New LunaML from DataStax supports Kaskada deployments

Published on May 17, 2023

DataStax is now offering a new support service for Kaskada, an open-source unified events processing engine for machine learning applications, which it acquired in January. Dubbed LunaML, the service will provide customers with mission-critical support and options for incident response times as short as 15 minutes.

Enterprises can escalate issues to Kaskada engineers for further review and troubleshooting. LunaML Premium and LunaML Standard offer ticket raising options that promise a one-hour and four-hour response time, respectively. DataStax believes that using Kaskada’s capabilities, machine learning applications can be scaled and cost-effective.

DataStax acquired Kaskada in January, an open-source-based unified events processing engine that enables enterprises to build real-time machine learning applications.

DBaaS provider DataStax has announced a new support service for Kaskada, an open-source unified events processing engine for machine learning applications.

The new service, LunaML, will provide customers with “mission-critical support and options for incident response times as short as 15 minutes,” according to the company. In addition, enterprises can escalate issues to Kaskada engineers for further review and troubleshooting.

According to a blog post published on Thursday, LunaML Premium and LunaML Standard provide ticket raising options that promise a four-hour and one-hour response time, respectively.

Enterprises can raise 18 tickets under the standard plan and 52 tickets under the Premium plan. Pricing was not immediately available.

Purchasing Kaskada in January, DataStax aims to integrate Kaskada’s capabilities into its offerings, such as its serverless, NoSQL database-as-a-service AstraDB.

Based on expected demand for machine learning applications, DataStax acquired Kaskada.

Using Kaskada’s capabilities, the company believes that machine learning applications can be scaled and cost effective. Using Kaskada’s time-based capabilities, you can create and update features for machine learning models based on sequences of events, or over time, as the technology is designed to handle large amounts of event data.