Businesses are becoming increasingly digital. Real-time big data analytics can handle different kind of data's.
Forecasting on real-time data sets and monitoring streaming data from (Internet of things) based devices are among the most exciting applications today. ML algorithms process real-time data streams from devices and apps. Our open source platform sets the stage for you to build an AI system that will help gain deep insights out of high-velocity and high-volume data without compromising on security, scalability and flexibility.
Apache Spark Streaming is the tool in which we specify the time-based window to stream data from our message queue. So it does not process every message individually. We can call it as the processing of real streams in micro batches. Whereas Apache Storm and Apache Flink can stream data in real-time.
A lot of Manufactures are trying to collect as much data from machines as they can regarding their products, services or even their organizational activities like tracking employees activities through various methods used like log tracking of machines and devices, taking survey at regular intervals. So our system allows us to convert this data into basic formats and Data Analysts then turn this data into useful results which can help the organization to improve their customer experiences and also boost their employee’s productivity. But when we talk about log analytics, fraud detection or real-time analytics, this is not the way we want our data to be processed. The actual value data is in processing or acting upon it at the instant it receives.
Improving asset on maintaining production equipment in peak condition, keeping expensive downtime to a minimum and maximising its working life. The combination of predictive analytics, Not only can preventive action be taken to avoid downtime, but maintenance operations themselves can be based on predicted conditions rather than a regular schedule. Both should generate substantial savings for manufacturers as well as improve asset productivity.