Large Language Models (LLM) are changing the way developers and QA engineers solve problems. They allow for quicker code generation, debugging, and automated testing, reducing development time by up to 40%. This shift has prompted 67% of senior IT leaders to focus on generative AI, with 33% planning to make it a top priority within the next 18 months. However, while LLM models offer immense potential, understanding how to get the most out of them while maintaining quality is important.

According to Gartner, 87% of organizations are classified as having low business intelligence and analytics maturity, meaning they struggle to extract value from their data. This alarming statistic highlights a common struggle—turning raw data into actionable insights. Product teams often find themselves overwhelmed by the sheer volume of information they collect. Extracting meaningful patterns, deciphering user behavior, and predicting market trends from this sea of customer data can seem daunting.

Spring Boot is an open-source micro framework maintained by a company called Pivotal. It provides Java developers with a platform to get started with an auto configurable production-grade Spring application. With it, developers can get started quickly without losing time on preparing and configuring their Spring application. This post will guide you through understanding what the micro framework is and what it’s used for.

System administrators, DBAs, and application developers all know about the need for data integration. That’s when you have to pull data from one database, application, or web service to another. Fortunately, this task is a lot less challenging than it used to be because APIs (application programming interfaces) are so popular today. APIs provide a standard way to share the data and services of one system with another, even if the two systems are otherwise incompatible.

As I eagerly awaited the start of Day 3 at the Global Fintech Fest 2024, the excitement was palpable. The added certainty that our very honorable Prime Minister Shri Narendra Modi would be in attendance boosted that expectation. His attendance was to take the day to another level since everyone eagerly awaited his views on India’s position in the global fintech industry. One could easily note preparations for his arrival were in full swing all over Mumbai.

The excitement built as we kicked off Day 2 of the Global Fintech Fest 2024! The day was packed with insightful sessions led by some of the brightest minds in the fintech world, including top CEOs and founders. The energy was palpable, with everyone eager to dive into discussions that would shape the future of digital finance.

The retail industry has always been competitive, and it is important for retailers to thrive in this competition by adapting to new technologies and trends. In the current age of e-commerce, any retailer who does not adapt to the changes will stay behind their peers. With ever-changing customer behaviors and their requirements for personalized shopping experiences, it has become essential for retailers to invest more in these areas.

Kotlin was built to replace Java. Or at least, supersede it for a wide range of Android development tasks. Released by JetBrains in 2016, it is designed to address some of Java’s drawbacks and provide a smoother, faster alternative for devs everywhere. So really the question in the title is redundant, right? Kotlin is just better, surely? Well, actually it’s much more nuanced than that.

Manual testing is a fundamental yet essential process in software development where testers manually execute test cases without the use of automation tools. Despite advancements in automation, over 30% of global testing efforts are still carried out manually, according to industry research.

Businesses handle massive amounts of data daily. To make informed decisions, this data must be optimized for accuracy and consistency, which requires effective data management. However, for best results, data management must be coupled with data governance, which provides essential frameworks to manage data quality and security. Data stewardship takes it a step further.