# Distributed Query

## Scale SQL queries across distributed nodes

Extend from a single-node engine to a distributed cluster. Parallelize scans, joins, and inference across executors for high-performance, fault-tolerant processing.

[Get a demo](https://meetings.hubspot.com/lukekim/talk-to-sales) [View the docs](https://spiceai.org/docs/features/distributed-query)

### Do more with your data

Consolidate operational, analytical, and object store data into a governed and accelerated SQL query engine.

- **100x** up to 100x faster queries
- **80%** up to 80% cost savings on data lakehouse spend
- **2x** increase in data reliability for critical workloads

- Declarative cluster configuration
- Parallel execution engine
- Automatic dependency management
- Optimized query planning

#### Declarative cluster configuration

Define distributed clusters directly in your Spice environment. Designate a master node and executor pool that register automatically with the scheduler—no external orchestration or complex setup required.

[View the docs](https://spiceai.org/docs)

#### Parallel execution engine

Built on Apache Ballista and DataFusion, Spice distributes query stages across nodes for concurrent reads, joins, and aggregations. Each executor processes partitions independently and merges results in real time.

[View the docs](https://spiceai.org/docs)

#### Automatic dependency management

Spice executors automatically detect and load dependencies—like embedding models or UDFs—from the master node. Runtime optimizers ensure each executor fetches only what's required, reducing overhead.

[View the docs](https://spiceai.org/docs)

#### Optimized query planning

A custom optimizer decomposes large scans into shuffle partitions and pushes projections closer to the data source. Less data movement, faster results, and lower network cost.

[View the docs](https://spiceai.org/docs)

### Integrations across all your data sources

Accelerate your data stack with a library of 30+ prebuilt connectors for the most common databases, warehouses, and file stores—from Databricks and S3 to MySQL and PostgreSQL.

[See connectors](/content/integrations/index.html)

### Deployed in production

Run data-intensive workloads on a high-performance engine trusted by teams building real-time systems at scale.

#### “Spice opened the door to take these critical control-plane datasets and move them next to our services in the runtime path.”  
Peter Janovsky  
Software Architect, Twilio

0x

Faster queries

#### “It just spins up and works, which is really nice. The responsiveness is amazing, which is a huge gain for the customer.”  
Darin Douglass  
Principal Software Engineer, Barracuda

#### “Partnering with Spice AI has transformed how NRC Health delivers AI-driven insights. By unifying siloed data across systems, we accelerated AI feature development, reducing time-to-market from months to weeks - and sometimes days. With predictable costs and faster innovation, Spice isn't just solving some of our data and AI challenges - it's helping us redefine personalized healthcare.”  
Tim Ottersburg  
VP of Technology, NRC Health

### See Spice in action

Walk through your use case with an engineer and see how Spice handles federation, acceleration, and AI integration for production workloads.

[Talk to an engineer](https://meetings.hubspot.com/lukekim/talk-to-sales)