Leaders know that making good, fast decisions is challenging under the best of circumstances. But, the trickiest decisions are those we call “big bets” – unfamiliar and high-stakes decisions. When you have a crisis of uncertainty, such as the COVID-19 pandemic, which arrived at overwhelming speed and enormous scale, organizations face a potentially paralyzing volume of these big-bet decisions.
You did it. You have machine learning capabilities up and running in your organization. Success! What started as a few nascent experiments (and maybe a few failures) are now carefully constructed models racing along in full production—with the ability to scale into the hundreds or thousands of productional models in sight. Assembling your expert team of data scientists and custodians seems like a distant memory. Now you’re looking ahead to the future—growth, innovation, revenue!
It’s hard to believe that we are now over 30 years into data warehousing. In that time, we have seen major changes in tools to help user report on and analyse data. In the last twenty years, we have seen the evolution from reporting, ad hoc analysis and advanced analytics. Today, BI/Analytics is a mature market with self-service BI and visual analysis standards in most organisations with self-service data preparation also widely deployed.
This post describes an architecture, and associated controls for privacy, to build a data platform for a nationwide proactive contact tracing solution.
Effectively managing data in an edge-to-cloud world is becoming increasingly complex. Enterprises need data management simplicity and agility to maximize the benefits they can get from their data. The enterprise that will succeed will shift resources away from mundane data management tasks to focus on using data to innovate and add business value.
Yellowfin 9 is defined by the belief that design matters. The ability to create a cohesive design look and feel across analytics dashboards and reports is particularly crucial for independent software vendors (ISVs) that embed analytics into their applications. Interestingly, when you take a look at the wider analytics market, few vendors are providing the toolkit that designers and developers need to build the analytical experiences they want.
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 OpDB management tools and features in the Cloudera Data Platform. The tools discussed in this article will help you understand the various options available to manage the operations of your OpDB cluster.
There are many reasons to run a big data distribution, such as Cloudera Data Hub (CDH) and Hortonworks Data Platform (HDP), in the cloud with Infrastructure-as-a-Service (IaaS). The main reason is agility. When the business needs to onboard a new use case, a data admin can bring on additional virtual infrastructure to their clusters in the cloud in minutes or hours. With an on-prem cluster, it may take weeks or months to add the infrastructure capacity for the new use cases.
