Unveiled last June, the Apache Spark cloud-hosted platform from Databricks has now opened its doors for business.

Databricks believes that big data is a huge opportunity that is still largely untapped and wants to make it easier to deploy and use.

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 ...

Data + AI Summit -- Databricks, the Data and AI company, today announced it is open-sourcing the company's core declarative ETL framework as Apache Spark™ Declarative Pipelines. This initiative ...

Spark SQL, part of Apache Spark big data framework, is used for structured data processing and allows running SQL like queries on Spark data. In this article, Srini Penchikala discusses Spark SQL ...

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 ...

Databricks and Hugging Face integrate Apache Spark to more seamlessly load and transform data for AI model training and fine-tuning.

Azure Databricks Designed in collaboration with the founders of Apache Spark, the preview of Azure Databricks is a fast, easy and collaborative Apache Spark-based analytics platform that delivers ...

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 ...