Wil's Second Brain

Enterprise Cases of Duckdb

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2 min read

Typical use cases for Duckdb

  1. Fast browser-based Analytics and Dashboarding
    • enables ultra-fast analytical use cases on local machines
      • example: analyzing 1.5B rows of taxi data on a laptop
    • enables fast and responsive data visualization and exploration used in tools like Motherduck and Rill developer
    • brings data to users, reducing latency enabling high-performance queries, and running a full-blown analytics engine within a browser using WebAssembly (WASM)
  2. Data Pipeline Compute & Data Wrangling and Preprocessing
    • Single binary with no dependencies, suitable for use in AWS Lambda
    • Strong data wrangling and preprocessing capabilities for transformation before importing to a data warehouse or OLAP system
    • Enables integration with existing data ecosystems and tools
  3. Single File Analytical DB & Local Development and Testing
    • Save compute and infrastructure costs with optimized performance and enable local development and testing
    • Free and open source, reducing licensing costs for enterprises
    • Run tests with dbt or SQLGlot models locally on DuckDB for testing before running in production with cloud data warehouses
  4. Fast Universal Data Processor / Zero-Copy SQL Connector
    • Acts as an SQL data virtualization wrapper on top of Parquet, CSV, and JSON files in S3 or Postgres DB using a zero-copy mechanism
    • Used for lazy and efficient aggregation, data exploration, and wrangling in memory with common formats (CSV/Parquet/JSON)
  5. Secure and Compliant Data Processing
    • embedded operation keeps data within the process, enhancing security
    • supports transactional guarantees (ACID) properties for data integrity

Why duckdb in an enterprise?

Why should a big enterprise such as JFC use duckdb? As JFC is still decentralized in the context of data with a small Excel here and there, units from every country, region, and department can leverage duckdb to test and fix data.

The computational cost of such tests is usually expensive as similar queries are run repeatedly. A single-compute lakehouse such as DuckDB can save us a lot of time and cloud costs. Running these tests simply on a cheap machine can also save a lot of money.

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