Beaker Notebook vs. MicroPython vs. Spark Streaming vs. VBScript Comparison Chart

BeakerX is a collection of kernels and extensions to the Jupyter interactive computing environment. It provides JVM support, Spark cluster support, polyglot programming, interactive plots, tables, forms, publishing, and more. All of BeakerX’s JVM languages plus Python and JavaScript have APIs for interactive time-series, scatter plots, histograms, heatmaps, and treemaps. The widgets remain interactive in both notebooks saved to disk, and notebooks published to the web. They include unique features for handling many points, nanosecond resolution, zooming, and exporting. BeakerX’s table widget automatically recognizes pandas data frames and allows you to search, sort, drag, filter, format, select, graph, hide, pin, and export to CSV or clipboard. This makes connecting to spreadsheets quickly and easy. BeakerX has a Spark magic with GUIs for configuration, status, progress, and interrupt of Spark jobs. You can either use the GUI or create your own SparkSession with code.

Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part. By running on Spark, Spark Streaming lets you reuse the same code for batch processing, join streams against historical data, or run ad-hoc queries on stream state. Build powerful interactive applications, not just analytics. Spark Streaming is developed as part of Apache Spark. It thus gets tested and updated with each Spark release. You can run Spark Streaming on Spark's standalone cluster mode or other supported cluster resource managers. It also includes a local run mode for development. In production, Spark Streaming uses ZooKeeper and HDFS for high availability.