This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, thatDot COO Rob Malnati explains the key differences of stream processing vs. batch processing.
Our world happens in real-time, and so should our decisions. For example, fraud prevention is often more beneficial than fraud detection. Increasing demand for data-driven decision-making puts pressure on organizations to move data from legacy batch-processing data infrastructures to cloud-based, real-time data processing pipelines that can scale to meet increasingly high volumes of data.
Batch data processing involves collecting and storing data before it can be processed. A batch processing job runs over that slice of historical data. Any data that arrives after the batch window has closed and points to an important event – early indications of an imminent cyber-attack, the tell-tales of a fraudulent transaction – will be excluded from the batch job and missed. So enterprises are shifting toward real-time data streams to get actionable insights when available, sparking a debate over which way to process data is better – stream vs. batch processing?
Below outlines the processing types, the pros and cons, and the benefits of embracing real-time streaming data.
Stream Processing vs. Batch Processing; What’s the Difference?
What is Batch Processing? Batch Processing Defined
Batch processing in its simplest form is when data is collected and processed all at once as a batch through an analytics system. Batch jobs are long-running, unsupervised, and capable of processing enormous amounts of historical data. The latter point is where batch processing has in the past had an advantage over stream processing.
An example of batch processing is billing or payroll, which are processed by week or month. The company will store the larger data set before processing it for deep analysis.
What is Stream Processing? Stream Processing Defined
Stream processing is a constant stream of data flows through a data processing pipeline as soon as it is collected or generated. Due to the processing speed, companies can gain valuable insight instantly in real-time. Stream processing struggles when data input volumes exceed the system’s ability to process and output results. A common workaround is to confine processing to events that arrive within a rolling time window.
An example of real-time stream processing ranges from social media feeds to retail inventory management. The real-time processing allows the consumer the most accurate information on hand.
Giving Momentum to Real-time at a Massive Scale
Until recently, massive data processing jobs were the exclusive domain of batch processing. The advent of stream-management systems like Kafka feeding data to real-time analytics systems like Flink and Quine, along with the proliferation of autoscaling cloud infrastructure, means enterprises now have a real-time alternative that can scale to batch processing data volumes.
2.5 quintillion bytes of data are generated daily, an enormous volume. Most of that data is simply ignored. Much of the rest is stored to be processed later. Stream processing alleviates the question of “where are we going to put this if we don’t want to lose it? By extracting only the valuable insights and discarding the rest.
Companies embracing real-time event processing will have a competitive advantage. Companies that can process data instantaneously can solve a myriad of problems in a world that values instant gratification, accuracy, and personalization. From instant fraud detection to real-time audience sentiment analysis, embracing real-time stream processing can only bolster.
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