Systems | Development | Analytics | API | Testing

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12 different types of mobile testing with real-life examples

Today we are using mobile phones for social media, shopping, banking, movies, online meetings, emails, and whatnot. Can we have a single day in our life without a mobile phone today? You’d be thinking of a no right? Something like ‘it’s near to impossible’. Mobiles have different models, screen resolutions, operating systems, network types, hardware configurations, etc. How to make sure that an application works fine on all these different combinations?

Why and When Should Businesses invest in Mobile Automation Testing?

Every year, the number of hours people spend online keeps increasing. Our needs are met online instantly. As mobile device usage is growing, the time spent on it is also increasing. Today, if I list what these mobile devices are doing, it will be very amateurish and naive. The usage of smartphones has increased because of the interesting mobile apps installed on them. Therefore, it becomes imperative to make an app bug-free and enjoyable.

Cloud Orchestration 101 | A Complete Guide for Beginners

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.

Container Orchestration | Definition, Benefits & How It Works

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.

How to Show the Business Value of Your APIs with Embedded Metrics

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.

Improving Node.js Application Performance With Clustering

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.

Enterprise CXO Priorities for 2021

One of the reasons we selected Sierra Ventures as one of our seed investors is because of their CXO Advisory Board. They have dozens of knowledgeable advisors across a wide variety of verticals: Healthcare, Consumer, Retail, Finance, Technology, Media and Telecom. Each year they conduct a broad survey of major trends. This kind of survey data is gold for Enterprise-focused startups.

Introducing real-time data integration for BigQuery with Cloud Data Fusion

Businesses today have a growing demand for real-time data integration, analysis, and action. More often than not, the valuable data driving these actions—transactional and operational data—is stored either on-prem or in public clouds in traditional relational databases that aren’t suitable for continuous analytics.

Continuous model evaluation with BigQuery ML, Stored Procedures, and Cloud Scheduler

Continuous evaluation—the process of ensuring a production machine learning model is still performing well on new data—is an essential part in any ML workflow. Performing continuous evaluation can help you catch model drift, a phenomenon that occurs when the data used to train your model no longer reflects the current environment.