Ever wonder how fast Apache NiFi is? Ever wonder how well NiFi scales? When a customer is looking to use NiFi in a production environment, these are usually among the first questions asked. They want to know how much hardware they will need, and whether or not NiFi can accommodate their data rates. This isn’t surprising. Today’s world consists of ever-increasing data volumes. Users need tools that make it easy to handle these data rates.
Recently, Databricks introduced Delta Lake, a new analytics platform that combines the best elements of data lakes and data warehouses in a paradigm it calls a “lakehouse.” Delta Lake expands the breadth and depth of use cases that Databricks customers can enjoy. Databricks provides a unified analytics platform that provides robust support for use cases ranging from simple reporting to complex data warehousing to real-time machine learning.
With Yellowfin 9, we introduced to the world an incredibly flexible, action-based dashboard builder and progressive data storytelling capabilities that advance the capability of the dashboard experience. We’ve received great feedback since then and this month, the newly-released 9.1 further enhances the user experience of analysts, developers, and business users in Yellowfin’s action-based dashboards, data storytelling, and data discovery products.
Data saturation is everywhere. We want to collect more data because we want better information from them. However, the rapid rise in our ability to collect data hasn’t been matched by our ability to get meaningful insights from the data.
Retailers are using big data to report on consumer demand, inventory availability, and supply chain performance in real time. Big data provides a convenient, easy way for retail organizations to quickly ingest petabytes of data and apply machine learning techniques for efficiently moving consumer goods. A top supermarket retailer has recently used Unravel to monitor its vast trove of customer data to stock the right product for the right customer, at the right time.
One key aspect of the Cloudera Data Platform (CDP), which is just beginning to be understood, is how much of a recombinant-evolution it represents, from an architectural standpoint, vis-à-vis Hadoop in its first decade. I’ve been having a blast showing CDP to customers over the past few months and the response has been nothing short of phenomenal…
The novel coronavirus, COVID-19, presents challenges the world hasn’t seen for decades. Humans have fought global pandemics before, and it isn’t easy. But we have an additional weapon on our side this time — data.
Back in 1985, Simple Minds sang “Don’t You (Forget About Me),” the soundtrack to – what is IMHO – one of the greatest movies of the ‘80s: “The Breakfast Club.” The song famously asks us not to forget, and – if any of the wedding parties I’ve attended are anything to go by – we certainly haven’t. However, when it comes to our sensitive data, that’s not always the case.
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.
