In some Caching strategies, especially for dynamic resources, the cache can store not only the complete response but also the individual elements or changes that make up the response. This approach is known as “delta caching” or “incremental caching.” Instead of sending the complete response, delta caching sends only the changes or updates made to the cached version of the resource. ... Delta caching is particularly useful for scenarios where resources change frequently, but the changes are relatively small compared to the complete resource. For example, in a collaborative document editing application, delta caching can be employed to send only the changes made by a user to a shared document, instead of sending the entire document every time it is updated. ... Caching enhances application resilience by reducing the risk of service disruptions during periods of high demand. By serving cached responses, even if the backend servers experience temporary performance issues, the application can continue to respond to a significant portion of requests from the cache. The caching layer acts as a buffer between the backend servers and the clients.

People become nervous for many reasons. More than 75 percent of people report being nervous in high-stakes communication, be it planned or spontaneous. Past experience could be a factor, as well as high stakes and the importance of the goals you’re trying to achieve. Those of us who study this at an academic level believe that the nervousness is wired into being human. We see this across all cultures. We see it develop typically in the early teen years and progress from there. There’s an evolutionary component to it. One of the most helpful tips is normalizing the anxiety that you feel. You’re not alone. ... My anxiety Management plan has three steps. The first thing I do is hold something cold in the palms of my hand before I speak. That cools me down. Secondly, I say tongue twisters to warm up my voice and also to get myself in the moment. Third, I remind myself, “I am in service of my audience. I am here to help them.” That really gets me other-focused rather than self-focused. That’s my anxiety management plan. I encourage everybody to find a plan that works for them.

Being able to fine tune as well as train generative AI is a process that relies on data, lots and lots of data. For enterprise use cases, that data isn’t just generic data taken from a public source, but rather is data that an organization already has in its data centers or cloud deployments and is likely also spread across multiple locations. To help enable enterprises to fully benefit from data for generative AI, Dell is building out an open data lakehouse platform. The data lakehouse concept is one that was originally pioneered by Databricks, as a way of enabling organizations to more easily query data stored in cloud object storage based data lakes. The Dell approach is a bit more nuanced in that it is taking a hybrid approach to data, with a goal of being able to query data across on-premises as well as mutli-cloud deployments. Greg Findlen, senior VP data management at Dell explained during the press briefing that the open data lakehouse will be able to use Dell storage and compute capabilities as well as multi-cloud storage. 

In South Africa, data governance tends to be a grudge investment based on regulatory issues. However, organisations that don’t do the basics well, and don’t have mature data governance and established frameworks in place, may well find they are spending on analytics technologies that don’t live up to expectations. What stands in the way of getting governance right? Firstly, it’s not easy. It involves all stakeholders across all domains. It may require a mindset change, and users may need to learn to use new technology. Secondly, it can be expensive, and it may take time before the organisation sees the value of it. One of the biggest problems is that the value of data governance investments is difficult to quantify in monetary terms. ... Data products should be supported by the entire CDO capability – including the CDO, data owners and data stewards – as well as IT, to ensure the data products will add the required business value. Owners and stewards need to identify and curate the required data for the products, while also ensuring good quality data and metadata management to make it more usable for broader business.

Manufacturing engineers produce assembly lines and manufacturing processes that can produce those units of value. Software engineers are largely the same, also producing systems and processes that deliver units of value. The manufactured widget of software is actually the discrete user interactions with those features and pieces of software, not the features themselves. The assembly line in software engineering isn’t, as many think, the engineers producing features. ... Systems like Total Quality Management, which are focused on driving a cultural mindset of continuous improvement and an entire company focused on providing very low defect rates, easily translate to customer satisfaction in software organizations. Just to pick on TQM a bit, if we were to adapt it to software, we would focus on the number of times users are impacted by a defect more than the number of open bugs. Instead of tracking the number of defects and searching for more, we would be tracking the number of users who either failed to receive the promised value from the product or had severely diminished value.

“The basic idea of serverless computing has been around since the beginning of cloud computing. However, it has not become widely accepted,” explains Samuel Kounev, who heads the JMU Chair of Computer Science II (Software Engineering). But a shift can currently be observed in the industry and in science, the focus is increasingly moving towards serverless computing. A recent article in the Communications of the ACM magazine of the Association for Computing Machinery (ACM) deals with the history, status and potential of serverless computing. Among the authors are Samuel Kounev and Dr. Nikolas Herbst, who heads the JMU research group “Data Analytics Clouds”. ... “NoOps” is the first, which stands for “no operations”. This means, as described above, that the technical server management, including the hardware and software layers, is completely in the responsibility of the cloud provider. The second principle is “utilisation-based billing”, which means that only the time during which the customer actively uses the allocated computing resources is billed. 

Some software development issues can be fixed later. Building an application that scales efficiently to handle millions or billions of events isn’t one of them. Creating effective code with no bottlenecks that surprise everyone when the app finally runs at full scale requires plenty of forethought and high-level leadership. It’s not something that can be fixed later with a bit of targeted coding and virtual duct tape. The algorithms and data structures need to be planned from the beginning. That means the architects and the management layer need to think carefully about the data that will be stored and processed for each user. When a million or a billion users show up, which layer does the flood of information overwhelm? How can we plan ahead for those moments? Sometimes this architectural forethought means killing some great ideas. Sometimes the management layer needs to weigh the benefits with the costs of delivering a feature at scale. Some data analysis just doesn’t work well at large scale. Some formulas grow exponentially with more users. 

For better understanding and evaluation, the study was able to categorize the responding organizations into "secure creators" and "prone enterprises." The grouping was done on the basis of the number of solutions used, the adoption of emerging technologies, and the use of technologies to simplify their automation environments. The study found that secure creators are more satisfied with their approach to cybersecurity, experience fewer cybersecurity incidents, and can detect and respond to incidents quicker. About 70% of them are early adopters of emerging technologies. The secure creators are also more focused on extracting the most value from specific advanced solutions, with 62% already using or in the late stages of implementing AI/ML solutions, as compared to only 45% of the prone enterprises. "When it comes to technology, the more clutter an organization has in its armory, the harder it is to pick up signals and get on top of issues quickly," Watson said.