How dataops improves data, analytics, and machine learning

Source :- infoworld.com A dataops team will help you get the most out of your data. Here’s how people, processes, technology, and culture bring it all together Have you noticed that most organizations are trying to do a lot more with their data? Businesses are investing heavily in data science programs, self-service business intelligence tools, artificial intelligence programs, and organizational efforts to promote data-driven decision making. Some are developing customer facing applications by embedding data visualizations into web and mobile products

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DataOps: The New DevOps of Analytics

Source- insidebigdata.com According to Gartner’s report, Innovation Insight for DataOps, 27 December 2018, “DataOps is a collaborative data management practice focused on improving the communication, integration, and automation of data flows across an organization.” A relatively new approach, DataOps represents a change in culture that focuses on improving collaboration and accelerating service delivery by adopting lean or iterative practices. Unlike its close cousin DevOps, which focuses on operations and development teams, DataOps is geared towards the data developers, data analysts or data

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Emerging Data Center Trends: From DevOps To DataOps

Source – forbes.com 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

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What is DataOps? Collaborative, cross-functional analytics

Source – cio.com.au What is DataOps? DataOps (data operations) is an emerging discipline that brings together DevOps teams with data engineer and data scientist roles to provide the tools, processes and organizational structures to support the data-focused enterprise. “You’ve got the modern trend for development of DevOps, but more and more people are injecting some sort of data science capability into development, into systems, so you need someone on the DevOps team who has a data frame of mind,” says Ted

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