Nowadays, data is the fuel that drives many decision-making processes for companies globally. As such, it helps them understand and improve their business operations and processes, understand existing customers and attract new ones, and solve business problems. Considering this, it’s easy to see why so many companies use it to make better decisions, improve their relationships with their customers, and drive key strategic initiatives. It’s not all good news, though, as there are challenges companies should overcome to get value from their data. So, with that in mind, we’ll look at one of the main challenges in this post and present the solution to this challenge.

Too Much Data?

Gathering the data that enable businesses to make better decisions is relatively easy, especially considering that most companies use multiple data sources. For example, a recent survey shows that about 25% of companies use between five and nine internal data sources, while about 19% of companies use between five and nine external data sources to support their decision-making.

The problem is that, to take full advantage of the benefits that data offers, companies must know how to unlock the value from their data. Typically, this would involve gathering, processing, and analyzing data. However, as the number of data sources increases, so does the amount of time it takes to analyze and get insights from it.

And in a competitive market, companies can simply not waste any time on processing and analyzing data. That’s where stream processing comes in. In simple terms, it allows companies to gather, process, analyze, and react to their data in real-time. But what exactly is stream processing and how does it differ from batch processing? Let’s take a look.

What Is Stream Processing?

So, what exactly is stream processing? It’s a data management technology that allows companies to ingest a continuous data stream from a variety of sources and processes, transform, and analyze the data in real-time. Once this is done, the data is then passed on to another application, data store, or stream processing engine where companies can gain valuable insights from it. Typically, stream processing takes place through a workflow referred to as a stream processing pipeline which includes the process of gathering the data, processing it, and delivering it to the right location. Typically, companies use some of the following tools for stream processing:

Throughout the processing pipeline, several actions can be implemented depending on a company’s specific needs and requirements. These actions can, for instance, include calculations, analytics, enrichment, and ingestion. A good example and use case for stream processing can be found in digital marketing. Here companies can use stream processing to deliver relevant, personalized marketing content to the right customers at the right time. This can for example include a discount on a product a customer just added to their cart without completing the sale or recommending a product to a customer that viewed a similar product just moments ago.

What’s the Difference Between Batch Processing and Stream Processing?

To better understand the benefits of stream processing, it’s necessary to see how it compares with batch processing. With batch-file based processing, companies would generally process their data in batches on a specified schedule or based on a specific threshold. So, for example, they would process their data at the same time every night or when a specific amount of data was gathered.

The problem with batch processing is that the pace of data has accelerated significantly. In simple terms, companies now need to process data faster and get insights quicker than ever. So, data that has value now might not have much value tomorrow. Because stream processing allows companies to respond to new data events as new data is generated, it effectively solves this problem.

Another challenge is that companies are now generating more data from various sources than ever before. Traditionally, with batch processing, companies would then need to process vast amounts of data at certain times. In contrast, stream processing spreads out this processing over time which then results in companies needing fewer computing resources to effectively manage their data.

The Future of Stream Processing

Although many might think that stream processing is something new, it was first conceived as far back as 1992 with the first specialized data stream managers developed in the early 2000s.

However, it’s grown significantly since then and as an increasing number of companies realize the benefits stream processing can bring to their business, it’s expected that the demand for it will increase in the coming years. And here, its use cases will evolve beyond just data analytics.

For example, the use of stream processing for artificial intelligence (AI) and machine learning (ML) applications has grown significantly and those companies already using stream processing for AI and ML applications expect this trend to continue.

Stream processing will also find increasing application event-driven architectures, cloud services, and microservice orchestration especially considering the widespread use and popularity of these technologies.

So, as the amount of data generated by companies continues to increase and new technologies and business challenges emerge, stream processing will continue to evolve with it and play an increasingly important role in business.

Considering the amount of data that companies generate and the speed at which it’s consumed, it’s easy to see why stream processing is becoming increasingly popular. In simple terms, it allows companies to improve their business processes, make them more efficient, serve their customers better, and generate more revenue.

To find out more about stream processing and how it can help your business, visit our website for more details. At Scramjet, we believe all companies should reap the benefits of stream processing without having to deal with the inherent complexity. As such, we provide seamless data transport and scalability mechanisms to cloud and on-premises, real-time and offline processing pipelines, allowing them to focus on their business logic.

Photo by Joshua Sortino