AWS Redshift is a managed data warehouse solution from Amazon Web Services. It’s part of their popular cloud-based computing platform and used by many familiar enterprises, such as Lyft and McDonald’s. Data warehouses are storage and analytical solutions for large amounts of data. They take data gained via ETL or ELT services like or AWS Glue and turn it into useful information and datasets that businesses can analyze and utilize for strategic insights. Unlike Postgres databases, Redshift deals with columns instead of rows and can handle multiple simultaneous parallel queries at speed. Here are eight great reasons enterprises choose AWS Redshift instead of Postgres or other options like Snowflake for their business data warehousing.

Table of Contents

  1. AWS Redshift Is Fast
  2. Redshift Offers Value for Money
  3. Redshift Is Scalable and Customizable
  4. Redshift Is Easy to Use
  5. Redshift Is Highly Secure
  6. It's part of the AWS Cloud Computing Platform
  7. Redshift Connects to Most Data Sources
  8. It's Cloud-Based and Managed
  9. How and Redshift Work Together

1. AWS Redshift Is Fast

If you’re looking for the best data warehouse, speed and high performance are definitely primary factors. Amazon claims that Redshift is three times quicker at dealing with data than comparably priced alternatives. This is because Redshift works with “clusters” of data, built around nodes. Each primary node connects to several others and these nodes can work in parallel to optimize fast data processing. This gives Redshift a huge performance advantage over older database technologies such as Postgres although, at its core, Redshift carries a souped-up version of the PostgreSQL relational database management system (RDBMS) as well as the technology from ParAccel, the first database to provide functional Massive Parallel Processing (MPP). Redshift also utilizes machine learning capabilities to maximize performance and throughput, so it's constantly updating and improving. Redshift can also query data using serverless query compilation, so it's not limited by CPU or memory usage.

2. Redshift Is Cost-Effective

Amazon provides Redshift on a sliding price scale, making it accessible for smaller businesses yet powerful enough for huge enterprises dealing with data in varying formats. Businesses can pay upfront for planned instances of running their clusters. Alternatively, they can opt for an on-demand structure. As your business grows, you can change the plan you’ve paid for and ensure you have the capacity to deal with spikes in your data volume. If you need to run more queries in concurrency, you simply add more compute nodes and pay for them accordingly.

Amazon’s pricing is easy to follow and has no hidden surprises, allowing enterprises to optimize their budget. Running queries in Redshift prioritizes columns instead of the traditional Postgres method of querying rows. Because of this columnar storage method, it can glean useful insights from much lower volumes of data. Redshift also allows users to prioritize data columns using the sort keys function. Other cluster-based big data services like Hadoop tend to be much more expensive for comparable amounts of data. 

3. Redshift Is Scalable

Because the pricing is so flexible, Redshift is a completely scalable data warehousing option for data integration. The amount of data businesses consume fluctuates for many reasons – peak seasons, general demand, and external events that businesses have no control over. The ability to remove or add nodes with ease makes Redshift an appealing option for businesses of all sizes with full scalability. Businesses that have a sudden spike in data or experience unprecedented growth have the comfort of knowing their data warehouse can easily grow with them, without the inconvenience of having to find another vendor. Redshift can deal with workloads at a petabyte-scale. This makes it ideal for dealing with big data or large volumes of raw or unstructured data from a data lake, making it useable for your BI tools.

4. Redshift Is Easy to Use  

Users of SQL commands will find the Redshift ecosystem absurdly simple to use. On top of this, the AWS Management Console makes the Redshift data warehouse easy to get to grips with, allowing users to add, remove or scale the Amazon Redshift clusters up or down with a few clicks. Administrators can even deploy clusters in a virtual private cloud (VPC). There is also plenty of documentation from Amazon to help beginners get to grips with the node types and other features. Beyond the intuitive interface, Redshift offers automation of many common administrative tasks to help monitor and manage existing or new data with ease for many use cases, as well as allowing admins to make data processing parameter adjustments in real-time. BI tools then use data visualization techniques to make this data useful for businesses.

5. Redshift Is Highly Secure

It’s hard to overstate how important data security is. Every organization has to comply with data regulations, like the GDPR ensuring data storage and management is secure and safe prevents financial losses and the loss of trust with clients and partners. Redshift is a cloud data warehouse that delivers end-to-end encryption, network isolation, data masking, and a range of other features to help businesses remain data compliant, regardless of the data types they use. Redshift also supports SSL connections for SQL queries.

6. It's part of the AWS Cloud Computing Platform

Because Redshift is an Amazon product, it already has built-in ways to connect with all the other AWS Cloud Computing products. We just touched on the importance of data security. Redshift integrates with another service called AWS CloudTrail which allows users to audit the API calls made from the data warehouse for added security. These logs can then be saved securely in Amazon S3, helping businesses get the most benefit from all their AWS services.

7. Redshift Connects to Most Data Sources

Redshift clusters connect to most data sources via SQL client tools, normally installed by the user or by a third-party data management service. Setting up data transfer connections uses Python, JDBC, or ODBC drivers, which Amazon will provide as downloads. Users can also use Postgres drivers, but the AWS Redshift team doesn’t offer any support for this. Many business apps provide their own APIs that you can use to obtain to load data for storage and analysis into the warehouse. Plus, administrators can plug in pipelines to their traditional Postgres databases for effective data collation.

8. It's Cloud-Based and Managed

Because Redshift is a data warehouse service that's hosted on the cloud by Amazon, it doesn’t take up any space on your servers or require any maintenance beyond your own instructions and configuration for how you want your data pipelines to work. Managing your own data warehouse or in-house Postgres databases means constantly having to find more server space as your business grows and expands. This is never an issue with Redshift, which as we’ve already seen, can scale to handle petabytes of data. Users of AWS S3 also enjoy automated backups of data, for added peace of mind.

How and Redshift Work Together

If you decide to use AWS Redshift for your enterprise data warehousing needs, it’s important to consider how you will set your data pipelines up. Manually creating connections to each individual app or data source is a task for a skilled data manager. As the volume of this data grows, you may need to invest in a dedicated and highly qualified team if you decide to keep your data management entirely in-house. can help. provides a fully scalable cloud-based Extract, Transform, and Load (ETL) tool for businesses. has a user-friendly interface, allowing users to easily create data pipelines from all their business data sources to their Redshift warehouse, which allows them to collate and aggregate their data efficiently.’s out-of-the-box integrations include:

  • Other AWS products like Aurora and RDS
  • PostgreSQL
  • com
  • Google My Business
  • HubSpot
  • Salesforce
  • Facebook Ads
  • LinkedIn

Integrations include links to other databases to allow you to collate all your data in one place, regardless of differing data formats. Plus, offers over a hundred preexisting connections to SaaS and the business apps you use every day. helps you transform your Redshift warehouse into a practical hub for data analysis by your business intelligence tools.

We’d be happy to show you more about how and Amazon Redshift work effortlessly together for effective business data integration. Contact us today and ask about a seven-day demo of the ETL product to find out how you can get the most out of your data warehouse or data lake.