AWS Data Pipeline is a data movement and data processing service provided by Amazon. Using Data Pipeline you can perform data movement and processing as per your requirement. Data pipeline also supports scheduling of Pipeline processing. You can also perform data movement residing on on-prem.
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Data Pipeline provides you various options to customize your resources, activities, scripts, failure handling, etc. In the Pipeline you just need to define the sequence of data sources, destinations along data processing activities depending on your business logic and the data pipeline will take care of data processing activities.
Similarly, you can perform Aurora to Redshift Replication using AWS Data Pipeline. This article introduces you to Aurora and Amazon Redshift. It also provides you the steps to perform Aurora to Redshift Replication using AWS Data Pipeline.
Introduction to Aurora
Aurora is a Commercial Database Engine from Amazon. It provides exemplary performance and speed at an affordable rate. The key feature of Aurora is that it backs up data to AWS S3 in real-time without degrading the performance. This saves Database Administrators (DBAs) time as they do not require to backup their data manually.
For more information on Aurora, click here.
Introduction to Amazon Redshift
Amazon Redshift is a Cloud-based Data Warehousing Solution from Amazon Web Services (AWS). It provides you a centralized and secure location that you can use to store your historical data for easy access and use. It also allows you to work with Business Intelligence Tools on the data stored in Amazon Redshift. This helps you extract meaningful insights and make informed business decisions.
For more information on Amazon Redshift, click here.
Steps to Perform Aurora to Redshift Replication using AWS Data Pipeline
This is a method that demands technical proficiency and experience in working with Aurora and Redshift. This is a Manual Integration using AWS Data Pipeline.
Follow the steps below to perform Aurora to Redshift Replication using AWS Data Pipeline:
- Step 1: Select the Data from Aurora
- Step 2: Create an AWS Data Pipeline to Perform Aurora to Redshift Replication
- Step 3: Activate the Data Pipeline to Perform Aurora to Redshift Replication
- Step 4: Check the Data in Redshift
Step 1: Select the Data from Aurora
Select the data that you want for Aurora to Redshift Replication as shown in the image below.
Step 2: Create an AWS Data Pipeline to Perform Aurora to Redshift Replication
For MySQL/Aurora MySQL to Redshift, AWS Data Pipeline provides an inbuilt template to build the Data Pipeline. You will reuse the template and provide the details as shown in the image below.
Note: Check all the pre and post conditions in the Data Pipeline before activating the Pipeline for performing Aurora to Redshift Replication.
Step 3: Activate the Data Pipeline to Perform Aurora to Redshift Replication
Data Pipeline internally generates the following activities automatically:
- RDS to S3 Copy Activity (to stage data from Amazon Aurora)
- Redshift Table Create Activity (create Redshift Table if not present)
- Move data from S3 to Redshift
- Perform the cleanup from S3 (Staging)
Step 4: Check the Data in Redshift
Pros of Performing Aurora to Redshift Replication using AWS Data Pipeline
- AWS Data Pipeline is quite flexible as it provides a lot of built-in options for data handling.
- You can control the instance and cluster types while managing the Data Pipeline hence you have complete control.
- Data pipeline has already provided inbuilt templates in AWS Console which can be reused for similar pipeline operations.
- Depending upon your business logic, condition check and job logic are user-friendly.
- While triggering the EMR cluster you can leverage other engines other than Apache Spark i.e. Pig, Hive, etc.
Cons of Performing Aurora to Redshift Replication using AWS Data Pipeline
- The biggest disadvantage with the approach is that it is not serverless and the pipeline internally triggers other instance/clusters which runs behind the scene. In case, they are not handled properly, it may not be cost-effective.
- Another disadvantage with this approach is similar to the case of copying Aurora to Redshift using Glue, data pipeline is available in limited regions. For the list of supported regions, refer AWS website.
- Job handling for complex pipelines sometimes may become very tricky in handling unless. This still requires proper development/pipeline preparation skills.
- AWS Data Pipeline sometimes gives non-meaningful exception errors, which makes it difficult for a developer to troubleshoot. Requires a lot of improvement on this front.
Hevo Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 100+ data sources including Aurora, etc., and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well.
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- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Conclusion
The article introduced you to Amazon Aurora and Amazon Redshift. It provided you a step-by-step guide to replicate data from Aurora to Redshift using AWS Data Pipeline. Furthermore, it also provided you the pros and cons to go with AWS Data Pipeline.
Amazon Aurora to Redshift Replication using AWS Data Pipeline is convenient during the cases where you want to have full control over your resources and environment. It is a good service for the people who are competent at implementing ETL solution logic. However, in our opinion, this service has not been effective and not that much success as compared to other data movement services.
This service has been launched quite a long back and is still available in a few regions. However, having said that since AWS data pipeline support multi-region data movement, you can Select Pipeline in the nearest region and perform the data movement operation using resources of the region for you movement (be careful about security and compliance).
With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continous Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, Hevo Data is the right choice for you! It will help simplify the Marketing Analysis. Hevo Data supports platforms like Aurora, etc.
While you rest, Hevo will take responsibility for fetching the data and moving it to your destination warehouse. Unlike AWS Data pipeline, Hevo provides you with an error-free, completely controlled setup to transfer data in minutes.
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Share your experience of setting up Aurora to Redshift Integration in the comments section below!
