DataStax Astra develops upon Cassandra to become serverless

DataStax is known for Enterprise Apache Cassandra solutions makes serverless the new norm.

This giant step by DataStax is in line with the development of its managed Astra cloud service. The tech company believes that with the use of Astra serverless, DataStax customers can achieve a 3 to 5 times better total cost of ownership on a "typical user application" than by provisioning for maximum use.

DataStax Astra, which is built on Kubernetes, makes Cassandra's deployment and application creation a convenience. Astra has simplified the process of getting started with Cassandra, the established NoSQL, open-source database, by providing self-service options, numerous learning tools, and developer-friendly APIs. Sam Sgro, Chief Architect at Lending Club said, “to keep millions of data points accurate and relevant for our decision-making systems, we use Cassandra and AWS. We're really excited about DataStax Astra, which provides a NoOps experience on Cassandra and enables us to respond to demand with ease.”

DataStax Astra features a modern, microservices-based architecture that separates computing and storage resources, allowing database resources to scale up and down on-demand to meet application requirements and traffic, regardless of computing resources. This technological advancement has the potential to change the game for developers and IT by reducing the costs, complexity, and developer velocity associated with running a modern data architecture.

Ed Anuff, chief product officer at DataStax says,

Most companies size their infrastructure for peak workload use to provide the best experience for end-users.

In continuation of his statement, he says,

We're adjusting the math with Astra's latest serverless architecture, which matches data consumption to workload peaks and valleys. Users no longer have to be concerned about over-provisioning for storage or workload tuning – they can feel confident that their workloads, no matter how volatile, will be managed in the most cost-effective manner possible.

Though serverless computing has been around for a while, serverless data has been difficult to keep up due to technical difficulties in separating compute and storage. To meet more demand or store more data, scaling a database usually necessitates the addition of more server nodes, which necessitates the “rebalancing” of the entire data set across the nodes to maintain the ratio of storage and processing capability equal.