Big Data, Cloud & DevOps

Whether you represent a brand-new startup or you’ve been in the game a while and your company is just ready for a change, choosing a new IT vendor is a decision you’ll want to make in equal parts on merit, culture, and personality. Here’s what to look for and how to find it. Does the company you’re looking at support a wide variety of technologies and brands, or only one or two? Do they provide other managed services you might want to branch out with in the future, or do they just do one or two things well?

Few organisations realise that almost 80% of what they store is ‘dark data’, or data they do not even know exists. Dark data is all of the information collected and stored by a digitalised organisation that is not maintained or monitored. To effectively manage and control data, organisations should set up a single location where all its data assets can be discovered, classified and categorised. This is all part of proper data management, which is not only important for compliance and security, it can also lead to better business decisions.

Workplace safety is a tremendous responsibility. Keeping personnel and assets safe is becoming an increasingly manageable challenge, thanks to modern technologies — including big data and predictive analytical tools. Physical and stress-related health and safety issues are common in offices and warehouses alike. What are some of the ways technology can help keep us safer and better attuned to workplace risks? Companies can use predictive modeling to learn where injuries tend to occur and are likely to happen in the future.

Analytics projects fail not because the solution doesn’t work, but because the business fails to realise value from its investment, or the technology is not used at all. The cost of this failure is enormous. The first step towards having analytics take its rightful place in the organisation is for data to be regarded as an asset, on par with every other asset owned by the business. There are seven key factors that can mean the difference between an analytics project succeeding, or adding to the high statistic of big data project failures.

A change management process that requires software license contracts to be considered for every change request can help organizations avoid unnecessary costs resulting from a software audit. However, an inexperienced license expert on the CAB might approve changes that could incur more costs simply because they do not understand their software license agreements and the impact of the RFC. Be sure to hire or partner with the expertise required for evaluating software license contracts.

Unless you have come into Data Science and Machine Learning (ML) from an IT background and have tangible experience into building enterprise, distributed, solid systems, your Jupyter notebook does not qualify as a great piece of software and sadly does not make you a Software Engineer! This blog shows you how you can build a scalable architecture to surround your witty Data Science solution! This will cover the basics of software engineering with regards to architecture and design and how to apply these on each step of the Machine Learning Pipeline.

Welcome to the introduction of Big data and Hadoop where we are going to talk about Apache Hadoop and problems that big data bring with it. And how Apache Hadoop helps to solve all these problems and then we will talk about the Apache Hadoop framework and how it’s work. You will learn all the basic of the Hadoop framework and can work on your further skill to be an expert in data engineer. 

Chief data officers are central to the success of organisations. The way that data is managed and indeed tended and nurtured, maybe in much the same way a gardener tends and nurtures the garden, is vital. That means the chief data officer has been catapulted to somewhere near the top. The future role of the CDO is one of increasing importance. It’s about people, process and technology, but the CDO sits at top of that pyramid.

The need to release software at a rapid pace requires continuous integration (CI) and deployment (CD) is the key to drive the frequency in which code is pushed to production. It is important for testing to start right from the requirements phase and continue all the way till production deployment and monitoring. This is what we call continuous testing (CT). There have been various challenges related to test automation, but the biggest challenge that still continues to haunt teams is the challenge of Maintenance.

Unlike engineers, designers, and project managers, data scientists are exploration-first, rather than execution-first. Data science roles are a little different. They vary greatly depending on the team structure and size, but generally speaking, execution isn’t where they are at their best. Their most valuable work often comes from exploration. When it comes to complex questions and hypotheses, execution isn’t the answer. Someone has to dive in and figure things out on a deeper level. They have to thoroughly analyze and explore the problem. Data scientists are the perfect candidates to take this on.

People working in science potentially can benefit from every piece of free software code—the operating systems and apps, and the tools and libraries—so the better those become, the more useful they are for scientists. But there’s one open-source project in particular that already has had a significant impact on how scientists work—Project Jupyter. Project Jupyter is a set of open-source software projects that form the building blocks for interactive and exploratory computing that is reproducible and multi-language.