Hadoop and big data platforms were originally known for scale, not speed. But the arrival of high performance compute engines like Spark and streaming engines have cleared the way for bringing batch ...

When the big data movement started it was mostly focused on batch processing. Distributed data storage and querying tools like MapReduce, Hive, and Pig were all designed to process data in batches ...

The Big Data streaming project Apache Kafka is all over the news lately, highlighted by Confluent Inc.'s new update of its Kafka-based Confluent Platform 2.0. On the same day as the MapR announcement, ...

As more and more data comes into the enterprise, companies are looking to build real-time big data architectures to keep up with an increased amount of information. In order to do this efficiently, ...

Analytics is often described as one of the biggest challenges associated with big data, but even before that step can happen, data has to be ingested and made available to enterprise users. That’s ...

eSpeaks host Corey Noles sits down with Qualcomm's Craig Tellalian to explore a workplace computing transformation: the rise of AI-ready PCs. Matt Hillary, VP of Security and CISO at Drata, details ...

It’s become almost a standard career path in Silicon Valley: A talented engineer creates a valuable open source software commodity inside of a larger organization, then leaves that company to create a ...

After Apache Hadoop got the whole Big Data thing started, Apache Spark emerged as the new darling of the ecosystem, becoming one of the most active open source projects in the world by improving upon ...

I spoke briefly about the evolution of modern data apps in a previous Forbes piece on the use of big data in the cloud. Essentially, the history of big data can be divided into thread broad ...

In the first half of this JavaWorld introduction to Apache Kafka, you developed a couple of small-scale producer/consumer applications using Kafka. From these exercises you should be familiar with the ...