How Stream Processing is Changing the Game of Big Data

In today’s corporate world, valuable data and data analytics are crucial to getting ahead of the competition – those companies that operate within a similar business sphere and are constantly competing for a larger share of the overall market. Using big data analytics to find hidden patterns within complex data sets of your market sector can give you greater insight when making business decisions. New technologies such as artificial intelligence and machine learning will definitely move your business from a position of knowing your market sector to learning your market’s competitors, understanding your target customers, and optimizing your supply chain avenues.

Yet, since so much business is conducted over the internet in real-time through instantaneous processing, there exists the real chance of basing decisions on information that changes so quickly, diminishing the value significantly. This is where data stream processing steps in. Real-time stream processing can process data streams in seconds to provide faster market insight and faster awareness of the changing patterns of your most valuable asset – your customers.

What is Data Stream Processing?

Big Data is generally processed in batches. At some point, the data collecting stops, the data is stored, and the data is processed. Additionally, each batch of data must be aggregated which combines the disparate batches of data into a shared whole. In contrast, when big data is processed, there is a continuous cycle of data collection, storage, and analysis. This enables a must faster and up-to-the-minute detection of market patterns that are occurring in real-time at different levels within the market.

Streaming data in this manner is also called real-time analytics. This big data technology originated with Apache Storm, a complex computation system that made processing unlimited amounts of data in real time. Today, processing systems in big data is very fault-tolerant and guarantees the data will be processed when using any programming language.


One of the first benefits you will reap from streaming data analytics is that only the most useful bits of data are stored. Since storing these huge amounts of data can be problematic, stream processing does not require a build-up of data, and eliminates the need to store massive amounts of information that may not be useful. A less robust hardware system is needed when compared to batch processing.

Also, with so many customer transactions and other processes such as manufacturing IoT sensor data, supplier shortages, and financial fluctuations happening every minute of the day, this type of data processing and analysis is more natural to the real world. Streaming data is optimum for processing recent data in a single pass or data has a temporary existence. Partner with an IT data engineering firm that can give your company access to streaming data that can benefit many areas of business operations:

  • Customer acquisition and customer retention
  • Resolve advertising problems
  • Insight into rapidly changing markets
  • Support risk management decisions
  • Drive innovative product development

Need Real-Time Stream Processing? Come to Sequent Solutions

Contact Sequent Solutions for expert, real-time data streaming and analytics to increase your data-driven decision-making. Our cost-competitive services can be scaled to fit large or small companies, with one goal in mind – to accelerate your business with outsourced data solutions.

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