We're looking for a Developer Relations Manager at Eventual to help manage and grow the Daft community! We're looking for folks that are: 🔥 Passionate about Open Source 🤝 Enjoy engaging with users and community ✒ Are great technical writers 👯 Tag anyone ⤵ you who would think would be a good fit! https://lnkd.in/e4y8iivq
Eventual
Software Development
San Francisco, California 599 followers
The Data Warehouse for Computer Vision
About us
Eventual is building a Data Warehouse from the ground up that is designed to tackle the challenges of working with traditional data engineering and analytics alongside modern ML/AI workloads. Eventual has raised over $2.5M from investors including YCombinator, Array VC, Caffeinated Capital and top Silicon Valley executives and founders in companies such as Meta, Lyft and Databricks.
- Website
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https://www.eventualcomputing.com
External link for Eventual
- Industry
- Software Development
- Company size
- 2-10 employees
- Headquarters
- San Francisco, California
- Type
- Privately Held
Locations
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Primary
Get directions
2 Embarcadero Center
San Francisco, California 94111, US
Employees at Eventual
Updates
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And it's a wrap for the #dataaisummit! 🤝 Unity Catalog Partnership: Daft joins the Unity Catalog OSS project as an official partner (https://lnkd.in/eDNPbFB2), and support for it is already slated for the next release. 🌊 Table Format support: Daft is the first non-Spark query engine to integrate all 3 of Iceberg, DeltaLake and Hudi. 🎨 Multimodal data: strong support for multimodal data functionality around URLs, images, embeddings and more. Big shoutout to our partners at Databricks and Tabular (now part of Databricks) (Denny Lee Michelle Leon Tathagata Das Fokko Driesprong) for making all this happen. Thanks for coming out and meeting us, looking forward to a great next year of Data + AI!
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Our talk recording from Iceberg Summit 2024 is up! (https://lnkd.in/gt84a9qB) Learn about how the Python ecosystem around Iceberg is rapidly growing, and how Daft is playing a big part in it :) #apacheiceberg #datalakehouse #dataengineering
Building Iceberg native applications in simple Python (Eventual)
https://www.youtube.com/
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We just merged PR #2000 in Daft! https://lnkd.in/g6puc3dR Really excited at the velocity of the project with new contributors joining us every day :)
[FEAT] Top level global expressions by kevinzwang · Pull Request #2000 · Eventual-Inc/Daft
github.com
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Got tons of tiny files in S3? 🐹 Or maybe some giant parquet files! 🦒 Check out Kevin W. work on building a read optimizer that allows Daft to read both efficiently! https://lnkd.in/gDzkn3He
Adversarial file reading: from 10,000 small CSVs to massive Parquet files
blog.getdaft.io
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Daft now has read support for both Iceberg and Delta Lake!
Daft just launched #deltalake support! Out of the box Daft supports: 🏎️ Distributed Reads 👩🏽⚖️ Predicate Pushdowns based on Statistics ✈️ Partition Pruning Check out the PR: https://lnkd.in/gi7_hcdS #apacheiceberg #apachespark #datalakehouse
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More contributions... MORE! If you're interested in getting your hands dirty with some Rust and data code, come make a PR in Daft! Shoutout to Chand Bud for merging PR #1960! Our functionality for mathematical operations are filling up quite nicely :) https://lnkd.in/gSHgaHeG
[FEAT] adding floor function by chandbud5 · Pull Request #1960 · Eventual-Inc/Daft
github.com
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TFW Nick Salerni knocks out 3 PRs in a row on the Daft issue page 🎉 🎉 🎉 Here's one of them :) https://lnkd.in/gaPA66F4 Daft now has `.str.lower/upper/lstrip/rstrip` functionality because of Nick's work. Each PR was ~160 lines long. I wonder if we can bring that even lower to lower the barrier to entry for writing new expressions in Daft. #dataframes #python
[FEAT] Add str.lstrip() and str.rstrip() functions by nsalerni · Pull Request #1944 · Eventual-Inc/Daft
github.com
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Daft Python Dataframes ❤️ Apache Iceberg We've been working really hard on integrating with the Apache Iceberg project and are really excited to release Daft + Iceberg reads. This works seamlessly with the excellent PyIceberg Python library from Fokko Driesprong at Tabular! Using this functionality is as simple as `daft.read_iceberg(table)`: 1. Full distributed reads (spread the workload across your cluster!) 2. Integration with the Daft query optimizer for data pruning (only read the data that you need) Learn more about the integration here: https://lnkd.in/gvUK2Nbw #datalake #distributedsystems #apacheiceberg #dataengineering #datalakehouse
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