MotherDuck | LinkedIn (original) (raw)
Data Infrastructure and Analytics
Making analytics fun, frictionless and ducking awesome with an unorthodox cloud data warehouse for the post-Big Data era
About us
Making analytics fun, frictionless and ducking awesome with a cloud data warehouse based on DuckDB's efficiency, ergonomics and performance in collaboration with the folks at DuckDB Labs.
Industry
Data Infrastructure and Analytics
Company size
11-50 employees
Headquarters
Seattle
Type
Privately Held
Founded
2022
Locations
Employees at MotherDuck
Updates
- Small Data is bigger (and hotter 🔥 ) than ever. Catch up on the action with our recap -
- Is BI 'Too Big' for Small Data? 🤔
- 🦆 Iceberg Analytics Night at the Duck Pond! Join us Nov 21st at 5:30pm. Join MotherDuck, RisingWave, and Confluent for an evening of Apache Iceberg and streaming analytics insights at our Seattle HQ! 💡 Tech talks from: -Kafka Meets Iceberg: Kasun Indrasiri-Ingesting and Querying Iceberg with the Postgres Protocol: Yingjun Wu-Using SQL in Your Data Lake: Jacob Matson🎟 Sign up today: https://lu.ma/1xokjeia
- MotherDuck reposted this
Data eng | Making data & tech topics fun and easy to swallow | Youtuber 🎥
4d
Can DuckDB be the ultimate portable catalog for Data Lakes and Lakehouses? DuckDB is great at integrations, whether with file formats like Parquet, JSON, and CSVs, table formats such as Iceberg and Delta Lake, or even databases like Postgres and MySQL. This makes it a strong candidate. To explore this idea further, I wrote a blog post showcasing a pragmatic use case🧑💻 I created a 268KB DuckDB file that references data stored across AWS S3, Google Cloud Storage, and a Postgres table hosted on Neon. The next step is to sync it to MotherDuck for seamless sharing. While there are trade-offs and some current limitations, it sounds very promising. Read more : https://lnkd.in/ep8Pswuj #dataengineering #duckdb -
18,265 followers
5d Edited
Who's running DuckDB in production, and how? Check out the extensive guide (longest blog ever published on MotherDuck!) from Simon Späti! In this second part of his series, he highlights practical use cases of DuckDB in production across the various categories he defined in part one.https://lnkd.in/eDtfxMKJ - MotherDuck reposted this
Data Engineer, Author & Educator | ssp.sh, dedp.online
6d
I often saw the question of whether DuckDB runs in production and what the use case would be. With the help of many of you, I explored (https://lnkd.in/ed9-zTys) this over the last few weeks and prepared an extensive guide highlighting real-world DuckDB use cases, categorized into the five categories identified in Part 1. From Fortune 500 companies processing trillions of security records to innovative startups building interactive data tools, DuckDB is changing how organizations handle analytical workloads. This guide showcases 15+ use cases with companies such as: > Atlan - Replaced Spark with DuckDB > Count - Local database analytics > Evidence - Universal SQL engine > Figma - WebAssembly implementation > FinQore - Financial data pipeline > GoodData - Analytics platform > Government of South Australia - Climate change dashboard > Hex - Notebook-based analytics > Hugging Face - Dataset access > Mode - Visual data exploration > Mosaic - Browser analytics tool > NSW Department of Education - Data portal > Observable - Data visualization platform > Okta - Security data processing > Rill - Visual analytics platform > Spare Cores - Cloud infrastructure price comparison service > Watershed - Carbon footprint analytics Organizations' benefits from DuckDB were exceptional performance gains, ranging from Watershed's 10x #speedup in carbon analytics to FinQore's lowering pipeline processing time from 8 hours to 8 minutes. Hex saw 5-10x faster notebook execution times, while Okta processed a staggering 7.5 trillion security records across 130 million files using thousands of concurrent DuckDB instances. Beyond raw performance, companies leveraged DuckDB's #versatility through browser-based analytics with WebAssembly (Figma, Evidence), zero-copy data sharing that eliminated transfer bottlenecks (Hugging Face's 150k+ datasets), and embedded analytics that brought compute directly to applications (Mode, Rill). These gains came with #reduced operational complexity and costs. Okta cut its data processing expenses while maintaining enterprise-grade security and performance. --- All the use cases are explored through 1️⃣ Zero-Copy SQL 2️⃣ Lightweight Analytics 3️⃣ Pipeline Component 4️⃣ Interactive Apps 5️⃣ Secure Enterprise. Find all links to videos, case studies, blogs, and source code in the relevant chapters. Feel free to use the quick navigation to jump directly to your most exciting example. I hope you enjoy it. Thanks to all of you who suggested their use case and commented on how you use it at your workplace. - 🦆 Seattle Data Friends! Waddle over to our Iceberg Streaming Analytics Meetup this November 21! Join us at MotherDuck HQ with our friends from RisingWave and Confluent for an evening of streaming analytics, Apache Iceberg, and modern data architectures. 💡 What's Quacking: 5:30 PM: Doors open! Come for the food, stay for the data chat 6:00 PM: Kafka Meets Iceberg: Real-time streaming into modern data lakes by Kasun Indrasiri ⚡ 6:30 PM: Ingesting and Querying Iceberg with the Postgres Protocol by Yingjun Wu 🌊 7:00 PM: When All You Have is a Hammer: Using SQL in Your Data Lake by Jacob Matson 🔨 7:30 PM: Hang out and make new data friends! 📍 Location: MotherDuck's Seattle Office 📅 When: November 21st 🎟️ Save your spot! 👉 https://lu.ma/1xokjeia
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