Page 3 - Apache Pulsar News Today

By Ed Anuff, Chief Product Officer, DataStax - Enterprises across industries have been obsessed with real-time analytics for some time. The technology that powers this toolset that aims to make critical business decisions quickly is expected to amount to a $50.1 billion market by 2026.

DataStax raises $115M to grow its super-scalable, real-time Astra DB database - SiliconANGLE

By George Trujillo, Principal Data Strategist, DataStax - Any enterprise data management strategy has to begin with addressing the 800-pound gorilla in the corner: the “innovation gap” that exists between IT and business teams. It’s a common occurrence in all types of enterprises, and it’s difficult to wrestle to the ground. IT teams grapple with an ever-increasing volume, velocity, and variety of data, which pours in from sources like apps and IoT devices. At the same ti...

After two and a half years in the role, the CEO of DataStax is laser-focused on simplifying the task of using and managing NoSQL databases for developers, as he plots a path to $1 billion in annual revenue and an eventual IPO. Founded in Texas in 2010, DataStax made its name helping enterprise customers get

DataStax’s multi-cloud database-as-a-service Astra now has new capabilities for real-time event streaming, which aims to drive the next phase of the company’s growth.

Saving all of your data in a data warehouse and analyzing it using a nightly batch process is no longer sufficient to monitor and manage a business or process in a timely fashion. Instead, you should perform simple real-time analysis of data streams in addition to saving the data for later in-depth analysis. Apache Kafka,

Originally developed at LinkedIn, Apache Kafka is one of the most mature platforms for event streaming. Kafka is used for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.

Many of the best platforms for managing, storing, and analyzing streaming data are Apache open source projects, along with commercial and cloud implementations of those projects.

Streaming data is generated continuously, often by thousands of data sources, such as sensors or server logs. Streaming data records are often small, perhaps a few kilobytes each, but there are many of them, and in many cases the stream goes on and on without ever stopping. In this article, we will provide some background

Redpanda raises $50M to modernize stream processing - SiliconANGLE