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 ...

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, ...

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 ...

For data engineers looking to leverage Apache Spark™'s immense growth to build faster and more reliable data pipelines, Databricks is happy to provide The Data Engineer's Guide to Apache Spark. This ...

Apache Spark rose to prominence within the Hadoop world as a faster and easier to use alternative to MapReduce. But as fast as Spark is today, it won’t hold a candle to future versions of Spark that ...

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At a Spark Summit 2017 conference this week, Databricks announced that it will be making an instance of the Apache Spark in-memory computing framework available as a managed cloud service running on ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. This article dives into the happens-before ...

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 ...

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 ...