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.
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.
Keep the bigger picture in mind as you build and use your analytics dashboards. Between devices, websites, applications, online service providers, and platforms of all kinds, modern businesses rarely have a single data source to analyze in our continuously connected world. That’s why how information is presented is almost as important as the quality of the information itself, making the difference between leading with confidence or simply flying blind.
With the expectation to become a $623 billion market by 2023, cloud computing is firmly planted in the technological field. Specifically, the more information that’s created, the higher the demand there is for quick access and manipulation of that data. Therefore, what cloud services offer to end-users is usable and essential: unlimited data storage, disaster recovery, and worldwide access.
Not until the early did the concept of a container-based application emerge to transform the IT world. For the first time, the software was deployed consistently and reliably regardless of the target environment’s changes (private or public cloud, personal computer, etc.). With the development of containers, container orchestration has become a trending topic in recent years, with successful applications from the likes of tech giants Facebook, Google, Netflix, among others.
When you’re providing APIs to your customers, you want to ensure they are getting value from them. At the same time, the best APIs are designed to be fully automated without requiring human intervention. This can leave your customers in the dark on whether your API is even being used by the organization and if you’re meeting any SLA obligations in your enterprise contracts.
When building a production application, you are usually on the lookout for ways to optimize its performance while keeping any possible trade-offs in mind. In this post, we’ll take a look at an approach that can give you a quick win when it comes to improving the way your Node.js apps handle the workload. An instance of Node.js runs in a single thread which means that on a multi-core system (which most computers are these days), not all cores will be utilized by the app.
