Bring structured and unstructured data together with Amazon Quick

Organizations collect data in many forms. Structured data lives in databases and spreadsheets, whereas unstructured data sits in documents, reports, and files stored across file systems. Analyzing both together typically requires switching between multiple tools, running separate queries, and manually correlating results.

Amazon Quick addresses this with a unified experience. You can connect structured data through enriched datasets and unstructured data through S3-backed knowledge bases. You can then query both through a single conversational AI agent.

The following diagram illustrates how each component connects to deliver a unified data experience through Amazon Quick.

Figure 1: Solution architecture for combining structured and unstructured data in Amazon Quick

With this layered approach, you can expand the solution over time. When you add a new data source to the Space, the agent linked to that Space can use it, and responses respect the permissions of the user who asks.

To follow along with this walkthrough, you need:

This walkthrough uses two types of data:

Unstructured documents: Upload these to your Amazon S3 bucket for the knowledge base:

Structured data: Download this file to your computer. You upload it directly to Amazon Quick in Step 3:

Start by connecting Amazon Quick to your S3 bucket. From the Amazon Quick console, select Knowledge in the left navigation panel.

The knowledge page shows available connector types, including Amazon Q Business, Web Crawler, Amazon S3, and third-party options like Google Drive, OneDrive, and Microsoft SharePoint.

Under Amazon S3, choose Add. In the Create S3 knowledge base dialog, on the Connect S3 bucket page, provide the following details:

Figure 2: Create integration dialog showing the S3 connection fields

After the S3 integration is created, Amazon Quick prompts you to create a knowledge base. The knowledge base indexes your S3 content for AI-powered search.

Next: Additional settings opens optional settings for document-level access control lists (ACLs) and a metadata files folder. Decide whether to use ACLs before you create the knowledge base, because you can’t change that choice later. This walkthrough uses sample data, so the default settings are sufficient. For details, see Set up and manage Amazon S3 integration.

Choose Create. A success banner confirms the S3 integration, and the knowledge base begins indexing in the background.

Figure 3: Create knowledge base dialog with the Business Knowledge Base name and content options

A dataset is your structured data file loaded into Amazon Quick. By enriching the dataset with semantic descriptions, custom instructions, and correct data types, the AI agent can interpret your data more accurately. It can then translate natural language questions into SQL queries.

Amazon Quick ingests the file into SPICE (the in-memory calculation engine) and the dataset appears in your dataset list.

The dataset takes its name from the uploaded file, so it appears as Product_Sales.csv in your account.

Enriching your dataset with semantic information helps the AI understand your data in business context. The agent uses this context to translate natural language questions into more accurate SQL queries. From the dataset page, choose Edit dataset to enter the data preparation view.

In the Transform tab, update data types for columns that were auto-detected incorrectly. For this dataset:

Figure 4: Transform view showing data type changes applied to the Product_Sales.csv dataset

Select the Output tab to access the dataset-level enrichment fields. Here you provide context that helps the AI understand what your data represents and how to use it.

Description: Add a dataset-level description:

“Product sales transaction data for AnyCompany, covering orders from FY2024-FY2025 across North America, EMEA, Asia Pacific, and Latin America. Includes order details, customer information, product lines, and revenue metrics.”

Custom instructions: Add guidance for how the AI should query this data:

TAGS: Sales, revenue, orders, regional performance, product analytics

Column descriptions: Select each column in the left panel and add a description. For example:

Figure 5: Output view showing dataset details with Description and Custom instructions configured

Choose Save & publish to save your changes. The dataset summary now shows the columns with their correct data types and the descriptions you added.

Figure 6: Enriched Product_Sales.csv dataset summary showing all 16 columns, data types, and descriptions

This enriched dataset provides the structured data layer for your solution. When the agent receives a natural language question about sales data, it uses these descriptions and instructions to generate more accurate SQL queries. Combined with the knowledge base from Step 2, you now have both structured and unstructured data ready to bring together in a Space.

Spaces in Amazon Quick act as unified data workspaces. A Space can hold file uploads, dashboards, knowledge bases, actions, and datasets, giving your chat agent a single location to query across data types.

From the left navigation, select Spaces and create a new Space. Name it “Business Intelligence Hub”.

Add your knowledge base and dataset to the Space:

The Space now shows both your knowledge base (unstructured documents) and your enriched dataset (structured sales data) linked as data sources. This is the central hub where both data types come together, giving your chat agent a single place to query across everything. Figure 7 shows the Space with both resources in the Ready state.

Figure 7: Business Intelligence Hub Space showing the Product_Sales.csv dataset and Business Knowledge Base

Navigate to Chat agents in the left navigation and choose Create chat agent. Amazon Quick presents a prompt builder where you can describe your agent in natural language or choose from templates like Training Guide, Project Coordinator, and Onboarding Helper.

For this walkthrough, choose Skip to configure the agent manually.

Figure 8: Chat agent creation screen with the prompt builder and sample templates

The configuration panel has several sections. Start with the Agent Persona:

You are a Business Intelligence Assistant designed to help organizations break down information silos. Your primary functions are: 1. Analyze business data stored in S3 2. Retrieve relevant findings from the reports and memos in the knowledge base 3. Provide comprehensive insights that connect internal data with external trends 4. Generate clear, actionable recommendations based on data analysis When answering questions, always cite your sources and explain how you arrived at your conclusions. Focus on delivering practical business value and highlighting patterns across different information sources. A note appears confirming that your chat agent comes equipped with enterprise guardrails.

Figure 9: Agent persona configuration with identity and persona instructions filled in

Under Communication style, pick a response style preset. Options include Executive, Technical, and Creative. For a business intelligence use case, select Executive.

Under Knowledge Sources, choose Link spaces and select the “Business Intelligence Hub” Space you created earlier. This gives the agent access to your S3 knowledge base and other data sources in that Space. When you link a Space, the agent answers from large language model (LLM) knowledge and the linked resources only.

You can also attach reference documents (PDF, TXT, HTML, MD, CSV, DOC, DOCX) directly to the agent for additional context. Amazon Quick extracts up to 100,000 characters of text from the reference documents that you upload.

Figure 10: Knowledge Sources section showing the linked Business Intelligence Hub Space

Figure 11: Customization section with the welcome message and suggested prompts

Choose Launch chat agent. The right panel shows a live preview of your agent with the welcome message and suggested prompts displayed.

Figure 12: Launched Business Intelligence Assistant with the welcome message and suggested prompts in the chat panel

With the agent running, start with a question about your business results:

For this question, the agent combines results from the Product_Sales.csv dataset with the documents in the S3 knowledge base. It returns a response with regional sales breakdowns, trends, and citations that point back to each source.

Figure 13 shows the response. Each citation marker links a figure back to its source document.

Figure 13: Chat agent response showing regional sales analysis from the dataset and the knowledge base documents

When you ask a question about structured data, the agent uses text-to-SQL to translate your natural language into a SQL query. It runs the query against the dataset and returns the results in a readable format. This means you can ask complex analytical questions without writing SQL yourself.

What are the key strategic priorities outlined in our product strategy memo?

The agent summarizes the strategic pillars, the FY2026 roadmap, and the investment requirements from the Product Strategy Memo, as shown in Figure 14.

Figure 14: Chat agent response summarizing strategic priorities from the Product Strategy Memo

Finally, ask a question that needs both data types:

How do our 2025 sales results align with the market trends identified in our analysis report?

The agent compares the trends in the Market Analysis Report with results from your sales data and cites each source, as shown in Figure 15.

Figure 15: Chat agent response comparing 2025 sales results with market trends from the Market Analysis Report

This is the core value of combining data types in a single Space. The agent can pull from structured CSV data and unstructured documents in the same response. You don’t need to know where the data lives.

The Business Intelligence Assistant can also generate formatted documents, including Microsoft PowerPoint presentations, Microsoft Excel spreadsheets, and Microsoft Word documents. Try these prompts in the chat agent:

Figure 16: Chat agent confirming the request and preparing a PowerPoint outline for AnyCompany’s Q4 2025 sales performance

Figure 17: Generated eight-slide PowerPoint with a downloadable AnyCompany_FY2025_Sales_Performance.pptx file

When you launch a chat agent, it remains private until you share it. After you test the agent, share it with your team:

The agent returns permissions-aware responses, and a knowledge base is available only to its creator until you update its permissions. Make sure that the people you share the agent with also have access to the Space, the knowledge base, and the dataset. For details, see Custom chat agents.

Use the following practices as you move from this walkthrough to your own data:

Under the AWS shared responsibility model, you are responsible for configuring access to your data, knowledge bases, and agents. Consider the following as you adapt this solution:

To avoid ongoing charges, remove the resources you created:

Amazon Quick user subscriptions and account fees are billed separately from these resources. For details, see Amazon Quick pricing.

Amazon Quick helps you bring structured and unstructured data together. In this post, you connected S3 as a knowledge base, enriched a dataset, built a Space, and created a custom chat agent. Business users can now query both data types through a single conversational interface, without needing to know where the data lives or how to write queries.

Puneeth is a Principal Technical Account Manager at AWS. He is particularly passionate about monitoring and observability, cloud financial management, and generative AI domains. In his current role, Puneeth enjoys collaborating closely with customers, using his expertise to help them design and architect their cloud workloads for optimal scale and resilience.

Sneha is a Technical Account Manager at AWS. In her role, Sneha collaborates with enterprise customers to architect, implement, and scale cloud-based applications to meet their business goals. She is passionate about cloud computing, cloud economics, and generative AI domains.

Johana is a Solutions Architect based in Arlington, Virginia, with over three years of experience designing and implementing cloud solutions. She specializes in generative AI and analytics, and loves helping customers architect solutions with security and resilience in mind. In her spare time, she enjoys spending time with her two dogs, cat, or watching sports.

Ramon is a Principal Solutions Architect for Amazon Quick. With many years of experience building BI solutions and a background in accounting, he loves working with customers and creating solutions. When not working, he prefers to be outdoors in the ocean or up on a mountain.