Community Built for Dremio

Extend Dremio. Query everything. Analyze anything.

Community-built connectors and SQL functions that expand Dremio's federated query capabilities and unlock advanced analytics. Free, open source, and built by the people who use Dremio every day.

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Connect Dremio to more. Query it all with SQL.

A collection of community-built storage plugins extends Dremio's federated query reach far beyond official connectors, from real-time streaming systems to NoSQL stores to legacy spreadsheets, and many more.

ClickHouse

ARP/JDBC integration with extensive function pushdown. Supports ClickHouse Cloud for high-performance analytical queries at scale.

Apache Kafka

Exposes Kafka topics as bounded tables with JSON and AVRO schema support. Query streaming data directly from Dremio without a separate pipeline.

Apache Cassandra

Native CQL connector with predicate and projection pushdown. Brings Cassandra's distributed wide-column store into Dremio's federated query fabric.

Apache Hudi

Read COW and MOR Hudi tables directly from S3 or HDFS. No Spark required. Query your Hudi lakehouse through standard SQL.

Delta Lake

Access Delta Lake tables without Spark or Databricks. Read Delta format natively and join it with any other source in Dremio.

Apache Pinot

Expose real-time Pinot tables through ARP/JDBC. Bring sub-second OLAP queries into Dremio's unified query layer.

Amazon DynamoDB

Native DynamoDB queries with partition key routing optimization. Query operational NoSQL data alongside your lakehouse without moving it.

Splunk

Access Delta Lake tables without Spark or Databricks. Read Delta format natively and join it with any other source in Dremio.

Excel / CSV Importer

Web UI for importing spreadsheets directly to Iceberg tables in Dremio. Turn ad-hoc files into queryable, governed lakehouse assets in seconds.

202 new SQL functions. Advanced analytics, right in Dremio.

Four specialized function libraries that bring geospatial analysis, vector similarity search, machine learning scoring, and PII protection directly into SQL. No external tools required.

Geometry operations and hexagonal H3 grid indexing for spatial queries. Analyze location data at any scale without leaving SQL.

Open up what Dremio can reach.

Combine connectors and UDFs to build analytics pipelines that were not possible before, all through standard SQL.

Location intelligence

Join DynamoDB operational records with geospatial UDFs to run H3 hex-grid analysis on customer locations. No GIS tool required.

AI-ready data access

Use the Vector UDF to run semantic similarity queries on embeddings stored in any Dremio source. Bring AI-scale search to your lakehouse.

Privacy-safe analytics

Use the Vector UDF to run semantic similarity queries on embeddings stored in any Dremio source. Bring AI-scale search to your lakehouse.

In-database ML scoring

Score models, normalize features, and evaluate predictions directly in SQL. No Python runtime, no data movement, no pipeline complexity.

Real-time + historical joins

Join live Kafka or Pinot data with historical Hudi or Delta Lake tables in a single Dremio query. One SQL engine, every source.

Spreadsheet to lakehouse

Import business team Excel files directly to governed Iceberg tables. Stop emailing CSVs and start querying them alongside everything else.

Simple to install. Easier to use.

Both libraries ship as JAR files with automated install scripts. No rebuilding Dremio. No complex configuration required.

Connectors

Each connector includes an install.sh that supports Docker, bare-metal, and Kubernetes. A rebuild.sh utility handles version upgrades automatically.

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UDFs

Pre-built JARs: copy to your Dremio jars/3rdparty/ directory and restart. Docker users deploy with a single docker cp command.