How to Connect DynamoDB to S3 in 5 Easy Steps

on Tutorial, Data Integration, Database, ETL • February 26th, 2019 • Write for Hevo

dynamodb to s3 aws data pipeline

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.

Table of Contents

Prerequisites

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 ETL Pipeline.

What is Amazon DynamoDB?

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.

To know more about Amazon DynamoDB, visit this link.

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 100+ other sources (40+ 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.

Get Started with Hevo for Free

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?

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.

To know more about Amazon S3, visit this link.

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 check, 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.

To know more about AWS Data Pipeline, visit this link.

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 by the below image.

aws data pipeline dynamodb to s3 | Hevo Data
Image Source: Self
import dynamodb to s3 | Hevo Data
Image Source: Self
aws data pipeline configuration for dynamodb to s3 | Hevo Data
Image Source: Self

Step 2: Activate the Pipeline once done.

aws data pipeline export dynamodb to s3 | Hevo Data
Image Source: Self

Step 3: Once the Pipeline is finished, check whether the file is generated in the S3 bucket.

S3 bucket aws data pipeline for dynamodb to s3 | Hevo Data
Image Source: Self

Step 4: Go and download the file to see the content.

dynamodb to s3: download s3 bucket files | Hevo Data
Image Source: Self

Step 5: Check the content of the generated file.

dynamodb to s3: validate data in s3 | Hevo Data
Image Source: Self

With this, you have successfully set up DynamoDB to S3 Integration.

What Makes Your Data Integration Experience With Hevo Best-in-Class? 

Performing the Data Integration process manually is a tedious and time-consuming process. To keep your application live all the time, the organization can use Hevo Data, an automated No Code Data Pipelining solution that not only helps in seamless Data Integration but also automate the ETL process without writing a single line of code.

These are some other benefits of having Hevo Data as your Data Replication Partner:

  • Exceptional Security: A Fault-tolerant Architecture that ensures Zero Data Loss.
  • Built to Scale: Exceptional Horizontal Scalability with Minimal Latency for Modern-data Needs.
  • Built-in Connectors: Support for Amazon DynamoDB, AWS S3, and 100+ Data Sources, including Databases, SaaS Platforms, Files & More. Native Webhooks & REST API Connector available for Custom Sources.
  • Data Transformations: Best-in-class & Native Support for Complex Data Transformation at fingertips. Code & No-code Flexibility is designed for everyone.
  • Smooth Schema Mapping: Fully-managed Automated Schema Management for incoming data with the desired destination.
  • Blazing-fast Setup: Straightforward interface for new customers to work on, with minimal setup time.

With continuous real-time data movement, ETL your data seamlessly to your destination warehouse with Hevo’s easy-to-setup and No-code interface. Try our 14-day full access free trial.

Sign up here for a 14-Day Free Trial!

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 flexibility on your resources 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 doing further testing on the DynamoDB table and can revert back 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 instance and the cluster configuration is 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.

Conclusion

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 100+ data sources (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.

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Share your experience of connecting DynamoDB to S3 in the comments section below!