What Business Teams Can Do with Supper and Amazon Redshift
For many companies, Amazon Redshift contains years of work by data engineers and analysts. Transaction records, customer activity, financial data, and reporting models all meet inside the warehouse. Redshift can manage this data through provisioned clusters or Redshift Serverless, giving teams a managed SQL environment for large analytical workloads.
The next challenge is making that investment useful to more of the business. Prepared dashboards answer known questions, while unexpected changes still send employees back to analysts for another report or query.
Supper connects directly to the customer's existing Redshift environment and gives business teams a dedicated place to conduct those investigations. It develops a semantic model around the accessible warehouse, applies company definitions during analysis, and can use SQL or Python according to the work required. Redshift continues to manage warehouse data and query execution.
AWS already provides business-facing AI analytics through Amazon Quick Suite and the AWS Data Analytics plugin for ChatGPT Work. Supper complements those options for organizations that want its approach to semantic modeling, reusable Skills, cross-system analysis, and application-level administration.
Give business teams a direct way to investigate Redshift data
A dashboard might show that customer acquisition cost increased last quarter. Understanding the increase requires a deeper investigation into spending, conversion rates, customer mix, and changes between channels. The answer may involve several warehouse tables and calculations that were never anticipated when the original dashboard was created.
Supper lets a user begin with that business question. Its agents interpret the request using the company's semantic model, generate the necessary Redshift queries, and continue the investigation through follow-up questions. Python can be used for additional calculations or statistical work after the relevant data has been retrieved. The final analysis can be presented through written explanations, tables, or charts.
This gives employees a way to work with Redshift data according to the questions they need to answer. The warehouse remains the source for the underlying information, while Supper manages the analytical conversation and the context surrounding it.
AWS also gives customers other ways to provide conversational access to Redshift. Amazon Quick Suite combines business intelligence, research, chat, and workflow automation. The AWS Data Analytics plugin for ChatGPT Work can query Redshift through natural language and create dashboards. Supper provides another product choice with a distinct analytical model and workflow.
Apply company definitions during analysis
A well-managed Redshift environment can describe databases, schemas, columns, and data types. Accurate business analysis also depends on context that may not be obvious from that structure.
Consider a company's definition of an active customer. The calculation may depend on account status, recent product activity, exclusions, and the time period being analyzed. Those instructions need to be applied consistently whenever someone asks about active customers, even when the wording of the question changes.
Supper examines the accessible Redshift environment and creates an initial semantic model for its analytical agents. The model can account for relationships, data grain, source quirks, and company-specific instructions. Business Logic terms connect the language employees use to the technical treatment required in the warehouse.
Supper also generates evaluations that test how the model handles representative questions. Actual usage can reveal where a definition is missing or where several interpretations remain possible. Administrators can review those issues and supply the judgment needed to resolve them.
Some AWS customers already maintain business context through Redshift views, Quick Sight Topics, SageMaker Unified Studio, or internal documentation. That work can inform how Supper is configured. During implementation, the company should decide which definitions will be carried into Supper and where the authoritative version will be maintained.
Analyze Redshift alongside operational systems
A company may have extensive historical and reporting data in Redshift while current operational information remains in SaaS applications. Business questions can span both environments.
Supper can connect supported SaaS sources and make that information available alongside Redshift data. Business users can investigate warehouse history together with current details from CRM, finance, or support systems through the same analytical experience.
AWS has also expanded the ways customers can bring operational information into Redshift. Its zero-ETL integrations support AWS databases and applications including Salesforce, SAP, ServiceNow, and Zendesk. Some companies will use these services to centralize more data in Redshift, while others may use Supper's direct SaaS connections for selected analytical needs.
Both approaches can be part of the same data environment. Redshift remains the primary warehouse, and Supper gives teams another route for questions involving information that is currently spread across systems.
Save recurring analysis as Supper Skills
Some investigations follow an established method. The underlying data changes each time, but the calculations, decision rules, and expected output remain similar.
Supper Skills let teams preserve that method as a reusable analytical capability. A Skill can define the sources to examine, the business logic to apply, the analytical steps to follow, and the expected form of the result. The Skill can then run against current Redshift data whenever the process needs to be repeated.
This is useful for work that would otherwise depend on an analyst reconstructing the same process each week or month. It also makes the methodology available to other authorized users, giving the organization a more consistent way to perform recurring analysis.
Redshift continues to provide the underlying data and execute warehouse queries. Supper supplies the company context and saved analytical process that determine how the data should be used.
Control access through Redshift and Supper
Redshift remains responsible for warehouse-level security. Supper connects through a customer-provisioned account, and the permissions assigned to that account establish what the application can query. Redshift supports role-based access control, row-level security, and dynamic data masking.
Supper adds a separate entitlement layer for people using the application. Administrators can create allow and deny rules covering schemas, tables, and columns. These rules can also govern information from SaaS systems connected to Supper.
The two layers need to be considered together. Redshift applies its policies to the account executing the warehouse query. Supper's entitlements determine which parts of that permitted environment each Supper user can access. An individual user's personal Redshift roles are not automatically inherited through a shared Supper connection.
The Admin Control Center gives administrators visibility into how the product is being used, the questions people are asking, and areas where the semantic model may require attention. This provides a way to expand analytical access while retaining control over the application.
Connect Redshift and refine Supper through use
Getting started begins with a Redshift account that has access to the appropriate databases, schemas, and query resources. The connection must also satisfy the customer's network and authentication requirements.
Supper then examines the accessible warehouse objects and creates its initial semantic model. It generates an evaluation corpus to test whether the model can answer representative questions correctly. Once users begin working with Supper, new questions provide further evidence about which definitions are clear and where more context is needed.
This concentrates refinement on the business questions people actually ask. Supper's semantic agents can identify many potential model issues, while administrators provide the company knowledge required to settle ambiguous rules.
The result is a clear division of responsibilities. Amazon Redshift continues to manage the warehouse and SQL workloads. Supper gives business teams a dedicated environment for investigating that data and preserving recurring analytical work.
Frequently asked questions
What is the Supper and Amazon Redshift integration?
The integration connects Supper to a customer's existing Amazon Redshift environment. Supper queries the data permitted by the customer-provisioned connection and uses its own semantic model, analytical agents, and Skills to answer business questions.
Does Supper replace Amazon Redshift?
No. Redshift remains the company's warehouse and executes the SQL queries used to retrieve data. Supper works with that environment as a business analytics application.
Does Supper replace Amazon Quick Suite or the AWS Data Analytics plugin?
No. AWS provides several ways to conduct conversational analytics over Redshift. Supper can be used alongside those products when a company prefers its approach to semantic modeling, evaluations, SaaS connections, reusable Skills, or user administration.
Does Supper move Redshift data into another warehouse?
Supper queries the customer's Redshift environment directly, so the warehouse does not need to be migrated. Query results are returned to Supper for analysis and presentation. Connected SaaS applications may use a separate analytical-copy model depending on the source.
Can Supper use business definitions already maintained in AWS?
Existing definitions can inform Supper's implementation and semantic model. The method depends on where those definitions are maintained and which integrations are available. Companies should decide which system will remain authoritative for each definition.
Can Supper analyze Redshift data with information from SaaS applications?
Yes. Supper can make data from supported operational applications available alongside Redshift information. This allows users to investigate questions that span the warehouse and systems such as CRM, finance, or customer support applications.
How are permissions handled?
Redshift applies its database permissions and policies to the account used by Supper. Supper then uses its own entitlements to determine what each person can access through the application. Administrators should configure and test both layers.
What analytical work can Supper perform with Redshift?
Supper can query Redshift using SQL, use Python for further analysis, create charts, and save recurring methodologies as Skills. The exact work available to a user depends on the connected data, Supper's semantic model, and the user's entitlements.