Amazon Redshift gives data teams a warehouse for storing, processing, and analyzing company data within AWS. The harder part is making that data useful across the business, especially when an answer depends on company terminology or information that still lives in operational tools outside the warehouse.
Supper connects directly to Redshift so employees can ask questions, investigate the results, and work with data from other connected sources. Redshift remains responsible for the warehouse and query processing. Supper uses that environment alongside the company's business definitions and other data sources to answer questions in context.
For Redshift customers, getting started is fairly light. After the company provisions a system account, Supper can inspect the permitted data, build an initial semantic model, and create an evaluation corpus for testing. Users can typically begin asking questions within one or two days.
The roles of Supper and Amazon Redshift
Companies use Amazon Redshift for data warehousing and analytical queries within AWS. Amazon Q generative SQL can help users generate SQL inside Query Editor v2, while SageMaker Unified Studio and Amazon Quick Sight provide additional options for working with Redshift data.
Supper gives a wider group of employees a way to work with that data. Users can ask questions conversationally, continue an analysis through follow-up questions, create charts, and save recurring work as reusable Skills. Supper can use SQL to query Redshift and Python when an analysis requires calculations or additional processing.
Many business questions also depend on context that is difficult to capture in warehouse metadata alone. The meaning of a metric may depend on an internal definition, an exception to the normal calculation, or information maintained in another system. Supper keeps that context in its semantic model and applies it when answering questions.
Connecting Supper to Amazon Redshift
A company begins by provisioning a Redshift system account for Supper. Supper uses that account to inspect the database structure available to it, create the initial semantic model, and generate an evaluation corpus that tests how well the model answers questions.
The customer's part of the initial connection generally takes about five minutes. Supper completes the automated setup and testing in the background, with users typically able to begin asking questions within one or two days. The exact timeline depends on the size and condition of the Redshift environment.
Supper queries Redshift directly. The company can keep its existing AWS architecture and data pipelines, while Redshift continues to process the warehouse queries.
The semantic model develops further after launch. Questions from real users expose missing definitions and areas where Supper needs more context. Supper can identify many of these issues automatically, while decisions involving company-specific business rules remain with the customer. During the first month, this review usually requires anywhere from a few minutes to a few hours of customer involvement.
How Supper adds business context to Redshift
Redshift provides Supper with technical metadata about the schemas, tables, views, and columns available through the system account. Amazon Q generative SQL can also use administrator-supplied custom context to improve the SQL it suggests in Query Editor v2.
Supper uses the permitted warehouse structure to create its own semantic model. Existing definitions from Redshift metadata, dbt, internal documentation, or other approved sources can be incorporated during setup and refinement.
Supper's semantic model can include company terminology, calculation instructions, known data issues, source-selection guidance, and reusable analytical processes. This knowledge is maintained at the model level so it can be applied consistently across users and questions.
This means a company does not need to complete a separate semantic-layer project before it begins using Supper. The initial model is created automatically, and the data team can add company-specific context as real questions reveal where it is needed.
Analysis across Redshift and SaaS data
Important business information often remains spread across operational applications. A company may keep historical transaction and product data in Redshift while the latest account activity remains in its CRM.
Take cash collection forecasting as an example. Answering the question may require historical invoices and payment behavior from Redshift, along with current customer activity from a finance or account-management system. Supper can determine which sources are relevant, query them according to the user's permissions, and apply the company's approved definitions throughout the analysis.
The person asking the question does not have to know where each field lives or how the systems relate. They can ask the business question directly and continue investigating the result within the same conversation.
This lets teams analyze Redshift alongside connected SaaS applications. It also means that a useful answer does not have to wait until every relevant field has been loaded into Redshift and modeled there.
Permissions and administrative oversight
Amazon Redshift supports role-based access control, column permissions, and row-level security. These controls determine what the Supper system account is permitted to query.
Because Supper connects through a system account, it does not automatically assume each employee's individual Redshift identity. Administrators use Supper's user-level entitlements to control which parts of the available Redshift data and connected SaaS sources each person can access.
Administrators can review how people are using company data through Supper's Admin Control Center. They can see the questions being asked, investigate how data is being accessed, and review issues found in the semantic model. This gives the data team continued oversight after AI analytics has been made available to the wider company.
When should a Redshift customer use Supper?
Amazon Q generative SQL may cover the use case when users already work in Query Editor v2 and primarily want help writing SQL. SageMaker Unified Studio and Amazon Quick Sight provide additional options for teams working within the broader AWS analytics environment.
Supper is relevant when a company wants to make Redshift data accessible through a dedicated business analytics product or when questions regularly depend on information outside the warehouse. It also gives the data team a place to manage company-specific definitions and review how employees use AI to access data.
Supper uses seat-based pricing. The cost is tied to the number of users rather than the number of tokens consumed during an analysis, making costs easier to anticipate when questions require several analytical steps.
Frequently asked questions
Does Supper replace Amazon Redshift?
No. Amazon Redshift continues to provide the warehouse and query engine. Supper connects to Redshift and gives business users another way to analyze the data stored there.
How does Supper connect to Amazon Redshift?
The customer provisions a Redshift system account for Supper. Supper uses that connection to inspect the permitted database structure, build its initial semantic model, run evaluations, and query Redshift directly.
Does Supper copy Redshift data into another warehouse?
Supper queries Redshift directly for warehouse analysis. Companies do not need to migrate or duplicate their Redshift warehouse before they can begin using Supper.
How is Supper different from Amazon Q generative SQL?
Amazon Q generative SQL is a coding assistant that suggests SQL inside Redshift Query Editor v2. Supper provides a business-facing analytics experience with its own semantic model, reusable Skills, administrative controls, and support for questions that use Redshift alongside connected SaaS sources.
Does Supper require an existing semantic layer?
No. Supper can inspect the permitted Redshift environment and create the first version of its semantic model automatically. Existing company definitions can then be incorporated and refined as people use the product.
How long does the Redshift setup take?
Provisioning the connection generally takes about five minutes of customer work. Supper can usually prepare the initial semantic model and evaluation corpus within one or two days, after which users can begin asking questions.