Why DuckDB?

The data landscape is bloated.

Traditional “modern” data stacks rely on layers of infrastructure: warehouses, orchestrators, ETL pipelines, reverse ETL, cataloging systems, semantic layers, and more. All of it promises faster insights—but delivers complexity, fragility, and constant maintenance.

DuckDB is a reset button.

It’s a lightweight, embeddable analytical database designed for speed, flexibility, and interoperability. It runs where you are—on your laptop, in your backend, inside notebooks or containers—and speaks fluent SQL. It integrates natively with open standards like Apache Arrow, Parquet, and CSV. And with its growing ecosystem of extensions, DuckDB becomes not just a tool—but a platform.

Built for Now: Why DuckDB Fits Today’s Stack

⚡ Instant Analytics, Anywhere

DuckDB runs in-process with no server and no setup. It works in Python, R, JavaScript, or C++. Whether you’re analyzing a Parquet file in a notebook or powering a data product in production, DuckDB is ready in milliseconds.

🧠 Smarter Local Compute

Instead of copying data to the cloud just to run a query, DuckDB lets you compute where the data already lives—on your machine, on your service, in your flow. It’s optimized for vectorized execution and can outperform many cloud warehouses, especially on small to medium-sized workloads.

🔌 Extensions That Unlock New Modes

DuckDB is extensible—and Query.Farm and the community is building fast.

Here are just some extensions:

  • Airport: Query live, remote, or versioned data over Arrow Flight from APIs, NoSQL stores, or internal systems.
  • sqlite_scanner: Query SQLite databases directly from DuckDB using SQL.
  • postgres_scanner: Connect to live Postgres instances and query them without ETL.
  • httpfs: Read Parquet, CSV, or JSON directly from HTTP/S3/Google Cloud.
  • spatial: Work with geospatial data using familiar SQL operators.
  • fts: Full-text search over structured and semi-structured data.

Together, these extensions allow DuckDB to behave like a universal query engine for both analytical and operational use cases.

SQL as the API Layer

DuckDB lets you treat SQL as the interface to all your data:

  • Query a local Parquet file and join it with data from a live Postgres instance.
  • Pull data from an internal REST API using Airport and filter it with SQL.
  • Register UDFs (user-defined functions) in Python or Rust and call them inline.

This shift—from workflows that move data to interfaces that expose it—is transforming how teams build data applications, machine learning pipelines, and observability platforms.

Simpler Stacks, Faster Teams

Adopting DuckDB often reduces your stack, not adds to it. Instead of waiting for the data to land in a warehouse:

  • Analysts can query logs, metrics, or event data directly.
  • ML engineers can pull labeled data and output predictions in SQL.
  • Backend engineers can expose data services as Arrow Flight servers, instantly queryable from DuckDB.

The result: faster iteration, lower cloud spend, and fewer brittle pipelines.

A Foundation, Not Just a Tool

DuckDB isn’t just a faster SQL engine—it’s a new foundation for how we think about data:

  • Composable: Use it for small local jobs or power entire analytical systems.
  • Portable: Ship DuckDB with your app or embed it into your stack.
  • Interoperable: Composable with Apache ARrow, extensible with Flight, friendly with files, services, and code.

It’s SQL-first, developer-friendly, and built for the world we work in now—not the one we hoped the warehouse would solve.

What You Can Do Today

  • Try DuckDB in your browser or notebook—no setup required.
  • Use extensions like Airport to query live sources.
  • Embed DuckDB into your app, service, or ML pipeline.
  • Rethink what your team can do when data is queryable without moving it.

Rethink the Stack. Start with DuckDB.

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