What is Real-Time Data Streaming and Analytics

Real-time data streaming and analytics is the process that is used for analyzing the huge amount of data at the moment it is used or produced. In this, we extract valuable information for the organization as soon as it’s created or stored.

In other words, we can say that Real-time data streaming and analytics is a process that mainly focuses on the data produced or consumed or stored within a live environment. Let’s take an example of analyzing the huge amount of data as it is produced within banks and branches, stock exchanges throughout the globe. The scope of analytics can be from multiple sources. We can import or fetch the data and store it within a system and can execute data analysis algorithms over it. And further, these analytics data is delivered to the users/administrator through an analytics dashboard. Real-time analytics can be used for below-listed purposes:-

• To report historical data and current data concurrently.
• For receiving alerts on the basis of certain and predefined parameters.
• To build operational decisions and apply them on business processes or on other production activities based on real-time and on an ongoing basis.
• To apply pre-existing prescriptive models or predictive models.
• For outlook of real-time displays or dashboards in real time on constantly changing datasets.

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Benefits of Real-Time Streaming and Analytics

Data Visualization:- Set of historical datasets can be placed into a single screen in order to represent an overall point but on the other hand, streaming data can be visualized in such a way that it updates in real time in order to display what is occurring in each and every single second.

Business Insights:- When it’s about business, real-time analytics can be used for receiving alerts on the basis of certain and predefined parameters. For example, if any store there is a drop in sales, then an alert can be triggered to tell management about the serious problem.

Increase competitiveness:- Real-time analytics helps the companies to surpass competitors who are still based on batch processing analysis.

Security:- Take an example of fraud detection, fraud can be detected immediately whenever it happens and a proper safety precaution can be taken in order to limit the damage.

Limitations of Real-Time Streaming and Analytics

Compatibility:- In the case of historical big data analytics, Hadoop is the most widely used tool but in case of streaming and real-time data it is not. The better options are the use of spark streaming, Apache Samza, Apache Flink, or Apache Storm.

System Failure:- In term of business, real-time analytics or handling a data at rapid rates is not an easy job. It could lead to faulty analysis or even sometimes system failure.

What is Real-Time Streaming

Real-time streaming is defined as it is the process by which huge size/volumes of data are processed quickly such that a firm extracting the information from that particular data can react to changing conditions in real time.

In other words, we can say that real-time streaming is based on the queries that work on time and buffer windows. When we compare this real-time streaming process with traditional database model, then we found that there is a lot of differences between these two processes. Both processes are opposite to each other. Real-time streaming makes use of data while in motion through the server but on the other hand in traditional database model data was first stored and indexed and was then processed.

Real-time analytics are used in many applications. Below listed are one of the major applications in which real-time streaming are used:-

• E-Commerce
• Pricing and analytics
• Network Monitoring
• Risk Management
• Fraud Detection

#insights #data science