Daily Tech Digest - November 19, 2020

Japan Is Using Robots As A Service To Fight Coronavirus And For Better Quality Of Life

Connected Robotics has developed a number of other food-related robots, including a machine that produces soft-serve ice cream, another that prepares deep-fried foods often sold at Japanese convenience stores, and yet another that cooks bacon and eggs for breakfast. Along the way, it was selected for the Japanese government’s J-Startup program, which highlights promising startups in Japan, and has raised over 950 million yen ($9.1 million) from investors including Global Brain, Sony Innovation Fund and 500 Startups Japan. Connected Robotics wants robots to do more than just prepare food in the kitchen. It is collaborating with the state-backed New Energy and Industrial Technology Development Organization (NEDO) to tackle the task of loading and unloading dishwashers. Under the project, one robot arm will prewash dishes and load them into a dishwashing machine, and another arm will store the clean dishes. The company aims to roll out the machine next spring, part of its goal to have 100 robot systems installed in Japan over the next two years. From that point, it wants to branch out overseas into regions such as Southeast Asia.

DevOps Chat: Adopting Agile + DevOps in Large Orgs

The first step towards creating a change is to acknowledge that you need to change, because once everyone acknowledges that you need to change you can start to think about, well, what sort of change do we want to have? And this has to be a highly collaborative effort. It’s not just your CXOs locking themselves in a conference room at a resort for two days and whiteboarding this out. This has to be a truly collaborative exercise across the entire organization. There may need to be some constraints that are in place, and what I’ve typically seen is a board of directors might say, “Look, this is where we want to be five years from now,” not necessarily saying how that needs to be achieved but saying this is where we want to be. That in combination with creating that sense of urgency says, “Look, if we need to hit this sort of a target we need to be able to develop software faster or close out outages quicker or have a more Agile procurement process or whatever else it might be,” and that’s when you start to identify these sort of strategic and tactical initiatives. Because you’re working collaboratively you’re dispersing that cognitive effort, if you will. So it’s not just somebody from one particular vantage point saying, “As a CFO I think we are giving away too much money so we’re going to be cost-cutting only.”

How empathy drives IT innovation at Thermo Fisher Scientific

The development of an instrument here has always involved an incredible amount of scientific innovation and industrial design. Over the last few years, we have had the added complexity of building software and IoT sensors right into the products. We also develop software that enables our customers’ lab operations. My digital engineering organization has agile capabilities that we can carry over to the development we are doing on customer software solutions. The product groups make the decisions about what solution to develop, but our agile teams, who have been developing software for years, now help the product teams with delivery. ... Now that cloud services are mature, we are all in. We are also very excited about artificial intelligence and its transformational potential, particularly in life sciences. We are putting AI into our customer service operations so that agents spend more time helping a customer and aren’t worrying about how quickly they can finish their service call. AI is also becoming very important for gene sequencing and diagnostics in drug manufacturing. We are only scratching the surface there, but by creating hybrid AI teams made up of both IT and product people, we avoid reinventing the wheel.

CAP Theorem, PACELC, and Microservices

These architectural decisions have impacts that extend beyond data centers and cloud providers. They impact how users interact with the system and what their expectations are. It's important in a system that is eventually consistent that users understand that when they issue a command and get a response, that doesn't necessarily mean their command completed, but rather that their command was received and is being processed. User interfaces should be constructed to set user expectations accordingly. For example, when you place an order from Amazon, you don't immediately get a response in your browser indicating the status of your order. The server doesn't process your order, charge your card, or check inventory. It simply returns letting you know you've successfully checked out. Meanwhile, a workflow has been kicked off in the form of a command that has been queued up. Other processes interact with the newly placed order, performing the necessary steps. At some point the order actually is processed, and yet another service sends an email confirming your order. This is the out-of-band response the server sends you, not through your browser, synchronously, but via email, eventually. And if there is a problem with your order, the service that checked out your cart on the web site doesn't deal with it.

How to Evolve and Scale Your DevOps Programs and Optimize Success

First, there is the fact that simple DevOps managerial topologies might be effective at integrating the workflows of small teams dedicated to the development of a particular software product, but they are far less so when applied to a firms’ total software output. Techniques such as normalized production processes make it easier to catch bugs for large firms, but an over-eager application of these techniques runs the risk of homogenizing otherwise highly adapted teams. Secondly, there is the challenge of inconsistency. Because, as we’ve explained above, DevOps techniques generally arise within small, forward-looking teams, they are generally also highly adapted to the particular needs of these same teams. This can mean that managers trying to standardize DevOps across an organization can be faced with dozens 9if not hundreds) of different approaches, many of which have proven to be very effective. Finally, there is a more basic problem – that of communication. Where DevOps works best, it facilitates close and continuous communication between operations and development staff. Achieving this level of communication not only involves that communications technologies and platforms are in place; it also demands that teams be small enough that they can genuinely talk to each other.

Majority of APAC firms pay up in ransomware attacks

The complexity of having to operate cloud architectures also had a significant impact on the organisation's ability to recover following a ransomware attack, according to Veritas. Some 44% of businesses with fewer than five cloud providers in their infrastructure needing fewer than five days to recover, compared to 12% with more than 20 providers doing likewise. And while 49% of businesses with fewer than five cloud providers could restore 90% or more of their data, only 39% of their peers running more than 20 cloud services were able to do likewise. In Singapore, 49% said their security had kept pace with their IT complexity. Their counterparts in India, at 55% were most confident amongst other in the region about their security measures keeping pace with their IT complexity. Just 31% in China said likewise, along with 36% in Japan, 39% in South Korea, and 43% in Australia.  With ransomware attacks expected to continue to increase amidst accelerated digital transformation efforts and the normalisation of remote work, enterprises in the region will need to ensure they can detect and recover from such attacks. Andy Ng, Veritas' Asia-Pacific vice president and managing director, underscored the security vendor's recommended three-step layered approach to detect, protect, and recover.

How to speed up malware analysis

The main part of the dynamic analysis is to use a sandbox. It is a tool for executing suspicious programs from untrusted sources in a safe environment for the host machine. There are different approaches to the analysis in sandboxes. They can be automated or interactive. Online automated sandboxes allow you to upload a sample and get a report about its behavior. This is a good solution especially compared to assembling and configuring a separate machine for these needs. Unfortunately, modern malicious programs can understand whether they are run on a virtual machine or a real computer. They require users to be active during execution. And you need to deploy your own virtual environment, install operation systems, and set software needed for dynamic analysis to intercept traffic, monitor file changes, etc. Moreover, changing settings to every file takes a lot of time and anyway, you can’t affect it directly. We should keep in mind that analysis doesn’t always follow the line and things may not work out as planned for this very sample. Finally, it’s lacking the speed we need, as we have to wait up to half an hour for the whole cycle of analysis to finish. All of these cons may cause damage to the security if an unusual sample remains undetected. Thankfully, now we have interactive sandboxes.

Graph Databases Gaining Enterprise-Ready Features

Enterprise graph database vendor TigerGraph recently unveiled the results of performance benchmark tests conducted on representative enterprise uses of its scalable application. Touted by the company as a comprehensive graph data management benchmark study, the tests used almost 5TB of raw data on a cluster of machines to show the performance of TigerGraph. The study used the Linked Data Benchmark Council Social Network Benchmark (LDBC SNB), which is a reference standard for evaluating graph technology performance with intensive analytical and transactional workloads. The results and performance numbers showed that graph databases can scale with real data, in real time, according to the vendor. TigerGraph claims it is the first industry vendor to report LDBC benchmark results at this scale. The data showed that TigerGraph can run deep-link OLAP queries on a graph of almost nine billion vertices (entities) and more than 60 billion edges (relationships), returning results in under a minute, according to the announcement. TigerGraph’s performance was measured with the LDBC SNB Benchmark scale-factor 10K dataset on a distributed cluster for the analysis. The implementation used TigerGraph’s GSQL query language, which were compiled and loaded into the database as stored procedures.

Solving the performance challenges of web-scale IT

Two other components to be considered when it comes to performance monitoring for web-scale IT are an in-memory database, and a strong testing protocol. Kieran Maloney, IT services director at Charles Taylor, explained: “Utilising an in-memory database and caching are ways to improve the performance of web-scale applications, particularly for things like real-time or near-time analytics. “The majority of cloud infrastructure service providers now offer PaaS services that include in-memory capabilities, which increase the speed at which data can be searched, accessed, aggregated and analysed – examples include Azure SQL, Amazon ElastiCache for Redis, Google Memorystore, and Oracle Cloud DB. “The other consideration for solving performance management is a testing approach for identifying how the actual performance is, and also determine whether there are specific aspects of the application that are not performing, or could be “tuned” to improve performance or the cost of operation. “There are a number of Application Performance Testing tools available that provide business and IT teams with real-time usage, performance and capacity analysis – allowing them to proactively respond and make interventions to improve performance, as required.”

Optimising IT spend in a turbulent economic climate

Having complete transparency and visibility of an organisations entire IT ecosystem is an essential first step to optimising costs. This includes having a full, holistic view across all solutions, whether they are on-premises or in the cloud. The reality, however, is that many businesses have a fragmented view over the technology applications within their organisation, which makes identifying inefficiencies extremely difficult. Even before the shift to remote work, the evolution of department-led technology purchasing had caused many IT teams to lose visibility of their technology estate, including accounting for what’s being used, how much and what tools are left inactive but still paid for. ... Once a clear view of all technology assets has been defined, IT teams can then start to assess the current usage and spend of the organisation. With many employees working from home, it is likely they will be using a variety of new tools to work effectively. Whilst it can be difficult to determine exactly what is being used and by who when many workers are remote, having this information is crucial to effectively reducing redundancies.

Quote for the day:

"Uncertainty is not an indication of poor leadership; it underscores the need for leadership." -- Andy Stanley

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