Systems | Development | Analytics | API | Testing

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.

Insurance companies around the world are striving ahead with innovative offerings that are fundamentally changing the insurance landscape. Insurance companies are creating personalized offerings and products that are tailored to the specific needs of their customers. For example, they are implementing usage-based insurance (UBI) based on driving habits, miles driven and driving history and discounts on health insurance based on health trackers, etc.).

The Qlik April 2020 product release accelerates the path to success for our customers, delivering significant improvements across the Qlik product portfolio. This release is our first synchronized release across the Data Integration and Data Analytics product platforms.