Automating the processes of device discovery and configuration validation allows you to enforce good network security by making sure that your devices and configurations not accidentally leaving any security holes open. Stated differently, the goal of automation is to guarantee that your network policies are consistently applied across the entire network. A router that’s forgotten and left unsecured could be the avenue that bad actors exploit. Once each device on the network is discovered, the automation system downloads its configurations and checks them against the configuration rules that implement your network policies. These policies range from simple things that are not security related, like device naming standards, to essential security policies like authentication controls and access control lists. The automation system helps deploy and maintain the configurations that reflect your policies. ... A network-change and configuration-management (NCCM) system can use your network inventory to automate the backup of network-device configurations to a central repository.
The difficulty comes from avoiding “retry storms” or a “retry DDoS,” which is when a system in a degraded state triggers retries, increasing load and further decreasing performance as retries increase. A naive implementation won’t take this scenario into account as it may require integrating with a cache or other communication system to know if a retry is worth performing. A service mesh can do this by providing a bound on the total number of retries allowed throughout the system. The mesh can also report on these retries as they occur, potentially alerting you of system degradation before your users even notice. ... The design pattern of sidecar proxies is another exciting and powerful feature, even if it is sometimes oversold and over-engineered to do things users and tech aren’t quite ready for. While the community waits to see which service mesh “wins,” a reflection of the over-hyped orchestration wars before it, we will inevitably see more purpose-built meshes in the future and, likely, more end-users building their own control planes and proxies to satisfy their use cases.
A classification model is a technique that tries to draw conclusions or predict outcomes based on input values given for training. The input, for example, can be a historical bank or any financial sector data. The model will predict the class labels/categories for the new data and say if the customer will be valuable or not, based on demographic data such as gender, income, age, etc. Target class imbalance is the classes or the categories in the target class that are not balanced. Rao, giving an example of a marketing campaign, said, let’s say we have a classification task on hand to predict if a customer will respond positively to a campaign or not. Here, the target column — responded has two classes — yes or no. So, those are the two categories. In this case, let’s say the majority of the people responded ‘no.’ Meaning, the marketing campaign where you end up reaching out to a lot of customers, only a handful of them want to subscribe, for example, this can be you offering a credit card, a new insurance policy, etc. The one who subscribed or is interested would request more details.
Motivational debt is a hidden cost to product delivery. It’s the rust that is accruing on aged PBIs, the sludge at the bottom of the Sprint Backlog and the creaking of the process when needing to do something new. Technical debt is to quality what motivational debt is to process. It’s important to remember that whilst motivational debt is shouldered by the entire Scrum Team, there is an individual element of accrual to it as well. Both short-term stresses which bounce back quickly (“I didn’t get any sleep last night”) to long-term tensions which don’t (“My parents are ill) all contribute to the motivational complexities of a Scrum Team. Moving to address these actively is an ethical quandary, as individuals have different coping mechanisms, meaning efforts to help may actually exacerbate the issue. Remember that whilst some team members may be feeling down, others may be up, therefore being conscious of the overall direction of pull is vital as a Scrum Master. Holistically, it is fair to say that motivational debt is felt both individually and collectively and it is everyone’s responsibility to create an environment where it can be minimised. But how can you do this?
Those responsible for addressing the government’s current levels of wasted IT expenditure may find that businesses offer positive, proactive case studies that highlight the value of embracing digital transformation. A 2020 study from Deloitte, for instance, has found that digitally mature companies – those that have embraced various aspects of digital transformation – saw net revenue growth of 45% and net profit growths of 43% compared to industry averages. The same study has found that the benefits of digital maturation are not limited to profits, but to a range of outcomes including increased efficiency, better product and service quality, and higher levels of both customer satisfaction and employee engagement. A study from McKinsey is even more strident, noting that “by digitising information-intensive processes, costs can be cut by up to 90% and turnaround times improved by several orders of magnitude.” Part of the ‘Organising for Digital Delivery Report’ includes a commitment to “investing in developing the technical fluency of senior civil service leadership.”
Process mining is used to obtain a wide lens over business processes and workflows within a company by examining event logs across systems, including how variable they are and where there are bottlenecks. The less variable the process, the greater its potential candidacy for RPA/IA, though other factors must be considered as well. Task mining is used to understand how a user is interacting with systems and where there are opportunities for automation. Both of the above help identify automation candidates throughout an organization. IDP is a use case of IA and is growing in popularity, as there are so many document-intensive processes across organizations that impact many employees. ... Data governance, visibility of shadow deployments (and having guardrails in place for them), and security are all important to set in place ahead of RPA/IA to ensure architectural readiness. Another challenge is ensuring that the infrastructure is able to handle the increased speed and volume of transactions related to automated processes, whether it’s their own or someone they do business with.
From a software development perspective, DevOps automation enhances the performance of the engineering teams with the help of top-notch DevOps tools. It encourages cross teams to work together by removing organizational silos. The reduced team inter-dependencies and manual processes for infrastructure management have enabled the software team to focus on frequent releases, receiving quick feedback, and improving user experience. From an organizational point of view, DevOps automation reduces the chances of human errors and saves the time used for error detection with the help of auto-healing features. Additionally, it minimizes the time required for deploying new features significantly and removes any inconsistencies caused due to human errors. Enterprises should first focus on the areas where they face the most challenges. The decision on what to automate depends on their organizational needs and technological feasibility. The DevOps automation teams should be able to analyze which areas of the DevOps lifecycle needs automation.
The obvious concern about being the victim of a ransomware attack is being locked out from data, applications, and systems – making organizations unable to do business. Then, there is the concern of what an attack is going to cost; the question of whether or not you need to pay the ransomware is being forced by cybercriminal gangs, as 77% of attacks also included the threat of leaking exfiltrated data. Next are the issues of lost revenue, an average of 23 days of downtime, remediation costs, and the impact on the businesses’ reputation. But those are post-attack concerns, and you should, first and foremost, be laser-focused on what effective measures you can you take to stop ransomware attacks. Organizations that are truly concerned about the massive growth in ransomware are working to understand the tactics, techniques and procedures used by threat actors to craft preventative, detective and responsive measures to either mitigate the risk or minimize the impact of an attack. Additionally, these organizations are scrutinizing the technologies, processes and frameworks they have in place, as well as asking the same of their third-party supply chain vendors.
If your organization is looking to hire data engineers in the next 12 months, be prepared to move quickly in your hiring process and think carefully before you waste time negotiating salaries. That’s some of the advice for hiring managers from the first edition of Salaries of Data Engineering Professionals from the quantitative executive recruiting firm Burtch Works. Known for its work with data scientists and analytics professionals, and its annual salary surveys that look at the employment trends for those professionals, this year, Burtch Works has expanded by offering this new survey for data engineers, conducted in individual interviews with 320 of these professionals based in the United States. The survey looks at salaries, demographics, and trends among data engineers. What is a data engineer? These are the professionals responsible for building and managing the data and IT infrastructure that sits between the data sources and the data analytics. They report into the IT department, the data science department, or both. According to the Burtch Works survey, these professionals command a high rate of pay.
Scientists have claimed victory against future diseases after successfully decoding the human genome. The marriage of this knowledge to the health data generated by patients would enable clinicians to make better decisions about our care. The two benefits of using predictive analytics: better care and lower costs. The biggest lesson of the recent global health issues such as COVID-19, SARS, dengue and malaria outbreaks is that pharma and healthcare companies cannot afford merely to react to every emerging situation. They need to track several data streams of local, regional, and global trends, create a database, and then predict various scenarios. Data analytics helps companies develop their predictive models, enabling them to make quicker, intelligent decisions, build partnerships, and resolve bottlenecks before the crisis hits the shore. Such data-driven measures aim to save invaluable lives and allow care to be personalized for each individual. Predictive analytics can classify particular risk factors for diverse populations. This is very useful for patients suffering from multiple ailments with complex medical histories.
Quote for the day:
"Every great leader has incredible odds to overcome." -- Wayde Goodall