AI is not a distant vision of the future; it has already permeated our lives, reshaping industries and challenging conventional notions of work and innovation. In the ongoing dialogue about AI, there are two prevalent perspectives: one prophesizes a future where AI replaces human roles and responsibilities, while the other dismisses AI as a mere buzzword, suggesting that it lacks the transformative power to bring about substantial change. As professionals navigating this evolving landscape, it is crucial to refrain from taking extreme sides and instead strive to understand the nuanced reality that lies somewhere in between.

Chapter 1: The Great Debate: Monoliths vs. Microservices As software engineers, we face numerous challenges on a daily basis. Perhaps your team is struggling to stay productive, your application isn’t scalable enough, or your response times are sluggish. Or maybe you’re simply having a hard time acing those job interviews. It can be overwhelming to tackle each problem individually, which is why many software gurus are promoting a single solution for all of these issues: microservices.

During a casual discussion at the Bangalore Java User Group, the topic of JSON parsers sparked a debate on their pros and cons. This led to the inception of an innovative idea – creating yet another JSON parser, one that is tailor-made and hackable to cater specifically to the needs of API development. In this blog post, we’ll delve into the reasons behind the creation of SJSON and explore its unique features.

When it comes to protecting confidential information, we find that clients require different approaches or pose different protection needs. Some clients need to protect the information on their mobile computers or laptops in case they are lost. Others want to keep their documentation protected on file servers so that it can even be protected from improper access by IT staff. Sometimes some customers need to protect documentation when it travels attached to an email because they use managed email servers or in the cloud.

Recently, I had a conversation with a college student who was interested in finding job opportunities at Tier 1 (MAANG/FAANG)companies (those offering high salaries). He asked me for advice and I shared what I knew about microservices, cloud computing, and JAMstack. However, he insisted that he was good at competitive programming and only wanted a job where he would be asked to solve programming puzzles every day and paid a high salary in return.

Introduction: When it comes to software development, there is a significant difference between working software and quality software. While the former focuses on achieving functional requirements and basic functionality, the latter encompasses a broader spectrum of factors that contribute to a software system’s overall excellence. Code quality plays a crucial role in determining the reliability, maintainability, and efficiency of software applications. Defining Code Quality: Code quality refers to the measure of how well a software system meets certain characteristics or attributes that contribute to its overall excellence.

Introduction: Over the years, Java’s interaction with relational databases has undergone significant transformations. As the backbone of enterprise application development, the way Java applications connect with and interact with databases has evolved to meet the demands of modern software development. These changes have not only influenced the way developers write code but also brought about major migrations and improvements in the database connectivity landscape. Code migrations are no small feat and often come with considerable costs and time investments.

Logging Greatness: For many years, logs have been an essential part of troubleshooting application and infrastructure performance. They help provide visibility into how our applications are running on each of the various infrastructure components. Log data contains information such as out of memory exception or hard disk errors. This is very helpful information that will help us identify the “why” behind a problem either that a user has brought to our attention or that we have uncovered.