Introduction to real – time/streaming data analytics

Introduction to real-time/streaming data analytics

Talking about Big Data sometimes take us to a very steady process of data analysis, Real-Time/Streaming Data Analytics. Companies who are looking for better aspects for their business and customers’ activities such as billing/metering, server activity, website clicks and so on, are into Real-Time Streaming Data Analytics. What allows any business to react without getting delayed to any of their insights is Real-Time Analysis. But what it is and where it could be used? Let’s learn about Real-Time/Streaming Data Analytics and its interior.

What is Real-Time/Streaming Data Analytics?

Real-Time/Streaming Data Analysis is the analysis that processes as soon as the data is accessible in the system. It manipulates, normalizes, cleanses and changes the pattern of interested detections of the receiving data before it gets stored or is in motion. Any user can get data insights very easily and can draw any conclusion right after data gets into the system. This makes easier for any organisation to identify potential opportunities to target or prevent any problems that may occur. In short, “Real-time analysis is the analysis of the data as soon as it becomes available”.


As mentioned earlier, this steady growth of data sometimes makes any business to act quickly whenever needed. Here are some uses where Real-Time/Streaming Data Analytics proves its power to any organisation,
-Real-Time/Streaming Data Analytics is to query a continuous data stream and detect conditions.
-It creates the value of insights which has derived from the processing data.
-Real-Time Data Analytics detects pattern, results and looks over the data from multiple streams simultaneously.
-It is used for the huge data that cannot be stored as it processes simultaneously.
-As the data is increasing, there is the availability of streaming data.

Usages of Real-time Analytics

Many businesses are using Streaming Analytics. But there are some particular parts where it’s point-blank actions are needed. Below mentioned are some of the usages of Real-Time/Streaming Data Analytics,
-CRM and CXM use it to update customers’ real-time information.
-It tracks the number of clicks on any website.
-Streaming Analytics is used in Continuous Monitoring such as Healthcare, Sensors, etc.
-Records users’ experience (VR, Drones, Robotics).
-It is used in churn detection, recommendation engines and gaming data feeds.
-Real-Time/Streaming Data Analytics is used in financial trading.
-It is also used in booking applications such as Hotels, Rideshares and many more.


Identifying potential opportunities to target or preventing problems that may occur on time, Streaming Analytics comes along with many advantages.
-It helps in providing insights faster.
-There is no requirement for storing large data.
-It establishes insights from processing data.
-Latency is eliminated.
-It makes error detection easy whenever required.
-It helps in boosting the productivity of any business.

Challenges in the data processing

During analysis, events are processed and analyzed in real-time as they trigger. Many times, decisions are time-taking as there is high availability of data and lesser time to act on it. Sometimes, it crosses in Terabytes. As latency is eliminated, the time of action has to get fasten according to requirements.


Handling huge data is a challenge like an ant has to handle the weight of an elephant. There are many tools which help in managing massive data such as Kafka, Flink, Spark and many more. These tools help in the live streaming of data and make the process faster. In the next section, we will talk about the details of a component of Real-Time/Streaming Data Analytics- Kafka.


Real-Time/Streaming Data Analytics permits end-users as the data is increasing with time. It is designed in such a way that it leverages the full power of standard processors and memories. It is more affordable than the analysis that includes storing process of data. Real-Time/Streaming Data Analytics is the most effective analytical method.

About the Author

Ravi Kumar Rachuri is a seasonal analytical professional with 7+ years of experience in analytical problem solving and building data engineering solutions. He loves to play cricket and keep himself up to date with the growing trends in the data world.

In the next section, we will talk about details of the component of Real-Time/Streaming Data Analytics- Kafka.

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