Connect your data to Spice
40+ data connectors to operational databases, warehouses, data lakes, HTTP APIs, and more. Query data where it already lives using standard SQL, accelerate working sets locally, and integrate operational and analytical datasets into one unified runtime.
Showing 32 of 32 integrations
ABFS
The Azure Blob Filesystem driver (ABFS) is a dedicated Azure Storage driver for Hadoop. The Azure BlobFS (ABFS) Data Connector enables federated SQL queries on files stored in Azure Blob-compatible endpoints.
ADBC
ADBC (Arrow Database Connectivity) is a columnar alternative to JDBC/ODBC for analytical data access using Apache Arrow. The ADBC connector supports federated SQL through runtime-loaded drivers and a managed connection pool.
Amazon DynamoDB
Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. This connector enables using DynamoDB tables as data sources for federated SQL queries in Spice.
Amazon Redshift
Amazon Redshift is a columnar OLAP database compatible with PostgreSQL. In Spice, Redshift is connected via the PostgreSQL data connector using Redshift cluster connection parameters.
Amazon S3
Amazon Simple Storage Service (S3) is a service offered by Amazon Web Services (AWS) that provides object storage. The S3 Data Connector enables federated SQL querying on files stored in S3 or S3-compatible systems (e.g., MinIO, Cloudflare R2, NetApp OnTap).
Apache Iceberg
Apache Iceberg is a high-performance format for huge analytic tables. The Iceberg Data Connector helps query Apache Iceberg tables using federated SQL.
Apache Kafka
Kafka is an open-source distributed event streaming platform used for high-performance data pipelines, streaming analytics, and data integration. The Kafka Data Connector enables direct acceleration of data from Apache Kafka topics.
Apache Spark
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning. Use the connector for federated SQL queries against a Spark Cluster using Spark Connect
AWS Glue
AWS Glue is a serverless data integration service that simplifies the process of discovering, preparing, moving, and integrating data from multiple sources. The Glue Data Connector enables federated SQL querying on tables in an AWS Glue Data Catalog.
Azure Cosmos DB
The Azure Cosmos DB Data Connector exposes Cosmos DB containers as SQL tables in Spice by inferring Arrow schemas from sampled documents and streaming results into DataFusion for federated SQL.
Clickhouse
ClickHouse is a fast, open-source columnar database management system designed for online analytical processing (OLAP) and real-time analytics. This connector enables federated SQL queries from a ClickHouse server.
Databricks
Databricks is a cloud-based platform for data analytics and artificial intelligence. Use the connector for federated SQL queries against Databricks using Spark Connect, directly from Delta Lake tables, or using the SQL Statement Execution API.
Debezium
Debezium is an open-source platform that enables Change Data Capture (CDC) for efficient real-time updates of locally accelerated datasets. Spice supports connecting to a Kafka topic managed by Debezium to keep datasets up-to-date with source data.
Delta Lake
An open-source storage layer that improves the reliability and performance of data lakes by bringing database-like features such as ACID transactions and schema enforcement. The Delta Lake data connector enables SQL queries from Delta Lake tables.
Dremio
Dremio is a data lake engine that enables high-performance SQL queries directly on data lake storage. This connector enables using Dremio as a data source for federated SQL queries, including running Spice side-by-side during a phased migration off Dremio.
DuckDB
DuckDB is an in-process SQL OLAP (Online Analytical Processing) database management system designed for analytical query workloads. This connector supports DuckDB persistent databases as a data source for federated SQL queries.
DuckLake
DuckLake is an open lakehouse format that stores metadata in a SQLite-compatible database (or PostgreSQL) and data in Parquet files. The DuckLake Data Connector enables querying individual DuckLake tables as datasets in Spice.
Elasticsearch
The Elasticsearch Data Connector exposes Elasticsearch indexes as SQL tables in Spice. Index mappings are translated to Arrow schemas so documents can be queried with federated SQL.
FlightSQL
FlightSQL is an open-source protocol built on Apache Arrow Flight for high-performance, columnar data transfer between SQL databases and clients. Use the connector for federated SQL queries across any Flight SQL compatible server.
GCS
The GCS Data Connector enables federated SQL queries on files stored in Google Cloud Storage. Both gcs:// and gs:// URI schemes are supported.
GitHub
GitHub is a developer platform that allows developers to create, store, manage, and share their code. The GitHub Data Connector enables federated SQL queries on various GitHub resources such as files, issues, pull requests, and commits.
GraphQL
GraphQL is an open‑source query language for APIs and a server‑side runtime. The GraphQL Data Connector enables federated SQL queries on any GraphQL endpoint
HTTP
The HTTP(s) Data Connector enables federated SQL queries across supported file formats stored at HTTP(s) endpoints, with dynamic querying and refresh via SQL-based filtering.
Microsoft SQL Server
Microsoft SQL Server (MSSQL) is a proprietary relational database management system (RDBMS). The Microsoft SQL Server Data Connector enables federated/accelerated SQL queries on data stored in MSSQL databases.
MongoDB
MongoDB is an open-source NoSQL database that stores data in flexible, JSON-like documents, allowing for dynamic schemas and easy scalability. The MongoDB Data Connector enables federated/accelerated SQL queries on data stored in MongoDB databases.
MySQL
MySQL is an open-source relational database management system that uses structured query language (SQL) for managing and manipulating databases. The MySQL Data Connector enables federated/accelerated SQL queries on data stored in MySQL databases.
ODBC
ODBC (Open Database Connectivity) is a standard API that allows applications to connect to and interact with various database management systems using a common interface. Connect to any ODBC database for federated/accelerated SQL queries with the Spice ODBC Connector.
Oracle
Oracle offers a comprehensive and fully integrated cloud platform service. The Oracle Data Connector enables SQL queries on data stored in Oracle databases, including on-premises instances, Oracle Cloud User-Managed Databases, and Oracle Cloud Autonomous Databases (ADB).
PostgreSQL
PostgreSQL is an advanced open-source relational database management system known for its robustness, extensibility, and support for SQL compliance. The PostgreSQL Server Data Connector enables federated/accelerated SQL queries on data stored in PostgreSQL databases.
ScyllaDB
The ScyllaDB Data Connector enables federated SQL queries on data stored in ScyllaDB clusters using CQL (Cassandra Query Language).
SharePoint
The SharePoint Data Connector enables federated SQL queries on documents and tabular data stored in SharePoint or OneDrive.
Snowflake
Snowflake is a cloud data platform that allows for data analysis and simultaneous access of data sets with minimal latency. The Snowflake Data Connector enables federated SQL queries across datasets in the Snowflake Cloud Data Warehouse.
Deployed in production
Teams trust Spice to bring inference closer to their data, enabling low-latency, enterprise-grade AI across industries.
“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
Integrations across all of your data sources
Accelerate your data and AI stack with a library of 40+ prebuilt connectors for the most common databases, warehouses, and file stores, from Databricks and S3 to MySQL and PostgreSQL. Govern AI sandboxes with the same policies as your production data environments.
FAQs
Common questions about connecting data sources to Spice.
Spice data connectors cover more than 40 sources across databases, warehouses, data lakes, and streaming systems. Examples include PostgreSQL, MySQL, MongoDB, Snowflake, Databricks, Amazon S3, Apache Iceberg, and Apache Kafka. For a source without a dedicated connector, the ODBC, ADBC, and Flight SQL connectors work when the source exposes that protocol. The HTTP connector queries supported file formats served from HTTP(S) endpoints.
You add a data source as a dataset in the spicepod.yaml file. The from field names the connector and the source object, for example postgres:public.orders. The params field holds the connection details, such as the host and the database name. After the runtime loads the Spicepod, SQL queries can use the dataset by its name.
Yes. Spice federates the query, so one SQL statement can join a PostgreSQL table with Parquet files in Amazon S3. Spice pushes filters down to each source, which reduces the data that moves over the network. See data virtualization for how this approach compares to copying data into a warehouse.
No. Without acceleration, Spice sends each query to the source system and returns the result. With acceleration enabled, Spice queries a local copy of the dataset in an engine such as Arrow, DuckDB, SQLite, or Spice Cayenne. See SQL federation and acceleration for how the two modes work together.
An accelerated dataset refreshes from its source with the mode that you set in refresh_mode. The default full mode replaces the whole dataset on each refresh, and the append mode adds new rows. The changes mode applies row-level changes from a change data capture source, such as the Debezium connector. See change data capture for how log-based CDC works.
See Spice in action
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