Why Stream Processing Is Set to Be the Breakout Star of Big Data

Why Stream Processing Is Set to Be the Breakout Star of Big Data

The flow of information never stops. From the globally linked pages of the World Wide Web to the POS systems in brick and mortar storefronts, new data is constantly being created as pre-existing bits move across the digital universe. The volume is tremendous. The variety is impressive. And the velocity is downright staggering. Companies seeking a competitive edge should capitalize on its speed and power while they can, preferably in the efficient manner possible. A select few organizations have found a novel solution in stream processing.

What is Stream Processing?

Stream processing describes the in-depth analysis of machine data in continuous motion. The concept makes it possible to follow the flow of steadily changing information before it is even delivered to the server. By nature, this method is an ideal fit for big data applications. Stream processing is designed to harness actionable data at the tune of millions of records per second, making it a valuable tool for the company that needs to extract intellgience from dynamic data streams.

In practice, stream processing distinguishes itself from traditional big data tools like Haoop by digging deeper into the targeted system. Each node maintains a contiuous stream of data, which undergones a series of operations ranging from filtering to analytics. A stream processing platform will posses the following qualities:

• In-memory analytics that performs continuous query processing from within system memory rather than data on phyiscal disk.

• Flexible IT infrastructure scaled and optimized for big data needs.

• Enterprise-wide distribution and deployment across multiple servers.

• Around the clock availability and low latency for heavy-duty performance.

• Live integration with existing databases and storage environments.

Stream Processing In Big Data Industries

The concept of stream processing for big data taking off, slowly but surely. And from web servers to telecommunication systems, it can be deployed wherever Internet access and machine data cross paths. For example, security experts can use the technology in both desktop computers and biometric systems to prevent fraud and detect intrusion. Manufacturing plants can integrate stream processing with their automated systems, lending real-time predictive analytics to production processes and quality assurance.

Stream processing is on the verge of mainstream relevance and being ready to explode. It’s an upstart industry getting support from some pretty big players, including long-time IT giant IBM. Driven by the company’s proprietary language Streams Processing Language or SPL, IBM is out in front of the market with a product it calls InfoSphere Streams, an advanced big data platform that focuses on speedy, high-performance analytics. Open source alternative Apache Spark is another option on the market, and slowly picking up commercial adoption.

New Big Data Challenges

While stream processing is a promising big data solution, it does bring unique challenges to the table. Above all, it creates a more strenous workload, so organizations need to make sure they have the resources to keep up with an infrastructure that is constantly processing data non-stop.

Why Stream Processing Is Set to Be the Breakout Star of Big Data
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