Hadoop ecosystem now serves as a comfortable home to Big Data now, and the Hadoop data stores now have a greater acceptance across the world by programmers, developers, data scientists, and database management experts. These ecosystems are as convenient as the data storages; however, the inherent reporting system of Hadoop poses a few challenges for the users to overcome.

Time Series as Fast Analytics on Fast Data Since the open-source introduction of Apache Kudu in 2015, it has billed itself as storage for fast analytics on fast data. This general mission encompasses many different workloads, but one of the fastest-growing use cases is that of time-series analytics. Time series has several key requirements: At first glance, it sounds like these requirements would demand a special-purpose database system built specifically for time series.

The telecommunications industry is in the midst of a fundamental reinvention and transformation. Faced with a range of emerging pressures – including consolidation, a changing competitive landscape, and commoditization of traditional services – communication service providers (CSPs) are seeking new revenue streams and novel business approaches.

The theoretical bases for Machine Learning have existed for decades yet it wasn’t until the early 2000’s that the last AI winter came to an end. Since then, interest in and use of machine learning has exploded and its development has been largely democratized. Perhaps not so coincidentally, the same period saw the rise of Big Data, carrying with it increased distributed data storage and distributed computing capabilities made popular by the Hadoop ecosystem.

Augmented analytics is when you take what was traditionally a very manual workflow and automate it. This gives you the ability to analyze data far more rapidly and to package up changes for humans to interpret. Essentially you’re augmenting a human experience, so rather than spending all your time looking for a needle in the haystack, the machine finds the needle and gives it to you.

Automation and robotics in retail is rapidly changing the retail landscape – so much so that there are clearly winners and losers. I’m not talking about the war between brick and mortar stores and digital marketplaces, but rather I’m talking about the retail digital revolution where the winners are delivering greater than 4.5% comparable store/ channel sales growth compared to their brothers that have not embraced automation and robotics.

For this blog post, I’m going to take a step back and not go into data visualization best practices. Rather, I’m going to explore what you can do with your data before arriving at a final visualization – what I like to call “re-expressing” your data. Accordingly, we are going to look at the topic of transforming your data.