It's easy to get lost in today's continuously changing landscape of cloud native technologies. The learning curve from a beginner's perspective is quite steep, and without proper context it becomes increasingly difficult to sift through all the buzzwords. If you have been developing software, chances are you may have heard of Kubernetes by now. Before we jump into what Kubernetes is, it's essential to familiarize ourselves with containerization and how it came about.

Over the last decade, data collection has become a commodity. Consequently, there has been a tremendous deluge of data in every area of industry. This trend is captured by recent research, which points to growing volume of raw data and growth of market segments fueled by that data growth.

A Forbes survey shows that data scientists spend 19% of their time collecting data sets and 60% of their time cleaning and organizing data. All told, data scientists spend around 80% of their time on preparing and managing data for analysis. One of the greatest obstacles that make it so difficult to bring data science initiatives to life is the lack of robust data management tools.

For some, this may look like a new category at this year’s Data Impact Awards. However, the Enterprise Data Cloud category marks the evolution of what was once the Data Anywhere category. The main reason for this change is that this title better represents the move that our customers are making; away from acknowledging the ability to have data ‘anywhere’.

Cloudera Data Platform 7.2.1 introduces fine-grained authorization for access to Azure Data Lake Storage using Apache Ranger policies. Cloudera and Microsoft have been working together closely on this integration, which greatly simplifies the security administration of access to ADLS-Gen2 cloud storage.

In 2020, however, contining to rely just on dashboards for your BI needs isn't enough. Why? Data is growing exponentially - in both size and complexity - within every business today. Manually keeping track of performance and searching for insights has become difficult for many users, and it's fostered new expectations - to be able to do more with analytics - including making it faster and easier to keep on top of changes or opportunities.

For our Head of Product Design and Creative Director, Tony Prysten, design is always top of mind. In analytics platforms, good design plays an important role in how people understand and use data. Here Tony shares how Yellowfin has been created with designers and developers in mind.

There seems to be universal acceptance that effective use of data can help maximize bottom line value. However, many businesses still aren’t successfully leveraging data to its full extent due to people, processes and technology roadblocks. Thankfully, there is a unifying approach to unlocking the immense opportunity for enterprises to more effectively leverage data to create new products, services and business models.

In this tutorial, we will look into how to integrate performance testing with k6 into your Jenkins Pipeline setup. By integrating performance tests into your CI pipelines, you can catch performance issues earlier and ship more stable and performant applications to production.

This is the second in a series of playbooks that walks you through the steps required to automate key monitoring and altering functions within your product. Through automation, Moesif will save you time, resources and energy. Automating repetitive processes is a key attribute in whole-product design. By combining payload observability with behavioral emails, Moesif’s able to make you more productive through sophisticated workflows of many functional, performance and product issues.