Real Time Analytics

Real-time analytics or Real-time analysis is the analysis of data as soon as that data enters the system. In other words, users gain insights or can draw conclusions very rapidly after the data becomes available. Real-time analytics is also known as real-time data integration and real-time intelligence.

Depending on the case scenario and using a variety of tools, Cathyos provides real-time
Analytics which follows:

  • Apache Spark is an open-source cluster-computing framework. Spark supports Hadoop Distributed File System (HDFS), MapR File System (MapR-FS), Cassandra, OpenStack Swift, Amazon S3, Kudu, or a custom solution can be implemented.
  • Apache Kafka is developed in Scala and Java. It is an open-source stream-processing software platform. Apache Kafka is formed on the commit log. Apache Kafka permits users to subscribe to it. It also allows users to publish data to real-time applications or any number of systems.
  • Apache Flume is available and reliable software for systematically gathering, aggregating, and operating large amounts of log data. It is based on streaming data flows. It has a simple and flexible architecture. Apache Flume is robust. It is a distributed software and fault tolerant with tunable reliability mechanisms. It also has many failover and recovery mechanisms. It makes use of a simple extensible data model that allows for the online analytic application.


Apache Flink offers a high-throughput. It also provides low-latency streaming engine as well as support for event-time processing and state management. Programs can be written in languages like Java, Python, SQL, and Scala that are compiled automatically and are optimized into data flow programs that are implemented in a cloud environment. Apache Flink deliver sink connectors and data source to systems namely Apache Kafla, HDFS, Amazon Kinesis, Apache Cassandra, and Elasticsearch