Eventual: The Data Warehouse for ML/AI

The all-in-one platform for modern data workloads across data engineering, machine learning and analytics.

Modern data workloads incorporate diverse modalities stored in various formats and systems. Eventual simplifies this with a Python-native platform that integrates seamlessly with existing tools, supports scalable data processing, and interoperates with diverse data types.

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Amazon uses Daft to manage exabytes of Apache Parquet in our Amazon S3-based data catalog. Daft improved the efficiency of one of our most critical data processing jobs by over 24%, saving over 40,000 years of Amazon EC2 vCPU computing time annually.

Patrick Ames

Principal Engineer @ Amazon

Daft has dramatically improved our 100TB+ text data pipelines, speeding up workloads such as fuzzy deduplication by 10x. Jobs previously built using custom code on Ray/Polars has been replaced by simple Daft queries, running on internet-scale unstructured datasets.

Maurice Weber

PhD AI Researcher @ Together AI

Daft was incredible at large volumes of abnormally shaped workloads - I pointed it at 16,000 small Parquet files in a self-hosted S3 service and it just worked! It's the data engine built for the cloud and AI workloads.

Tony Wang

Data @ Anthropic, PhD @ Stanford

Daft as an alternative to Spark has changed the way we think about data on our ML Platform. Its tight integrations with Ray lets us maintain a unified set of infrastructure while improving both query performance and developer productivity. Less is more.

Alexander Filipchik

Head Of Infrastructure at City Storage Systems (CloudKitchens)

OUR TEAM

Eventual is built by experts in distributed data systems, machine learning, and high performance computing.