Query Kinetica Directly from DuckDB with Our New Postgres-Protocol Connector

Today we’re excited to share a new integration that brings the speed and flexibility of DuckDB together with the real-time analytical power of Kinetica. By leveraging DuckDB’s Postgres protocol extension alongside Kinetica’s support for the PostgreSQL wire protocol, you can now query live Kinetica datasets directly from DuckDB — mixing real-time operational analytics with local, interactive SQL.

This unlocks an incredibly productive developer experience: use DuckDB as your local analytics workbench, join Kinetica data with Parquet/CSV files, prototype queries against live tables, and orchestrate workflows without writing custom connectors or glue code.

Why This Matters
Tools like DuckDB have transformed local, interactive analytics. Analysts and engineers can explore datasets stored in many formats right from their laptop — fast and without infrastructure overhead.

But until now, extending that same ergonomics to live analytics databases required someone to build and maintain a custom connector layer.

Now, because Kinetica natively speaks the Postgres wire protocol and DuckDB supports attaching Postgres-compatible endpoints, you can treat Kinetica just like another data source in DuckDB — no translation layer, no REST API wrangling, no extra drivers.

This opens up a host of use cases:

• Interactive analytics on real-time data: Query fresh Kinetica data alongside local files.
• Prototyping before deployment: Build queries in DuckDB before promoting them to production workloads.
• Ad-hoc joins across formats: Seamlessly join Kinetica tables with Parquet, CSV, or other DuckDB-native sources.
• Low-latency insights: Leverage Kinetica’s real-time ingestion and DuckDB’s interactive engine together.

How It Works

At a high level:

1. DuckDB’s Postgres extension allows you to ATTACH any Postgres-protocol target as a new database.
2. Kinetica’s Postgres wire protocol implementation makes it behave like a native Postgres endpoint.
3. Once attached, you can query, create, insert, and drop Kinetica tables directly from DuckDB.

Here’s an example workflow:

-- Create credentials for Kinetica
CREATE SECRET (
    TYPE postgres,
    HOST '127.0.0.1',
    PORT 5432,
    DATABASE 'KINETICA',
    USER 'admin',
    PASSWORD 'admin_password'
);

-- Attach Kinetica as a Postgres database in DuckDB
ATTACH '' AS kinetica (TYPE postgres);

-- Create a table directly in Kinetica
CREATE OR REPLACE TABLE kinetica.ki_home.test_export (a1 INTEGER, a2 INTEGER);

-- Insert rows
INSERT INTO kinetica.ki_home.test_export VALUES (1,2), (2,3), (NULL,NULL);

-- Query from DuckDB
SELECT * FROM kinetica.ki_home.test_export;

-- Perform pass-through updates using postgres_execute
CALL postgres_execute('kinetica', 'update ki_home.test_export set a1 = 10');

-- Drop the table when done
DROP TABLE kinetica.ki_home.test_export;

 Note: DuckDB’s native UPDATE and DELETE syntax isn’t supported over the Postgres extension for all cases, because those forms depend on Postgres-specific semantics. For complete DML support, use postgres_execute() to run statements directly on Kinetica.

What You Can Do Now
This integration makes DuckDB a powerful ad-hoc interface to Kinetica’s real-time analytical platform:

• Data exploration: Spin up DuckDB locally and query live Kinetica tables without moving data.
• Hybrid analytics: Combine real-time data with large offline datasets stored in Parquet, Iceberg, or CSV.
• Rapid prototyping: Build and test analytical logic in DuckDB before operationalizing it.
• Simplified tooling: Leverage the same DuckDB workflows you already use — now with real-time data.

If you’ve been building custom ETL or connector logic just to bridge your analytics environment with Kinetica, this integration means you can stop writing glue and start writing queries.

What’s Next
We see this as the first step toward a richer ecosystem of interactive analytics on top of real-time data platforms. Future work includes:

• Enhanced metadata discovery (schema introspection inside DuckDB)
• Better error reporting and type mapping
• Deeper support for Postgres-protocol features in Kinetica
• Samples and templates for common analytical patterns

We can’t wait to see what you build with DuckDB + Kinetica together — whether it’s real-time dashboards, priority scoring systems, fraud detection analytics, or hybrid OLAP workflows.