It seems like only yesterday that the cloud was considered the destination. For technology decision-makers, choices boiled down to: “Do I want this thing in my data center, or would it be better, cheaper and faster in the cloud?” The cloud was the place to be.
Adobe is a legendary Silicon Valley company. From the desktop publishing era of the 1980s, powered by the Adobe Postscript page description language, through the creation and marketing of Photoshop, Illustrator, and other creative power tools, the digital revolution is unthinkable without Adobe.
Like most of our customers, Cloudera’s internal operations rely heavily on data. For more than a decade, Cloudera has built internal tools and data analysis primarily on a single production CDH cluster. This cluster runs workloads for every department – from real-time user interfaces for Support to providing recommendations in the Cloudera Data Platform (CDP) Upgrade Advisor to analyzing our business and closing our books.
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.
We've moved from desktop to SaaS, to a real UX focus. Now we're seeing new vendors that are analytics-first. They’re creating new applications that are challenging the established players. Historically, applications were transaction-first; you build your software thinking about your workflow or the transactions that you want people to do.
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.
