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.
Kotlin Multiplatform apps are a great option for big projects. They allow us to unify the business logic under a Kotlin module while using the latest perks of each platform’s individual UI, significantly reducing development costs. Debugging in Kotlin Multiplatform is easy, as you can use typical tools like adding breakpoints and printing logs in the console – and in Bugfender. Continue reading for a full tutorial. KMM stands for Kotlin Multiplatform Mobile.
As most developers know, their code doesn’t always behave as they expect it to and they’re not quite sure why. This is most apparent when it comes to debugging their software. In order to figure out what the problem is, developer teams must go through a very long process in order to get the data they need to understand the problem. This can involve unnecessary wait times, unproductive hours, and a lot of wasted resources.
Business Intelligence (BI) tools allow businesses to gather, process and analyse large amounts of data so that they are able to make decisions and see opportunities as well as visualise and report on key metrics for Stakeholders. The range of Business Intelligence tools available to use is an ever-expanding list with newcomers to the market appearing every year.
With the massive explosion of data across the enterprise — both structured and unstructured from existing sources and new innovations such as streaming and IoT — businesses have needed to find creative ways of managing their increasingly complex data lifecycle to speed time to insight.
