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.
- Increased performance of queries and reports with automatic indexing and sort keys
- Easy integration with other AWS products
- Most established data warehouse
- Flexibility to pay for compute independently of storage by specifying the number of instances needed
- 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.
- 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
- 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
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.
Fred Bliss is the CTO at Aptitive. He brings over 15 years of experience solving complex business problems through data solutions including cloud integration, data warehouse modeling, ETL, and front-end reporting implementations.