Turn your data lake into a real-time engine
Bring data closer to your app. Spice accelerates queries on your data lake for up to 100x faster performance.
Do more with your data
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up to 100x faster queries
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up to 80% cost savings on data lakehouse spend
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increase in data reliability for critical workloads
Data lakes weren't designed for operational workloads
Modern data lakes are great for scale and cost, but they weren't built for interactive workloads. Every query triggers a network round-trip or a cluster spin-up. Teams often overpay for compute or wait minutes for results. What if you could make your lake behave like a local database, without moving the data?
Operationalize and accelerate your data lake
Run sub-second SQL directly on Parquet, Iceberg, S3, and more with local acceleration and real-time sync.
Accelerate your data lake locally
Spice pulls frequently queried datasets from your data lake into a local Data Accelerator, reducing latency by eliminating network overhead. Data stays synced in real-time or on schedule, so queries always see the latest state.
Explore Spice Cayenne Data Accelerator
Lower cost, higher reliability
Spice increases reliability and reduces expenses by materializing frequently accessed data in the runtime. If data is not available locally, queries automatically fall back to your underlying lakehouse or data warehouse source. Consistent access and increased reliability, even during service outages.
Read how Spice makes object storage operational
Any source, one runtime
Accelerate and unify over 30 data sources in one SQL runtime. Automatic schema detection, indexing, and retention are built in.
See 40+ supported connectors
Reduce cold start times
Go from zero to ready with zero ETL. Acceleration snapshots bootstrap local tables from object storage like S3.
Read about operationalizing S3 for AI
Transform your data lake into a low-latency operational engine
Federate, accelerate, and query in one engine, without standing up clusters or pipelines.
Local Acceleration
Execute queries with in-memory or on-disk acceleration for sub-second results.
Snapshots
Restore local tables instantly using versioned acceleration snapshots.
Source Agnostic
Accelerate data from S3, RDS, Snowflake, and more, all with SQL.
Governed and Reliable
Schema validation, key constraints, retention policies, and predictable refresh behavior ensure that accelerated data remains consistent with its source.
Automatic Source Fallback
If data isn't cached locally, Spice routes to the original data source or lakehouse, ensuring reliability and business continuity.
Deployment Flexibility
Run Spice anywhere: as a sidecar, microservice, cluster, or on the managed Spice Cloud Platform.
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
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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
“Spice AI grounds AI in our actual data, using SQL queries across many data sources. This brings accuracy to probabilistic AI systems, which are very prone to hallucinations.”
Rachel Wong
CTO, Basis Set
Trusted by global enterprises
Build an accelerated data lake
Guides and examples to learn more about querying data, building apps, and integrating AI with Spice.
Data acceleration with DuckDB
This recipe will walkthrough how to accelerate a local copy of the taxi trips dataset stored in S3 using DuckDB as the data accelerator engine.
Spice Acceleration docs
Datasets and views can be locally accelerated by the Spice runtime, pulling data from any Data Connector and storing it locally in a Data Accelerator for faster access
Making Object Storage Operational for Real-Time and AI Workloads
TLDR Introduction Although legacy systems and workflows remain common, many enterprises are re-evaluating their architectures to meet new demands – driven in part, but not exclusively, by AI – that require support for more data-intensive and real-time applications. The underlying storage needs for these novel workloads are generally outside the bounds of a traditional operational […]
FAQs
Answers to common questions about accelerating data lake queries with Spice
What is a data lake accelerator?
A data lake accelerator materializes frequently queried datasets from object storage into a fast local engine so queries avoid network round-trips to the lake. Spice pulls hot data into local Data Accelerators, keeps it synced, and serves sub-second SQL directly on formats like Parquet and Iceberg. It builds on the SQL federation and acceleration engine in the Spice runtime.
How does accelerated data stay in sync with the source?
Spice refreshes accelerated datasets in real time or on a configurable schedule, so queries always see the latest state. Schema validation, key constraints, retention policies, and predictable refresh behavior keep accelerated data consistent with its source. For streaming sources, real-time change data capture applies changes as they occur.
What happens when data is not available locally?
Queries automatically fall back to the underlying lakehouse or data warehouse source when data is not cached locally. This automatic source fallback maintains consistent access and business continuity, even during service outages.
How does acceleration reduce data lake costs?
Materializing frequently accessed data in the Spice runtime avoids repeated network round-trips and cluster spin-ups against the data lake, which lowers compute spend. Spice reports up to 80% cost savings on data lakehouse spend and up to 100x faster queries for accelerated workloads.
Do I need to build ETL pipelines to accelerate my data lake?
No. Acceleration snapshots bootstrap local tables directly from object storage like S3 with zero ETL, reducing cold start times. Spice runs as a sidecar, microservice, or cluster, or as a fully managed service on the Spice Cloud Platform.
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