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 ...

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 ...

“Many organizations are interested in using a single software environment for streaming and batch processing, while taking advantage of the power of the Apache Spark compute platform for analytics and ...

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 ...

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, ...

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 ...

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, ...

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 ...

Organizations building real-time stream processing systems on Apache Kafka will be able to trust the platform to deliver each messages exactly once when they adopt new Kafka technology planned to be ...