Las Vegas began deploying edge computing technology in 2018 while working on smart traffic solutions. A key driver for analyzing data at the network edge came from working with autonomous vehicle companies that needed near real-time data, Sherwood says. “Edge computing allowed for data to be analyzed and provided to the recipient in a manner which provided the best in speed,” Sherwood says. Visualizing data in a real-time format “allows for decision-makers to make more informed decisions.” The addition of predictive analytics and artificial intelligence (AI) is helping with decisions that are improving traffic flows, “and in the near future will have dramatic impacts on reducing traffic congestion and improving transit times and outcomes,” Sherwood says. To help bolster its data analytics operations overall and at the edge, the city government is developing a data analytics group as an offshoot of the IT department. The Office of Data and Analytics will drive how data is governed and used within the organization, Sherwood says. “We see lots of opportunities with many new technologies coming onto the market,” he says.
In order to learn how to test with databases, one must first ‘unlearn’ a few things starting with the concept of unit tests and integration tests. To put it bluntly, the modern definitions of these terms are so far removed from their original meanings that they are no longer useful for conversation. So, for the remainder of this article, we aren’t going to use either of them. The fundamental goal of testing is to produce information. A test should tell you something about the thing being tested you may not have known before. The more information you get the better. So, we are going to ignore anyone who says, “A test should only have one assertion” and replace it with, “A test should have as many assertions as needed to prove a fact”. The next problematic expression we need to deal with is, “All tests should be isolated”. This is often misunderstood to mean each test should be full of mocks so the function you’re testing is segregated from its dependencies. This is nonsense, as that function won’t be segregated from its dependencies in production.
Is the Great Resignation a temporary trend or a long-term structural change? There’s no way to know but my money is on the latter. Life-changing events change lives, whether or not we realize it as it is occurring. An individual crisis changes individual behavior, worldwide crises cause lasting social and cultural consequences. The pandemic completely upended the employee experience, and while many employers continued to monitor productivity, most didn’t devote nearly the same amount of effort to soliciting real-time, real-world feedback from remote workers about the challenges, struggles and stresses they were facing. McKinsey identified “employees prioritize relational factors, whereas employers focus on transactional ones”. By neglecting to engage with remote employees, not listening to nor addressing their issues and concerns, employers missed a once-in-a-lifetime opportunity to build trust in within the organization and loyalty from workers. As the Great Resignation plays out and the workforce reshuffles, it will be interesting to see if employers and workers can engage, listen, and trust each other enough to find common ground.
Ransomware offers a low-investment, high-profit business model that’s irresistible to criminals. What began with single-PC attacks now includes crippling network-wide attacks using multiple extortion methods to target both your data and reputation, all enabled by human intelligence. Through this combination of real-time intelligence and broader criminal tactics, ransomware operators have driven their profits to unprecedented levels. This human-operated ransomware, also known as “big game ransomware,” involves criminals hunting for large targets that will provide a substantial payday through syndicates and affiliates. Ransomware is becoming a modular system like any other big business, including ransomware as a service (RaaS). With RaaS there isn’t a single individual behind a ransomware attack; rather, there are multiple groups. For example, one threat actor may develop and deploy malware that gives one attacker access to a certain category of victims; whereas, a different actor may merely deploy malware.
Simply put, the phishing “game” only has two moves: the scammers always play first, trying to trick you, and you always get to play second, after they’ve sent out their fake message. There’s little or no time limit for your move; you can ask for as much help as you like; you’ve probably got years of experience playing this game already; the crooks often make really silly mistakes that are easy to sp …and if you aren’t sure, you can simply ignore the message that the crooks just sent, which means you win anyway! How hard can it be to beat the criminals every time? Of course, as with many things in life, the moment you take it for granted that you will win every time is often the very same moment that you stop being careful, and that’s when accidents happen. Don’t forget that phishing scammers get to try over and over again. They can use email attachments one day, dodgy web links the next, rogue SMSes the day after that, and if none of those work, they can send you fraudulent messages on a social network: The crooks can try threatening you with closing your account, warning you of an invoice you need to pay, flattering you with false praise, offering you a new job, or announcing that you’ve won a fake prize.
As technology extends deeper into every aspect of business, the tip of the spear is often some device at the outer edge of the network, whether a connected industrial controller, a soil moisture sensor, a smartphone, or a security cam. This ballooning internet of things is already collecting petabytes of data, some of it processed for analysis and some of it immediately actionable. So an architectural problem arises: You don’t want to connect all those devices and stream all that data directly to some centralized cloud or company data center. The latency and data transfer costs are too high. That’s where edge computing comes in. It provides the “intermediating infrastructure and critical services between core datacenters and intelligent endpoints,” as the research firm IDC puts it. In other words, edge computing provides a vital layer of compute and storage physically close to IoT endpoints, so that control devices can respond with low latency – and edge analytics processing can reduce the amount of data that needs to be transferred to the core.
Automating software and security testing in software development is an ongoing process, yet truly reaching full automation may never happen. In SmartBear Software’s “2021 State of Software Quality | Testing” the percentage of organizations that conduct all tests manually rose from 5% in 2019 to 11% in 2021. This does not mean that automation is not happening. On the contrary, both manual and automated tests are being conducted. The biggest challenge to test automation is no longer dealing with changing functionality but instead not having enough time to create and conduct tests. Testers are not being challenged by demands to deploy more frequently but instead to test more frequently across more environments. Testing of the user interface layer is more common, and to address this 50% currently conduct some automated usability testing as compared to just 34% in 2019. The remainder of the article provides additional highlights on this and two other reports that highlight DevSecOps metrics and practices.
Slack’s list of design principles begins with each API doing one thing well and the developer experience. The first is that APIs should focus on a specific use case, thus becoming more straightforward, safer and easier to scale. The authors believe that APIs should be so well designed and documented that developers should be able to build a simple use case in a matter of minutes and discover parts of the API intuitively. In case of errors, the API should return all the information necessary for developers to understand the cause of the error and take the first steps towards solving it. The fifth principle concerns scale and performance. The authors provide concrete advice, recommending pagination of big collections, avoiding nesting big collections inside other big collections, and implementing rate limiting on the API. The last principle enumerated by the authors is that breaking changes should be avoided.
The first step toward creating a comprehensive DRP strategy is to identify the specific business needs the retention policy must address. The next step should be reviewing the compliance regulations that are applicable to the entire organization. “Designate a team of individuals across various business practices to begin data inventorying and devising a plan to implement and maintain a data retention policy that meets your business requirements while adhering to compliance regulations,” Gandhi advises. The enterprise's chief data officer (CDO) should oversee the DRP's design and implementation, Ferreira recommends. “However, everyone who deals with the data must be aware of the mechanisms implemented ... so that they can behave in ways that facilitate the implementation of the DRP,” he adds. “Implementing a robust DRP may be a top-down decision, but it requires buy-in from all levels of the organization.” Stakeholders from records, legal, IT, security, privacy, and other relevant posts and departments all need a chance to weigh in on an enterprise's data retention policy, Read says.
In addition to its far-reaching geographical footprint, FSU has a broad range of operational needs to support the diversity of work typical of a university. It also has distributed IT. All those factors make for additional levels of complexity within disaster recovery and business continuity plans. Furthermore, at the time of the audit, the university had 307 different units expected to devise their own disaster and recovery plans as well as complete an annual 140-question risk assessment. Hunkapiller sought to overcome those complexities by using a multipronged approach to first tackle the inadequacies in the university’s business continuity, disaster preparedness and response capabilities and then encourage continuous improvement. “The idea was to better identify risks, improve our vulnerability management and resiliency plans, ensure continuity of operations and bring risk down to a level that was tolerable,” says Hunkapiller, who worked with FSU’s Department of Emergency Management to devise Seminole Secure.
Quote for the day:
"So much of what we call management consists in making it difficult for people to work." -- Peter Drucker