Venice is a derived data storage platform designed for planet-scale workloads. It offers a range of features that make it a powerful solution for various applications. With Venice, you can benefit from high throughput asynchronous ingestion from batch and streaming sources, ensuring efficient data processing.

The platform also provides low-latency online reads through remote queries or in-process caching, allowing for quick access to data. With active-active replication between regions and CRDT-based conflict resolution, Venice ensures data consistency and reliability. It also supports multi-cluster environments, offering multi-tenancy, horizontal scalability, and elasticity within each cluster.

Venice is particularly well-suited for AI applications, enabling ML training jobs to feed data into the platform for use during online inference workloads. The write path in Venice allows for full dataset swaps, insertion of rows, and updates to columns, supporting a wide range of operations. Additionally, Venice offers various read APIs such as single get, batch get, and read compute, allowing for flexible data retrieval and computation.

Venice offers two client modes: Classical Venice for remote queries against the backend service, and Da Vinci for eager loading and querying local cache. Getting started with Venice is easy using the quickstart guide, where you can explore features like data store creation, batch push, incremental push, and single get. Stay updated with the latest stable release for the best experience with Venice.

With a vibrant community and various resources like Slack workspace, LinkedIn group, and GitHub issues, you can actively engage and contribute to the Venice project. Follow Venice on their official blog, LinkedIn page, Twitter handle, and YouTube channel to stay up to date with the progress of the platform