Chronon is a data platform designed to simplify and standardize data computation and serving for AI/ML applications. It enables users to define features as data transformations, supporting both batch and streaming computations, scalable backfills, low-latency serving, and robust observability. This platform is targeted at ML practitioners and engineers who need to leverage diverse data sources for model training and real-time inference without managing complex data infrastructure.

How It Works

Chronon utilizes a declarative API for defining features through GroupBy (aggregations over data sources) and Join (combining features for specific keys and timestamps) constructs. It translates these definitions into Spark jobs for scalable batch computation and backfills, ensuring point-in-time accuracy. For online serving, Chronon supports uploading computed features to a key-value store (like MongoDB) and provides APIs for low-latency fetching, enabling consistent feature retrieval for real-time model inference.

Quick Start & Requirements

• Install/Run: Download docker-compose.yml and run docker-compose up.
• Prerequisites: Docker.
• Setup: Minimal setup via Docker.
• Links: chronon.ai, Introductory Documentation, Benefits of Chronon.

Highlighted Details

• Supports complex transformations and windowed aggregations.
• Guarantees and measures online/offline consistency.
• Handles scalable, resilient, and point-in-time accurate backfills.
• Offers managed pipelines for batch and real-time feature computation.

Maintenance & Community

• Community support via Slack workspace.
• Contributions welcomed via GitHub issues.

Licensing & Compatibility

• License: Apache 2.0.
• Compatibility: Suitable for commercial use and integration with closed-source applications.

Limitations & Caveats

The provided quickstart does not cover running streaming jobs. The Java client example for online fetching is illustrative and not runnable within the Docker environment.