Category: Blogs

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

    DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

    Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

    Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

    The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

    We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

    Indeed.com

    Citi
    New York, New York
    DevOps Engineer – Vice President
    $137,610 to $206,420

    SimplyHired.com

    Metrum Research Corp.
    Remote
    Senior DevOps Engineer
    $135,000 to $195,000

    CareerBuilder.com

    Peraton
    San Diego, California
    DevOps Systems Engineer
    $112,000 to $179,000

    Dice.com

    Stamford Technology Solutions
    Redmond, Washington
    Site Reliability Engineer (SRE)/DevOps Engineer
    $100,000 to $110,000

    LinkedIn

    TIAA Bank
    Jacksonville, Florida
    Senior DevOps Engineer
    $92,500 to $154,100

  • Chronosphere Adds Professional Services to Jumpstart Observability

    Chronosphere Adds Professional Services to Jumpstart Observability

    Chronosphere has added a professional services capability through which it will provide the resources and expertise needed to deploy and manage its observability platform.

    Ian Smith, field CTO for Chronosphere, said the Chronosphere Quick Start program will make it easier for organizations to transition away from legacy monitoring tools that only track a set of pre-defined metrics and embrace an observability platform that enables DevOps teams to surface issues before an IT environment is disrupted.

    The scope of the services provided spans everything from data ingestion to providing training so that management of the Chronosphere observability platform can eventually be handed over to an internal DevOps team.

    The professional services team will also guide DevOps teams through setting up data control mechanisms and retention polices in addition to crafting dashboards to monitor specific processes.

    Finally, there’s also a self-paced online learning platform, dubbed Chronosphere University, that provides a comprehensive curriculum to help DevOps teams navigate fundamentals, advanced features and best practices.

    It’s still early days as far as the adoption of observability platforms is concerned. But it’s apparent that as application environments become more complex, the plethora of monitoring tools that DevOps teams rely on will need to be streamlined. Observability platforms promise to unify logs, metrics and traces in a way that makes it simpler to launch queries to identify the root cause of an issue.

    The rate at which DevOps teams will embrace observability will naturally vary, but the biggest technical obstacles are deploying and configuring the platform, loading data and managing the cost of storing all the data that is collected.

    In addition, DevOps teams also need to understand what queries to launch to investigate specific issues. Many DevOps teams lack the expertise required to frame those queries today, but it’s expected that machine learning algorithms and other forms of artificial intelligence (AI) will automatically identify issues that might lead to disruption.

    In the meantime, DevOps teams should be evaluating where it makes sense to continue to rely on monitoring tools. In many instances, an observability platform might obviate the need for that tool, but there may also be plenty of instances where a simple monitoring tool is sufficient for the task at hand.

    One way or another, however, more advanced observability capabilities will be needed as application environments continue to become more complex in the cloud-native era. Application environments now consist of a mix of monolithic and microservices-based applications that gain more dependencies with each passing day. An application may not be as prone to crash as it once was, but determining the root cause of a performance issue can still take weeks given all the dependencies that exist between various services.

    Regardless of approach, observability has always been a core tenet of DevOps. As organizations’ DevOps maturity increases, the greater the appreciation for it becomes. The challenge has been that observability is one of many DevOps things that are easier said than done.

  • Friend or Foe? ChatGPT’s Impact on Open Source Software

    Friend or Foe? ChatGPT’s Impact on Open Source Software

    OpenAI’s ChatGPT took the world by storm, amassing 100 million users in the first two months after its public launch. The continued interest in the tool has created a buzz among developers, especially those in the open source community. But this begs the question: How will ChatGPT impact open source software?

    Amid the excitement and promise that ChatGPT holds, many wonder whether the open source community, just like any other organization, should fear or embrace the technology. Certain concerns for open source contributors and developers are sparking debate as questions surrounding the origin of generated source code surface, and the possible ethical and legal implications are discussed. While valid, developers should not fear or stray away from using ChatGPT but rather shift their focus to understanding how to embrace it for positive outcomes.

    The Top Three Open Source ChatGPT Concerns

    Collaboration between contributors is the cornerstone of open source projects, which some fear will be disrupted with the increased use of AI-based tools. However, the level of synergy within the community cannot be easily taken away or replaced by tools. Instead, ChatGPT or other AI-based tools, including GitHub Copilot, allow developers to produce code more quickly and more efficiently.

    As developers increasingly use AI tooling to assist with new or enhanced code, project collaboration and oversight will help improve the AI-generated code.

    Debunking and navigating the top concerns will be imperative to take full advantage of the technology’s promise and potential.

    Validity. While the creation of code by ChatGPT has generated excitement among developers, critics argue that—without context—the validity of code can be questioned. Some open source developers worry that teams will begin to rely entirely on ChatGPT to generate code, but this concern fails to consider the humans involved in the process—today and far into the future.

    Developers don’t take ChatGPT’s output as the final word; rather, they use it as a baseline and jumping-off point to streamline their code. In fact, code is rarely created from scratch anymore. Developers rely on others’ source code. For example, code from Stack Overflow, GitHub and thousands of open source libraries that are available on public registries like npm, Maven, Nuget and PyPI. Bringing ChatGPT into the fold will not significantly change how developers source their code, but will improve development velocity, saving valuable time and associated cost.

    Training Data. Machine learning (ML) and deep learning (DL) model training must be fair, robust and explainable to avoid biases. If the data is wrong, the results will be as well. Garbage in, garbage out. Coming from ML/DL models, the code ChatGPT returns in response to prompts could raise concerns surrounding its accuracy. Like any other source (Stack Overflow, GitHub, etc.), ChatGPT’s code outputs are not guaranteed to be perfect, and developers must be mindful of this.

    However, there are more benefits of ChatGPT-trained models concerning code outputs. ChatGPT can also explain new or existing code, it can also effectively provide unit tests for code, which will help with writing better software faster.

    Ownership. Questions of ownership have surfaced regarding the use and distribution of the code that the AI tool generates. While the code that ChatGPT generates is the result of ML/DL inference from many sources, it’s the developer’s responsibility to use that code ethically and safely. It should be treated as any other public data or open source software, as not a final product and must be used in the context of your requirements. It’s also important to carefully review any code generated by ChatGPT and ensure that it doesn’t introduce vulnerabilities.

    Similar to GitHub Copilot, ChatGPT is trained by millions of lines of open source software. Code posted in prompts is likely to make it to models, too. Unlike copyrighted pieces of art and writing, the code that ChatGPT outputs should not be considered in a final state and should not be used in a way that is subject to licensing restrictions or legal implications that have dominated the conversations around its use.

    Ownership closely relates to the ethical dilemma of using AI-generated text, code and more. To address that, there are new tools that help detect whether AI generated the content and how much, which will be useful for educators concerned with students overusing the tool. This emerging area of checks and balances exemplifies the constantly evolving and improving technologies that allow humans, and in the case of open source, contributing developers to improve their craft and produce better open source software.

    ChatGPT’s Impact on Open Source Talent

    Discussions about whether ChatGPT will require new skills for existing positions or new jobs with specialized experts are ongoing. While new and exciting, ChatGPT won’t immediately create new or different jobs. Like any new tool introduced to developers, it takes time for developers to become familiar with the technology and understand how to use it best. ChatGPT is no different. For example, consider the previous push to use low-code/no-code technology. While this great technology is used to speed up the creation of apps and improve the usability for non-developers, it has taken time for organizations to properly use low-code/no-code technology. A similar perspective and trajectory will extend to the use of ChatGPT for all software development, including open source projects.

    In the coming weeks and months, it will be essential to encourage the open source community to embrace ChatGPT and explore its possibilities. Technology has already proven to be an effective educational tool. Consider asking ChatGPT for book recommendations about programming languages and coding; it delivers short descriptions for each book. Or, prompt it for the top takeaways from one specific book. By doing this, individuals can make learning so much easier. By learning from other developers and sharing resources, including results from ChatGPT, the community roots of open source will keep thriving.

    Human Involvement is Always Needed

    The public’s reaction to ChatGPT may be novel, but the idea behind the tool’s relationship to open source is not. Analyzing how ChatGPT works within the open source community reveals that it will be useful because it enhances the capabilities of developers, it doesn’t replace them.

    Whether it’s reviewing code, pair programming or learning from fellow developers, humans will not be replaced by generative AI, only enhanced. Leveraging the tool will decrease the time and effort needed to complete tasks, increasing developer quality and efficiency. The use of ChatGPT does not create new positions or overhaul current skill sets; it makes them better and empowers developers to expand their opportunities and enhance their abilities.

    Soon, ChatGPT and other AI tools will be standard practice within the open source community—or until a new technology is created and shakes up software development once again.

  • VMware Streamlines IT Management via Cloud Foundation Update

    VMware Streamlines IT Management via Cloud Foundation Update

    VMware this week updated its Cloud Foundation platform and rolled up recent updates to the company’s virtualization portfolio all at once.

    Version 5.0 of VMware Cloud Foundation (VCF) encompasses the latest versions of VMware vSphere 8.0u1 for workload management, VMware vSAN 8.0u1 for scale-out storage, VMware NSX 4.1 for networking and VMware vRealize LifeCycle Manager 8.10 (Aria) for cloud management.

    In addition, VMware has added a single sign-on (SSO) capability, dubbed Isolated SSO Workload Domains, that enables administrators to configure new workload domains using SSO instances from different providers or platforms via NSX.

    Rick Walsworth, director of product marketing for the Cloud Infrastructure Business Group at VMware, said that as the management of IT continues to evolve in VMware environments, more organizations are opting to update their entire VMware software stack simultaneously rather than attempting to update components individually.

    The primary reason for that shift is that the company ensures that all the latest VCF component releases have been thoroughly stress-tested from an integration perspective, he noted.

    During uncertain economic times, VMware is seeing an increased focus on simplifying IT environments as IT teams are asked to do more with less, said Walsworth. As such, more organizations are looking to standardize on a stack of infrastructure software that can be managed more consistently, he added.

    More organizations are also looking to reduce the total cost of IT by employing the same stack of infrastructure software across hybrid cloud computing environments where workloads are deployed both in on-premises IT environments and public cloud, said Walsworth.

    The company is also seeing increased adoption of DevOps best practices to programmatically manage IT environments that need to respond to rapidly changing requirements, he noted. IT teams are looking to work more collaboratively rather than relying solely on specialists to manage specific functions, said Walsworth.

    Some of that shift is also being driven by Tanzu, a distribution of Kubernetes that the company makes available as part of VCF, he noted. Most application developers don’t have a preference for a specific Kubernetes distribution, so many organizations view Tanzu as an opportunity to extend their existing management framework to a de facto standard for building the next generation of cloud-native applications, said Walsworth.

    VMware is in the midst of being acquired by Broadcom. Nevertheless, VMware this week reported revenues for the first quarter 2023 of $3.28 billion, a 6% year-over-year increase. Subscription and software-as-a-service (SaaS) revenue for the first quarter was $1.22 billion, an increase of 35% year-over-year. That increase in revenue suggests any concerns about the future direction of the company are not impacting the willingness of IT organizations to continue to rely on VMware.

    It may be a while before all the dust settles around Broadcom’s acquisition of VMware. In the meantime, however, it’s clear that the way IT is managed in the cloud era is continuing to rapidly evolve regardless of who owns VMware.

  • Revolutionizing the Nine Pillars of DevOps With AI-Engineered Tools

    Revolutionizing the Nine Pillars of DevOps With AI-Engineered Tools

    In my book “Engineering DevOps,” published a few years ago, I classified DevOps into “nine pillars of DevOps best practices” – a comprehensive framework that covered the full scope of DevOps practices. Each of these pillars represents a vital area of DevOps implementation and, together, they ensure a holistic approach to integrating development and operations for effective collaboration and continuous delivery.

    The current generation of AI-engineered tools is now being applied to each of the DevOps pillars and has the potential to significantly increase the efficiency, reliability and speed of the software delivery process. By automating repetitive tasks, improving decision-making with predictive insights and facilitating proactive issue resolution, AI can drive a higher level of quality, security and performance in the software development life cycle. Furthermore, AI’s capacity to learn and adapt over time means continuous improvement, fostering a truly data-driven, learning-oriented DevOps culture.

    AI Applications for the Nine Pillars of DevOps

    The following paragraphs briefly explain how the current generation of AI-engineered large language model (LLM) tools are being applied or could be practically applied to improve each of the nine pillars of DevOps.

    Leadership Practices: Leadership is vital to drive cultural changes, set vision and goals, encourage collaboration and ensure resources are allocated properly. Strong leadership fosters a successful DevOps environment by empowering teams and supporting innovation. AI can assist leaders in decision-making by analyzing large datasets to identify trends and predict outcomes, providing valuable insights to guide strategic planning.

    Collaborative Culture Practices: DevOps thrives in a culture of openness, transparency and shared responsibility. It’s about breaking down the silos that can exist between different teams (like development, operations and QA) and promoting effective communication and collaboration. AI-powered tools can improve collaboration through smart recommendations, fostering more effective communication and knowledge sharing.

    Design-for-DevOps Practices: This involves designing software in a way that supports the DevOps model. This can include aspects like microservices architecture, modular design and considering operability and deployability from the earliest stages of design. AI can be used in the software design phase to automatically suggest improvements in code quality or to identify potential performance issues early.

    Continuous Integration Practices: This is about developers frequently merging code changes into a central repository. After that, automated builds and tests are run. The main objectives are to detect problems early and reduce integration issues. AI can analyze patterns in past build failures to predict potential issues with new builds, leading to proactive error mitigation.

    Continuous Testing Practices: Continuous testing involves automating tests to provide instant feedback on business risks and allowing for faster, more reliable releases. This approach enhances the quality of the software and reduces the time it takes to resolve defects. AI can be used to generate and optimize test cases automatically, reducing manual effort and speeding up the testing process.

    Elastic Infrastructure Practices: Elastic infrastructure involves automated provisioning and management of computing resources, often leveraging cloud-based solutions for scalability and resilience. This includes practices like infrastructure-as-code (IaC). AI can help manage cloud resources by predicting future needs based on usage trends and automatically adjusting resources to optimize cost and performance.

    Continuous Delivery Practices: This is an approach where code changes are automatically built, tested and prepared for release to production. It aims to make releases less disruptive and more frequent, increasing speed and efficiency. AI can help predict the potential impact of new releases on the system, aiding in decision-making related to deployment.

    Continuous Security Practices: Also known as DevSecOps, this involves integrating security practices into the DevOps workflow. The goal is to introduce security considerations early and often in the life cycle rather than leaving them as an afterthought. AI can be used in anomaly detection to identify potential security threats and automate security policy enforcement.

    Continuous Monitoring Practices: Monitoring systems and application performance in real-time allows teams to detect and react to problems quickly, often before they impact users. It’s crucial for maintaining application health and informing future improvement efforts. AI can analyze logs and metrics to predict potential system failures or performance degradation, allowing for proactive maintenance and issue resolution.

    Pitfalls and Challenges

    Applying AI to DevOps is a complex process with certain challenges. Here are some potential pitfalls, along with ways to address them:

    Data Quality and Availability: AI relies heavily on data for training and making accurate predictions. If the data is inaccurate, incomplete or biased, it can lead to misleading outcomes. Ensure you’re collecting high-quality, relevant and unbiased data for training AI models. Regular data audits can help maintain data integrity.

    Over-Reliance on Automation: While automation is beneficial, over-reliance on it without human oversight can result in overlooked errors or system failures. Maintain a balance between automation and human intervention. Implement periodic manual checks and reviews to validate AI-generated results and actions.

    Security and Privacy Concerns: As AI tools are more integrated into the DevOps pipeline, they may become targets for security threats. Also, the handling of sensitive data used for AI training can raise privacy issues. Implement strong security practices, conduct regular security audits and consider privacy requirements during the data collection process.

    Black Box Problem: AI, especially complex models like neural networks, often operate as a “black box,” meaning it’s difficult to understand why they made a certain decision. This lack of transparency can make troubleshooting difficult. Consider using explainable AI (XAI) techniques, which aim to create more understandable AI systems, providing transparency into how decisions are made.

    Skills Gap: The application of AI in DevOps requires a certain level of expertise and understanding of both domains, which can be hard to find. Invest in training and upskilling existing staff, or consider bringing in external expertise through hiring or consultancy. Sharing knowledge and encouraging continuous learning can also help bridge this gap.

    Summary

    AI-engineered tools can enhance all of the nine pillars of DevOps by increasing efficiency, speeding up processes and making predictions to aid decision-making. However, implementing AI in DevOps comes with challenges such as ensuring data quality, maintaining a balance between automation and human intervention, addressing security and privacy concerns, managing the “black box” problem of AI and overcoming skills gaps. Each of these pitfalls can be addressed through proactive strategies like maintaining data integrity, implementing manual checks, bolstering security practices, using explainable AI techniques and investing in training and skills development.

  • No, Dev Jobs Aren’t Dead: AI Means ‘Everyone’s a Programmer’? ¦ Interesting Intel VPUs

    No, Dev Jobs Aren’t Dead: AI Means ‘Everyone’s a Programmer’? ¦ Interesting Intel VPUs

    Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

    This week: Nvidia’s CEO grabs headlines by saying your career is toast, and Intel is still fighting. (more…)

  • Logz.io Taps AI to Surface Incident Response Recommendations

    Logz.io Taps AI to Surface Incident Response Recommendations

    Logz.io this week added a supervised machine learning capability to its observability platform that reduces mean-time-to-remediation by surfacing recommendations for resolving incidents.

    Asaf Yigal, vice president of product for Logz.io, said the Alert Recommendation capability added to the Logz.io Open360 platform uses artificial intelligence (AI) to model the steps a DevOps team needs to complete to resolve an incident.

    The goal is to reduce the amount of time required to resolve incidents at a time when IT environments are becoming increasingly complex, he added.

    In fact, a recent Logz.io survey found 75% of respondents said it currently takes them hours to resolve production issues, with only 14% satisfied with their current mean-time-to-resolution (MTTR). A total of 41% specifically cited monitoring and observability of Kubernetes environments as a primary challenge.

    Alert Recommendation is the latest in a series of investments in AI that Logz.io has made. Previously, Logz.io integrated the ChatGPT generative artificial intelligence (AI) platform to surface links to related information and best practices for resolving IT issues.

    In general, AI tools should make it possible to manage IT at a level of scale that eliminates many of the low-level data engineering and analytics tasks that previously required manual effort from a DevOps engineering team. If, for example, the observability platform is generating recommendations to address issues, there may be less of a need to create runbooks that DevOps teams typically create to address a wide range of known issues.

    Along with making it simpler to discern the root cause of an issue, AI also makes it possible for less experienced members of a DevOps team to resolve an issue using guidance generated by the observability platform, noted Yidal.

    In effect, AI technologies are reducing the cognitive load required to be an effective member of a DevOps team, he added.

    One way or another, it’s not so much a question of whether AI will be applied to DevOps as much as it is to the degree. Many of the manual tasks that often conspire to create DevOps bottlenecks should be significantly reduced in the months ahead as more advances are made. The challenge now is determining how best to reallocate DevOps expertise in anticipation of those advances.

    Of course, there’s always going to be some sense of trepidation when it comes to AI. However, many of the tasks that are about to become automated tend to be tedious. Many DevOps professionals would just as soon see those tasks become automated in the expectation that more time will become available to take on more complex challenges.

    Regardless of the motivation, the way IT is managed is about to change. There may be plenty of instances where AI does not live up to its initial hype, but as AI models are exposed to more data, they will become more accurate. That doesn’t mean, however, there won’t always be a need for a DevOps engineer to ensure those algorithms are behaving as expected.

  • Technological Breakthroughs in Hurricane Forecasting

    Technological Breakthroughs in Hurricane Forecasting

    The 2023 Atlantic hurricane season marks a transformative era in tropical forecasting, driven by the remarkable progress in machine learning and big data analysis. It’s a rapidly evolving field, where technology and meteorology intersect to better prepare the public for the formidable forces of Mother Nature.

    NOAA’s 2023 Atlantic Hurricane Season Forecast

    The National Oceanic and Atmospheric Administration (NOAA) projects that this year’s Atlantic hurricane season will produce 12 to 17 named storms, with five to nine major (Category 3+) hurricanes. NOAA expresses 70% confidence in these ranges, bolstered by its evolving technology.

    Leveraging Technological Innovations for Advanced Hurricane Forecasting and Resilience
    A summary infographic showing hurricane season probability and numbers of named storms predicted from NOAA’s 2023 Atlantic Hurricane Season Outlook. (Image credit: NOAA)

    Upcoming Upgrades: Supercomputing System Expansion

    The recent implementation of the Probabilistic Storm Surge model improves storm surge forecasting for the U.S. mainland, Puerto Rico and the U.S. Virgin Islands. The model provides forecasters with a range of potential flooding scenarios, enhancing community preparedness. 

    NOAA plans to implement several more upgrades this summer, including a 20% expansion of its operational supercomputing system’s capacity. This enhancement will enable NOAA to run more complex forecast models, leading to more accurate predictions.

    In late June, the Hurricane Analysis and Forecast System (HAFS) becomes operational. Running in tandem with the current Hurricane Weather Research and Forecast Model System, HAFS is projected to show a 10-15% improvement in track forecasts over existing models.

    HAFS represents a significant leap forward in the integration of machine learning algorithms and sophisticated data analysis techniques. By leveraging state-of-the-art computational models and assimilating an extensive array of meteorological data, HAFS has the potential to unlock new insights into the behavior and trajectory of tropical cyclones. This fusion of technology and meteorology is projected to surpass the capabilities of existing models.

    Harnessing Machine Learning for Advanced Forecasting

    The core strength of HAFS lies in its ability to process vast amounts of atmospheric and oceanic data, utilizing advanced machine learning algorithms to identify patterns and correlations. These algorithms can discern intricate relationships between various meteorological variables, such as sea surface temperatures, atmospheric pressure, and wind patterns, enabling more precise forecasting. By comprehensively analyzing historical storm data and incorporating real-time observations, HAFS can generate forecasts with higher accuracy and longer lead times.

    HAFS also harnesses the power of high-performance computing, allowing for faster and more complex simulations. This computational prowess enables the system to simulate a multitude of possible scenarios, accounting for various atmospheric conditions and factors that influence hurricane behavior. The ability to run multiple simulations and generate ensemble forecasts enhances the robustness of predictions and provides forecasters with a comprehensive range of possibilities.

    Innovative Technologies: Drones, Saildrones, Underwater Gliders, and Sounding Balloons

    Innovative technologies like small aircraft drone systems, additional Saildrones, underwater gliders, and WindBorne global sounding balloons will contribute to advancing knowledge and improving hurricane forecast accuracy. 

    Small aircraft drone systems offer a unique vantage point for data collection in the lower atmosphere, enabling meteorologists to gather critical information on temperature, humidity, wind patterns, and other atmospheric variables. These drones are equipped with advanced sensors and instruments, allowing them to navigate through hurricane environments and provide valuable real-time data that can significantly improve forecast models.

    Saildrones, alternatively, are autonomous surface vehicles equipped with an array of meteorological and oceanographic sensors. They harness wind and solar energy for propulsion, enabling them to traverse vast oceanic regions and collect crucial data on sea surface temperatures, ocean currents, and other parameters relevant to hurricane formation and intensification. By expanding the fleet of Saildrones, scientists can enhance their observational capabilities and obtain a more comprehensive understanding of the complex interactions between the ocean and the atmosphere during hurricane events.

    Underwater gliders, equipped with sensors and instruments, offer a unique perspective beneath the ocean’s surface. These autonomous vehicles can collect data on temperature, salinity, and ocean currents, providing valuable insights into the oceanic conditions that influence hurricane development and intensity. By combining data from underwater gliders with other observational platforms, scientists can gain a more holistic understanding of the underlying processes that fuel tropical cyclones.

    WindBorne global sounding balloons ascend to the upper levels of the atmosphere, collecting data on temperature, humidity, and wind patterns. This information is vital for initializing and validating numerical models used in hurricane forecasting, enhancing the accuracy of predictions.

    Leveraging Technological Innovations for Advanced Hurricane Forecasting and Resilience
    NOAA GOES satellite captures Hurricane Ian as it made landfall on the barrier island of Cayo Costa in southwest Florida on September 28, 2022.

    Improving El Niño & La Niña Forecasts

    The Tropical Atmosphere Ocean buoy array is undergoing modernization and upgrades, bolstering the accuracy of El Niño and La Niña forecasts. These climate patterns have significant impacts on hurricane activity, and accurate predictions of their behavior are crucial for understanding the overall climate system. 

    By modernizing and enhancing the buoy array, scientists can gather more precise data on oceanic and atmospheric conditions associated with El Niño and La Niña, leading to more accurate long-range hurricane forecasts.

    New tools and upgrades have also been introduced for more precise hurricane analysis and forecasting. The National Hurricane Center has expanded its Tropical Weather Outlook graphic forecast range to seven days from five. To give communities more time to prepare, the Weather Prediction Center has also extended the Excessive Rainfall Outlook by two days.

    Leveraging Technological Innovations for Advanced Hurricane Forecasting and Resilience
    A summary graphic showing an alphabetical list of the 2023 Atlantic tropical cyclone names as selected by the World Meteorological Organization. The official start of the Atlantic hurricane season is June 1 and runs through November 30. (Image credit: NOAA)

    Bolstering Resilience Against Future Hurricanes

    The integration of these innovative technologies and the modernization of existing observational systems expands observational capabilities both in the atmosphere and the ocean to track and monitor storms. The evolution of forecasting techniques holds promise for even greater resilience against future hurricanes, showcasing the remarkable impact of technological advancements in safeguarding coastal communities.

  • Why You Need a Multi-Cloud and Multi-Region Deployment Strategy

    Why You Need a Multi-Cloud and Multi-Region Deployment Strategy

    As I began writing this article, I encountered some technical challenges in my attempt to provision three cloud providers in three separate regions to simulate multi-cloud and multi-region deployments.

    I couldn’t get one of the regions to behave as I expected when provisioning the environment. As it turned out, the region (and by association, cloud) was down due to a cut undersea cable, and it would take several weeks until the outage was mitigated.

    Fortunately, there are plenty of clouds in the sky to choose from, but this experience emphasized the importance of multi-cloud redundancy, especially in a production environment.

    This article examines the benefits of adopting a multi-region and multi-cloud deployment strategy for better resiliency, regulatory compliance and optimal user experience.

    Resilient Reasons to Choose Multi-Cloud and Multi-Region Architectures

    The first reason to choose a multi-cloud or multi-region environment is for data resilience. When our data is in a single region or on a single cluster, there is a good chance that you will experience data failure. You need a mitigation strategy if you want to keep the data lights on 24/7. These strategies fall into two buckets: Multi-region and multi-cloud.

    We first need to define the differences between multi-cloud and multi-region. The architectures are simply the implementation details of how we plan for one versus the other. However, depending on your database provider, those details can represent a significant time investment, which we’ll look at later.

    Multi-Region

    With multi-region, we typically deploy our applications to globally distributed regions. However, regional deploys are subjected to physical faults. Inclement weather, political unrest, acts of foreign or domestic terrorism and other physical variables have all led to regional outages in the past.

    Regardless of the cloud, the regions are exposed to similar physical faults. Instead of losing your data, we can switch to another region to handle the workload. Even though much as our industry hates latency, it is always preferred over loss.

    Deploying to multiple regions allows for failover handling. If the Sydney region goes down, Singapore may still be online. It would be a globally bad day if the same physical faults simultaneously impacted Frankfurt, Sydney and Seattle, for example.

    Multi-Cloud

    Physical faults are only one of the problems that public and private clouds encounter. User error, poorly configured networks and even banal errors can have substantial consequences that impact entire clouds, either from the provider or the user.

    In these circumstances, regional failover won’t help you. An expired certificate, accidental deletion or disabling the wrong port will take you offline, regardless of whether you’re in Seattle or Sydney. If that happens, someone somewhere is most definitely awake and is likely very unhappy.

    Choosing a multi-cloud configuration protects you from these types of faults so that if Google, for example, has interruptions, AWS is still online—or IBM, Deutsche Telekom, OVH or Hetzner.

    The Correct Answer: Deploy Both!

    Multi-region is a go-to method to mitigate against mostly physical faults. Multi-node clusters in each of those regions handle general data corruption or failures on a more granular level.

    Multi-cloud is a go-to method to mitigate configuration or software-level faults, which are not that uncommon.

    The correct answer is to blend the two, working within operational cost constraints and general survivable requirements from regulatory and business objective use cases. For example, I do want access to my heart-rate data from two years ago, but the order of browsing across Amazon shopping is less important.

    Regulatory Reasons for Multi-Region Architecture

    Apart from data resiliency, most companies serving a global market are subject to a number of geopolitical influences, as well. Most users will be familiar with the GDPR regulations ensuring European data stays on European soil. Further regulations require the ability for government oversight to request data at will from service providers, which requires its own set of reasonable isolation from those not in those jurisdictions.

    Using a multi-region database provider allows companies to ship single binaries of software without creating isolated applications for different countries—a practice that was the norm not too many years ago.

    Performant Reasons for Multi-Region Architecture

    Co-locating data and access provides the optimal pathway for getting a user’s data to the end destination. It goes without saying, but the less copper your data has to traverse, the faster the experience will be for your users. Retention and regulation aside, in many cases, you need to mitigate latency caused by region separation.

    It’s not just the individual user’s data. Imagine a sales enablement platform where an account executive needs to manage all of APAC or EMEA. If these accounts are optimally stored for reads on geo boundaries, you can remove seconds of delay between page navigations for your account manager. We all know how much account executives like to click!

    Choosing a DB Provider With Multi-Region and Multi-Cloud

    At this point, you should be convinced of the benefits of multi-region and multi-cloud deployments. But at what cost? Creating replication, failover, logging and synchronization mechanisms is a big, expensive ask. Even with deployment tooling, coordinating all the pieces for multi-region deploys is still a significant operational lift, let alone multi-cloud, where variables in APIs come into play.

    Choosing a single provider that deploys across regions and clouds means you get to benefit from their collective experience while reducing a significant burden.

    Having a database provider offer one-click multi-region deploys is the way to go, but you still need to handle the data access layer. The role-based access, federation and other settings that enable your developers still need to be distributed along with the underlying data sources.

    You need a provider that makes it simple to deploy the same configuration for horizontally scaled instances, leaving you to pick your geo router of choice to distribute the request to the nearest point of presence.

    Closing

    Multi-cloud and multi-region deployments provide significant redundancy, scalability and flexibility. Not only does this enable faster access times and improved reliability, but also provides the flexibility required to adapt to disruptions or unpredictable increases in traffic.

    Deploying multi-cloud and multi-region is no small feat–but the peace of mind and security it provides make it worth the effort.