations: Per account, you can have a maximum of 100 pipelines and objects per pipeline.

Steps to Connect DynamoDB to S3 using AWS Data Pipeline

You can follow the below-mentioned steps to connect DynamoDB to S3 using AWS Data Pipeline:

Step 1: Create an AWS Data Pipeline from the built-in template provided by Data Pipeline for data export from DynamoDB to S3 as shown in the below image.

DynamoDB to S3 - Create pipeline | Hevo Data
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DynamoDB to S3 - import | Hevo Data
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aws data pipeline configuration for dynamodb to s3 | Hevo Data
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Step 2: Activate the Pipeline once done.

DynamoDB to S3 - Activate the pipeline | Hevo Data
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Step 3: Once the Pipeline is finished, check whether the file is generated in the S3 bucket.

DynamoDB to S3 - S3 bucket | Hevo Data
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Step 4: Go and download the file to see the content.

DynamoDB to S3 - download s3 bucket files | Hevo Data
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Step 5: Check the content of the generated file.

DynamoDB to S3 : validate data in s3 | Hevo Data
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With this, you have successfully set up DynamoDB to S3 Integration.

Advantages of exporting DynamoDB to S3 using AWS Data Pipeline

AWS provides an automatic template for Dynamodb to S3 data export and very less setup is needed in the pipeline.

  1. It internally takes care of your resources i.e. EC2 instances and EMR cluster provisioning once the pipeline is activated.
  2. It provides greater resource flexibility as you can choose your instance type, EMR cluster engine, etc.
  3. This is quite handy in cases where you want to hold your baseline data or take a backup of DynamoDB table data to S3 before further testing on the DynamoDB table and can revert to the table once done with testing.
  4. Alarms and notifications can be handled beautifully using this approach.

Disadvantages of exporting DynamoDB to S3 using AWS Data Pipeline

  1. The approach is a bit old-fashioned as it utilizes EC2 instances and triggers the EMR cluster to perform the export activity. If the instance and the cluster configuration are not properly provided in the pipeline, it could cost dearly.
  2. Sometimes EC2 instance or EMR cluster fails due to resource unavailability etc. This could lead to the pipeline getting failed.

Even though the solutions provided by AWS work but it is not much flexible and resource optimized. These solutions either require additional AWS services or cannot be used to copy data from multiple tables across multiple regions easily. You can use Hevo, an automated Data Pipeline platform for Data Integration and Replication without writing a single line of code. Using Hevo, you can streamline your ETL process with its pre-built native connectors with various Databases, Data Warehouses, SaaS applications, etc.

You can also check out our blog on how to move data from DynamoDB to Amazon S3 using AWS Glue.

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Overall, using the AWS Data Pipeline is a costly setup, and going with serverless would be a better option. However, if you want to use engines like Hive, Pig, etc., then Pipeline would be a better option to import data from the DynamoDB table to S3. 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.

Hevo Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. Hevo caters to 150+ data sources (including 50+ 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.

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Share your experience of connecting DynamoDB to S3 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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