As the Apache Spark userbase grows, the developer community is working to adapt it for ever-wider...
As the Apache Spark userbase grows, the developer community is working to adapt it for ever-wider use cases. 2014 saw fast adoption of Spark in the enterprise and major improvements in its performance, scalability and standard libraries. In 2015, we also want to make Spark accessible to a wider set of users, through new high-level APIs targeted at data science: machine learning pipelines, data frames, and R language bindings. In addition, we are defining extension points to let Spark grow as a platform, making it easy to plug in data sources, algorithms, and third-party packages. Like all work on Spark, these APIs are designed to plug seamlessly into existing Spark applications, giving users a unified platform for streaming, batch and interactive data processing.