Best Streaming Analytics Platforms for Deep.BI

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

Apache Flink is a distributed processing engine and framework for stateful computations using unbounded and bounded data streams. Flink can be used in all cluster environments and perform computations at any scale and in-memory speed. A stream of events can be used to produce any type of data. All data, including credit card transactions, machine logs, sensor measurements, and user interactions on a website, mobile app, are generated as streams. Apache Flink excels in processing both unbounded and bound data sets. Flink's runtime can run any type of application on unbounded stream streams thanks to its precise control of state and time. Bounded streams are internal processed by algorithms and data structure that are specifically designed to process fixed-sized data sets. This results in excellent performance. Flink can be used with all of the resource managers previously mentioned.