Knowing best practices for Amazon Web Services (AWS) data pipelines is essential for modern companies handling large datasets and requiring secure ETL (Extract, Transform, Load) processes. In this article, we discuss AWS data pipeline best practices to ensure top performance and streamlined processes — without complications that can impede the execution of data transfer.

Table of Contents:

  1. What is AWS?
  2. Incorporate Amazon Redshift
  3. Balance File Sizes
  4. Keep ETL Runtimes Consistent
  5. Combine Multiple Steps
  6. Monitor ETL Health
  7. Summary
  8. How Xplenty Can Help

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What is AWS?

Amazon Web Services is a broad cloud platform — one of the most comprehensive cloud services on the market today. It provides a wealth of benefits to companies across various industries that are storing and managing data.

AWS services are especially beneficial for the ETL processes of data storage warehouses that allow data extraction. For example, the AWS data pipeline provides a web service that can efficiently and reliably move data between storage and computation sources.

Incorporate Amazon Redshift

Amazon Redshift is a fully managed data warehouse that is part of AWS. It uses parallel processing along with compression to increase the speed of execution. This makes it perfect for analyzing and storing vast chunks of data. 

Companies wanting a fully managed data warehouse solution should consider using Amazon Redshift. Effectively incorporating these options and transferring your existing data allows for the smooth operations of database connections, job schedules, I/O, and parallelism. 

With the right tools and knowledge, transferring data to Amazon Redshift produces better results and facilitates more data storage while providing better control and enhancing visibility. For example, users can consistently see the data to be loaded from S3.

Balance File Sizes

When you copy data and balance file sizes, it is easier for the database to divide and parallelize. Users should ensure they have evenly sized files copied from multiple data sets.

This best practice ensures the data nodes slice into even sizes to do an equal amount of work. Keep in mind that the slice with the heaviest load will determine the spread of the process. 

Keep ETL Runtimes Consistent 

One of the best ways to keep ETL runtimes consistent is to have fewer slots in the queue. Because the ETL process commit is high, this helps reduce wait time for the commit queue. 

Companies can claim additional memory in the wlm_query_slot_count, which speeds up the copy process, allowing downstream ETL jobs to parallel sooner. Users can also use the memory from the reporting queue rather than the ETL once the process is complete. 

Combine Multiple Steps 

To reduce ETL processes, it's good to perform multiple tasks in one transaction, optimizing the AWS data pipeline. This approach improves speed and lowers commit costs by conducting one commit at the end transformation logic executions. 

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Monitor ETL Health

Monitoring scripts help the user monitor the health of their ETL. For example, you could implement commit_stats.sql to previous queue lengths and times. This strategy can help determine when the execution times are longer than usual or when there are multiple options in progress. 

Sometimes the daily COPY takes longer than expected to finish executions, in which case the copy_performance.sql script would analyze incoming datasets and provide insight regarding growth. 

Summary

So you get the most from AWS data pipelines, incorporating these best practices will produce better results and prevent issues. These beneficial tips enable data to flow smoothly and make AWS more effective. 

If used correctly, the AWS pipeline can be a true asset to businesses across many industries. It also helps to reach out to knowledgeable companies offering services designed to make using the AWS pipeline easier and more effective.

How Xplenty Can Help

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Xplenty is an all-in-one solution for importing and exploring data while improving and streamlining the process. This platform allows clients to design and execute data pipelines without code, so users of all skill sets can build a no-code data pipeline. The complete toolkit helps you implement your data integration as well as customize your products and services.

Our cloud-based ETL solutions and AWS provide clients with automated data flows to various destinations. We pride ourselves on ensuring customers get the help and support they need with around-the-clock, 24/7 help. No matter what the issue is, an expert is standing by to assist whenever it's needed.

Contact a representative today and ask about our 14-day demo.