Building generational technology that makes data processing across all modalities simple, reliable and performant regardless of scale.

We heard that the Open Lakehouse event is coming to the Bay Area soon, subscribe to the Luma page to stay updated!

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What an honor to speak at the first-ever #OpenLakehouse + AI Meetup in Amsterdam! We loved seeing how vibrant the open source and data engineering community in Europe is and connecting over the latest advancements in open lakehouse and AI architectures. Thank you to

Last call! We're hosting the Image Intelligence Hack Night TONIGHT with @daytonaio, kicking off at 4:30pm. Build the best image intelligence app for a chance to win $200 Amazon gift card and $2000 platform credits. See you tonight!

RT @daytonaio: We’re partnering with @daftengine for Image Intelligence Hack Night this Thursday, Aug 28 @ 4:30pm at Daft HQ 🚀. Bring your….

RT @Sammy_Sidhu: It was a blast giving a talk on multimodal Data Lakehouses and @daftengine at @databricks Amsterdam! . Thanks for hosting….

@huggingface 5/ What's Next?.• Reading private datasets.• Native support for Hugging Face image, audio, and video data types.• Specifying the dataset split or revision to read from. Check it out in our documentation:

@huggingface 4/ And finally, Daft can write Parquet files to Hugging Face datasets. Uploading data from Daft to Hugging Face is as easy as calling DataFrame.write_huggingface! Daft automatically takes advantage of Parquet Content-Defined Chunking ( and Xet.

@huggingface 3/ Not only can you read entire datasets with Daft, you can also read individual files from a dataset. Use any of Daft's read functions that take in a path and specify a Hugging Face dataset path via the hf://datasets/ prefix. df =.

@huggingface 2/ Daft can read entire datasets directly from Hugging Face, simply pass in the Hugging Face repo name and Daft will handle the rest - no matter the file types of size of the data. df = "username/dataset_name").

1/ It's now easier than ever to use @huggingface 🤗 Datasets at scale! Introducing Daft's native support for reading from and writing to Hugging Face.

Watch the full video here:

Batch inference doesn't have to be complex. With Daft, it takes 3 lines of code. Transform thousands of prompts into predictions. Scale from laptop to cluster without changing a single line. This is how modern data teams ship ML to production.

@databricks @awscloud @puppyquery @dremio @ClickHouseDB @wherobots @streamnativeio @Minio @redpandadata @CelerData @confluentinc @Microsoft @bufbuild @RisingWaveLabs Want to be a part of this grand event? We’re looking for talks! If you’ve been working with Iceberg — whether at scale, in production, or through creative integrations — we’d love for you to share your story 👉

Our biggest meetup yet, featuring cohosts from @databricks, @awscloud, @puppyquery, @dremio, @ClickHouseDB, @wherobots, @streamnativeio, @minio, @redpandadata, @Celerdata, @confluentinc, @Microsoft, @bufbuild, and @RisingWaveLabs!.

Get ready folks for THE Iceberg meetup of the year: October 1st in San Francisco at the Contemporary Jewish Museum.

Leverage the power of data and AI to build the best image intelligence app you can in one night. Register now: Date & time: Thursday Aug 28 @ 4:30pm.Location: Daft HQ in Mission District. Can’t wait to see what you all will build!.

Get your hands dirty with Daft & Daytona at our upcoming Image Intelligence Hack Night next Thursday Aug 28! A chance to win a $200 gift card and 2000 platform credits that you don’t want to miss out on.

RT @lhoestq: Wow the team at @daftengine cooked!. You can now read/write to 🤗Hugging Face with Daft!. > DataFrame engine in 🦀 runs distribu….

RT @essential_ai: Really proud of Essential's collaboration with @daftengine. It simplified web-scale data processing.

RT @ashVaswani: Proud of @RitvikKapila and team’s collaboration with DAFT. It really simplified our web-scale data processing.