Since childhood, we’ve been taught about the power of coalitions: working together to achieve a shared objective. In nature, we see this repeated frequently – swarms of bees, ant colonies, prides of lions – well, you get the idea. It is no different when it comes to Machine Learning models. Research and practical experience show that groups or ensembles of models do much better than a singular, silver bullet model. Intuitively, this makes sense.

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. This blog post gives you an overview of the operational database (OpDB) administration tools and features in the Cloudera Data Platform.

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.

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.

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…

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.

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.

Cloudera’s operational database (OpDB) in CDP delivers a real-time, always available, scalable OpDB that serves traditional structured data alongside new unstructured data within a unified Operational and Warehousing platform. Cloudera delivers an operational database that serves traditional structured data alongside new unstructured data within a unified open-source platform.

Currently, many enterprises, including many Cloudera customers, are experimenting with machine learning (ML) and creating models to tackle a wide range of challenges. While today, many models are used for dashboards and internal BI purposes, a small and rapidly growing group of enterprise leaders have begun to realize the potential of ML for business automation, optimization and product innovation.

Apache Ozone is a distributed object store built on top of Hadoop Distributed Data Store service. It can manage billions of small and large files that are difficult to handle by other distributed file systems. Ozone supports rich APIs such as Amazon S3, Kubernetes CSI as well as native Hadoop File System APIs.