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.
Keeping your customers happy is paramount. And nothing alienates them more than stopping their service after they’ve made an innocent mistake. This playbook walks you through the steps required to keep your customers in the loop when quota or billing issues arise.
If there was a theme around Appian 20.3, it is building on top of an already capable platform. For example, Appian records have been a customer favorite for several years. Records are easy-to-build, and they centralize data from multiple sources into a single actionable view. In this release, we build on the power of Appian records with record-powered grids.
Spark is known for its powerful engine which enables distributed data processing. It provides unmatched functionality to handle petabytes of data across multiple servers and its capabilities and performance unseated other technologies in the Hadoop world. Although Spark provides great power, it also comes with a high maintenance cost. In recent years, innovations to simplify the Spark infrastructure have been formed, supporting these large data processing tasks.
Everybody needs more data and more analytics, with so many different and sometimes often conflicting needs. Data engineers need batch resources, while data scientists need to quickly onboard ephemeral users. Data architects deal with constantly evolving workloads and business analysts must balance the urgency and importance of a concurrent user population that continues to grow.
