Systems | Development | Analytics | API | Testing

APIs are the backbone of many modern applications, enabling them to interact seamlessly with other services and platforms. Whether you’re running a small app or a large enterprise system, monitoring and checking the performance of your API is crucial. Why? Because slow or unreliable APIs can frustrate users, harm your business reputation, and lead to lost revenue. In this article, we’ll explore how you can effectively monitor and check the performance of your API deployed on Microsoft Azure.

As global app downloads have doubled every quarter since 2015, and users now spend more time on apps than websites, the need for thorough mobile app testing is more critical than ever. The app testing market is projected to reach $13.6 billion by 2026. This blog will cover key mobile app testing statistics and explore why it is essential for delivering high-quality user experiences and maintaining competitive advantage.

If 2023 was the year of gen experimentation, 2024 is the year of gen AI implementation. As companies embark on their implementation journey, they need to deal with a host of challenges, like performance, GPU efficiency and LLM risks. These challenges are exacerbated in highly-regulated industries, such as financial services and telecommunication, adding further implementation complexities. Below, we discuss these challenges and present some best practices and solutions to take into consideration.

Since it was created by Facebook in 2012 and made publicly available in 2015, GarphQL has changed everything about how we fetch data from servers for our front-end apps. Most front-end clients typically use REST APIs to retrieve data from the server, this includes mobile apps for platforms like Android, iOS, and Flutter, as well as JavaScript frameworks like React, Angular, Vue, and Next. A huge advantage of GraphQL is that it enables front-end clients to request only the API they require.

Most developers who upload mobile apps to the App Store or Play Market have likely encountered unexpected bugs in new releases. To address this issue, a new build with the fix must be created and checked again by the platform where they are uploading. They also have to wait for the approval process again for the new version to be released. Unfortunately, this entire process takes a considerable amount of time, but there is an alternative way.

IoT is driving the digital metamorphosis in the banking industry. The surge in internet-enabled devices, like smartphones, tablets, and smartwatches, signifies an increase in opportunities for fintech software development to scale their business through the Bank of Things (BoT). IoT technologies are enabling real-time connections, making banking more efficient and customer-centric.

‍McKinsey’s latest research projects that generative AI could contribute between $2.6 trillion and $4.4 trillion annually across various sectors. Experts have also observed that integrating AI-driven automation, threat detection, and low-code platforms redefines next-gen software development. Whether it is code generation, bug fixing, or even designing a new digital component, generative AI is seeping into all product engineering processes.

Testing tools in AI and data automation have evolved to offer sophisticated features that ensure the quality of the end product and reduce its time to market. With reports suggesting 50% of manual testing being replaced by automated, these tools can help with various aspects of software testing, including unit testing, performance testing, and security testing. They can be integrated practically anywhere across the CI/CD pipeline for continuous testing and shift left testing.