Local laws and requirements are causing cloud computing users to rethink their strategies.
Traditionally, operational databases and platforms for data analysis have been two different worlds. This has come to be seen as natural, as after all the requirements for use cases that need immediate results and transactional integrity are very different from those that need complex analysis and long-running processing.
Remember how we noted data is going the way of the cloud? While there are no signs of this slowing down, there's another interesting trend unraveling, the so-called Insight Platforms as a Service (IPaaS). The thinking behind this is simple: if your data is in the cloud anyway, why not use a platform that's also in the cloud to run analytics on them, and automate as much of the process as possible?
If a cloud data center supports an orchestrator that's capable of spinning up dozens of the same microservice, and one goes wrong, how does it know which one? And how can an audit determine who's to blame? How far does identity go when identifying things that are things rather than people?
The endless streams of data generated by applications lends its name to this paradigm, but also brings some hard to deal with requirements to the table: How do you deal with querying semantics and implementation when your data is not finite, what kind of processing can you do on such data, and how do you combine it with data from other sources or feed it to your machine learning pipelines, and do this at production scale?
Researchers at Secureworks say trojan malware is being distributed in phishing emails using the lure of a fake job advert
Hybrid cloud technology is becoming a standard model for many modern enterprises, but the terminology can be difficult to fathom. This glossary of 25 hybrid cloud terms will help you gain an understanding...
This 'smartpad' solution sounds good on (digital) paper, but the implementation could be better.
The pace of change is catalyzed and accelerated at large by data itself, in a self-fulfilling prophecy of sorts: data-driven product -> more data -> better insights -> more profit -> more investment -> better product -> more data. So while some are still struggling to deal with basic issues related to data collection and storage, governance, security, organizational culture, and skillset, others are more concerned with the higher end of the big data hierarchy of needs.
As descriptive and diagnostic analytics are getting commoditized, we are moving up the stack towards predictive and prescriptive analytics. Predictive analytics is about being able to forecast what's coming next based on what's happened so far, while prescriptive analytics is about taking the right course of action to make a desirable outcome happen.
Another quarter of increased revenue confirms return to growth, says IDC research.