Apache Spark is a project designed to accelerate Hadoop and other big data applications through the use of an in-memory, clustered data engine. The Apache Foundation describes the Spark project this ...

Apache Spark has become the de facto standard for processing data at scale, whether for querying large datasets, training machine learning models to predict future trends, or processing streaming data ...

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 ...

The immensely popular open-source cluster computing framework Apache Spark has just reached version 2.0, according to an announcement by the Apache Software Foundation (ASF) yesterday. Spark’s ...

The cloud-hosted environment, described by Databricks as being deployed by more than 150 firms, aims to simplify the use of the open-source cluster compute engine and cut the time spent developing, ...

Databricks, the company founded by the creators of Apache Spark—the powerful open-source processing engine that provides blazingly fast and sophisticated analytics—announced today the launch of ...

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 ...

Databricks, provider of the Unified Analytics Platform and founded by the team who created Apache Spark, is releasing Apache Spark 2.3.0 on Databricks’ Unified Analytics Platform. Databricks is the ...

Spark Declarative Pipelines provides an easier way to define and execute data pipelines for both batch and streaming ETL workloads across any Apache Spark-supported data source, including cloud ...