Modern applications nowadays use NoSQL Databases to manage flexible data. Many micro-services require a real-time stream of data to power up the application. There are many NoSQL databases available in the market. Amazon DynamoDB is a powerful Database that can auto-scale automatically and deliver high performance to the applications.

Amazon Redshift is a Data Warehouse service that stores business data for analysis. Connecting Redshift to DynamoDB allows users to transfer data from Redshift Data Warehouse to DynamoDB Database. It helps developers to use historical data from Redshift to DynamoDB and power up the application.

Connecting Amazon Redshift to DynamoDB Database enables easy data flow. In this article, you will learn about Amazon Redshift, DynamoDB, and its features. You will learn the steps to connect Amazon Redshift to DynamoDB Database.

Prerequisites

  • An active Amazon Redshift Cluster.
  • An active Amazon DynamoDB Database.
  • Access to AWS S3 Bucket.

What is Amazon Redshift?

Redshift to DynamoDB: Amazon Redshift Logo | Hevo Data

Amazon Redshift is a fully managed Data Warehouse by Amazon Web Service (AWS) that can handle petabytes of data. It delivers high performance for querying data and real-time analysis. As it is a part of AWS so it can easily integrate with other AWS products to expand its functionalities. Amazon Redshift supports standard SQL to query data, and analyze structures as well as semi-structured data across Data Warehouses, Operational Databases, and Data Lakes.

Key Features of Amazon Redshift

Some of the main features of Amazon Redshift are listed below:

  • Query Editor: Amazon Redshift comes with a powerful query editor that supports SQL to allow users to access data from Data Warehouses and Data Lakes. It helps you visualize query results in a single click.
  • Fault Tolerant: Amazon Redshift continuously monitors the health of the clusters and automatically replicates data from nodes as necessary for fault tolerance which increases the reliability.
  • Redshift ML: Amazon Redshift comes with its ML environment that makes it easier for users to create, train, and deploy Amazon SageMaker models using SQL.

To know more about Amazon Redshift, click here.

What is Amazon DynamoDB? 

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Amazon DynamoDB is a fully managed NoSQL Database that supports key–value and document data structures. It is a part of the Amazon Web Services portfolio. DynamoDB is designed to run a high-performance application at any scale. It is well suited for real-time data capture, similar to CDC (Change Data Capture), it comes with DynamoDB Stream that stores the events in a log for 24 hours with no flexibility to extend. 

Key Features of Amazon DynamoDB

Some of the main features of Amazon DynamoDB are listed below:

  • Serverless: Amazon DynamoDB doesn’t require any server to provision, patch, or manage and no software to install or maintain. It automatically scales tables to maintain performance and storage.
  • DynamoDB Accelerator (DAX): Amazon DynamoDB comes with a highly available in-memory cache that improves the performance by 10 times by adding in-memory acceleration to the tables.
  • Auto-Scaling: Amazon DynamoDB automatically scales the throughput and storage of the Database based on the previous capacity.

To learn more about Amazon DynamoDB, click here.

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Steps to Connect Redshift to DynamoDB

In this section, you will go through the manual process to connect Amazon Redshift to DynamoDB. As DynamoDB doesn’t directly load data from Amazon Redshift. So for this, you need first extract data from Amazon Redshift to the S3 bucket and then load this data from AWS S3 to DynamoDB. The following steps to connect Redshift to DynamoDB via S3 are listed below:

Step 1: Unloading Data from Redshift to S3

  • Go to your Amazon Redshift Console.
  • Here, you are going to use the UNLOAD command with the SELECT statement to query your table and locate data then you need to provide the location of your S3 bucket where you want to load your data. 
  • Also, you need to provide your IAM Role. The following command is given below:
unload ('select * from venue')   
to 's3://mybucket/tickit/unload/venue_' 
iam_role 'arn:aws:iam::0123456789012:role/MyRedshiftRole';
  • Here, the example table name is “venue” and in the S3 location, the “venue_” is the prefix of the files that will save in your bucket. The files will save in a similar as shown below:
venue_0000_part_00
venue_0001_part_00
venue_0002_part_00
venue_0003_part_00
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Step 2: Importing Data from Amazon S3 to DynamoDB

To connect Amazon Redshift to DynamoDB, you need to export Redshift Data saved in the S3 bucket to DynamoDB. For this, your DynamoDB should already have the table created as per the data format of Redshift.

To load data from Redshift to DynamoDB, your table should not be empty. Though, while importing data from Redshift to DynamoDB, it will replace the existing data with a new one that has the same keys. To import data from S3 to DynamoDB follow the steps given below:

  • Sign in to your AWS Management Console and open the AWS Data Pipeline from here
  • Here, click on the Create new pipeline button.
  • Here, it will open a  Create Pipeline page where you need to provide all the essential details.
  • In the Name text field, you need to provide the name of your Pipeline to connect Redshift to DynamoDB. For example, you can name the Pipeline “RedshiftToDynamoDBPipeline”
  • In the source parameter, you need to select the Build using a template option. Now from the drop-down list of templates, select the  Import DynamoDB backup data from the S3 option.
  • In the Input S3 Folder text field, you need to provide the URI of the location of the S3 bucket where your Redshift data exists. In this example, the location of the S3 bucker is “s3://mybucket/tickit/”. 
  • In the Target DynamoDB table name text field you need to provide the name of the DynamoDB table where you want to load your Redshift data.
  • In the S3 location for the logs text field, you need to provide the URI of the S3 bucket where you want to write the log file for the import of data from Redshift to DynamoDB. For example, the URI can be “s3://mybucket/logs”
  • Now, add a tag with the Key dynamodbdatapipeline and the Value true.
  • After reviewing all the configurations, click on the Activate button.
  • This will create your data pipeline to import data from Redshift to DynamoDB via AWS S3. The process may take several minutes to complete depending on the volume of data.

Learn more about: Connecting Amazon Redshift to SQL Server

Connect Redshift to Databricks
Connect Redshift to Databricks
Connect DynamoDB to Redshift

Conclusion

In this article, you learned about Amazon Redshift, Amazon DynamoDB Database and their main features. You also went through the steps to how to transfer data from Amazon Redshift to DynamoDB Database. DynamoDB is a NoSQL Database and Amazon Redshift is a widely used Data Warehouse for fast performance and real-time analysis. Connecting Redshift to DynamoDB powers backend services, applications and web services.

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Amazon Redshift stores data from multiple sources, and every source follows a different schema. Instead of manually writing scripts for every source to perform the ETL (Extract Transform Load) process, one can automate the whole process. Hevo Data is a No-code Data pipeline solution that can help you transfer data from 150+ data sources to Amazon Redshift or other Data Warehouse of your choice. Its fault-tolerant and user-friendly architecture fully automates the process of loading and transforming data to destination without writing a single line of code. 

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Share your experience of learning about the Connecting Redshift to DynamoDB in the comments section below!

Frequently Asked Questions

1. Is it possible to load data from Amazon DynamoDB into Amazon Redshift?

Yes, it is possible to load data from Amazon DynamoDB into Amazon Redshift.

2. Does DynamoDB replicate data?

Yes, Amazon DynamoDB replicates data to ensure high availability and durability.

3. What does DynamoDB do?

Amazon DynamoDB is a fully managed NoSQL database service provided by AWS that offers high performance, scalability, and reliability for applications that require consistent, single-digit millisecond response times.

Aditya Jadon
Research Analyst, Hevo Data

Aditya Jadon is a data science enthusiast with a passion for decoding the complexities of data. He leverages his B. Tech degree, expertise in software architecture, and strong technical writing skills to craft informative and engaging content. Aditya has authored over 100 articles on data science, demonstrating his deep understanding of the field and his commitment to sharing knowledge with others.