We did the research and pulled together some useful articles on Amazon Redshift so you don’t have to! Read this post to find out some great information on this cloud-based data warehouse so you can decide on your own data warehousing platform.
Amazon Redshift is a fully managed, petabyte-scale cloud based data warehouse service. It allows businesses to store and analyze large scale data sets and perform large database migrations. Amazon Redshift is based on PostgreSQL, and is a column-oriented database management system, enabling fast querying and efficient performance.
For more on Amazon Redshift, review Amazon Redshift’s documentation for details on management, features, and security.
In most cases, businesses will get a better price-to-performance ratio for analyses using Amazon Redshift. With a transparent pricing model, and efficiency for large data sets, Amazon Redshift will be competitive option for scaling startups to large enterprises.
While Amazon Redshift is a managed warehouse, you are still required to perform certain maintenance procedures, while you don’t have to worry about maintenance with Google BigQuery.
If you are comparing Amazon Redshift against another warehousing option, there are many specific comparisons already written on using Redshift vs other solutions, some of our favorites are:
As mentioned earlier in this article, Amazon Redshift is best known for its query speed on large data sets due to columnar and compressed storage. If you are running many queries on-demand, Amazon Redshift can perform extremely fast and efficiently.
Amazon Redshift is relatively easy to manage. As a fork of Postgres, you can utilize SQL, providing an easy learning curve for administrators and end users. Additionally, Amazon Redshift works with a huge catalog of integrations, giving you the flexibility to build out your data stack with whatever tools you need. There are few maintenance overheads, saving management time.
Here are some more articles about Amazon Redshift and its unique features:
Amazon Redshift does not enforce uniqueness and other types of data constraints like foreign keys, requiring some internal diligence for data hygiene. Additionally maintenance is important to the extent that you need someone focused on managing your Amazon Redshift instance, whereas other cloud warehouses like Google BigQuery or Snowflake require less work.
Self-managed ETL with Amazon Redshift is non-trivial. There are a lot of nuances with the COPY command and data migration. You can work with a third-party ETL provider to bypass these issues, depending on your security requirements.
Here are some more articles around considerations for Amazon Redshift:
Amazon Redshift works with a variety of partners from data management to analytics to help you load or extract data from your warehouse for meaningful insights.
Amazon Redshift’s data integration partners provide solutions for data integration such as ETL / ELT, data modeling, data cleansing, or data migration.
Amazon Redshift’s business intelligence partners provide solutions for reporting, sharing, embedding, analyzing and visualizing your data.
System integration and consulting partners provide trusted and validated experts who can provide training, expertise or consulting work on Amazon Redshift.
For more resources on building a data stack with Amazon Redshift:
Considerations for using an Amazon Redshift warehouse include scalability, cost and maintenance, but also should also focus on the ease of extracting your data into useful business insights. Amazon Redshift is optimized for data analysis, so that many queries can be run quickly, leading to real-time reporting.
Amazon Redshift easily integrates with analytics tools such as Chartio through the partner network, as well as through the AWS Digital Customer Experience Competency Partner Network.
Learn more about visualizing Amazon Redshift with Chartio:
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