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What Is Streaming Data? Guide To Real-time Data And Stream Processing

Data is indeed the new oil, and real-time data processing & real time stream processing is the one that unlocks its potential to drive businesses in this technologically advanced era.

Now, businesses need high-speed data pipelines more than before, to streamline their processes and match the customer’s expectations. A report by Gartner states that by 2022, more than half of new businesses will use Real-time Data streaming to leverage its power in making on-time decisions that matter to the company.

What Is Streaming Data?

Today, the data sources are infinite- IoT sensors, servers, business applications, the list can go on. The number of IoT connected devices alone is estimated to be around 45 billion in 2021.

Real-time Data streaming is the continuous flow of data from these multiple data sources. The data generated and published are real-time in nature. These are stored and processed in small packages in the size of Kilobytes. It mainly involves two functions such as storing and processing.

Data storage includes capturing and recording large volumes of data streams coherently and systematically.

Data stream processing interacts with the generated data and processes data in a way that makes sense. In short, it makes the data useful for its intended applications.

For example, consider real-time streaming in an online food delivery application. As soon as you order your meal, a chain of data streams across various devices. The restaurant owner and nearest delivery person get a message instantaneously to approve or ignore the delivery request.

Here real-time streams of data create a seamless user experience. The data is captured, processed, analyzed, and published, and you get your order confirmation within minutes. The data streams help the food-delivery application to put together various crucial information such as restaurant availability, food tracking, pricing, offers, discounts, and much more.

Thus, even a single streaming source has the potential to create tons of data that is difficult to process without proper structuring. It is where streaming data architecture holds value.

What is Data Streaming Architecture?

Real-time Data streaming architecture provides a framework where large chunks of data can undergo real-time data stream processing, even when the data is from multiple sources.

Unlike conventional data solutions, data streaming architecture processes the data as soon as it gets out of its shell and stores it. Depending on the use-case, it may also use tools that help in real-time data processing, real-time data analytics, etcetera.

The four main elements of real-time data stream processing architecture are:

The Message Broker –

It captures the data from the source or the producer. It translates the data into a message format and streams them in real-time. The message format helps other components to understand the data and trigger actions accordingly.

Real-time ETL tools:

It aggregates all the data produced by one or more message brokers and transforms them into a structured form. The structured data is then used to generate the event-specific result. It could be an API call, alert, notification, visualization, or may even trigger a new data stream.

Data Analytics Engine:

It imparts value to the structured data. Here a set of data analytics tools work together to analyze the well-structured data. The data analysis helps in understanding the relevance of data to the use case it is intended to serve.

Streaming Data Storage:

It stores the streaming data. Most companies now prefer storing their real-time stream data as it is a potent way to create credible records of crucial events.


Real-time data stream processing architecture



Gone are the days when businesses could rely on batch data processing alone. Now, companies are striving for real-time data that helps them get insights into consumer behavior, product acceptance.

As per IDC, almost 30% of data generated by 2025 will be real-time in nature. Therefore, businesses need real-time data processing to harness the power of data to create exceptional products and customer services. Real-time data processing also helps in alleviating cybersecurity risks and keeps them aware of the fluctuating stock markets. The possible use-cases of data streaming are many. Therefore, data streaming has now become an essential part of the data-driven digital present and the future.

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