Mobile App Development

Real-Time Big Data Analytics: The key to exceptional business growth

Jul 26, 2022

Kamlesh Devaliya

By Kamlesh Devaliya


If innovation is an unstoppable juggernaut that rolls on, then quality data is its fuel, and data analytics is its engine.

Real-time big data analysis is a cutting-edge innovation that is revolutionizing the way IT businesses acquire actionable business knowledge, identify cyber security threats, and gauge the performance of crucial web- and cloud-deployed services. Several big data analysis techniques have been helping businesses to keep an edge.

More software applications, whether web apps or mobile apps, than ever before are being deployed to the cloud by enterprises today.

Event logs are computer-generated records of each application or server’s operations. Organizations rely on real-time big data analytics to quickly shift through the millions of new event logs created every day for pertinent trends and insights that drive quick IT and business decisions.

Aside from the fact that they generate enormous amounts of data, they also run the risk of getting into the wrong hands or experiencing a security breach. Real-time big data analytics are useful in this situation.

Real-time big data analytics: What you need to know

Big data has advanced with the development of real-time big data analytics. Real-time big data analysis converts the raw files as they are being formed, as opposed to big data, which converted the database immediately after the raw files were generated. 

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Precisely, a few milliseconds after being produced, the hazy-looking raw data transforms into useful data. There is no delay in the reply. Big data analysis tools help analyze a lot of important things that can make or break a business.


Real-time data processing, as used in computers, basically indicates that we are processing the data just milliseconds after it becomes accessible.

Real-time response is essential for monitoring your security posture, spotting risks, and launching quick quarantine responses in order to prevent cyberattacks before hackers may harm systems or steal data.

By reviewing event logs milliseconds after they are created, real-time big data analytics enables enterprises to mitigate cyberattacks as they take place. Any hybrid app development company would be able to help you make the best use of this.

Big Data

The increasing use of software programs throughout the digital age has led to the production of enormous amounts of data.

The parallel emergence of more expensive and space-efficient hardware storage devices has made it possible to store this data.

It became obvious that conventional data processing systems could no longer match the requirements of these firms as the top data collectors in the world produced data sets with numerous cases and high levels of complexity.

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Analytics software can receive data input from many sources, look for trends, evaluate those patterns, and then convey the findings in a way that is understandable to humans.

Analytics software searches through data sets that are too complex and diverse to be effectively evaluated manually for hidden links using mathematics, statistics, probability, and predictive models.

The greatest analytics solutions available today combine various software capabilities with cutting-edge technology like machine learning and pattern recognition to accomplish a specific aim.

To find Indicators of Compromise (IoCs) and respond to security issues, analytics tools are used in IT organizations to monitor event logs and correlate events from many applications.


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The advantages of real-time big data analytics

IT firms most frequently use this technology in sectors like logistics, finance, or IT that generate or collect massive amounts of data quickly.

Here are three ways that real-time big data analytics can help IT organizations. The best framework for mobile apps uses big data analysis.

Rapid monitoring and troubleshooting for IT operations

The regular operational and maintenance duties required to ensure the smooth operation of the IT infrastructure are carried out by IT operations teams.

IT Ops is in charge of maintaining a specific set of control tools (SEM, SIM, or SIEM tools, for example), monitoring the IT infrastructure, backing up databases to prevent data loss, and restoring the system in the event of an outage. 

Reviewing event logs from all over the network in real-time using big data analytics enables quick discovery and correction of problems that are affecting customers.

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Improve IT Security by Using Rapid Incident Response Tools

IT security analysts are responsible for upholding the IT organization’s security posture and defending against cyberattacks while working at the security operations center (SOC).

In the current IT security environment, analysts sift through millions of aggregated log files from across the network to look for evidence of a network attack using real-time data and analytics.

Security analysts employ analytics tools to acquire threat intelligence, automate threat detection and response, and carry out forensic analyses after a cyberattack takes place. An able android developer company will be able to help you get sorted with that.

Data Management and Collection to Support Business Decision-Making

Real-time big data analytics have an influence that goes beyond only keeping an eye on and protecting the IT infrastructure.

This technology can also be used to monitor application usage and evaluate how well cloud-deployed services are working.

By prioritizing the appropriate features and advancements at the appropriate time, organizations may use that application performance data to inform decisions about product development that promote customer engagement.

Examples of big data analysis techniques useful for businesses

Web monitoring

When data abnormalities happen, digital service providers cannot wait for a historical post-mortem.

For instance, in order to respond quickly and improve the situation, suppliers of network infrastructure, such as Content Delivery Networks (CDNs), network providers, and cybersecurity services, require immediate access to information regarding new downtime incidents. iOS app development companies can get you started.

Financial markets monitoring

Up until the development of real-time seismic monitoring systems and cybersecurity monitoring frameworks, tracking stock market values was perhaps the subject of the most in-depth research and development.

In order to keep humans informed and to support automated trading algorithms powered by stock market machine learning models, the volatility of trading markets necessitates fast warning of stock swings.

Real-time streaming financial applications present a unique challenge to data systems developers in terms of live analysis, as opposed to the straightforward reporting of changed values, because the flood of data as markets open and close around the world is not consistent over a working day in terms of volume or origin.

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How to use big data analytics in real-time

Here, we’ll examine some of the prerequisites for real-time big data streaming and contrast the advantages of open-source software to commercial hyper-scale SaaS services for in-house data streaming implementations.

Real-time stream processing

A real-time big data analytics system uses a lot of previous data. The configuration you offer to your end users and the extent to which your back-end architecture can handle high-volume data (or is capable of extracting broad trends from older data to lighten the processing load) while maintaining dashboard responsiveness will determine how far back the data goes.

The ability to “inject” current events into this batch of historical data is essential for the construction of a real-time data reporting system.

Real-time stream processing (RTSP) is what is used for this, and it comes as no surprise that the biggest tech companies are at the forefront of the technology.

Streaming options from FAANG

Dataflow is a service provided by Google Cloud, whose fundamental technologies have been driving the company’s Google Analytics architecture for 16 years.

As many as a dozen additional frameworks, filtering techniques, load-balancing systems, and other components may be used in a typical pipeline application to make sure the data is kept up to date.

Open-source stream processing

For a company’s RTSP requirements, the FAANG corporates provide functionality that is somewhat akin to on-demand, but, as with other types of cloud infrastructure, their main selling point is still their massive network and processing power.

No matter how you go through the pricing calculators of Google, Azure, and Amazon (among many others), this rentier model invariably has a price premium.

It is possible to avoid the main cloud providers and build internal real-time streaming systems utilizing open-source repositories, often the same repositories that the FAANG solutions employ if your business can predict capacity and has clearly defined growth plans.

Examples of big data analytics in usage

Here are a few instances of how big data analytics may benefit businesses:

Acquisition and retention of customers

Companies’ marketing initiatives can benefit from consumer data so they can take advantage of trends and improve customer satisfaction.

Personalization tools for services like Spotify, Netflix, and Amazon, for instance, can enhance user loyalty and experiences.

Targeted ads

Users can benefit from effective targeted ad campaigns that are created for them both on an individual level and a broader scale using personalization data from sources including prior purchases, interaction patterns, and product page viewing histories.

Product development

Big data analytics can offer information to help with product viability, development choices, progress tracking, and guiding improvements toward what works for a company’s clients.

Price reduction

In order to increase profits, retailers may choose pricing models that model and utilize data from various data sources.

Supply chain and channel analytics

Predictive analytical models can assist with proactive restocking, B2B supplier networks, inventory management, route improvements, and delivery delay notice.

Risk management

Effective risk management techniques can be developed using big data analytics to find new dangers in data trends.

More effective decision-making

Businesses may make quicker and better decisions with the help of the insights business users derive from pertinent data.

Wrapping up

Real-time big data analytics can unquestionably become a great differentiator if well-planned and executed.

Real-time can be interpreted in a variety of ways, therefore it’s critical to understand exactly what the business expects from the analytical system.

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