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Our Criblpedia glossary pages provide explanations to technical and industry-specific terms, offering valuable high-level introduction to these concepts.
Data streaming is a method of continuously transmitting and receiving data in real-time, enabling analysis and processing of the data as it flows. Unlike traditional batch processing, where data is collected, stored, and processed in chunks, data streaming handles information incrementally. This allows companies to act on insights as they emerge. The approach is particularly crucial in today’s fast-paced, data-intensive world, where timely decision-making can provide a significant competitive advantage.
In a data streaming system, data is generated from various sources. Some of them include IoT devices, social media, server logs, or sensors. The data is then sent to a central platform where it’s ingested, processed, and often stored in-memory for immediate analysis. This continuous flow of data is processed in small, manageable chunks, and companies can apply various techniques for real-time data transformation, aggregation, and enrichment.
Let’s break down the process and explore the different stages:
When assessing the merits of data streaming, it’s important to explore its specific pros and cons. Let’s break them down real quick.
Pros:
Cons:
Data streaming has a broad range of applications across industries.
The benefits of data streaming are evident in its ability to offer up-to-the-moment insights, facilitate quicker decision-making, and improve the overall efficiency of data-driven operations.
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