What is Streaming Analytics? Streaming Analytics is a type of data analysis that processes data streams for real-time analytics. It continuously processes data from multiple streams and performs simple calculations to complex event processing for delivering sophisticated use cases. The primary purpose is to present the most up-to-date operational events for the user to stay on top of the business needs and take action as changes happen in real-time.

In our previous blog post in this series, we explored the benefits of using GPUs for data science workflows, and demonstrated how to set up sessions in Cloudera Machine Learning (CML) to access NVIDIA GPUs for accelerating Machine Learning Projects.

According to IDG, when customers consider updating to the latest release of a product, they expect new features, enhanced security, and better performance, but increasingly want a more streamlined upgrade process. With each new release of CDP Private Cloud, this is exactly what we strive to deliver. Along with a host of new features and capabilities, we are improving the upgrade process to be as painless as possible.

Cloudera Data Engineering is a serverless service for Cloudera Data Platform (CDP) that allows you to submit jobs to auto-scaling virtual clusters. CDE enables you to spend more time on your applications, and less time on infrastructure. CDE allows you to create, manage, and schedule Apache Spark jobs without the overhead of creating and maintaining Spark clusters.

The blog “Migrating Apache NiFi Flows from HDF to CFM with Zero Downtime” detailed how many common NiFi dataflows can be easily migrated when the Hortonworks DataFlow and Cloudera Flow Management clusters are running side-by-side. But what if you lack the resources to run multiple NiFi clusters concurrently? Not a problem.

What’s the fastest and easiest path towards powerful cloud-native analytics that are secure and cost-efficient? In our humble opinion, we believe that’s Cloudera Data Platform (CDP). And sure, we’re a little biased—but only because we’ve seen firsthand how CDP helps our customers realize the full benefits of public cloud.

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. It’s a pie-in-the-sky statement that sounds great, right? And while you’d be forgiven for thinking that it might sound too good to be true, operational ML is, in fact, achievable and sustainable. You can get the very kind of ML you need to increase revenue and lower costs. To help teams work smarter and do things faster.

The United States Veterans Administration (VA) over the last decade underwent a massive enterprise-wide IT transformation, eliminating its fragmented shadow IT and adopting a centralized system capable of supporting the agency’s 400,000 employees and more effectively utilizing its $240 billion-plus annual budget. The result: A more reliable and modern IT environment that improves access, availability, and user experience -ultimately supporting the VA mission more effectively.

When working on complex, or rigorous enterprise machine learning projects, Data Scientists and Machine Learning Engineers experience various degrees of processing lag training models at scale. While model training on small data can typically take minutes, doing the same on large volumes of data can take hours or even weeks. To overcome this, practitioners often turn to NVIDIA GPUs to accelerate machine learning and deep learning workloads.

This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization.