Software has become an essential part of our daily lives, from the apps on our phones to the programs we use at work. However, software, like all things, has a lifecycle, and as it approaches its end-of-life (EOL). 
Then it poses risks to the security, privacy, and performance of the system on which it runs.

In today’s data-driven world, businesses across industries are increasingly dependent on data warehouse and data lake solutions to store, process, and analyze their ever-growing volumes of data. These traditional approaches have played a crucial role in helping organizations unlock the value hidden within their data, driving informed decision-making. However, as the data management landscape continues to evolve, businesses face new challenges related to data volume, variety, and velocity, coupled with the need for real-time insights, advanced analytics, and machine learning capabilities.
Overcoming Limitations: Data Lakehouses Emerge as the Next-Gen Data Management Solution
Traditional data warehouses excel at handling structured data and providing fast query performance, but often struggle with scalability and rigidity when incorporating new data sources or adapting to changing business needs. Data lakes, in contrast, offer flexible storage solutions for diverse data types but may lack the necessary performance, governance, and advanced analytics support modern organizations require.

Machine learning has become an integral part of many industries, from healthcare to finance and beyond. It provides us with the tools we need to derive meaningful insights and make better decisions. However, even the most accurate and well-trained machine learning models are useless if they’re not deployed in a production environment. That’s where machine learning model deployment comes in.
Deploying a machine learning model can be a daunting task, even for experienced engineers. There are many challenges to overcome, from choosing the right deployment platform to ensuring your model is optimized for production. But fear not; in this article, you’ll learn advanced tips and techniques to help you optimize your machine learning model deployment process and avoid common pitfalls.

Cloud-native technologies like Kubernetes enable companies to build software quickly and scale effortlessly. However, debugging these Kubernetes-based applications can be quite challenging due to the added complexity of building service-oriented architectures (microservices) and operating the underlying Kubernetes infrastructure. 
Bugs are inevitable and typically occur as a result of an error or oversight made during the software development process. So, in order for a business to keep pace with app delivery and keep their end users happy, developers need an efficient and effective way to debug. This involves finding, analyzing, and fixing these bugs. 

Providing a personal ReadMe to your new teammates and stakeholders as a Scrum Master is a great way to build trust and rapport while managing expectations at the same time. I do so regularly, and having a template for that purpose comes in handy.
Therefore, I thought it also might be an excellent exercise to test ChatGPT on more practical aspects of a Scrum Master’s work. So, please follow the complete path to having ChatGPT create a decent personal readme template for Scrum Masters — which took me less than 20 minutes.

In the following videos, I explain, using scenarios and examples, how to view the contents of a deployed message flow and how to retrieve and import resources that are deployed to an integration server in IBM App Connect Enterprise.
In the first video, I use a scenario where an HTTP request is sent to a message flow. The message flow sends a REST Request to a back-end REST API, and the REST API then returns a population estimate for a European city that was entered in the original request message that was sent to the message flow.

If you’ve ever implemented a Java project using a mainstream build system such as Ant, Maven, or Gradle, you’ve probably noticed that you need to use extra language to describe how to build your project.
While this may seem appealing for basic tasks, it can become trickier for more complicated ones. You need to learn a specific soup of XML, write verbose configurations, or write Kotlin DSLs that are intertwined with complex tooling. 

Previously we had a look at the lock class and how it works behind the scenes.In this post, we shall look at LockSupport and its native methods which are one of the building blocks for locks and synchronization classes.
As we can see the methods of lock support are static and behind the scenes, they are based on the Unsafe class.The Unsafe class is not intended to be used by your code directly thus only trusted code can obtain instances of it.

AI is the fastest-moving technology with a solution for every security concern for an enterprise. From building a privacy layer for data management systems to using natural language processing for detecting fraud in inbound messages such as emails, there’s an abundance of whitespace to create. However, while communicating with several business leaders, I have found that most ignore the severity of what may seem like a minor problem: phishing scams through emails. 
Since emails are the primary mode of corporate communications, millions of employees worldwide risk attracting spam and exposing sensitive information. In fact, as per Proofpoint’s findings, 83% of organizations were under email phishing attacks in 2022. 

I’ve heard a lot about Jira not being optimized for QA as, at its core, it is specifically a Project Management solution, and, therefore, it is not about test management. But let’s be honest here, Jira feels unnecessarily complicated when you get started with it, regardless of your position or goals. And given it’s the go-to standard for organizing and managing software development projects – of which QA is an integral part – you’ll barely have a choice in the matter. 
That being said, Jira can (and should) be optimized in a way that is equally efficient for developing new features, testing, and releasing them.