This blog post is part of a series on Cloudera’s Operational Database (OpDB) in CDP. Each post goes into more details about new features and capabilities. Start from the beginning of the series with, Operational Database in CDP. Cloudera’s OpDB provides a rich set of capabilities to store and access data. In this blog post, we’ll look at the accessibility capabilities of OpDB and how you can make use of these capabilities to access your data.
We’re continuing our series on the slick new features and design that you can find in Yellowfin 9 - a game-changing analytics product packed with new capabilities to help you get to actionable insights faster. It was time for a change to the look and feel of the Yellowfin platform and we also knew that some of the workflows could be enhanced. So, the major release of Yellowfin 9 was our chance to give Yellowfin a new look and improve the user interface and workflows while we were at it.
As a kid, I loved the TV show “Knight Rider.” But, for me, the star of the show wasn’t David Hasselhoff, it was the intelligent automobile KITT. KITT – the Knight Industries Two Thousand – was smart, funny and sarcastic, which is always well received by us Brits.
We’re announcing a key capability to help organizations govern their data in Google Cloud. Our new BigQuery column-level security controls are an important step toward placing policies on data that differentiate between classes. This allows for compliance with regulations that mandate such distinction, such as GDPR or CCPA.
The advantages of remote work have been praised for a while now. Team members gravitate towards the flexible work schedule and enjoy an increase in personal freedom, while companies benefit from a talent pool unencumbered by geographical borders. From remote-first tech giants like Automattic (which powers a third of the internet), to newbies such as NASA, companies are steadily progressing towards telecommuting. But working from home comes with its own unique set of challenges.
Data is the new oil and a critical differentiator in generating retrospective, interactive, and predictive ML insights. There has been an exponential growth in the amount of data in the form of structured, semi-structured, and unstructured data collected within the enterprise. Harnessing this data today is difficult — typically data in the lakes is not consistent, interpretable, accurate, timely, standardized, or sufficient. Scully et. al.
Think about your experience when you book a hotel room, order a taxi, or purchase something online. You reach the best offer in a few clicks and you get additional guidance with ratings to so you can predict the quality of the goods or services you’re buying. It’s really helpful to have all that additional information. So why don’t we get a similar experience when consuming data?
A secret to adopting machine learning that has nothing to do with the actual technology. Machine learning has the potential to transform your business. To automate processes, uncover new insights, make your products and services better, and customers happier. Integrating the capability into your organization requires operational transformation and lots (and lots) of experimentation. But, you know this already.
