The Fourth Industrial Revolution has not halted despite the global pandemic. If anything, COVID-19 has only accelerated the adoption of Industry 4.0, leading businesses across industries to use Internet of Things (IoT) technology and Big Data in a more sophisticated manner.
With 2.1 billion smartphone users and 10,000+ mobile apps released daily, the mobile application field seems lucrative yet more competitive than ever, especially the testing landscape. To account for such demands, entering cloud mobile testing: transparency and mobility allow businesses and developers teams to optimize their mobile app testing pipeline while ensuring the application quality.
If you are working in an agile environment, you may have come across the term ‘code review’. It is a strategic operation where you break the source code into snippets in order to audit the code before it is moved to the testing phase.
If your COVID-19 reopening plans have gone through more phases than you can count, you’re not alone. What was once planned for October has pushed to January or June or maybe never. Even the companies that have managed to get back onsite or into the office often live in constant vigilance, knowing that a single positive COVID-19 test could send them into lockdown.
Software testing methods are essential in building software. It helps developers deal with different types of bugs. As we all know, these bugs may range from a missing semicolon to a critical business requirement. Thus, software testing becomes an essential part of a test driven development environment. We are in the era of process automation. Today, businesses are dependent on one or more software products. Therefore, software quality becomes crucial.
As we covered in part 1 of this blog series, Snowflake’s platform is architecturally different from almost every traditional database system and cloud data warehouse. Snowflake has completely separate compute and storage, and both tiers of the platform are near instantly elastic. The need to do advanced resource planning, agonize over workload schedules, and prevent new workloads on the system due to the fear of disk and CPU limitations just go away with Snowflake.
Today SEO is much more than just finding high converting keywords for better ranking. Most marketers and content writers nowadays rely on different strategies to stay in the game. Imagine handling such intricate tasks manually or shuffling through several tools daily to get this done. Sounds hectic, right? But what if we told you, there’s a single package out there to make your work easier.
This post is about Kafka and the process I have been through recently writing a performance test for an application that subscribes to messages from this technology. The test I ended up with was in the end very straightforward but there were several hurdles that took a while to resolve. I hope that reading this post will hopefully help you avoid them.
Let’s precisely define the different kinds of data repositories to understand which ones meet your business needs. October 29, 2020 A data repository serves as a centralized location to combine data from a variety of sources and provides users with a platform to perform analytical tasks. There are several kinds of data repositories, each with distinct characteristics and intended use cases. Let’s discuss the peculiarities and uses of data warehouses, data marts and data lakes.
The way we write, ship, and maintain software today has evolved drastically in the last few years. How we consume underlying infrastructure to run our software has matured significantly, in that we have seen a transition from bare metal to virtual machines to containers to micro-VMs.
