What metrics should you include in your healthcare KPI dashboard? According to 8 healthcare businesses/agencies, here are the 18 most important ones.

Worried about your bounce rate? This article tells you whether your bounce rate is good or it needs to be improved (we’ll also show you how)

We are thrilled to announce that the new DataFlow Designer is now generally available to all CDP Public Cloud customers. Data leaders will be able to simplify and accelerate the development and deployment of data pipelines, saving time and money by enabling true self service.

We just announced the general availability of Cloudera DataFlow Designer, bringing self-service data flow development to all CDP Public Cloud customers. In our previous DataFlow Designer blog post, we introduced you to the new user interface and highlighted its key capabilities. In this blog post we will put these capabilities in context and dive deeper into how the built-in, end-to-end data flow life cycle enables self-service data pipeline development.

SQL or NoSQL? That is the question. Successful companies need reliable, robust databases to handle their day-to-day data management needs. However, with so many technologies on the market, it can be difficult to know which database provider is right for your company. Firebase and MySQL are two database solutions built very differently.

A guide to finding the happy balance between enough governance to tame data chaos and enough self-service to empower stakeholders.

Apache Spark and Hadoop MapReduce are two popular big data processing frameworks used in the industry. Both of these frameworks have the capability to handle large-scale data processing, but they differ in terms of their architecture and design.

How Sift Delivers Fraud Detection Workflow Backtesting at Scale powered by BigQuery.

Effective data governance builds a culture of trust and collaboration around data.

Data management is quite complex. Organizations continue to struggle with their data initiatives irrespective of how mature their organizations' data management environments are. And the reality in most cases, is that many companies and data teams cannot keep up with overall data pipeline needs. Different teams across departments have a variety of data needs and all are important for visibility - but budgets, resources, and execution gaps creates limitations in delivery.