Daily Tech Digest - July 28, 2021

DevOps Is Dead, Long Live AppOps

The NoOps trend aims to remove all the frictions between development and the operation simply removing it, as the name tells. This may seem a drastic solution, but we do not have to take it literally. The right interpretation — the feasible one — is to remove as much as possible the human component in the deployment and delivery phases. That approach is naturally supported by the cloud that helps things to work by themself. ... One of the most evident scenarios that explain the benefit of AppOps is every application based on Kubernetes. If you will open each cluster you will find a lot of pod/service/deployment settings that are mostly the same. In fact, every PHP application has the same configuration, except for parameters. Same for Java, .Net, or other applications. The matter is that Kubernetes is agnostic to the content of the host's applications, so he needs to inform it about every detail. We have to start from the beginning for all new applications even if the technology is the same. Why? I should explain only once how a PHP application is composed. 

Thrill-K: A Blueprint for The Next Generation of Machine Intelligence

Living organisms and computer systems alike must have instantaneous knowledge to allow for rapid response to external events. This knowledge represents a direct input-to-output function that reacts to events or sequences within a well-mastered domain. In addition, humans and advanced intelligent machines accrue and utilize broader knowledge with some additional processing. I refer to this second level as standby knowledge. Actions or outcomes based on this standby knowledge require processing and internal resolution, which makes it slower than instantaneous knowledge. However, it will be applicable to a wider range of situations. Humans and intelligent machines need to interact with vast amounts of world knowledge so that they can retrieve the information required to solve new tasks or increase standby knowledge. Whatever the scope of knowledge is within the human brain or the boundaries of an AI system, there is substantially more information outside or recently relevant that warrants retrieval. We refer to this third level as retrieved external knowledge.

GitHub’s Journey From Monolith to Microservices

Good architecture starts with modularity. The first step towards breaking up a monolith is to think about the separation of code and data based on feature functionalities. This can be done within the monolith before physically separating them in a microservices environment. It is generally a good architectural practice to make the code base more manageable. Start with the data and pay close attention to how they’re being accessed. Make sure each service owns and controls access to its own data, and that data access only happens through clearly defined API contracts. I’ve seen a lot of cases where people start by pulling out the code logic but still rely on calls into a shared database inside the monolith. This often leads to a distributed monolith scenario where it ends up being the worst of both worlds - having to manage the complexities of microservices without any of the benefits. Benefits such as being able to quickly and independently deploy a subset of features into production. Getting data separation right is a cornerstone in migrating from a monolithic architecture to microservices. 

Data Strategy vs. Data Architecture

By being abstracted from the problem solving and planning process, enterprise architects became unresponsive, he said, and “buried in the catacombs” of IT. Data Architecture needs to look at finding and putting the right mechanisms in place to support business outcomes, which could be everything from data systems and data warehouses to visualization tools. Data architects who see themselves as empowered to facilitate the practical implementation of the Business Strategy by offering whatever tools are needed will make decisions that create data value. “So now you see the data architect holding the keys to a lot of what’s happening in our organizations, because all roads lead through data.” Algmin thinks of data as energy, because stored data by itself can’t accomplish anything, and like energy, it comes with significant risks. “Data only has value when you put it to use, and if you put it to use inappropriately, you can create a huge mess,” such as a privacy breach. Like energy, it’s important to focus on how data is being used and have the right controls in place. 

Why CISA’s China Cyberattack Playbook Is Worthy of Your Attention

In the new advisory, CISA warns that the attacks will also compromise email and social media accounts to conduct social engineering attacks. A person is much more likely to click on an email and download software if it comes from a trusted source. If the attacker has access to an employee's mailbox and can read previous messages, they can tailor their phishing email to be particularly appealing – and even make it look like a response to a previous message. Unlike “private sector” criminals, state-sponsored actors are more willing to use convoluted paths to get to their final targets, said Patricia Muoio, former chief of the NSA’s Trusted System Research Group, who is now general partner at SineWave Ventures. ... Private cybercriminals look for financial gain. They steal credit card information and health care data to sell on the black market, hijack machines to mine cryptocurrencies, and deploy ransomware. State-sponsored attackers are after different things. If they plan to use your company as an attack vector to go after another target, they'll want to compromise user accounts to get at their communications. 

Breaking through data-architecture gridlock to scale AI

Organizations commonly view data-architecture transformations as “waterfall” projects. They map out every distinct phase—from building a data lake and data pipelines up to implementing data-consumption tools—and then tackle each only after completing the previous ones. In fact, in our latest global survey on data transformation, we found that nearly three-quarters of global banks are knee-deep in such an approach.However, organizations can realize results faster by taking a use-case approach. Here, leaders build and deploy a minimum viable product that delivers the specific data components required for each desired use case (Exhibit 2). They then make adjustments as needed based on user feedback. ... Legitimate business concerns over the impact any changes might have on traditional workloads can slow modernization efforts to a crawl. Companies often spend significant time comparing the risks, trade-offs, and business outputs of new and legacy technologies to prove out the new technology. However, we find that legacy solutions cannot match the business performance, cost savings, or reduced risks of modern technology, such as data lakes. 

Data-Intensive Applications Need Modern Data Infrastructure

Modern applications are data-intensive because they make use of a breadth of data in more intricate ways than anything we have seen before. They combine data about you, about your environment, about your usage and use that to predict what you need to know. They can even take action on your behalf. This is made possible because of the data made available to the app and data infrastructure that can process the data fast enough to make use of it. Analytics that used to be done in separate applications (like Excel or Tableau) are getting embedded into the application itself. This means less work for the user to discover the key insight or no work as the insight is identified by the application and simply presented to the user. This makes it easier for the user to act on the data as they go about accomplishing their tasks. To deliver this kind of application, you might think you need an array of specialized data storage systems, ones that specialize in different kinds of data. But data infrastructure sprawl brings with it a host of problems.  

The Future of Microservices? More Abstractions

A couple of other initiatives regarding Kubernetes are worth tracking. Jointly created by Microsoft and Alibaba Cloud, the Open Application Model (OAM) is a specification for describing applications that separate the application definition from the operational details of the cluster. It thereby enables application developers to focus on the key elements of their application rather than the operational details of where it deploys. Crossplane is the Kubernetes-specific implementation of the OAM. It can be used by organizations to build and operate an internal platform-as-a-service (PaaS) across a variety of infrastructures and cloud vendors, making it particularly useful in multicloud environments, such as those increasingly commonly found in large enterprises through mergers and acquisitions. Whilst OAM seeks to separate out the responsibility for deployment details from writing service code, service meshes aim to shift the responsibility for interservice communication away from individual developers via a dedicated infrastructure layer that focuses on managing the communication between services using a proxy. 

Navigating data sovereignty through complexity

Data sovereignty is the concept that data is subject to the laws of the country which it is processed in. In a world where there is a rapid adoption of SaaS, cloud and hosted services, it becomes obvious to see the issues that data sovereignty can have. In simpler times, data wasn’t something businesses needed to be concerned about and could be shared and transferred freely with no consequence. Businesses that also had a digital presence operated on a small scale and with low data demands hosted on on-premise infrastructure. This meant that data could be monitored and kept secure, much different from the more distributed and hybrid systems that many businesses use today. With so much data sharing and lack of regulation, it all came crashing down with the Cambridge Analytica scandal in 2016, promoting strict laws on privacy. ... When dealing with on-premise infrastructure, governance is clearer, as it must follow the rules of the country it’s in. However, when it’s in the cloud, a business can store its data in any number of locations regardless of where the business itself is.

How security leaders can build emotionally intelligent cybersecurity teams

EQ is important, as it has been found by Goleman and Cary Cherniss to positively influence team performance and to cultivate positive social exchanges and social support among team members. However, rather than focusing on cultivating EQ, cybersecurity leaders such as CISOs and CIOs are often preoccupied by day-to-day operations (e.g., dealing with the latest breaches, the latest threats, board meetings, team meetings and so on). In doing so, they risk overlooking the importance of the development and strengthening of their own emotional intelligence (EQ) and that of the individuals within their teams. As well as EQ considerations, cybersecurity leaders must also be conscious of the team’s makeup in terms of gender, age and cultural attributes and values. This is very relevant to cybersecurity teams as they are often hugely diverse. Such values and attributes will likely introduce a diverse set of beliefs defined by how and where an individual grew up and the values of their parents. 

Quote for the day:

"The mediocre leader tells The good leader explains The superior leader demonstrates The great leader inspires." -- Buchholz and Roth

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