Accelerating Data Insights with Amazon Q in QuickSight: Self-Service Analytics for Everyone
Business intelligence is only as valuable as it is accessible. Traditionally, data teams have built dashboards and reports while business users relied on them for insights. Amazon Q in QuickSight changes this dynamic by bringing natural language querying directly into dashboards, empowering users to ask questions and get instant visual responses — all without writing SQL or waiting on analysts.In this guide, we’ll explore what Amazon Q in QuickSight is, how it works, how to enable it, and how it improves
Published May 26, 2025
What Is Amazon Q in QuickSight?
How Amazon Q in QuickSight Solves This
End-to-End Implementation Steps
What Happens Behind the Curtain With Glue:
Step 3: Steps to Use Athena Table in QuickSight
Step 4: Enable Amazon Q in QuickSight
Step 5: Use Magic of GEN-AI in Quicksight
Build visuals with Generative BI:
Creating executive summaries with Amazon Q in QuickSight:
Converting to the Generative Q&A experience
Working with scenarios in Amazon QuickSight
Detailed Features of Amazon Q in QuickSight
3. Scenario Analysis (What-If Analysis)
Amazon Q is a generative AI-powered assistant developed by AWS. It’s designed to help users — technical or non-technical — interact with enterprise data, code, systems, and documentation using natural language. Amazon Q exists in various AWS services (like Amazon Q in QuickSight and Amazon Q in IDEs), and it enables users to ask questions like
- “What were our top-selling products last month?”
- “What does this error message mean?”
- “Summarize our Q1 sales performance.”
Amazon Q uses foundational models from Amazon Bedrock under the hood and is designed with enterprise-grade security, privacy, and governance in mind.
Amazon QuickSight is AWS’s cloud-native business intelligence (BI) service. It allows organizations to create and share:
- Interactive dashboard
- Paginated reports
- Visual analytics
QuickSight connects to multiple data sources (e.g., Amazon Redshift, Athena, S3, RDS, Salesforce, Snowflake) and lets business users derive insights from data without needing deep technical expertise.
Amazon Q in QuickSight combines the strengths of both Amazon Q and QuickSight. It brings natural language querying, generative BI, and automated insight discovery to QuickSight, enabling anyone in an organization to:
- Ask questions in plain English
- Build visuals and dashboards without coding
- Generate summaries and narratives from data
- Perform “what-if” analysis
- Get contextual insights from multiple datasets
As a Platform/DevOps engineer, you’re responsible for ensuring network reliability, security posture, and performance across hundreds (or thousands) of VPCs. Common challenges include:
- Massive Log Volumes: VPC Flow Logs generate millions of records per day — troubleshooting spikes or anomalies requires complex SQL queries and long Athena scans.
- Slow Iteration: Each ad-hoc query can take minutes to complete, slowing incident response and root-cause analysis.
- Toolchain Friction: You must bounce between S3, Athena, CloudWatch, and custom scripts to extract, transform, and visualize.
- Skill Gaps: Not every on-call engineer is comfortable writing Athena SQL or building QuickSight dashboards from scratch.
Amazon Q in QuickSight embeds a natural-language assistant directly into your VPC Flow Logs dashboards, letting you:
- “Ask” Instead of “Query”: Simply type “Which subnets saw the highest reject rate this morning?” and get an immediate visual — no SQL required.
- Context-Aware Insights: Q respects your dashboard filters (time range, VPC ID, subnet) and the underlying SPICE or live Athena table schema.
- Rapid Triage: From incident to insight in seconds, you can drill down on anomalies without waiting for analysts or hand-crafting queries.
- Self-Service Analytics: On-call teams and even application owners can explore traffic patterns on their own.
This approach transforms Flow Logs analysis from a manual, code-heavy chore into an intuitive, conversational workflow.
- Prepare an S3 bucket
In the AWS Console, create (or pick) an S3 bucket.
Ensure its bucket policy allows the CloudWatch Logs service principal (
logs.<region>.amazonaws.com) to PutObject- Open the VPC console
Sign in to the AWS Management Console → VPC → Your VPCs Repost.
- Select your VPC
Click the VPC you want to monitor, then go to the Flow logs tab.
- Create the flow log
Click Create flow log.
Set Filter to
All (or Reject / Accept as needed).For Destination, choose Send to S3 and enter your bucket ARN (e.g.
arn:aws:s3:::my-flow-logs-bucket/prefix/).Save and verify
Click Create flow log.
After a few minutes, check your S3 bucket prefix — GZIP-compressed log files should start appearing.
- Enable VPC Flow Logs and configure delivery to an S3 bucket.
- Note the S3 path where logs are stored, e.g.
s3://my-vpc-logs/AWSLogs/<account-id>/vpcflowlogs/<region>/ - Open Athena Console: https://console.aws.amazon.com/athena/
- Set Athena query result location (e.g.
s3://my-athena-results/) under Settings. - Create a Database in Athena
- Create an external table for VPC Flow Logs with this schema:
- Athena uses AWS Glue as its Data Catalog service.
2. When you run a
CREATE EXTERNAL TABLE statement in Athena:- A Glue table is created under the specified database.
- The table stores metadata such as:
- Column names and data types
- Partition keys
- File format and delimiter
- S3 location of the data
3. This Glue table can also be:
- Queried by Athena
You can view the table in the AWS Glue Console:
- Go to AWS Glue Console
- Navigate to Data Catalog → Databases → Select your database → See your table.
- Ensure QuickSight has permission to access Athena & S3
- In the QuickSight console, go to Manage QuickSight > Security & Permissions
- Enable access to Amazon Athena
- Amazon S3 buckets (your VPC flow log bucket and Athena query result bucket)
- If needed, add the IAM role used by QuickSight to access the correct S3 paths.
2. Ensure your Athena table is created and working
- Run a test query in the Athena Console to confirm data is being read.
3. Open QuickSight Console:
4. Create a new dataset:
- Go to Datasets > New Dataset
- Choose Athena as the data source
- If prompted, create a new data source (e.g., name it
vpc_flow_logs_source) - Choose your Athena database (e.g.,
vpc_logs) and table (e.g.,vpc_flow_logs)
5. Import or use direct query:
Choose either:
Choose either:
- Direct Query: Fetches data live from Athena (good for large data sets)
- SPICE: Ingests and caches data in QuickSight for faster performance
To upgrade a user to a Pro role
- Open the QuickSight console.
- Choose the user icon at the top right, and then choose Manage QuickSight.
- Choose Manage users to open the Manage Users page.
- To change the role of an existing user, locate that user on the Manage Users table and choose the role that you want to grant them from the Role dropdown. The image below shows the Manage users table with the Role dropdown opened.
QuickSight authors can use the Build a visual button to create custom visuals using natural language input. You can either type a custom description or select from suggestions generated by Amazon Q based on the analysis topic.
For example, when I asked for a summary of my VPC Flow Logs, it returned the correct response, generating an accurate visual.
With Amazon Q in QuickSight, you can leverage large language models (LLMs) to generate executive summaries of dashboards. Executive summaries are based on QuickSight’s suggested insights for a dashboard. Executive summaries help readers find key insights at a glance without the need to pinpoint specific data from a dashboard’s visuals.
Below is an example of summary of our vpc flow logs.
If you have existing Amazon Q topics, you can easily convert these to leverage our new generative capabilities. Navigate to a topic, and then choose Convert next to the topic name. You will then be prompted to Duplicate & Convert Topic in a dialog box. We duplicate your topic for you so that the conversion to our beta experience does not impact your end users. Once you are satisfied with topic performance in the new experience, you can unshare the original topic and share the new one.
QuickSight users with Admin Pro, Author Pro, or Reader Pro roles can use scenarios with Amazon Q to analyze complex business problems using simple natural language.
To begin, users describe the problem and either attach relevant data from QuickSight or upload it from their computer. Alternatively, Amazon Q can automatically search for related data. It then generates analyses, suggests follow-up prompts, or allows users to input their own. Amazon Q breaks down each prompt into actionable steps, delivering insights, interactive visuals, and business interpretations with recommended next actions.
Below is an example of asking anomaly data against our vpc flow logs.
Ask questions like:
“What’s the source and dest ADDR for the last 6 months by region?”
Amazon Q translates your question into SQL behind the scenes and generates charts or textual answers automatically.
With Generative BI, Amazon Q can:
- Build visuals automatically based on your question
- Generate executive summaries highlighting key metrics
- Explain charts by summarizing trends and anomalies in simple language
- Create data stories that narrate insights in paragraph form, useful for presentations and reports
Amazon Q supports what-if scenario modeling. For example:
“What happens to the vpc that reject addr has been increased”
This helps users simulate business outcomes without needing complex models.
Amazon Q can analyze data across multiple sources and dashboards. You don’t need to open different reports — just ask one question, and Q fetches answers from wherever relevant data lives.
You can embed Amazon Q’s interactive, conversational UI directly into web applications or internal portals. This enables your business users to get data-driven answers without logging into QuickSight.