Cloud-based data warehouse based on S3 with query engine that enables queries on linked data from Redshift and Data Lake (S3).
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Amazon, founded in 1994 in Washington, initially started as an online bookstore but has since transformed into a global technology company. Headquartered in Seattle, WA, Amazon is widely known for its e-commerce platform, Amazon.com, and its leadership in cloud computing through Amazon Web Services (AWS) founded in 2006. AWS holds 33 percent of the cloud computing market and works with over 90 percent of Fortune 100 companies.
Amazon Web Services (AWS) provides over 200 services that span data storage, computing, machine learning (ML) and artificial intelligence (AI). The AWS Marketplace hosts more than 4,700 data products and over 4,000 ML products, connecting third-party sellers with buyers. AWS serves diverse sectors, including government, enterprises, educational institutions and individuals. AWS operates in 108 Availability Zones across 34 global regions, along with many edge locations and local zones.
Amazon Redshift is a key service in AWS’s data and analytics offerings. It is a cloud-based relational data warehouse designed for large-scale data processing. Redshift integrates seamlessly with other AWS services, such as Amazon Data Lake and Amazon SageMaker, enabling users to leverage advanced analytics and machine learning within the same ecosystem. With its columnar storage and massively parallel processing (MPP) architecture, Redshift provides optimized query performance for analytics and reporting.
Redshift’s data management is built on efficient workflows. Data can be loaded from Amazon S3 into Redshift, processed and then unloaded back into S3 for further analysis. Redshift Spectrum allows users to query data stored in S3 without moving it into Redshift, saving time and storage.
Recent enhancements to Amazon Redshift include:
• Support for zero-ETL integration: Customers can leverage new flexibility to control the frequency of data replication. The refresh interval provides new granular control.
• Data sharing for data lakes tables: Enables customers to securely share data at various levels, including tables and views via Redshift and Lake Formation.
• Amazon Sagemaker JumpStart for Redshift ML: Enables customers to create, train and deploy machine learning models on their Redshift data using SQL commands.
BARC’s viewpoint on the product’s strengths and challenges.
The information contained in this section is based on user feedback and actual experience with Amazon Redshift.
The information and figures are largely drawn from BARC’s The BI & Analytics Survey, The Planning Survey, The Financial Consolidation Survey and The Data Management Survey. You can find out more about these surveys by clicking on the relevant links.
All key figures for Amazon Redshift at a glance.
Close to PostgreSQL, as it's a fork.
Performance is constantly a challenge.
Move to Snowflake.
Rather poor - it requires a shift on how they do things.
It helps get the data to where it needs to be faster.
Lack of expert knowledge within my org.
Get people trained or bring in an expert.
Positive.
