A key defining factor of how software applications and information architecture are developed is through data streaming. With more focus on infrastructure automation, when data streaming capabilities are added to data infrastructures, it enables the ability to automate data flow in real-time. It combines the flexibility to process the data coming in from social media, among others and managing these sources side-by-side with traditional batch-based data. However, the difference in real-time data flows is that the data is more time-sensitive, more significant in volume and may not hold long-term value post-processing.
It holds the promise of enabling organizations to become more efficient and also opens up a whole range of new opportunities. Streaming data architecture assumes that data is in constant motion, which is different from the traditional assumption that information is static. This is also something that vendors are incorporating into their platforms. There has been an increasing interest in data streaming-based processing from mainstream companies. However, they lack the skills and resources to integrate the new technology efficiently and effectively. There has been an increasing interest in data streaming-based processing from mainstream companies but as they lack the skills and resources to integrate the new technology efficiently and effectively.
Another of these causes is merely being pressed for time, fully understanding and incorporating new data streaming technology takes time before it can be incorporated into software-defined cloud-centric environments.
The challenge for companies is that data streaming is not a plug-and-play solution in any way and with the market still being documented by Hadoop that distributes more massive data sets, it is still early days for data streaming, and it still needs time to secure a foothold in the continually changing market space.
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