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## What is Spice?
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**Spice** is a portable runtime providing developers with a unified SQL interface to materialize, accelerate, and query data sourced from any database, data warehouse, or data lake.
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**Spice** is a portable runtime offering developers a unified SQL interface to materialize, accelerate, and query data from any database, data warehouse, or data lake.
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📣 Read the [Spice.ai OSS announcement blog post](https://blog.spiceai.org/posts/2024/03/28/adding-spice-the-next-generation-of-spice.ai-oss/).
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Spice connects, fuses, and delivers data to applicationsand AI, acting as an application-specific, tier-optimized Database CDN.
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Spice connects, fuses, and delivers data to applications, machine-learning models, and AI-backends, functioning as an application-specific, tier-optimized Database CDN.
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The Spice runtime is written in Rust and is built-with industry leading technologies like [Apache DataFusion](https://datafusion.apache.org), Apache Arrow, Apache Arrow Flight, SQlite, and DuckDB.
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The Spice runtime, written in Rust, is built-with industry leading technologies such as [Apache DataFusion](https://datafusion.apache.org), Apache Arrow, Apache Arrow Flight, SQLite, and DuckDB.
Spice makes querying data by SQL across one or more data sources simple and fast. Easily co-locate a managed working set of data with your application or ML, accelerated with in-memory Arrow, with SQLite/DuckDB, or with attached PostgreSQL for high-performance, low-latency queries. Accelerated engines run tier-native in your infrastructure giving you flexibility and control over cost and performance.
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Spice makes it easy and fast to query data from one or more sources using SQL. You can co-locate a managed dataset with your application or machine learning model, and accelerate it with Arrow in-memory, SQLite/DuckDB, or with attached PostgreSQL for fast, high-concurrency, low-latency queries. Accelerated engines give you flexibility and control over query cost and performance.
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### How is Spice different?
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1.Tier-optimized Acceleration with **both OLAP (Arrow/DuckDB) and OLTP (SQLite/PostgreSQL)** databases at dataset granularity compared to other OLAP only or OLTP only systems.
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1.**Application-focused:** Spice is designed to integrate at the application level; 1:1 or 1:N application to Spice mapping, whereas most other data systems are designed for multiple applications to share a single database or data warehouse. It's not uncommon to have many Spice instances, even down to one for each tenant or customer.
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2.**Separation of materialization and storage/compute**compared with monolith data systems and data lakes. Keep compute colocated with source data while bringing a materialized working set next to your application, dashboard, or data/ML pipeline.
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2.**Dual-Engine Acceleration:**Spice supports both **OLAP** (Arrow/DuckDB) and **OLTP** (SQLite/PostgreSQL) databases at the dataset level, unlike other systems that only support one type.
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3.**Edge to cloud native**. Designed to be deployed standalone, as a container sidecar, as a microservice, in a cluster across laptops, the Edge, On-Prem, to a POP, and to all public clouds. Spice instances can also be chained, and deployed distributed across tiers of infrastructure.
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3.**Separation of Materialization and Storage/Compute:** Spice separates storage and compute, allowing you to keep data close to its source and bring a materialized working set next to your application, dashboard, or data/ML pipeline.
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4.**Edge to Cloud Native**. Spice is designed to be deployed anywhere, from a standalone instance to a Kubernetes container sidecar, microservice, or cluster at the Edge/POP, On-Prem, or in public clouds. You can also chain Spice instances and deploy them across multiple infrastructure tiers.
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