Edge computing supports the metaverse by minimizing network latency, reducing bandwidth demands and storing significant data locally. Edge computing, in this context, means compute and storage power placed closer to a metaverse participant, rather than in a conventional cloud data center. Latency increases with distance—at least for current computing and networking technologies. Quantum physics experiments can convey information at a distance without significant delay, but those aren’t systems we can scale or use for standard purposes—yet. In a virtual world, you experience latency as lag: A character might appear to hesitate a bit as it moves. Inconsistent latency produces movement that might appear jerky or communication that varies in speed. Lower latency, in general, means smoother movement. Edge computing can also help reduce bandwidth, since calculations get handled by either an on-site system or one nearby, rather than a remote location. Much as a graphics card works in tandem with a CPU to handle calculations and render images with less stress on the CPU, an edge computing architecture moves calculations closer to the metaverse participant.
Survey participant, Andrew Whytock, head of digitalization in Siemens pharmaceutical division, crystallized the criticality of employee recruitment, training and retention, explaining, “It’s great having a big tech strategy, but employers are struggling to find the people to execute their plans.” In addition to growth needs, staffing problems extend to fortifying cybersecurity. Nearly 60% of respondents reported that cybersecurity objectives are behind schedule. When asked to identify the “internal challenges” driving delays, executives ranked “lack of key skills” and “cultural obstacles” highest. That’s inexcusable. Lax tech controls and strategy acceleration pressure make a dangerous mix. To thrive, “digitally mature” enterprises need top talent in supportive cultures to unlock the transformative value of their sizable IT modernization investments. ... Despite huge investments in job training and leadership development, broad business perspective remains a widespread skill gap.
“Data architecture is many things to many people and it is easy to drown in an ocean of ideas, processes and initiatives,” says Tim Garrood, a data architecture expert at PA Consulting. Firms need to ensure that data architecture projects deliver value to the business, he adds, and this needs knowledge and skills, as well as technology. However, part of the challenge for CIOs and CDOs is that technology is driving complexity in both data management and how it is used. As management consultancy McKinsey put it in a 2020 paper: “Technical additions – from data lakes to customer analytics platforms to stream processing – have increased the complexity of data architectures enormously.” This is making it harder for firms to manage their existing data and to deliver new capabilities. The move away from traditional relational database systems to much more flexible data structures – and the ability to capture and process unstructured data – gives organisations the potential to do far more with data than ever before. The challenge for CIOs and CDOs is to tie that opportunity back to the needs of the business.
Cloud orchestration is the coordination and automation of workloads, resources, and infrastructure in public and private cloud environments and the automation of the whole cloud system. Each part should work together to produce an efficient system. Cloud automation is a subset of cloud orchestration focused on automating the individual components of a cloud system. Cloud orchestration and automation complement each other to produce an automated cloud system. ... Cloud orchestration supports the DevOps framework by allowing continuous integration, monitoring, and testing. Cloud orchestration solutions manage all services so that you get more frequent updates and can troubleshoot faster. Your applications are also more secure as you can patch vulnerabilities quickly. The journey towards full cloud orchestration is hard to complete. To make the transition more manageable, you can find benefits along the way with cloud automation. For example, you might automate the database component to speed up manual data handling or install a smart scheduler for your Kubernetes workloads.
From tech giants to small businesses: we will all have to make sure our hardware and software is updated so that our data is protected against the arrival of quantum computers. It seems far away, but it’s not a problem for later: any encrypted data captured today can be broken by a sufficiently powerful quantum computer in the future. ... How does it work? cloudflared creates long-running connections to two nearby Cloudflare data centers, for instance San Francisco and one other. When your employee visits your domain, they connect to a Cloudflare server close to them, say in Frankfurt. That server knows that this is a Cloudflare Tunnel and that your cloudflared has a connection to a server in San Francisco, and thus it relays the request to it. In turn, via the reverse connection, the request ends up at cloudflared, which passes it to the webapp via your internal network. In essence, Cloudflare Tunnel is a simple but convenient tool, but the magic is in what you can do on top with it: you get Cloudflare’s DDoS protection for free; fine-grained access control with Cloudflare Access and request logs just to name a few.
The benefit of a microservice architecture is that developers can deploy features that prevent cascading failures. A variety of tools are also available, from GitLab and others, to build fault-tolerant microservices that help improve the resilience of the infrastructure. A microservice application can be programmed in any language, so dev teams can choose the best language for the job. The fact that microservices architectures are language agnostic also allows the developers to use their existing skill sets to maximum advantage – no need to learn a new programming language just get the work done. Using cloud-based microservices gives developers another advantage, as they can access an application from any internet-connected device, regardless of its platform. A microservices architecture lets teams deploy independent applications without affecting other services in the architecture. This feature will enable developers to add new modules without redesigning the system's complete structure. Businesses can efficiently add new features as needed under a microservices architecture.
According to TechRepublic, data preparation is “the process of cleaning, transforming and restructuring data so that users can use it for analysis, business intelligence and visualization.” AWS’s definition is even simpler: “Data preparation is the process of preparing raw data so that it is suitable for further processing and analysis.” But what does this actually mean in practice? Data doesn’t typically reach enterprises in a standardized format and, thus, needs to be prepared for enterprise use. Some of the data is structured—like customer names, addresses and product preferences — while most is almost certainly unstructured—like geo-spatial, product reviews, mobile activity and tweets. Before data scientists can run machine learning models to tease out insights, they’re first going to need to transform the data, reformatting it or perhaps correcting it, so it’s in a consistent format that serves their needs. ... In addition, data preparation can help to reduce data management costs that balloon when you try to apply bad data to otherwise good ML models. Now, given the importance of getting data preparation right, what are some tips for doing it well?
The SnapshotRead isolation level, although not an ANSI standard, has been gaining popularity. This is also known as MVCC. The advantage of this isolation level is that it is contention-free: it creates a snapshot at the beginning of the transaction. All reads are sent to that snapshot without obtaining any locks. But writes follow the rules of strict Serializability. A SnapshotRead transaction is most valuable for a read-only workload because you can see a consistent database snapshot. This avoids surprises while loading different pieces of data that depend on each other transactionally. You can also use the snapshot feature to read multiple tables at a particular time and then later observe the changes that have occurred since that snapshot. This functionality is convenient for Change Data Capture tools that want to stream changes to an analytics database. For transactions that perform writes, the snapshot feature is not that useful. You mainly want to control whether to allow a value to change after the last read. If you want to allow the value to change, it will be stale as soon as you read it because someone else can update it later.
Data tells the story of what works – and perhaps more importantly, what doesn’t work – for your team. It provides a clear and unbiased picture of how new transformations are netting out and where opportunities lie to increase efficiency and value. Utilizing the right metrics reveals which innovations are most effective for the team, letting IT managers know how transformations are running. Focusing on these results helps organizations streamline business processes and leads to higher team productivity. It also puts IT leaders on the path to sunset legacy solutions that require large budgets or lots of manual work to keep them functional. These changes impact all business areas, allowing employees anywhere and everywhere – not just those in IT – to be more innovative and effective. ... As business leaders focus on meeting the needs of today’s evolving workforce and customers’ desires, operating with a data-driven strategy lets managers stay agile and confident in their next steps. Allowing data to drive decisions also provides a means to back those decisions with clear evidence.
Alarmingly — or perhaps unfairly — only 8 per cent of executives said that their CISO or equivalent performs above average in communicating the financial, workforce, reputational or personal consequences of cyber threats. At the same time, under 15 per cent of executives gave their CISOs or equivalent a top rating from a scale of one to ten. Maintaining a bridge between business and tech is vital when it comes to ensuring all are on the same page regarding security. “It is no surprise that one of the main challenges companies face when implementing a cyber risk mitigation or resiliency plan is the communication gap between the board and the CISO,” said Anthony Dagostino, founder and CEO of cyber insurance and risk management provider Converge. “Cyber resiliency starts with the board because they understand risk and can help their organisations set the appropriate strategy to effectively mitigate that risk. However, while CISOs are security specialists, most of them still struggle with adequately translating security threats into operational and financial impact to their organisations – which is what boards want to understand.
Quote for the day:
"You may be good. You may even be better than everyone esle. But without a coach you will never be as good as you could be." -- Andy Stanley