Data analytics contender Databricks offers a platform that, along with the open source Apache Spark technology on which its core is based, has long been a favorite for attacking streaming data, data ...

Databricks Inc., the primary commercial steward behind the popular open source Apache Spark data processing framework for Big Data analytics, published a new report indicating the technology is still ...

First created as part of a research project at UC Berkeley AMPLab, Spark is an open source project in the big data space, built for sophisticated analytics, speed, and ease of use. It unifies critical ...

Today to kick off Spark Summit, Databricks announced a Serverless Platform for Apache Spark — welcome news for developers looking to reduce time spent on cluster management. The move to simplify ...

Apache Spark 3.0 is now here, and it’s bringing a host of enhancements across its diverse range of capabilities. The headliner is an big bump in performance for the SQL engine and better coverage of ...

As well as access control, Databricks 2.0 now offers use of the popular R statistical programming language, support for multiple versions of Spark, and notebook versioning. Spark started in 2009 as a ...

Databricks Lakehouse Platform combines cost-effective data storage with machine learning and data analytics, and it's available on AWS, Azure, and GCP. Could it be an affordable alternative for your ...

Two years in the making, Apache Spark 2.0 will officially debut in a few weeks from Databricks Inc., which just released a technical preview so Big Data developers could get their hands on the "shiny ...

For those of you just tuning in, Spark, an open source cluster computing framework, was originally developed by Matei Zaharia at U.C. Berkeley’s AMPLab in 2009, and later open-sourced and donated to ...

The Apache Spark community last week announced Spark 3.2, a significant new release of the distributed computing framework. Among the more exciting features are deeper support for the Python data ...