Pivotal swings to Kubernetes

Source:- devclass.com Pivotal has done a major turn in the direction of Kubernetes, announcing that its flagship PAS platform will run on the container orchestration platform. The announcements come a month after the Dell/VMware backed firm got a roasting from investors over its tardiness embracing Kubernetes, after scaling back its financial forecasts. The new services are not a reaction to that – CEO Rob Mee had flagged up the Kubernetes announcements during the earnings call. But it is notable that they

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How to create an access key for the root user in AWS

source:- techrepublic.com Performing tasks with root user credentials opens AWS to potentially catastrophic security vulnerabilities. Creating and managing access keys mitigates the risk. istock-1083685790.jpg Image: marchmeena29, Getty Images/iStockphoto Best practices for Amazon Web Services (AWS) dictate that you should not perform basic everyday tasks using the original root user credentials created when you first registered your AWS account. Root user credentials allow access to everything in your AWS system, without any restrictions, creating a potentially catastrophic security vulnerability. Must-Read Cloud Google

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7 key characteristics of cloud computing

Source:-searchcloudcomputing.techtarget.com Companies rely on the cloud for modern app development. Learn the key features that differentiate cloud computing from traditional, on-premises IT architectures. Cloud entails an ever-expanding list of tools and techniques, but the key characteristics of cloud computing have always remained the same. AWS was the first to popularize cloud computing as an alternative to on-premises infrastructure when it began selling compute and storage instances in 2006. Google and Microsoft followed soon after. Today, cloud computing extends from IaaS

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How DevOps enables organisations to deliver true value to customers: A guide

Source :- cloudcomputing-news.net Gone are the days when teams could work on a project for months (or even years) before releasing it to production. Now even two-week cycles are too long due to ever more demanding customer expectations and an always-growing field of competitors. Organisations today expect to deliver new features to production on a weekly, daily or even hourly basis. This accelerated timeline lets organisations adapt to market shifts and technological changes. It keeps companies on the same pace as

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Microservices and big data start to get closer

Source:- searchdatamanagement.techtarget.com Microservices are riding a wave of user interest, leading to changes in IT organization. ThoughtWorks’ expert, Zhamak Dehghani, discusses what that means for big data. Database development and management are changing dramatically, as microservices gain wider use in the enterprise. That’s according to Zhamak Dehghani, principal consultant at Chicago-based ThoughtWorks, a global technology consulting firm specializing in Agile methods, distributed systems and open source software adoption. The firm has been active in bringing together microservices and big data.

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Attunity automates streaming data pipelines for AI, ML

Source:- itbrief.com.au. Data integration and big data management solutions provider Attunity has released an analytics platform designed to automate streaming data pipelines for AI, ML, and data science initiatives. According to Attunity, data engineering teams struggle to keep up with demand for real-time data sets for machine learning applications. In addition, data integration and migration can be a manually intensive and complex endeavour; challenging to assemble and often resulting in outdated data when it is finally ready for data scientists. Attunity

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The Power of Crunching Big Data Effectively

Source- insidebigdata.com According to an Accenture study, 79% of enterprise executives agree that companies not embracing big data will lose their competitive edge, with a further 83% affirming that they have pursued big data projects at some point to stay ahead of the curve. Considering that data creation is on track to grow 10-fold by 2025, it’s crucial for companies to be able to process it more quickly, and meaningfully. The expression “big data” is often bandied around in the business and tech

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DevOps & machine learning improves app performance!

Source – devopsonline.co.uk Machine learning has been hyped (and in some cases overhyped). However, one successful DevOps use case for machine learning is application performance management. Why? Simply put, machine learning is needed to analyse the volume, velocity, and variety of big data generated by today’s dynamic application environments. By applying machine learning to identify patterns and anomalies, DevOps teams can better troubleshoot intermittent and complex problems, understand usage patterns, reduce bug rates, and improve the customer experience. What’s changed? Modern applications based on

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Applying machine learning to DevOps

Source – jaxenter.com DevOps methodologies are rapidly increasing and generating vast and diverse data sets across the life cycle of entire application including development, deployment, and performance management. Only a robust analysis and monitoring layer can particularly harness this data for the ultimate DevOps goal that is end-to-end automation. The rise of machine learning and its related capabilities, such as artificial intelligence and predictive analytics, has pushed organizations to explore implementing new analysis models that mainly rely on mathematical algorithms.

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DevOps 101: Adopt Continuous Innovation

Source – informationweek.com DevOps requires some hard work and tough choices, but in the end can keep a business competitive and innovative. I’ve spent a good portion of my career interacting with DevOps, but from an infrastructure and data center perspective. In that role, my goal was to eliminate legacy components from infrastructure so that the entire process could then impact both DevOps and the business overall. Today, my focus has shifted into the world of cloud, DevOps, and advanced technology

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