Buster - Query your data with plain english

For engineers & data professionals

Have your own 24/7 AI data engineer

Use our AI data engineers to 10x your engineering workflows.

Optimize your data models on auto-pilot, generate new models, handle ad-hoc user requests, auto-document business terminology, & more.

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Monthly Sales by Sales Team

Show me our monthly sales by sales team

Sure, I’ll create a chart that shows you your monthly sales by sales rep. Lets build your chart:

Monthly Sales by Sales Team

Thought for 13.1 seconds

Can you make it a nice soft purple and pinkish red?

Let me change the colors to be a softer purple/pinkish red color.

Monthly Sales by Sales Team

Thought for 1.1 seconds

The colors on the chart have been changed to a purple/pinkish red color. Let me know if you need me to tweak it some more.

Edit the chart or follow up...

Our AI may make mistakes. Check important info.

Chart

Results

Thought

Monthly Sales by Sales Team

Last 6 months

•

What are our monthly sales, broken down by sales team?

$48,080

Team 1

$56,192

Team 2

Jul

Aug

Sep

Oct

Nov

Dec

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Update Sales Pipeline Dataset

can you push a change to sales_pipeline? i need to add a column that calculates time since previous stage

Sure, I’ll spin up a new column in sales_pipeline to add a column that calculates the total time since the previous stage. Lets add that column.

Update Sales Pipeline Dataset

Thought for 14.8 seconds

I was able to add that column to the dataset. It takes calculates the amount of time (days) an account has spent in their current stage.

Edit the chart or follow up...

Our AI may make mistakes. Check important info.

Sales Pipeline Dataset

Reset

Publish

SQL

Metadata

Open repo

Copy SQL

Run

Account_ID

Nate Kelley

Blake Rouse

Gregory

Dallin Bentley

Another name

Account_Name

9.1

4.3

8.9

1.3

Stage_Name

Raisin bran

Pizza

Quinoa

Meats

Various Items

Stage_Start_Date

Raisin bran

Pizza

Quinoa

Meats

Various Items

Days_In

Raisin bran

Pizza

Quinoa

Meats

Various Items

Favorite food

Raisin bran

Pizza

Quinoa

Meats

Various Items

Favorite food

Raisin bran

Pizza

Quinoa

Meats

Various Items

Favorite food

Raisin bran

Pizza

Quinoa

Meats

Various Items

Open source

Built by devs, for devs

Code-based

Use git & deploy via CI/CD

dbt Integration

100% dbt compatible

SOC 2 Compliant

Enterprise grade security

AI data engineer overview

What can Buster’s AI data engineers actually do?

We believe that the future of AI analytics is about helping engineers & data teams build powerful, self-serve experiences for their users. We think that requires a new approach to the analytics stack. One that allows for deep integrations between products & allows data teams to truly own the entire experience.

Below is an overview of how Buster can help you manage & optimize your data stack.

Deploy & manage your data warehouse

Unify your data & mange ETL pipelines

Rapidly build data models

Implement guardrails & AI safety

Improve your data models on auto-pilot

Generate & auto-update your documentation

Code-based & Git-native

Open source

Deploy & manage your data warehouse

Optimize your data warehouse on auto-pilot

Buster can spin up & fully manage a data warehouse on your behalf.

Under the hood, Buster has access to a headless data warehouse. It's an open-source, high-performance OLAP data warehouse that uses Apache Iceberg, StarRocks, & S3. It's blazing fast, real-time, & 10x cheaper than Snowflake. Buster can even deploy the warehouse in your own cloud.

Once deployed, Buster can do lots of cool things to optimize your warehouse. For example, it can run background jobs to identify common query patterns & auto-partition your data for faster query times.

If you already have a data warehouse that you love using, Buster can work with those as well. We seamlessly integrate with all major databases & data warehouses.

Unify your data & mange ETL pipelines

Manage & build ETL pipelines

Buster comes with pre-built ETL integrations via Airbyte and dltHub.

Once you've connected your various datasources, Buster can:

Manage pre-built ETL pipelines (powered by Airbyte)
Build custom ETL pipelines on demand (powered by dltHub)
Unify your data into a single schema
Automatically integrate new schemas into existing data models for end users to query

Buster can also set up real-time data ingestion pipelines via API.

Rapidly build data models

Rapidly build analytics-ready data models

Buster can generate entire models on your behalf. It's specifically taught to build a single, shared data model. It's main goal is to integrate and unify all of your company data into a single data model. This approach provides a strong foundation for other AI workflows & agents to accurately work with your data.

When building ad-hoc models, Buster also functions as a modeling copilot. It is context-aware of your data structures & can rapidly build robust datasets. It's like Cursor for data modeling.

Buster integrates seamlessly with dbt. Once connected, model and metadata changes from dbt will automatically sync with your datasets & documentation in Buster. You can also push datasets created in Buster to dbt, creating a new pull request in your git repo.

Implement guardrails & AI safety

Implement guardrails around AI querying capabilities

Buster is trained to strictly work within the bounds of the data models that have been granted query access (by you). Anytime Buster attempts to generate a query that is not explicitly defined in the underlying data model, the query gets flagged.

We do this by running a set of rigorous tests every time Buster generates SQL or Python. These evaluations detect lots of things, but the most important are:

Did the AI have to make assumptions about things that are not explicitly defined in the data model(s)?
If assumptions were made, how severe was each individual assumption?

You can do all kinds of automated things with flagged queries:

You can allow the AI data analyst to run the query & notify the end user of the assumptions that were made.

You can block the query from being run & return an error message to the end user.
You can send a ticket to Jira or Linear for review.
You can have Buster generate a pull request with a suggested model improvement or documentation update.

Improve your data models on auto-pilot

Self-improving data models

Buster was intentionally built to create strong feedback loops between the BI layer and the modeling layer. This allows Buster to constantly be optimizing your data stack.

Lets say a user requests some kind of analysis, but it introduces a new concept that isn’t clearly defined in your data model. Buster will identify this & flag the request. Then, Buster can do some really powerful things:

identify a potential model improvement → create a new branch → generate an update to your data model → send you a pull request

You can then review the request & merge it with one click.

Generate & auto-update documentation

Auto-documentation and constant learning

Buster documents your business terminology, data structures, and company goals—and keeps them up to date as things change.

All of this documentation is used as a context & memory layer for the AI. This also means that Buster will build a deeper understanding of your business & data stack over time.

Code-based & Git-native

Everything in Buster is code

Everything in Buster is code. Your data models, dashboards, metrics, documentation, permissions, etc all live (as files) in your own Github Repo. This enables you to manage everything from your CLI & CI/CD pipeline.

More importantly, it enables things like: