Redis Labs is introducing two new data models and a new data programmability paradigm for multi-model operations.
RedisTimeSeries collects and stores high volume and high velocity data, and organizes that data by time intervals. The solution also allows organizations to point out specific data points using capabilities such as downsampling, aggregation, and compression. According to Redis Labs, this model will allow organizations to query and extract data in real-time, enabling rapid analytics.
RedisAI allows users to run AI models within Redis, eliminating the need to migrate data to or from various environments. Redis Labs believes that this will significantly decrease the time spent conducting analytics. RedisAI also integrates with common deep learning frameworks, such as TensorFlow, PyTorch, and TorchScript. In addition, by using Redis Clusters instead of GPU-based servers, RedisAI reduces processing overhead.
Finally a new in-database serverless engine, RedisGears, allows for nearly infinite programmability options to support event-driven or transaction-based operations.
“Redis is the first, instant multi-model database with infinite in-database programmability options,” said Yiftach Shoolman, co-founder and CTO at Redis Labs. “With these new advancements, Redis users can process requests across multiple data models asynchronously or synchronously, with sub-millisecond latency at any scale.”