Which ETL tool is best for Amazon Redshift?

When dealing with data from multiple sources, you need to unify those sources in one place for better management and efficiency. To do that, you’ll need to use both an ETL pipeline and a cloud-based data storage solution.

One of the top data warehouse storage solutions currently available is Amazon Redshift, which launched in 2013. Amazon Redshift is completely hosted on Amazon’s Web Services (AWS), which means it has easy integration with existing services in the AWS ecosystem.

One of the main differences between Redshift and other data storage solutions is the pricing. With Redshift you pay by the instance rather than by the query. For this reason, you’ll likely need to have a dedicated administrator to handle the instances.

If you’re already familiar with AWS and are planning to use Redshift as your data storage solution, then you’ll need to consider the best ETL tool to build your pipeline.

Best ETL tools

An ETL tool is a software platform designed to help you build your ETL pipeline by bringing together data from your multiple sources into your storage solution – in this case, Amazon Redshift. When looking for your tool of choice, you should consider:

• Credential management
• Integrations
• Automation and scheduling
• Quality of performance
• The option to have both on-premises and cloud ETL tools
• Built-in data profiling
• Data governance
• Monitoring and alerting
• Ease of use

With all those features in mind, here are a few different types of ETL tools currently available. Each of them has its pros and cons, so depending on your use cases and resources you might find one type works better than others.

Open-source tools: If you or your team know your way around coding, you can use open-source ETL tools. The main benefit of going open-source is that it’s free and the code can be customized to fit your business needs. However, the main drawback is you don’t get any guarantees it will work, and no technical support to help when things go wrong.

DIY custom tools: If you have a team of talented developers, you can custom-build an ELT software that offers bespoke solutions for your needs. However, this level of customization comes with the cost of massive internal resources for regular updating, maintaining, and testing the software.

Enterprise tools: The final option is to use a ready-built enterprise tool. This option is great if your business has limited time and resources to dedicate to software development. It’s not likely to cost any more than building your own solution (after factoring in the hours of customer development saved) but comes with enterprise support.

What technology does Amazon Redshift use?

Amazon Redshift is a Relational Database Management System (RDBMS) built on PostgreSQL. It’s specifically designed for Online Analytical Processing (OLAP) and Business Intelligence (BI) that require complex queries and massive datasets. For this reason, although Redshift is built on PostgreSQL, its data storage schema and implementation are very different.

An easy example to explain this difference is that whereas Online Transaction Processing (OLTP) apps will typically store data in rows, Redshift stores it in columns for best memory and disk I/O performance. It also omits some PostgreSQL features such as secondary indexes and single-row data (which are better suited for smaller-scale processing) to improve performance.

Does Amazon Redshift use ETL or ELT?

Amazon Redshift supports data transformation both before and after loading. So you can decide whether you want to use an ETL or ELT pipeline.

However, Amazon does include helpful guides on different use cases for using either pipeline in Redshift – as some types of uses may benefit from an ETL approach versus an ELT approach, and vice versa.

What is the difference between Redshift and AWS?

AWS is the name of Amazon’s cloud-based services division, whereas Redshift is a specialized data warehouse that AWS hosts.

What is the difference between Redshift and S3?

Redshift has the tools for analyzing large and complex datasets, whereas S3 is a simple object storage platform.