Are you trying to derive deeper insights from your Aurora Database by moving the data into a larger Database like Amazon Redshift? Well, you have landed on the right article. Now, it has become easier to replicate data from Aurora to Redshift.
This article will give you a comprehensive guide to Amazon Aurora and Amazon Redshift. You will explore how you can utilize AWS Glue to move data from Aurora to Redshift using 7 easy steps. You will also get to know about the advantages and limitations of this method in further sections. Let’s get started.
Prerequisites
You will have a much easier time understanding the method of connecting Aurora to Redshift if you have gone through the following aspects:
- An active account in AWS.
- Working knowledge of Database and Data Warehouse.
- Basic knowledge of ETL process.
Introduction to Amazon Aurora
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Aurora is a database engine that aims to provide the same level of performance and speed as high-end commercial databases, but with more convenience and reliability. One of the key benefits of using Amazon Aurora is that it saves DBAs (Database Administrators) time when designing backup storage drives because it backs up data to AWS S3 in real-time without affecting the performance. Moreover, it is MySQL 5.6 compliant and provides five times the throughput of MySQL on similar hardware.
To know more about Amazon Aurora, visit this link.
Introduction to Amazon Redshift
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Amazon Redshift is a cloud-based Data Warehouse solution that makes it easy to combine and store enormous amounts of data for analysis and manipulation. Large-scale database migrations are also performed using it.
The Redshift architecture is made up of several computing resources known as Nodes, which are then arranged into Clusters. The key benefit of Redshift is its great scalability and quick query processing, which has made it one of the most popular Data Warehouses even today.
To know more about Amazon Redshift, visit this link.
Introduction to AWS Glue
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AWS Glue is a serverless ETL service provided by Amazon. Using AWS Glue, you pay only for the time you run your query. In AWS Glue, you create a metadata repository (data catalog) for all RDS engines including Aurora, Redshift, and S3, and create connection, tables, and bucket details (for S3). You can build your catalog automatically using a crawler or manually. Your ETL internally generates Python/Scala code, which you can customize as well. Since AWS Glue is serverless, you do not have to manage any resources and instances. AWS takes care of it automatically.
To know more about AWS Glue, visit this link.
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Steps to Move Data from Aurora to Redshift using AWS Glue
You can follow the below-mentioned steps to connect Aurora to Redshift using AWS Glue:
Step 1: Select the data from Aurora as shown below.
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Step 2: Go to AWS Glue and add connection details for Aurora as shown below.
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Similarly, add connection details for Redshift in AWS Glue using a similar approach.
Step 3: Once connection details are created create a data catalog for Aurora and Redshift as shown by the image below.
Load Data from PostgreSQL on Amazon Aurora to Redshift
Load Data from PostgreSQL on Amazon Aurora to Redshift
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Once the crawler is configured, it will look as shown below:
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Step 4: Similarly, create a data catalog for Redshift, you can choose schema name in the Include path so that the crawler only creates metadata for that schema alone. Check the content of the Include path in the image shown below.
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Step 5: Once both the data catalog and data connections are ready, start creating a job to export data from Aurora to Redshift as shown below.
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Step 6: Once the mapping is completed, it generates the following code along with the diagram as shown by the image below.
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Once the execution is completed, you can view the output log as shown below.
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Step 7: Now, check the data in Redshift as shown below.
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Advantages of Moving Data using AWS Glue
AWS Glue has significantly eased the complicated process of moving data from Aurora to Redshift. Some of the advantages of using AWS Glue for moving data from Aurora to Redshift include:
- The biggest advantage of using this approach is that it is completely serverless and no resource management is needed.
- You pay only for the time of query and based on the data per unit (DPU) rate.
- If you moving high volume data, you can leverage Redshift Spectrum and perform Analytical queries using external tables. (Replicate data from Aurora and S3 and hit queries over)
- Since AWS Glue is a service provided by AWS itself, this can be easily coupled with other AWS services i.e., Lambda and Cloudwatch, etc to trigger the next job processing or for error handling.
Limitations of Moving Data using AWS Glue
Though AWS Glue is an effective approach to move data from Aurora to Redshift, there are some limitations associated with it. Some of the limitations of using AWS Glue for moving Data from Aurora to Redshift include:
- AWS Glue is still a new AWS service and is in the evolving stage. For complex ETL logic, it may not be recommended. Choose this approach based on your Business logic
- AWS Glue is still available in the limited region. For more details, kindly refer to AWS documentation.
- AWS Glue internally uses Spark environment to process the data hence you will not have any other option to select any other environment if your business/use case demand so.
- Invoking dependent job and success/error handling requires knowledge of other AWS data services i.e. Lambda, Cloudwatch, etc.
Conclusion
The approach to use AWS Glue to set up Aurora to Redshift integration is quite handy as this avoids doing instance setup and other maintenance. Since AWS Glue provides data cataloging, if you want to move high volume data, you can move data to S3 and leverage features of Redshift Spectrum from the Redshift client. However, unlike using AWS DMS to move Aurora to Redshift, AWS Glue is still in an early stage.
Learn how to transfer data from AWS Glue to Redshift to enhance your data integration. Our guide provides clear steps for efficient data migration.
Job and multi-job handling or error handling requires a good knowledge of other AWS services. On the other hand in DMS, you just need to set up replication instances and tasks, and not much handling is needed. Another limitation with this method is that AWS Glue is still in a few selected regions. So, all these aspects need to be considered in choosing this procedure for migrating data from Aurora to Redshift.
If you are planning to use AWS DMS to move data from Aurora to Redshift then you can check out our article to explore the steps to move Aurora to Redshift using AWS DMS.
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Share your experience of connecting Aurora to Redshift using AWS Glue 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.
