Real-time Big Data Processing: How & Where?

By processing the data in motion, real-time Big Data Processing enables you to walk in parallel with the current landscape of your Business and turn data intelligence into vital business decisions. Whether it is positive, negative or neutral, a clear picture can be visualized about the current status of the projects.

The demand of Real Time Streaming Platforms is high these days. No doubt that the data being generated today every second is colossal, but what use is the data of if it is not processed to draw conclusions, trends, patterns, and outliers? Not merely the processing, but a quick processing should be done quickly so that a firm can react to the changing patterns in the business in real-time.

Some data streaming platforms

Apache Storm

Apache Storm is a real time computation system which reliably processes unbounded streams of data, just like what Hadoop does in batch processing. It’s simple and can be used with any programming language. It is fast, fault tolerant, scalable, easy to setup, easy to operate and is used for real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more.

Apache Kafka

Apache Kafka is an open-source stream processing platform which is written in Scala and Java. It has features like scalability, data partitioning, low latency, and the ability to handle a large number of diverse consumers. The users of Kafka process data in processing pipelines which consist of multiple stages. There the raw input data is consumed from Kafka topics and is aggregated, enriched, and transformed into new topics for further consumption or follow-up processing.

IBM Infosphere Streams

IBM Infosphere Streams is a computing platform which allows the users to quickly and continuously ingest, analyze and correlate massive volumes of data as it arrives from the real-time sources. It adapts to the rapidly changing data forms and types. It also easily visualizes data with its out-of-the-box ability to route all streaming records into a single operator and display them in a variety of ways on an HTML dashboard.

Areas Using Real-time Big Data Processing

Fraud Detection

Detecting fraud in real time provides a safe environment to run the business. The online retailers process the sales transactions against defined fraud patterns for detection. It becomes too late to catch the fraudsters if it is not done in real time. For example, IBM Infosphere Streams, a data streaming platform, analyzes telephone call records to perform fraud detection in real-time.

E-commerce

Real-time big data processing in commerce can help optimize customer service processes, update inventory, reduce churn rate, detect customer purchasing patterns and provide greater customer satisfaction.

The e-commerce companies use big data to find the warehouse nearest to you so that the delivery charges cut down.

On Amazon, the price typically changes after every 10 minutes as the big data is updated and analyzed. By optimizing the price, Amazon attracts more customers and increase profits by 25% annually.

Social Networks

For Social Network Sites the flow of continuous data makes real-time big data processing a necessity.

To address Twitter's real-time processing needs, Heron, a “real time, distributed, and fault-tolerant-stream processing engine,” is in use since 2014. It helps in quickly taking actions against fraudulent twitter accounts, improves the speed of real-time trending, classifies the data as it is created by the users, does a fast analysis of machine (server) functionality which helps in predicting the probability of possible failures within the network and memory capacity.

Healthcare

A real-time monitoring of patients provides an insight to the situation of the patient continuously. The care provider or physician is instantly informed when the condition of the patient changes. This helps in making life-saving decisions through wearable sensors and devices.  

In Healthcare, 75% of patient’s data is unstructured. A real-time big data analysis helps in getting a comprehensive view of the patient. Not only this, it also supplements by eliminating unnecessary and expensive tests, and help in prescribing relevant drugs.

Conclusion

Jay Kreps, CEO of Confluent opines that very less data is batch in nature and that "Data in real life is not produced when the sun goes up or down -- when your business is digital data keeps coming all the time". Thus, it can be deduced that real-time Big Data Processing is of a great importance as it helps in identifying significant events, provides valuable insights and allows you to respond in right time.

We at NewGenApps have an expertise in providing Big Data Solutions. Get in touch to know more about real-time Big Data Processing and how it can benefit your business.