Over the last few days, I have been hard at work writing an up to date comparison of Kubernetes tooling (check out the first and second posts if you haven’t already, which cover tools that help you reproduce issues locally). Going through the sprawling Kubernetes ecosystem and curating the knowledge that would be the most interesting to fellow developers and engineering managers has been no small task. That’s why section 3 will cover the heart of cloud-native development: the IDE.

Over the last few blog posts, I have covered critical elements of developer tooling for Kubernetes and how things are looking in 2021. As we continue to dive into that discussion, we must not forget the process of building container images. Of course, most of us create our images by writing Dockerfiles and building them with the Docker engine. And yet, more and more teams are adopting newer alternatives.

We live in a world where FinTech automation is forcing traditional banks to move faster and deliver better customer experiences. This new world demands a completely different business model from traditional financial institutions.

With product analytics, you collect data about how your users are interacting with features and how frequently they use your app. With website analytics, you know your top pages, devices, and locations, and you can drill down into activity per visitor. But what about manual app testing analytics?

Xplenty provides features to efficiently extract, transform, and store data from various sources. Chartio provides Visual SQL features that let us explore and analyze data. Furthermore, it includes functionality to arrange charts and metrics in dashboards that can be shared. Both these tools can be used synergically. In this post, we will cover how you to configured Xplenty to use Chartio data. In a subsequent post, we will explain how to visualize the data provided by Xplenty in Chartio.

Financial services institutions need the ability to analyze and act on massive volumes of data from diverse sources in order to monitor, model, and manage risk across the enterprise. They need a comprehensive data and analytics platform to model risk exposures on-demand. Cloudera is that platform. I am pleased to announce that Cloudera was just named the Risk Data Repository and Data Management Product of the Year in the Risk Markets Technology Awards 2021.

Artificial intelligence (AI), automation and machine learning (ML) are rapidly transforming the analytical experience for everyday business users in 2021. Whether it’s automated visualizations, continuous analysis, or reduced time-to-insight, there are many practical benefits of augmented analytics that are well documented and fully realized today.

You may have read about Snowflake’s IPO last year. But you probably didn’t hear about all the work that the Snowflake security team did in preparation. Our corporate security program went through a security analytics review to ensure that it satisfied the new security policy requirements resulting from the IPO. Here are a few lessons that we learned when setting up automated security control validation on our Snowflake security data lake.

Digital transformation has been a top enterprise priority for years, and in the wake of the global pandemic, that urgency has only increased. Many industries have had to manage in weeks or months what previously would have taken years. According to surveys conducted for our “State of the API Economy 2021” report, three-quarters of enterprises remained focused on digital transformation in 2020, and two-thirds of those companies actually increased their investments.