Kubernetes: A Data Management Game Changer

February 12, 2019 | 130 views

The only sure thing in the data integration and management world is that technology will continually change. While this is good for companies looking for a better way to manage and use the explosion of data now available, it also presents a challenge: How do you know what technology is right for your needs, and will it really move you closer to your business goals? Every time a new tool is created, there is a lot of buzz about its capabilities, innovative approach to a problem or potential to move data integration or management to the next level.

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BankVault

Imagine owning a business – and have to temporarily shut your business to deal with a computer virus that’s wreaked havoc on your business simply because your antivirus, firewall, and off-site data backups failed. The pain is truly excruciating. Yet it's happening to more and more businesses every day. BankVault is simple yet powerful software which protect your most valuable assets - your computer, online credentials, and ultimately the money in your bank account. It is the best way to win new business and customer confidence in this digital age.

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SOFTWARE

AI's Impact on Improving Customer Experience

Article | July 14, 2022

To enhance the consumer experience, businesses all over the world are experimenting with artificial intelligenace (AI), machine learning, and advanced analytics. Artificial intelligence (AI) is becoming increasingly popular among marketers and salespeople, and it has become a vital tool for businesses that want to offer their customers a hyper-personalized, outstanding experience. Customer relationship management (CRM) and customer data platform (CDP) software that has been upgraded with AI has made AI accessible to businesses without the exorbitant expenses previously associated with the technology. When AI and machine learning are used in conjunction for collecting and analyzing social, historical, and behavioral data, brands may develop a much more thorough understanding of their customers. In addition, AI can predict client behavior because it continuously learns from the data it analyzes, in contrast to traditional data analytics tools. As a result, businesses may deliver highly pertinent content, boost sales, and enhance the customer experience. Predictive Behavior Analysis and Real-time Decision Making Real-time decisioning is the capacity to act quickly and based on the most up-to-date information available, such as information from a customer's most recent encounter with a company. For instance, Precognitive's Decision-AI uses a combination of AI and machine learning to assess any event in real-time with a response time of less than 200 milliseconds. Precognitive's fraud prevention product includes Decision-AI, which can be implemented using an API on a website. Marketing to customers can be done more successfully by using real-time decisioning. For example, brands may display highly tailored, pertinent content and offer to clients by utilizing AI and real-time decisioning to discover and comprehend a customer's purpose from the data they produce in real-time. By providing deeper insights into what has already happened and what can be done to facilitate a sale through suggestions for related products and accessories, AI and predictive analytics are able to go further than historical data alone. This increases the relevance of the customer experience, increases the likelihood that a sale will be made, and increases the emotional connection that the customer has with a brand.

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SOFTWARE

The Evolution of Quantum Computing and What its Future Beholds

Article | July 8, 2022

The mechanism of quantum computers will be entirely different from anything we humans have ever created or constructed in the past. Quantum computers, like classical computers, are designed to address problems in the real world. They process data in a unique way, though, which makes them a much more effective machine than any computer in use today. Superposition and entanglement, two fundamental ideas in quantum mechanics, could be used to explain what makes quantum computers unique. The goal of quantum computing research is to find a technique to accelerate the execution of lengthy chains of computer instructions. This method of execution would take advantage of a quantum physics event that is frequently observed but does not appear to make much sense when written out. When this fundamental objective of quantum computing is accomplished, and all theorists are confident works in practice, computing will undoubtedly undergo a revolution. Quantum computing promises that it will enable us to address specific issues that current classical computers cannot resolve in a timely manner. While not a cure-all for all computer issues, quantum computing is adequate for most "needle in a haystack" search and optimization issues. Quantum Computing and Its Deployment Only the big hyperscalers and a few hardware vendors offer quantum computer emulators and limited-sized quantum computers as a cloud service. Quantum computers are used for compute-intensive, non-latency-sensitive issues. Quantum computer architectures can't handle massive data sizes yet. In many circumstances, a hybrid quantum-classical computer is used. Quantum computers don't use much electricity to compute but need cryogenic refrigerators to sustain superconducting temperatures. Networking and Quantum Software Stacks Many quantum computing software stacks virtualize the hardware and build a virtual layer of logical qubits. Software stacks provide compilers that transform high-level programming structures into low-level assembly commands that operate on logical qubits. In addition, software stack suppliers are designing domain-specific application-level templates for quantum computing. The software layer hides complexity without affecting quantum computing hardware performance or mobility.

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AI TECH

Language Models: Emerging Types and Why They Matter

Article | July 20, 2022

Language model systems, often known as text understanding and generation systems, are the newest trend in business. However, not every language model is made equal. A few are starting to take center stage, including massive general-purpose models like OpenAI's GPT-3 and models tailored for specific jobs. There is a third type of model at the edge that is intended to run on Internet of Things devices and workstations but is typically very compressed in size and has few functionalities. Large Language Models Large language models, which can reach tens of petabytes in size, are trained on vast volumes of text data. As a result, they rank among the models with the highest number of parameters, where a "parameter" is a value the model can alter on its own as it gains knowledge. The model's parameters, which are made of components learned from prior training data, fundamentally describe the model's aptitude for solving a particular task, like producing text. Fine-tuned Language Models Compared to their massive language model siblings, fine-tuned models are typically smaller. Examples include OpenAI's Codex, a version of GPT-3 that is specifically tailored for programming jobs. Codex is both smaller than OpenAI and more effective at creating and completing strings of computer code, although it still has billions of parameters. The performance of a model, like its capacity to generate protein sequences or respond to queries, can be improved through fine-tuning. Edge Language Models Edge models, which are intentionally small in size, occasionally take the shape of finely tuned models. To work within certain hardware limits, they are occasionally trained from scratch on modest data sets. In any event, edge models provide several advantages that massive language models simply cannot match, notwithstanding their limitations in some areas. The main factor is cost. There are no cloud usage fees with an edge approach that operates locally and offline. As significant, fine-tuned, and edge language models grow in response to new research, they are likely to encounter hurdles on their way to wider use. For example, compared to training a model from the start, fine-tuning requires less data, but fine-tuning still requires a dataset.

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SOFTWARE

Low-code and No-code: A Business' New Best Friend

Article | July 5, 2022

Businesses are starting to integrate artificial intelligence (AI) into their workflow in greater numbers as a result of the growth of digital transformation and developments in machine learning (ML). As a result, platforms that need no coding, as well as their low-code counterparts, are becoming more popular. This development is a step toward computer science's long-term objective of automating manual coding. Low-code/no-code AI platforms will be beneficial to businesses in more data-driven industries like marketing, sales, and finance. AI can assist in a variety of ways, including automating invoicing, evaluating reports, making intelligent suggestions, and anticipating churn rates. How Does an Organization Look at Low-code/No-code as the Future? Developers and other tech-related positions are in high demand, particularly in the fields of AI and data science. Organizations have the chance to close the gap with the aid of citizen data scientists who don't require an AI professional to design unique AI solutions for many scenarios, thanks to low-code and no-code AI technologies. The demand for technological solutions and AI technologies is rising significantly as the technological landscape rapidly changes. AI systems, for example, require complex software that uses a lot of code, a variety of frameworks, and the Internet of Things (IoT). One person's capacity to comprehend every technical detail is strained by the array of complicated technology. Software delivery must be timely, effective, and secure while maintaining high standards. Conclusion Low-code AI solutions offer the speed, ease of use, and adaptability of ready-made software solutions while also drastically reducing the time to market for AI solutions and the cost of recruiting software and computer vision engineers. Organizations are free to construct the architecture, functionality, or pipeline that best suits their project, the sky being the limit. However, creating such unique models may be both costly and time-consuming. Therefore, employing low-code/no-code platforms would apply to particular pipeline actions that would streamline and accelerate the processes.

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Spotlight

BankVault

Imagine owning a business – and have to temporarily shut your business to deal with a computer virus that’s wreaked havoc on your business simply because your antivirus, firewall, and off-site data backups failed. The pain is truly excruciating. Yet it's happening to more and more businesses every day. BankVault is simple yet powerful software which protect your most valuable assets - your computer, online credentials, and ultimately the money in your bank account. It is the best way to win new business and customer confidence in this digital age.

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Kubernetes’ Push to the Edge Shows Innovation, Challenges

SDxCentral | March 19, 2019

Kubernetes continues to proliferate across the cloud ecosystem, with one of its latest efforts focused on pushing the container orchestration platform further toward the edge. This is becoming more important as organizations look to extend the orchestration capabilities of Kubernetes across their cloud infrastructure and as the overall edge market sees increased service provider attention tied to 5G deployments. A recent example of this push toward the edge was Rancher Labs’ launch of its K3s platform. That platform is basically a slimmer version of Kubernetes, which is often referred to as “K8s.” That slimness is important because edge locations are more resource constrained compared with data center or network core locations. Shannon Williams, co-founder and vice president of sales at Rancher Labs, said the vendor pulled “alpha-level features that were still in development and also deprecated features” that were no longer needed or supported. This allowed the vendor to push out a platform that consumes just 40 megabytes of space and can run on x86_64, Armv8-A, and Armv7-A architectures. “We completely changed how Kubernetes works,” Williams said. “We removed drivers that were not essential for edge deployments, but still allow a customer to pull down those drivers if they need them.” Full to the Edge: While Rancher Labs moved to strip down Kubernetes, other vendors have been plugging the full version into their efforts. Mirantis last year plugged Kubernetes into its Cloud Platform Edge product to allows operators to deploy a combination of containers, virtual machines (VMs), and bare metal points of presence (POP) that are connected by a unified management plane.

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Kubernetes, Docker, ContainerD Impacted by RunC Container Runtime Bug

SDxCentral | February 11, 2019

The Linux community is dealing with another security flaw, with the latest bug impacting the runC container runtime that underpins Docker, cri-o, containerd, and Kubernetes. The bug, dubbed CVE-2019-5736, allows an infected container to overwrite the host runC binary and gain root-level code access on the host. This would basically allow the infected container to gain control of the overarching host container and allow an attacker to execute any command. “It is quite likely that most container runtimes are vulnerable to this flaw, unless they took very strange mitigations beforehand,” explained Aleksa Sarai, a senior software engineer at SUSE and a maintainer for runC, in an email posted on Openwall. Sarai added that the flaw is blocked by the proper implementation of user namespaces “where the host root is not mapped into the container’s user namespace.” The bug has received an “important” impact rating from some vendors. Sarai said the flaw has a 7.2 out of 10 CVSSv3 vector score. A patch for the flaw has been developed and is being sent out to the runC community. A number of vendor and cloud providers have already taken steps to implement the patch. RunC was initially spun out of work done by Docker Inc. It’s an Open Container Initiative (OCI)-compliant command line interface (CLI) tool for spawning and running containers.

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DriveScale Says Composable Is the Answer to Data-Intensive Compute

SDxCentral | January 15, 2019

DriveScale’s founders wanted to solve the problem of infrastructure over-provisioning when they started their company. But the technology wasn’t called composable infrastructure back then. “That was before 2013, and they were looking at the changing data center applications and workloads being created in the hyperscale clouds,” said Denise Shiffman, chief product officer at DriveScale. These were workloads like Hadoop, Casandra, and Spark, and they behaved differently than traditional data center workloads like Oracle and SAP. “These applications were much more dynamic and the demands for latency were very high, while the history had been running predictable applications in the data center,” she said. In addition to performance problems, data-intensive workloads also lead to server sprawl. Companies can’t predict how much storage and compute capacity they need. So they over-buy. “Many customers we talk to are really only using about 20 percent of the infrastructure they’ve deployed,” Shiffman said. “The [DriveScale] founders believed there was a new way to fix this and that was taking storage outside the server and putting it on the network.” Duane Northcutt, Satya Nishtala, and Tom Lyon founded DriveScale in March 2013. Northcutt, CTO at DriveScale, previously held that post at other tech companies including the Connected Home Division of Technicolor, Trident Microsystems, and Silicon Image. Nishtala, also a DriveScale CTO, is a former Cisco distinguished engineer. And Chief Scientist Lyon previously co-founded several companies including Ipsilon Networks and Nuova Systems, which was acquired by Cisco and became the basis for its UCS servers.

Read More

Kubernetes’ Push to the Edge Shows Innovation, Challenges

SDxCentral | March 19, 2019

Kubernetes continues to proliferate across the cloud ecosystem, with one of its latest efforts focused on pushing the container orchestration platform further toward the edge. This is becoming more important as organizations look to extend the orchestration capabilities of Kubernetes across their cloud infrastructure and as the overall edge market sees increased service provider attention tied to 5G deployments. A recent example of this push toward the edge was Rancher Labs’ launch of its K3s platform. That platform is basically a slimmer version of Kubernetes, which is often referred to as “K8s.” That slimness is important because edge locations are more resource constrained compared with data center or network core locations. Shannon Williams, co-founder and vice president of sales at Rancher Labs, said the vendor pulled “alpha-level features that were still in development and also deprecated features” that were no longer needed or supported. This allowed the vendor to push out a platform that consumes just 40 megabytes of space and can run on x86_64, Armv8-A, and Armv7-A architectures. “We completely changed how Kubernetes works,” Williams said. “We removed drivers that were not essential for edge deployments, but still allow a customer to pull down those drivers if they need them.” Full to the Edge: While Rancher Labs moved to strip down Kubernetes, other vendors have been plugging the full version into their efforts. Mirantis last year plugged Kubernetes into its Cloud Platform Edge product to allows operators to deploy a combination of containers, virtual machines (VMs), and bare metal points of presence (POP) that are connected by a unified management plane.

Read More

Kubernetes, Docker, ContainerD Impacted by RunC Container Runtime Bug

SDxCentral | February 11, 2019

The Linux community is dealing with another security flaw, with the latest bug impacting the runC container runtime that underpins Docker, cri-o, containerd, and Kubernetes. The bug, dubbed CVE-2019-5736, allows an infected container to overwrite the host runC binary and gain root-level code access on the host. This would basically allow the infected container to gain control of the overarching host container and allow an attacker to execute any command. “It is quite likely that most container runtimes are vulnerable to this flaw, unless they took very strange mitigations beforehand,” explained Aleksa Sarai, a senior software engineer at SUSE and a maintainer for runC, in an email posted on Openwall. Sarai added that the flaw is blocked by the proper implementation of user namespaces “where the host root is not mapped into the container’s user namespace.” The bug has received an “important” impact rating from some vendors. Sarai said the flaw has a 7.2 out of 10 CVSSv3 vector score. A patch for the flaw has been developed and is being sent out to the runC community. A number of vendor and cloud providers have already taken steps to implement the patch. RunC was initially spun out of work done by Docker Inc. It’s an Open Container Initiative (OCI)-compliant command line interface (CLI) tool for spawning and running containers.

Read More

DriveScale Says Composable Is the Answer to Data-Intensive Compute

SDxCentral | January 15, 2019

DriveScale’s founders wanted to solve the problem of infrastructure over-provisioning when they started their company. But the technology wasn’t called composable infrastructure back then. “That was before 2013, and they were looking at the changing data center applications and workloads being created in the hyperscale clouds,” said Denise Shiffman, chief product officer at DriveScale. These were workloads like Hadoop, Casandra, and Spark, and they behaved differently than traditional data center workloads like Oracle and SAP. “These applications were much more dynamic and the demands for latency were very high, while the history had been running predictable applications in the data center,” she said. In addition to performance problems, data-intensive workloads also lead to server sprawl. Companies can’t predict how much storage and compute capacity they need. So they over-buy. “Many customers we talk to are really only using about 20 percent of the infrastructure they’ve deployed,” Shiffman said. “The [DriveScale] founders believed there was a new way to fix this and that was taking storage outside the server and putting it on the network.” Duane Northcutt, Satya Nishtala, and Tom Lyon founded DriveScale in March 2013. Northcutt, CTO at DriveScale, previously held that post at other tech companies including the Connected Home Division of Technicolor, Trident Microsystems, and Silicon Image. Nishtala, also a DriveScale CTO, is a former Cisco distinguished engineer. And Chief Scientist Lyon previously co-founded several companies including Ipsilon Networks and Nuova Systems, which was acquired by Cisco and became the basis for its UCS servers.

Read More

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