A High-Level Overview of Amazon Redshift

Modern data warehouses, like Amazon Redshift, can improve the way you access your organization’s data and dramatically improve your analytics. Paired with a BI tool, like Tableau, or a data science platform, like Dataiku, your organization can increase speed-to-insight, fuel innovation, and drive business decisions throughout your organization.

In this post, we’ll provide a high-level overview of Amazon Redshift, including a description of the tool, why you should use it, pros and cons, and complementary tools and technologies.

Overview of Amazon Redshift

Amazon’s flagship data warehouse service, acquired from ParAccel originally, is a columnar database forked from Postgres. Similar to AWS RDS databases, pricing for Amazon Redshift is charged by size of the instance, along with how long it’s up and running.

Value Prop:

  • Increased performance of queries and reports with automatic indexing and sort keys
  • Easy integration with other AWS products
  • Most established data warehouse

Scalability:

  • Flexibility to pay for compute independently of storage by specifying the number of instances needed

Performance:

  • Instances maximize speed for performance-intensive workloads that require large amounts of compute capacity.
  • Distribution and sort keys are more intuitive than traditional RDBMS indexes, allowing for more user-friendly performance tuning of queries.

Features:

  • Easy to spin up and integrate with other AWS services for a seamless cloud experience
  • Native integration with the AWS analytics ecosystem makes it easier to handle end-to-end analytics workflows with minimal issues

Security:

  • Can be set up to use SSL to secure data in transit and hardware-accelerated AES-256 encryption for data at rest

Why Use Amazon Redshift

It’s easy to spin up as an AWS customer, without needing to sign any additional contracts. This is ideal for more predictable pricing and starting out.

Pros of Amazon Redshift

  • It easily spins up and integrates with other AWS services for a seamless cloud experience.
  • The distribution and sort keys are more intuitive than traditional RDBMS indexes, allowing for more user-friendly performance tuning of queries.
  • Materialized views support functionality and options not yet available in other cloud data warehouses, helping improve reporting performance.

Cons of Amazon Redshift

  • It lacks some of the modern features and data types available in other cloud-based data warehouses such as support for separation of compute and storage spending, and automatic partitioning and distribution of data.
  • It requires traditional database administration overhead tasks such as vacuuming and managing of distribution of sort keys to maintain performance and data storage.
  • As data needs grow, it can be difficult to manage costs and scale.

Select Complementary Tools and Technologies for Amazon Redshift

  • AWS Glue
  • AWS QuickSight
  • AWS SageMaker
  • Tableau
  • Dataiku

We hope you found this high-level overview of Amazon Redshift helpful. If you’re interested in learning more about Amazon Redshift or other modern data warehouse tools like Google BigQuery, Azure Synapse, and Snowflake, contact us to learn more.

The content of this blog is an excerpt of our 2021 Modern Data Warehouse Comparison Guide. Click here to download a copy of that guide.

Originally published at https://aptitive.com on June 30, 2021.

Original technology and enterprise content presented by Aptitive, a Chicago-based IT consulting firm with data, analytics, and custom application solutions