Started in 2009 as a research project at UC Berkeley, Apache Spark transformed how data scientists and engineers work with large data sets, empowering countless organizations to accelerate time-to-value for their analytics activities.
Apache Spark is now the most popular engine for distributed data processing at scale, with thousands of companies (including 80% of the Fortune 500) using Spark to support their big data analytics initiatives. As organizations increase investments in AI and ML technologies, we anticipate that Spark will continue to play a big role in the modern data analytics stack.
In this blog, we explore the evolution of Apache Spark, how the Spark framework is currently used on large data sets in the cloud, and our predictions for the future of Apache Spark in big data analytics.
What is the Future of Apache Spark in Big Data Analytics?
