In the ever-changing world of business and communications, we’ve never relied on data as much as we do today. This reliance has enabled industries in all sectors to use big data that they’ve captured for years. 

But now, the same industries are finding more value in real-time data to power their processes and infrastructure.

Data is vital for the proper functioning of any business, especially those that rely on customer data. Real-time data analytics enable businesses to swiftly see trends and take thoughtful action. Its value is felt greatest in customer service and marketing teams where customer behavior and customer experience both power campaigns.

Today’s article aims to provide you with all the information on real-time data. By the end of this article, you’ll be familiar with the benefits of using real-time data, why it’s important in today’s world, and some of the most popular use cases today.

What Is Real-Time Data?

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Understanding real-time data is fairly simple. Unlike traditionally stored data, real-time data is immediately accessible once it is created or obtained and is forwarded to users as soon as it is collected.

Companies use this type of data to power live and in-the-moment decision-making. You can see real-time data in action in the technology that supports our everyday lives. From GPS to live video streams, everything is powered by real-time data.

Its true potential is also realized by conventional businesses. Companies are concentrating on speeding up the entire data lifecycle since obtaining enormous amounts of big data and getting information from datasets has become easier. Real-time datasets are used today to optimize operations, enhance customer experience, and manage products. 

But perhaps the biggest and most valuable deployment of real-time data is in the monitoring of IT infrastructure. And to understand the true potential of real-time data processing, it’s important to know how the data is collected and processed.

What Is Real-Time Data Processing?

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Real-time data processing is essentially a system that works with data as it is produced and delivers results almost instantly. To understand the concept better, let’s contrast it with real-time data’s counterpart: batch data processing.

Data processing, in general, takes raw data and turns it into useful information. This raw data can be collected from a wide range of sources such as social media, marketing campaigns, and other data sources. Once processed, this data can help businesses make better decisions.

In batch data processing, the system looks back over the data source at a set interval, comparing the current state of the data to the previous time it checked. All the changes that occurred during that time window are gathered into a batch and then processed. By nature, batch processing introduces a time delay into the data lifecycle. 

Real-time data eliminates this delay with a mechanism called event streaming or stream processing. It’s a way of processing and transmitting data in real-time, as it is generated. It is a powerful tool for a wide range of applications, including 

  • Social media
  • Financial trading
  • Real-time analytics
  • Internet of Things (IoT)

The key to event streaming is the concept of an “event.” An event is a discrete piece of data or a change in state that is important enough to be recorded and transmitted. For example, a user clicking a “like” button on a social media platform is an event. An event can be simple, such as a single data point, or complex, such as a nested data structure with multiple fields and values. 

Unlike data batches, events are processed instantaneously.

There are several components that make up an event streaming system. 

  • The first is the event producer, which generates the events and sends them to the event stream. 
  • The event stream is a continuous flow of events that is transmitted between the producer and the consumer. 
  • The event consumer is the destination for the events, and is responsible for processing and storing them.

In between the producer and the consumer, there may be a number of intermediaries, such as brokers, routers, and transformers. 

  • Brokers are responsible for receiving events from the producer and forwarding them to the consumer. They may also store the events for a period of time, allowing the consumer to “catch up” if it falls behind the stream. 
  • Routers are responsible for routing events to the appropriate consumer based on rules or filters. 
  • Transformers are responsible for modifying the events in some way, such as enriching them with additional data or aggregating them.

The below-mentioned 6 steps are followed when turning raw data into actionable information. This process is repeated cyclically and continuously.

1. Collection

Any processing cycle begins with gathering data. A variety of sources, including data lakes, warehouses, network devices, online databases, and others, are used for the collection.

2. Preparation

Then, after cleaning to remove any instances of duplicate, missing, or corrupt data, the data is prepared and put into the proper format for analysis. Preparation is important since it ensures only the best quality data is processed.

3. Input

The raw data is next transformed into a machine-readable format before being transmitted to the processing system.

4. Processing

The transformed data is then further manipulated and processed with the help of machine ML and AI to create the desired output.

5. Output

A readable medium, such as audio, video, text, or data visualizations, is then used to deliver the data to the user.

6. Storage

Finally, data is stored and can be easily accessed when required for the future or as input in the next data processing cycle.

Both batch and real-time processing function in this fashion, but since they operate differently, it makes them suitable for various applications.

Batch Processing Vs Real-Time Processing

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As stated earlier, the true measure and use-case of real-time data processing are understood only once batch data processing is compared to it. For starters, batch data processing handles large volumes of data that are gathered over a predefined period and then stored. 

Then, the stored data is passed into the system at once and sent for bulk processing. A batch output is created once all the data has been processed.

One might assume that real-time data processing is technologically superior to batch data processing but the latter has several advantages and unique use cases too. 

Batch data processing is the preferred system for processing large volumes of data. And since this data isn’t bound by deadline constraints, data can be processed independently from the collection at a designated time. The process is also incredibly cost-effective and efficient.

The time between data collection and the output of processing, however, is longer. This delay makes batch data processing great for processing accounting data, such as billing and payroll.

Real-time data processing processes data in a very short time and produces instant outputs. Therefore, a continuous input data stream is required to generate a constant output data stream since the process is almost instantaneous.

It means lower latency as compared to batch processing, which is usually measured in seconds or milliseconds. The next time you head over to the ATM and withdraw money, real-time data processing is the key to a successful transaction.

And since data is processed and output in real-time, one of the major benefits of real-time data processing is its speed and only a minute lag between data intake and response. The process also ensures that all data processed is also up to date. 

All these benefits enable businesses to accurately process data for better actions in the shortest amount of time. But the processing is generally complex to set up and requires a lot of computing power, which can make it prohibitive for organizations to implement on their own.

Now let’s take a closer look into the advantages of real-time big data analytics.

Benefits Of Real-Time Big Data Analytics

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One study points to the importance of real-time analysis and stated that companies that use consumer data to produce behavioral insights outperform others by 25% in gross margin and 85% in sales growth. This shows the importance of real-time data analytics which has several benefits for companies. Let’s discuss the most prominent ones below.

I. Better Error Reporting

System and processing errors can take their time to show up in batch data processing but in real-time analytics, highlighting an error can enable businesses to take action instantly. This can reduce the impact of the error and most operational issues can be solved immediately. All in all, this reporting can prevent operations from slowing down and costing the company.

II. Dramatic Improvement In Service

Businesses can evolve quickly and generate greater revenue through higher conversion rates with the help of real-time data analytics. A great example of this can be seen in internet-powered network centers and consumer products.

Errors and maintenance schedules are updated continuously which can then notify the user of the device to update, repair, or replace a component that is not working optimally.

III. Cost Savings

While the initial costs associated with setting up real-time data analytics may be high, its ROI (return on investment) is fairly quick. In the long term, especially, real-time processing helps save companies a lot of money and even reduces the burden on the IT infrastructure present. All of this allows companies to manage quicker and better-targeted responses when and where they are needed.

IV. Immediate Cybercrime Detection

Companies and users are alerted of cyber-attacks instantly once real-time data is enabled and all safety measures are in place. They can then take appropriate measures to contain the threat and help your organization to better protect information security.

V. Improved Sales Insights

Greater business intelligence and sales insights help businesses generate greater revenue. Since real-time analytics provide better insights, users can expect higher revenues. Sales and eCommerce data can be evaluated in real-time too. Important sales characteristics like purchasing patterns and product fares can be assessed immediately and can help businesses anticipate the needs of the market.

Why Is Real-Time Analytics Important?

The importance of real-time data analytics cannot be downplayed in a world of connected devices and continuous measurement. The finer the data, the more users and businesses can learn from it. 

Real-time analytics can enable you to better understand your customers and develop better strategies to meet their needs. It can even be a powerful tool to help predict future sales and provide actionable results on what strategy or product to deploy when it is needed the most. 

A great example of this in action is examining the sales of umbrellas on an eCommerce platform. Over time, the demand for umbrellas is greatest in the rainiest months of the year or when temperatures are soaring with the sun in the sky for long durations such as in summer months. 

Businesses that have data on the number of umbrellas sold over the year may not be able to make accurate assumptions on how the weather or price may affect sales. But having historical data collected over multiple years under similar conditions can help them predict how much demand would increase if none of these variables changed.

Businesses can also respond to competition changes quicker and identify growth opportunities to create new products or services. And they can do this before their competitors follow similar strategies – helping them remain one step ahead at all times.

Real-time analytics solutions like Estuary can greatly benefit businesses and help transform and load data into any data environment of your preference. Estuary Flow helps produce operational information that further drives infrastructure improvements.

Use-Cases Of Real-Time Data

The ease of use of real-time data makes them popular across industries and applications. 

  • Architects can understand the supply chain and other processes. 
  • Stock and crypto traders can monitor patterns to avoid losses and maximize gains.
  • In healthcare, real-time data is used for issues such as monitoring a patient’s vital signs and diagnosing and treating at the point of care instead of waiting. And real-time data allows service providers to adapt to load and demand instead of dealing with potential, unexpected failures.
  • Real-time data powers many of the applications and services that inform our daily lives. It is very important for the accuracy of weather instruments and wind and earthquake systems.

To sum up, real-time use cases are everywhere around us and are necessary for making informed decisions quickly.


The implementation and requirement for real-time data will only grow in the future as operational processes and personalized experiences are needed by users. And as mobile devices get more sophisticated in how they handle large swathes of data with upcoming technologies, understanding how data works at the moment is crucial for any business.

Understanding your environment and making sure it operates to the highest of standards both depend on real-time data, which is the single most crucial component.

We hope our in-depth coverage of real-time data was the simplest you could find online and one that will help you and your businesses better understand data processing. If you’re interested in the product or want to learn more about Estuary, start your free trial or contact our team.

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