Luna ML Standard
Recommended for pre-production use cases
- 18 tickets per year
- 12x5 support coverage
- 4 hour response time
Luna ML
Kaskada Open Source for ML applications with Expert Support
Experience the power of real-time ML on event data from Kaskada with peace of mind.
Benefits
Build real-time, AI-powered apps
Rich temporal event processing
Easy to use, high-performance columnar computations over event-based data. Computations like windowed aggregations, over time, for present & historical values.
Unified batch & real-time event-processing
Operate directly on events in real-time & observe computations on demand or materialized to a table. Execute batch analytic queries over large historical events in seconds.
Powerful, declarative & composable queries
Easy-to-read, type-safe, declarative queries to compute stateful aggregations, automatic joins, temporal joins, event-based windowing and pipelined operations
Kaskada query engine
Kaskada is a modern, open source compute engine written in Rust and built on Apache Arrow.
Machine learning with Kaskada
Compute event-based features in historical feature computation. Prevent data leakage, or accidental computation of future events that contaminate ML models.
Develop real-time AI use-cases with peace of mind
Support engagement from DataStax to help your team develop real-time AI use cases that leverage Kaskada open source.
Packages
Luna ML Premium
Recommended for production use cases
- 52 tickets per year
- 24x7 support coverage
- 1 hours response time
FAQ
What is Kaskada?
Kaskada is a unified event processing engine that provides all the power of stateful stream processing in a high-level, declarative query language designed specifically for reasoning about events in bulk and in real time.
What is Luna ML?
Adding an open source component to your ML platform that supports experimentation and production environments can be complex. With Luna ML, customers can deploy Kaskada open source with the benefit of support from the Kaskada experts at DataStax.
Which Kaskada components are supported via Luna ML?
All unmodified Kaskada open source components are supported via Luna ML.
Do I need Luna ML to use Kaskada open source?
No. You don’t need Luna ML to use Kaskada open source; however, Luna ML offers support for your Kaskada deployment from the Kaskada experts at DataStax.
What is the benefit of Luna ML when using Kaskada open source?
Luna ML offers the peace of mind needed to manage your open source Kaskada deployment in production.
What are the advantages of Kaskada for real-time AI applications?
Kaskada’s declarative query language bridges the gap between batch and real time. Match model context in training and production for better performing models—without costly code rewrites.
How do I use Luna ML with Apache Cassandra and Apache Pulsar?
Depending on your requirements, you can use Cassandra and/or Pulsar as sources and sinks with Kaskada. Luna ML can help you deploy Kaskada with Cassandra and Pulsar.
Apache Cassandra can be an event store (source), feature store (sink) and prediction store (sink). Cassandra offers balanced write and query scalability, low latency, and reliability while supporting large data volumes required by ML at scale.
Pulsar can stream new data into Kaskada as a source and Kaskada can write query results by creating a materialization and writing to Pulsar as a sink in real-time.