With the advent of cloud services, IT is transforming and evolving from being traditionally data center-centric to data-centric. The data center is no longer a physical location. It extends beyond the walls of the enterprise, to the cloud, and the edge where the majority of data is being generated.

Whether you call it self-service analytics or self-service business intelligence (BI), there has been much discussion about the perils, myths, promises, and prospects of successfully building self-service capability. Going forward, I’ll use the phrase “self-service BI” but you are welcome to substitute the words “self-service analytics”. So, is self-service BI actually attainable or just snake oil?

Data migration can be a time-consuming and error-prone process. But with the right data migration tool, you can automate the migration workflows, ensure high data quality and save a lot of that precious time for more value-generating tasks. In this blog, we'll cover 7 of the best data migration tools available on the market today, along with their pros, cons, and ideal applications.

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.

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.

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.