One common misconception about DataOps is that it is just DevOpsapplied to data analytics. While a little semantically misleading, the name “DataOps” has one positive attribute. It communicates that data analytics can achieve what software development attained with DevOps. That is to say, DataOps can yield an order of magnitude improvement in quality and cycle time when data teams utilize new tools and methodologies. The specific ways that DataOps achieves these gains reflect the unique people, processes and tools characteristic of data teams (versus software development teams using DevOps). Here’s our in-depth take on both the pronounced and subtle differences between DataOps and DevOps. Read More

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#devops

Whether referred to as data science, data engineering, data management, big data, business intelligence, or the like, through our work we have come to value in analytics:

Individuals and interactions over processes and tools 
Working analytics over comprehensive documentation
Customer collaboration over contract negotiation
Experimentation, iteration, and feedback over extensive upfront design 

Cross-functional ownership of operations over siloed responsibilities 
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If asked to list the top trends that are shaping the enterprise data center today, most technologists and tech investors would likely agree on a core set. The list would include technologies like such as cloud computing, containers and virtualization, microservices, machine learning and data science, flash memory, edge computing, NVMe and GPUs. These technologies are all important for organizations pushing digital transformation.

The harder question: What’s coming next? Which emerging technologies or paradigm shifts are poised to be the next big thing? And what effects will they have on the hardware and software markets? Read More

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#devops

Artificial intelligence is still in its infancy. Today, just 15% of enterprisesare using machine learning, but double that number already have it on their roadmaps for the upcoming year. With public figures like Intel’s CEO stating that every company needs a machine learning strategy or risks being left behind, it’s just a matter of time before machine learning enters your organization, too—if it hasn’t already.

However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production. In other words, “The gap between ambition and execution is large at most companies,” as put by the authors of an MIT Sloan Management Review article. Ultimately, there’s a major difference between building a model, and actually getting it ready for people to use in their products and services. Read More

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