Apache Spark vs. Azure Data Explorer vs. Oracle Stream Analytics vs. dbForge Data Compare for MySQL Comparison Chart

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Oracle Stream Analytics allows users to process and analyze large scale real-time information by using sophisticated correlation patterns, enrichment, and machine learning. It offers real-time actionable business insight on streaming data and automates action to drive today’s agile businesses. Visual GEOProcessing with GEOFence relationship spatial analytics. New Expressive Patterns Library, including Spatial, Statistical, General industry and Anomaly detection, streaming machine learning. Abstracted visual façade to interrogate live real time streaming data and perform intuitive in-memory real time business analytics.