Big Data, Cloud & DevOps

IT services in India is highly vulnerable to the next phase of technological disruption in automation and AI as novel capabilities such as self-repairing code reduce the need for the large-scale deployment of cheap IT professionals. India is likely to witness a crisis like the one it has never seen before.

Tableau and Power BI offers different advantages and disadvantages in comparing BI solutions. However, need for a Business Intelligence solution for an enterprise depends on the nature and demands of its business niche, size, type and workforce necessities and customer segmentations it caters.

Many Hadoop installations have been focused on individual teams and their particular data analysis projects. But that’s changing as Scott Carey points out in “11 Hadoop case studies in the enterprise,” business from big banks to airlines and retailers are deploying Hadoop at the enterprise scale. Further he asserts, that “Forrester now says enterprise adoption of Hadoop is ‘mandatory,’ so any business that wants to derive value from its data should, at the very least, be looking at the technology.”   Installing Hadoop and enabling a single team to use it has become a simple process, particularly if a cloud-based offering of the platform is acceptable. Whether on Azure, AWS, the Google Cloud Platform or another cloud provider’s infrastructure, provisioning a multi-node Hadoop instance can be done quickly…point and click.

Does that mean, however, that the newly-minted Hadoop cluster is ready for the enterprise?

Good design achieves firmness, usefulness, and delight. It’s relatively easy to translate the first two of those ideas to workflow automation design: whatever we build, it should consistently do the job we want it to do at the scale, speed, and quality that we need. The first step of a good design is to decide what we want to accomplish. Encoding workflow saves time and effort over the long-run, but it also requires being explicit about what workflow is in the first place.

Ready to learn Big Data? Browse courses like Big Data – What Every Manager Needs to Know  developed by industry thought leaders and Experfy in Harvard Innovation Lab. There’s no doubt that AI has usurped big data as the enterprise technology industry’s favorite new buzzword. After all, it’s on Gartner’s 2017 Hype Cycle for emerging technologies, for a reason. While

Ready to learn Machine learning? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab.   In this section, we’ll discuss the results of the recent “insideHPC insideBIGDATA AI/Deep Learning Survey 2016” underwritten by NVIDIA. In  November 2016, insideHPC Media LLC conducted this audience survey to get readers thoughts

Need help with your Big Data project or simply need data scientists, data engineers and visualizers to augment your existing

An introduction to the role of analytics in HR departments along with some examples of analytics and data mining in the area. 

This fun-to-read post describes the similarities between implementing a data-driven culture within a sports team and within a company. 

As the volume of employee data at organizations’ disposal increases, novel ways to make use of this data can be defined. By using simple metrics to define your current, past and potential employees, you can increase retention and make sure you hire people who are a good fit culturally. 

Soccer’s dynamic nature makes it a notoriously difficult game to predict. But if you have enough data and attributes, you can increase the accuracy of your predictions by using advanced machine learning and artificial intelligence methods.