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Tag: DevOps

Building Resilient Infrastructure: Unleashing the Power of High Availability – Webinar
Despite being a well-known concept, implementing high availability (HA) remains a challenge for many companies. It’s critical to avoid single points of failure in company infrastructure, but it can be difficult to know where to start. In this Techstrong Learning Experience, we’ll shed light on this issue and explore the topic of HA. Our panel of industry experts will discuss the necessity of HA, clarify distinctions between disaster recovery and high availability and offer best practices for successful HA implementation.

You’ll learn:
- Considerations when implementing HA solutions
- Best practices from real-life experiences
- How to research and develop your own HA solutions
- Why HA remains relevant
- Differences between HA in on-premises and cloud environments
Presentation Materials:
October 4, 2023
GraphQL API Visibility Ensures Enterprises Can Escape Lock-In

While GraphQL is still early in its enterprise adoption curve, the runtime query language (and replacement for REST APIs) has already built an impressively robust ecosystem for jumpstarting major enterprise-grade deployments. Among the appeals of GraphQL are its open source implementations and breadth of available server types, which combine to offer enterprises flexibility and the benefits of best-fit solutions.

However, operating GraphQL in production, maintaining GraphQL APIs at enterprise scale and achieving the requisite GraphQL visibility and security require complementary tools and services. This is where enterprises can fall prey to vendor lock-in and lose the flexibility to change and control their own deployments if they aren’t careful.

Enterprises need vendor partners to manage their GraphQL deployments effectively, and vendors are glad to offer comprehensive SaaS solutions. But enterprises must understand that a vendor can seek to capture their long-term business by coercion—straitjacketing customers by hosting their entire GraphQL infrastructure and GraphQL gateway and making it, well, not exactly easy to let go. They can unleash particularly nefarious practices, using proprietary features to force customers to migrate to certain technologies or purchase expensive upgrades to keep their crucial GraphQL deployments afloat. Enterprises must be wary of allowing this vendor lock-in to happen for their own good.

Knowledge is essential to maintaining the balance of power in vendor relationships. Enterprises must understand their GraphQL graph and key measures to correctly assess current vendors and maintain agility and control over deployments by not entrusting their entire fate to any one partner.

These are the specific areas where enterprises should seek out intelligence and reliable metrics in order to choose their own future with GraphQL:

Understand Your Overall Graph (and Subgraph) Utilization

Establishing an accurate overview of your graph utilization—and harnessing actionable insights—are crucial, foundational steps that lay the groundwork for deeper analysis and better decision-making. You need to know what graph and subgraph objects and operations your most important API users heavily engage with and which objects and operations are underused or not used at all. You need visibility into who is using GraphQL APIs and how. Getting to actionable intelligence should also tell you where errors are most likely to occur and what’s causing them, as well as where major performance bottlenecks pop up during heavy load events.

With this overview available, the next steps call for building a depth of knowledge in each of these five areas:

1) Understand areas of heavy graph and subgraph usage

Knowing exactly where heavy usage occurs in your graph puts you in a position to upgrade or migrate your GraphQL API server or gateway on your terms while mitigating outage and error risks. Specifically, you want to know which operations in your graph are executed most and how often, which graph objects and fields are consumed most often, and which subgraphs support those most heavily burdened areas in your supergraph. This knowledge and some careful planning help to put successful GraphQL server or gateway changes within your power and also reduce risk when introducing changes to areas of the graph under heavy consumption.

2) Understand where your graph is underused

GraphQL migration and modernization efforts are simpler—and carry less risk—when you first remove any unused functionality and reduce your scope as much as possible. To do so, you need visibility into your graph’s least executed operations, objects and fields, and subgraphs, as well as any operations with zero recent usage. Then, flag low-use graph areas you can deprecate, replace or just remove to simplify your deployment before making changes.

3) Understand your GraphQL API users and usage

You want a seamless experience for your GraphQL API users throughout any changes you make, especially for the users and graph-usage patterns most crucial to your business. That means knowing the frequent users of the areas of your graph under the most heavy usage. You also want to recognize and bolster any GraphQL operations that your most important users frequently call, in addition to the objects and fields those users consume.

4) Understand graph errors and potential error hotspots

The right actionable intelligence will prepare enterprise teams to anticipate and address graph errors. This requires visibility into where, when, why, and how often graph errors occur and error hotspots (for example, high P95 latencies indicative of potential timeouts). Insights into which users feel the brunt of graph errors are also valuable in shaping mitigation strategies.

5) Understand graph performance bottlenecks

Bottlenecks caused by your GraphQL server or gateway itself may call for a shift in your graph’s underpinnings. Bottlenecks can also arise from costly queries of substantial depth and height, especially when these queries see intensive use.

To foresee and address bottlenecks, actionable insights include the average and P95 latencies of your graph’s most-called operations and the heights and depths of your graph queries with the worst performance metrics. You also want to know the number of times that bottlenecked operations get called each day, the subgraphs at the root of bottlenecks, the latency added by supergraph query planning, and which users are impacted the most by bottlenecks.

Graph Knowledge = The Power to Avoid Vendor Lock-In

Implementing the GraphQL API visibility and actionable intelligence strategies covered here help ensure that an enterprise has the means to modernize or migrate as necessary to preserve its strategic independence. Those unfortunate organizations already in the grip of vendor lock-in may need to bide their time but should still use these same techniques to improve and decouple their fundamental GraphQL services from their vendor-controlled GraphQL technology. There’s no acceptable reason why organizations relying on free and open source GraphQL servers and gateways should find themselves stuck in proprietary software arrangements. Thankfully, the right knowledge can deliver enterprises a more flexible future as they modernize API development with GraphQL.

October 4, 2023
Avoiding Cloud Lock-In Amidst Potential Regulatory Change

At the end of my street, there’s a beautiful and much-admired garden with vibrant flowers and a well-manicured lawn. Next to this splendid garden is an abandoned vacant lot, long-neglected and overgrown with weeds. Every time I walk by, I think about how this neglected space could be transformed into something magnificent (like this garden), but no one wants to take responsibility for it. Similarly, many CIOs and CTOs have “discarded vacant lots” to deal with in their own work lives: The problem of cloud lock-in.

Cloud lock-in can be a significant headache for business leaders. Even the UK’s communications sector regulator, Ofcom, has expressed concerns about the issue. Earlier this year, Ofcom announced that it would refer the UK cloud market for investigation to the Competition and Markets Authority, highlighting barriers to interoperability and concerns about high egress fees charged by the cloud hyperscalers.

But organizations shouldn’t just wait (and hope) for regulators to solve this problem. As a true believer in harnessing the full benefits of the cloud, I propose two practical ways to combat cloud lock-in.

The Upside of a Technical Approach

The first option is straightforward: Have an honest conversation with your cloud service account leader. Express your concerns about lock-in and negotiate more favorable terms and conditions. This approach might yield some results, but it’s not a guaranteed success. So, there’s a more effective strategy: Adopt a technical approach that empowers you to avoid vendor lock-in altogether, without costing you an arm and a leg.

Imagine that, instead of trying to turn your neglected space into a garden alone, you could access a ready-made gardening community, where multiple people work together to create something amazing. The same principle applies to cloud databases. Rather than relying exclusively on one hyperscaler’s technology stack, you can opt for platforms that offer portability and flexibility across different cloud providers.

Let’s consider databases, a critical underlying component of many mission-critical business applications. Each cloud hyperscaler will offer a range of SQL, NoSQL, and graph database options, which cater to various types and scales of technical problems. The challenge is to avoid getting locked into a single cloud stack, which hinders your ability to switch between clouds if and when needed.

One viable solution is to choose “vanilla Postgres” – the basic open source version of PostgreSQL available from postgres.org or any cloud provider. This will certainly provide initial flexibility, but you are likely to encounter limitations as your application grows and requires enterprise-level features.

The real benefit of the cloud lies in modernizing your data layer to fully exploit cloud-native features like scalability and resilience.

To achieve this, you need databases that run across different public clouds and share common APIs. Born-in-the-cloud technologies, designed from the ground up for cloud environments, offer the most flexibility and benefits.

Examining your current cloud vendor’s offerings is natural, as you’re already on their platform. However, keep in mind that these services are customized to their specific cloud environment only, so this could limit your future growth. If you decide to change cloud providers or adopt a multi-cloud strategy, moving across the cloud database “matrix” can be cumbersome due to the inconsistent APIs.

If you were restricted to a single set of gardening tools when trying to transform that discarded space into a beautiful community garden, it would be nearly impossible and would not be fit for purpose.

You need to realize that these services and their functionality will always need to be customized in some way to the specific cloud environment. AWS’s fully managed relational database engine, Aurora, is compatible with MySQL and PostgreSQL, but it was specifically designed to run on Amazon. This means it can’t run on-prem, and it certainly won’t work in GCP (Google’s cloud).

As a consequence, you will have to make time-consuming and costly changes to your application to accommodate a bespoke version of PostgreSQL that will run efficiently in the cloud environment of your hyperscaler partner. You’re going to be locked into that version of the application because it can’t really work anywhere else. The same applies to the Google equivalent of vanilla Postgres (CloudSQL for Postgres), or the Microsoft version (Azure PostgreSQL).

If you truly want a multi-cloud strategy, you need to consider how you go, for example, from mid-range Google to low-end Amazon. Scaling is much discussed, but surely one of the main selling points of cloud is that you can go down as easily as you go up.

The Business Advantages of Multi-Cloud are in Reach

To achieve the advantages of multi-cloud, you need to consider databases that offer common APIs and portability across clouds. There are several open source PostgreSQL-based database options available, which enable you to keep your cloud database options open and avoid the trap of cloud vendor lock-in.

The wait for regulatory intervention might be long and uncertain. Taking proactive steps now to mitigate cloud lock-in is crucial.

Adopt a scalable open source PostgreSQL option to immediately enjoy many of the benefits of multi-cloud. Embracing this strategy now can spare you potential panic moves when and if regulatory changes come into play.

By freeing your business from the shackles of cloud lock-in, you can unleash the true potential of multi-cloud environments. Just like that “discarded vacant lot” waiting to be transformed into a beautiful garden, your applications can blossom in a multi-cloud ecosystem, which allows you to easily scale up or down and enjoy the best that each cloud provider has to offer.

So, don’t wait for someone else to fix the problem. Take action today and start cultivating your multi-cloud future.

October 3, 2023
Best Practices for DevOps in the Hybrid Cloud

In rеcеnt years, various tools, technologies and frameworks have еmеrgеd with thе goal of enhancing flеxibility, performance and scalability. Traditional monolithic approaches have been replaced by more modular methods like microservices and nano-services. Additionally, the rise of cloud computing has led to on-prеmisеs software being replaced by cloud еnvironmеnts that offer a wide array of advantages and capabilities that were previously unavailable.

Bеing able to optimize spееd and efficiency through the adaptation of cloud settings is a vital capability. To leverage cloud-bursting capabilities and also safeguard their applications and data, businеssеs can embrace a hybrid cloud strategy combined with DеvOps. In this article, we will dеlvе into bеst practices for implеmеnting DеvOps in a hybrid cloud sеtting.

What is a Hybrid Cloud Environment?

A hybrid cloud, which combines on-premises and cloud computing modеls, allows you to harness the benefits of both approaches. Hybrid cloud computing allows businеssеs to blеnd their own on-prеmisеs infrastructurе with thе powеr and flеxibility of cloud computing. This approach allows them to optimize their current infrastructure while also benefiting from the scalability and cost efficiency offered by public cloud environments.

By lеvеraging thе strеngths of both, companies can optimizе their opеrations for scalability and flеxibility and еnhancе sеcurity for thеir applications and data. One notablе advantage of hybrid cloud еnvironmеnts is their ability to adapt rеsourcе capacity to mееt еvolving workload dеmands rapidly.

A hybrid cloud offers flexibility and cost optimization to businesses. With a hybrid cloud, enterprises can scale up their resources and adapt to changing needs, much like a public cloud. At the same time, it allows them to retain control over sensitive data and critical applications stored on-site.

By adopting a hybrid cloud environment, organizations gain enhanced data control and the ability to flexibly allocate workloads based on evolving business needs. Organizations can achieve improved flexibility, enhanced security and regulatory compliance by adopting a hybrid cloud model.

What are the Benefits?

Here are the benefits of a hybrid cloud:

Cost-effectiveness: The cloud offers a cost-effective solution for deploying workloads. When using the cloud, an organization does not have to spend much money to purchasе additional infrastructure. Moreover, most cloud environments provide support for a pay-as-you-go pricing model, which implies you only have to pay for what you consume.

Avoids Vendor Lock-in: Companies often face the challenge of becoming too dependent on one cloud provider during the transition to the cloud, making it difficult to switch to another provider if it becomes necessary to do so. However, by adopting a hybrid model and specifically migrating to a platform that supports multiple providers, companies can mitigate this risk and avoid being locked into one vendor.

Improved Developer Productivity: By deploying a hybrid cloud platform, developers will be able to significantly enhance their productivity. With this approach, developers can leverage Agile and DevOps methodologies more efficiently and deploy their applications across multiple cloud infrastructures simultaneously.

Infrastructure Efficiency: A hybrid cloud offers development and IT operations teams enhanced and smooth control over resources. Organizations can also leverage cloud services and combine them with data stored in the cloud or on-premises infrastructure to create more value through the hybrid cloud.

Better compliance and security: Hybrid clouds offer significant benefits for business growth and acceleration. They enable faster product development cycles, promote innovation and expedite time to market. Additionally, they facilitate prompt response to customer feedback and allow for the delivery of applications in closer proximity to customers. Hybrid clouds also facilitate the integration of new services from third-party providers.

Cloud-bursting: One valuable feature of hybrid cloud environments is called cloud bursting, which enables organizations to easily increase their resource capacity to meet varying work demands.

An Overview of Hybrid Cloud Architecture

A typical hybrid cloud architecture comprises of the following components:

On-Premises Infrastructure: This refers to your privately owned physical infrastructure that can be used for storage, network, host servers, etc.

Private Cloud: Generally speaking, a private cloud is an environment that is dedicated solely to the use of a single organization. It can be hosted either internally within the organization’s own premises or externally, but it is securely protected and not accessible to the general public.

Public Cloud: The term public cloud refers to a computing model in which services such as compute, storage, deployment, etc. is provided by third-party providers as a fully managed service over the internet.

Cloud Management Platform: A cloud management platform is responsible for managing the operation and allocation of computing resources across various cloud platforms. It also helps businesses effectively use their hybrid cloud environments.

Orchestration and Automation Tools: Streamlining tasks such as resource provisioning and data synchronization across different environments requires the use of orchestration and automation tools. These tools are crucial in ensuring efficiency and seamless operations.

Best Practices for DevOps in the Hybrid Cloud

Albeit the benefits it provides, successfully implementing DevOps in a hybrid cloud requires you to adhere to certain best practices:

Monitoring: Ensure your on-premises and cloud environments are monitored and managed effectively by using the right tools. These tools offer a comprehensive view of your resources, regardless of their hosting location.

Infrastructure-as-Code (IaC): Use tools like CloudFormation, Terraform or ARM to automate infrastructure provisioning and management. IaC ensures that infrastructure is built consistently and reliably.

Containerization: Container technologies such as Docker and container orchestration platforms like Kubernetes abstract the underlying infrastructure and make moving applications between on-premises and the cloud easier.

Centralized logging: Make sure you have a centralized solution for metrics, logs and traces (like ELK Stack, Prometheus and/or Grafana) that can collect data from both the platform and the cloud environment.

Automation: From CI/CD pipelines to infrastructure deployment, it’s critical to ensure that processes are automated by leveraging tools like GitLab CI, GitHub Actions and/or Jenkins.

Security: You should consistently implement a robust security strategy. Consider the unique security requirements of a hybrid system, such as security of data transfer between disk and the cloud, encryption at rest and in transit and consistent identity and access management.

Performance: Understand latency and bandwidth issues when transferring data between disk and the cloud. Choose the right architecture (such as edge computing) and optimize your applications to minimize these issues.

Data management and integration: You should understand where your data comes from and pay attention to data gravity. You should also link applications and data sources on the server and in the cloud to ensure smooth data flow.

Application architecture: You should design cloud-agnostic, modular and flexible applications. A microservices architecture often works well for hybrid cloud environments.

Disaster Recovery and Backup: Take advantage of both on-premises and cloud resources to establish a robust disaster recovery and backup strategy.

Administration and policy management: You should enforce consistent policies across both environments using policy-as-code by leveraging tools such as Open Policy Agent (OPA).

Training and skills development: Note that hybrid clouds require knowledge across multiple platforms. Ensure your team is trained and has the skills to manage and operate a hybrid environment.

Budget and cost management: You should monitor costs on the server and in the cloud. Tools such as AWS Cost Explorer, Azurе Cost Management and third-party solutions can provide insight into where your money is going.

Communication and collaboration: Although there are challenges and obstacles, you should encourage open communication and team collaboration.

Regular reviews and audits: Review your architecture, tools and procedures to ensure they align with business goals and are as efficient as possible.

What are the Challenges?

Despite its many advantages, hybrid cloud also comes with some challenges. These challenges arise from the complexities of managing multiple environments and integrating them together. Here are the key challenges of hybrid cloud at a glance:

Security Concerns: Hybrid cloud models can pose security vulnerabilities, particularly if there is inconsistent implementation of security protocols across different environments. It is important to properly secure data that moves between private and public environments to mitigate potential risks.

Data Transfer Costs: When moving data between private and public clouds, it’s important to consider the potential costs involved, particularly when dealing with large volumes of data. Being mindful of these expenses is essential when designing hybrid solutions.

Data Sovereignty and Compliance: You must ensure that data complies with regional and industry-specific regulations when it is stored or processed in multiple locations, including public clouds in different regions.

Latency Issues: One of the challenges that may arise when implementing cloud-based applications is the integration with existing legacy systems. This can be a problem, especially for applications that require real-time or high-performance capabilities.

Vendor lock-in: Vendor lock-in can still be a challenge even with a hybrid approach, especially if organizations heavily depend on proprietary tools or services from a specific cloud provider, making it difficult to migrate or integrate with other platforms.

Skills gap: One challenge in managing and operating a hybrid cloud is the potential for a skills gap, since you’ll need a diverse range of skills, such as knowledge of both on-premises and cloud technologies. To address this, organizations may need to provide training or hire new talent with the necessary expertise in both areas.

Monitoring: Achieving visibility and effective monitoring can be challenging when resources are spread across multiple environments. It often requires specialized tools or platforms to achieve end-to-end visibility and monitor effectively.

Governance and Policy Management: Although it is crucial for maintaining security and compliance, it can be challenging to implement consistent governance and policy management across both private and public clouds.

Cost Management and Optimization: While a hybrid cloud can provide cost benefits, it can also be difficult to track and manage costs across multiple platforms. This requires ongoing monitoring and the implementation of optimization strategies.

Network Connectivity: To achieve secure, reliable and fast connectivity between on-premises and cloud environments, a well-designed network infrastructure is essential. This may involve additional investments in network services or infrastructure to ensure seamless connectivity.

How Hybrid Cloud Supports DevOps Culture

Here’s how hybrid cloud supports DеvOps culture:

Automation: Automation plays a crucial role in both hybrid cloud and DеvOps. By utilizing tools and services provided by cloud providers, automation еnablеs the provisioning, scaling, and management of infrastructure. This aligns with the core principles of DеvOps, which include automated installation, integration, and utilization.

Flexibility: Unlike with a single platform or tool, DevOps teams have the freedom to select the most suitable services and platforms from various cloud providers and incorporate them into their own solutions.

Cost Efficiency: DevOps teams can achieve cost efficiency by using public cloud resources for temporary workloads like test environments or short-term projects. Your teams can optimize their costs by only paying for the resources they actually use, thereby optimizing the amount of money they spend on those resources.

Fast deployment of resources: The fast provisioning and de-provisioning of resources are crucial in a DevOps culture. Hybrid cloud allows for rapid deployment of environments, supporting the iterative and dynamic nature of DevOps practices.

Collaboration: Hybrid cloud solutions often include collaboration tools and platforms that promote effective communication and teamwork, aligning with the collaborative culture of DevOps.

Continuous Integration and Continuous Delivery (CI/CD): Hybrid cloud provides tools and platforms that support CI/CD pipelines and enable consistent and continuous integration, testing and transition to production (delivery/deployment).

Service Integration: Many cloud providers provide a wide range of services, including machine learning and databases, as part of their cloud offerings. Using these services will allow you to seamlessly integrate them into your applications, fostering a culture of continuous improvement and innovation.

Monitoring and feedback: Hybrid clouds provide robust monitoring, logging and analytics tools that offer valuable insights into application performance and user experiences.

Regulatory compliance: With a hybrid cloud approach, IT teams can maintain the security and compliance of sensitive data and applications by housing them in a private cloud or controlled environment. This way, organizations can meet their compliance and security standards effectively.

Conclusion

A hybrid cloud combines on-premises infrastructure with cloud computing, offering scalability, flexibility and improved security for applications and data. A hybrid cloud infrastructure should be more flexible than a traditional cloud to meet changing requirements. Therefore, businesses with hybrid cloud capabilities can adapt more quickly to business and technological changes.

Businesses can leverage several benefits by implementing DevOps practices in a hybrid cloud environment, such as improved agility, faster release cycles, enhanced scalability and better collaboration. With a hybrid cloud setup, companies can leverage the strengths of both on-premises infrastructure and cloud computing models to achieve scalability and flexibility.

October 3, 2023
The Role of Observability Engineers in Managing Complex IT Systems

The rise of microservices, the adoption of cloud-native architectures and the increasing shift to digital have led to the growing volume of telemetry data (logs, metrics, traces, events) contributing to the complexity of observing modern IT systems effectively and at the right cost. These challenges with processing the telemetry data on time and at the right cost make it difficult for organizations to maximize their observable surface area and ensure they perform at their best.

Enter observability engineers. These professionals are tasked with collecting, processing, analyzing and visualizing data from complex IT systems. They use this data to identify patterns, detect anomalies and understand system behavior. This awareness allows observability engineers to discover and address potential issues before they impact users.

Observability engineers play a critical role in ensuring the reliability, performance and security of complex IT systems, and are essential for organizations that want to stay ahead of the curve in today’s rapidly evolving technology landscape.

Let’s examine the essential characteristics and responsibilities of observability engineers to understand their role.

Characteristics of Observability Engineers

Observability engineers are problem solvers who specialize in optimizing system performance, ensuring reliability and driving actionable insights from telemetry data. They are the driving force behind data-driven solutions and operations. Their specialized skillset and unique focus on observability set them apart from other roles within IT operations. They may have previous backgrounds as system administrators, site reliability engineers (SREs) and IT operations members, who often react to incidents, but observability engineers are proactive and have adopted a shift-left approach to observability. They are adept at understanding the telemetry data and its formats, creating telemetry strategies, implementing comprehensive monitoring systems, and using advanced observability platforms to gain real-time insights.

Another important aspect of observability engineers’ expertise is their in-depth knowledge of telemetry data. They have the necessary skills to collect, analyze and process data such as metrics, events, logs and traces from various sources. They understand the transformations that the data needs for easy consumption by downstream SIEM, analytics, or visualization systems. They are well versed in the challenges posed by microservices architectures, cloud-native environments, and hybrid infrastructure setups and how to extract meaningful information from the system exhaust. This understanding enables them to design telemetry pipelines to move the right data, in the right format, to the right system; build monitoring systems; and implement observability practices that effectively capture, transform and analyze these complex systems.

Collaboration and cross-functional skills are other essential aspects of the observability engineers’ role. They break down the barriers between observability domains, including infrastructure, applications and networking. By coordinating and aligning the efforts of these domains, observability engineers create a more comprehensive understanding of the IT system’s behavior and performance. This approach allows for a holistic analysis beyond the limitations of individual domains and identifies cross-domain optimization and improvement opportunities.

Responsibilities

Observability engineers have many responsibilities focused on addressing problems within IT operations. Some of their key responsibilities are detecting anomalies, troubleshooting incidents, monitoring system health, optimizing resource allocation, enhancing user experiences, enabling data-driven decision-making and supporting compliance and security efforts. Through their skills and expertise, observability engineers empower organizations to maintain highly performant, reliable and secure IT systems.

They are needed throughout the lifecycle of IT systems. They play a vital role in system design and implementation, ongoing maintenance and monitoring, incident response and troubleshooting, optimization, performance enhancement and new feature development and releases. Their expertise ensures that observability is built into the system from the ground up, maintains optimal system performance, minimizes downtime and enhances the user experience.

How Observability Engineers Perform Their Tasks

Observability engineers use telemetry data to learn trends and patterns. Analyzing this data allows them to plan capacity, predict potential issues, and implement preventive measures. This approach enables organizations to continuously improve their IT systems, enhancing performance, reliability and user satisfaction.

To achieve their objectives, observability engineers use various tools for collecting and analyzing data. Telemetry pipelines are emerging as their primary tool for collecting data from various sources within an IT system, transforming it, and routing it to various destinations. These pipelines enable observability engineers to gather data from metrics, logs, events and traces. They process and transform this data to extract meaningful insights and send it to downstream analytics or visualization platforms or for long-term storage.

Various analytics and visualization APM and observability platforms allow observability engineers to analyze and visualize the data. Custom dashboards and visual representations offer them a comprehensive view of system behavior, performance trends and user experiences. With this information, observability engineers can troubleshoot and optimize system performance, finding root causes of issues and implementing effective solutions.

They also contribute to compliance and security efforts. By monitoring and analyzing telemetry data, observability engineers identify possible vulnerabilities and risks within the system. They help organizations maintain robust security practices and ensure compliance with regulations.

Investing in the Promise of Observability Engineering

Observability engineers are increasingly becoming essential for managing the complexity and unpredictability of modern IT systems. Their specialized skills and expertise enable them to address potential issues, optimize system performance and ensure reliability. As technology advances, the demand for observability engineers will only increase. Organizations striving to succeed in their IT operations must invest in these professionals.

October 2, 2023
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:

SimplyHired.com

Wolfe
Remote
Senior Staff DevOps Engineer
$162,000 to $172,000

LinkedIn

Justworks Labs
New York, New York
DevOps Engineer
$155,000 to $165,000

Dice.com

KBS Solutions
Annapolis Junction, Maryland
DevOps Engineer
$150,000

CareerBuilder.com

Fusion Global Solutions
Dallas, Texas
Azure Architect with DevOps Experience
$140,000 to $190,000

Indeed.com

Kaseware
Denver. Colorado
Senior DevOps Engineer (Kubernetes)
$140,000 to $180,000

October 2, 2023
What DevOps Teams Need to Know About Responsive Web Design

The role of DevOps in modern software development extends beyond just coding, testing and deployment. One of the significant areas where DevOps teams can bring value is delivering a positive user experience, a critical competitive advantage in the digital era. Responsive web design (RWD) is a cornerstone of a friendly user experience, allowing for seamless website functionality across various screen sizes and devices.

This article explores RWD and its fundamental principles and explains how RWD impacts web application development and deployment. We will review critical considerations for DevOps teams such as performance optimization across devices, the integration of responsive design into CI/CD pipelines, automated testing, the importance of a user-centric approach and consistent monitoring and troubleshooting. The objective is to equip DevOps teams with the necessary knowledge to effectively implement and support RWD, enhancing the user experience and significantly improving overall user satisfaction and engagement.

What is Responsive Web Design?

Responsive Web Design is a web development approach that makes your website layout adjust according to the size of the viewer’s screen. This means that your website looks and functions optimally on a wide range of devices, from desktop computers to tablets and smartphones. The primary goal of RWD is to ensure that users have an efficient and enjoyable experience, regardless of the device they are using.

With the increasing use of mobile devices for internet access, it is essential that your website is accessible and user-friendly on all platforms. By implementing RWD, your organization can ensure that your website offers a seamless user experience, no matter the device.

RWD removes the need for creating different versions of your website for different devices. Instead, you can have a single website that adjusts automatically to fit any screen size. This not only improves user experience but also saves you time and resources in website development and maintenance.

Understanding the Principles of Responsive Web Design

Fluid Grid Layouts

A fluid grid layout is a key principle of RWD. It involves designing your website layout in relative units, such as percentages, instead of absolute units like pixels. This allows your website layout to resize dynamically to fit the screen on which it is viewed.

The advantage of fluid grid layouts is that they offer more flexibility than fixed layouts. They allow your website to adjust smoothly to different screen sizes, ensuring that the user experience is not compromised. This is particularly important in today’s digital landscape, where users access websites from a wide range of devices with different screen sizes.

Flexible Images and Media

The second principle of RWD is responsive images and media. This means that your images and media files should be able to resize automatically to fit the screen size. This is essential to ensure that your images and media do not distort or overflow when viewed on smaller screens.

Flexible images and media enhance the visual appeal of your website on different devices. They also improve the loading speed of your website, which is a crucial factor for user experience and search engine ranking.

Mobile-First Design Philosophy

Mobile-first design philosophy is another key principle of RWD. As the name suggests, this approach involves designing your website primarily for mobile devices and scaling it up for larger screens.

The mobile-first design philosophy is based on the reality that a growing number of users are accessing the internet through their mobile devices. By designing your website with a mobile-first approach, you can ensure an optimal user experience on mobile devices.

Progressive Enhancement

Progressive enhancement is the fourth principle of RWD. It involves building your website in layers, starting with a basic layer that works on all devices and then adding more features and functionalities for devices with advanced capabilities.

Progressive enhancement allows you to provide a basic, functional website for all users regardless of their device or browser. At the same time, it enables you to offer an enhanced website experience for users with modern devices and browsers.

How RWD Affects Application Development and Deployment

Responsive web design has a significant impact on the development and deployment of web applications. It affects various aspects of the process, including the codebase, development and testing process, user interface design and deployment and updates.

Unified Codebase

One of the main benefits of RWD is that it allows you to maintain a unified codebase. Instead of creating separate versions of your application for different devices, you can have a single codebase that works across all platforms.

A unified codebase simplifies the development and maintenance process. It eliminates the need for duplicate coding and testing, saving you time and resources. It also ensures consistency in your application, as all users are accessing the same codebase, regardless of their device.

Development and Testing Process

RWD also influences the development and testing process. Since your application needs to work optimally on various devices and screen sizes, you need to incorporate responsive design considerations into your development and testing process.

Responsive design testing involves checking your application on different devices and screen sizes to ensure that it functions and displays correctly. This can be done manually, using device emulators or through automated testing tools.

User Interface Design

The user interface (UI) design of your application is also affected by RWD. A responsive UI design ensures your application is easy to use and visually appealing on all devices.

Responsive UI design involves creating a flexible layout that adjusts automatically to the screen size. It also requires you to design intuitive navigation and interaction elements that work well on touchscreens and desktop interfaces.

Deployment and Updates

Finally, RWD has implications for the deployment and updates of your application. With a responsive application, you can deploy and update a single version of your application for all devices.

This simplifies the deployment and update process and ensures that all users have access to the latest version of your application. It also makes it easier to fix bugs and issues, as you only need to update a single codebase.

Key Considerations for DevOps Teams

Implementing RWD is not a straightforward task. It requires careful planning and consideration. Here are some key considerations for DevOps teams when implementing RWD.

Ensuring Website Performance Across Different Devices

One of the main challenges in implementing RWD is ensuring that your website performs well on all devices. This requires careful optimization of your website elements, including images, CSS and JavaScript.

You also need to consider factors like loading speed and responsiveness, as these can significantly affect user experience. There are a number of available tools to help you analyze and optimize your website performance.

Incorporating Responsive Design into CI/CD

Continuous integration and continuous delivery (CI/CD) are key practices in DevOps. CI/CD involves integrating code changes regularly and delivering updates to users quickly and reliably. Incorporating responsive design in your CI/CD process can help you ensure that your application is responsive and user-friendly at all times.

Automation in Testing

Testing is a crucial part of the development process. With RWD, testing becomes more complex as your application needs to work on a wide range of devices and screen sizes. Automation can help streamline the testing process and ensure that your application is tested thoroughly and efficiently.

User-Centric Approach

A user-centric approach is essential in implementing RWD. This means considering the needs and preferences of your users at every stage of the development process. From the design phase to testing and deployment, the user experience should be at the forefront of your considerations.

Monitoring and Troubleshooting Responsive Design Issues

Finally, monitoring and troubleshooting are important aspects of implementing RWD. You need to monitor your website performance on different devices and troubleshoot any responsive design issues that arise.

Analytics tools can provide valuable insights into how your website is performing on different devices. They can also help you identify and fix any issues that are affecting your website’s responsiveness.

Conclusion

In conclusion, responsive web design is an essential aspect of modern web development. It ensures your website is accessible and user-friendly on all devices, improving user experience and engagement. By understanding the principles of RWD and considering the key aspects of implementing it, you can unlock the power of responsive design and take your website to the next level.

September 28, 2023
Post service upgrade pods are taking too long to serve traffic

After a service upgrade the pods are taking too long to serve traffic. What would you need to find out to debug?
1. The validating webhook for pods has failed
2. The cluster doesn’t have enough CPU/memory to schedule the pod to a node
3. The new code added an init container and the init container is taking a long time to finish
4. The new deployment changed the readiness probe
5. There is a bug in the code

Let’s break down why each of the provided choices is indeed a correct answer in the context of troubleshooting slow pod response times after a service upgrade:

The validating webhook for pods has failed: Validating webhooks in Kubernetes are responsible for ensuring that pods conform to certain policies before being created. If the validating webhook fails, it can prevent pod creation altogether, which would certainly impact the ability of new pods to serve traffic. Therefore, this is a correct choice to consider when debugging service issues.

The cluster doesn’t have enough CPU/memory to schedule the pod to a node: When a service is upgraded, its resource requirements may change. If the cluster lacks the necessary CPU or memory resources to schedule the new pods, it can lead to delayed pod scheduling and slow response times. This is a common issue that SREs and DevOps need to investigate when traffic-serving problems arise after an upgrade.

The new code added an init container, and the init container is taking a long time to finish: Init containers are executed before the main application containers in a pod. If a new init container is introduced during an upgrade and it takes an extended time to complete its tasks, it can indeed cause delays in the pod becoming ready to serve traffic. This is a relevant consideration when diagnosing slow response times post-upgrade.

The new deployment changed the readiness probe: The readiness probe determines when a pod is ready to accept traffic. If the readiness probe is modified during an upgrade and is now misconfigured, it can delay the point at which the pod is considered ready to serve traffic.
Therefore, this is an accurate choice for troubleshooting slow response times.
One of our earlier quiz questions dealt with a similar issue which we discussed in the following blog: https://webb.ai/blog/quiz-1-aug-1-23-healthcheck-port-mismatch/

There is a bug in the code: While the broadest option, it’s also valid. Code bugs can absolutely result in slow response times. However, it’s crucial to note that this choice doesn’t specify what type of bug or where in the code it might be. It highlights the importance of code review and debugging as part of the troubleshooting process.

In summary, all the provided choices are correct because they represent various potential causes of slow pod response times after a service upgrade. Troubleshooting such issues often requires considering a combination of factors, from resource constraints to configuration changes and code-related problems. SREs and DevOps professionals must approach the problem with a holistic perspective to identify and address the root causes effectively.

There are additional reasons, not included in the options provided, for slow pod response times after a service upgrade:
Network Latency/Issues: Connectivity problems, network misconfigurations, or high network latency between pods and services can cause delays in serving traffic.
Storage Latency: Slow storage I/O or inadequate storage resources can impact data retrieval and processing, affecting pod performance.
Resource Contentions: Other resource-intensive workloads on the same nodes can lead to resource contentions, resulting in slow pod response times due to competition for CPU, memory, or disk resources.

Analyzing each potential root cause for each failure is not possible in today’s highly dynamic environments. Leveraging software and the recent advancements in AI to automate root cause analysis is one way to reduce toil and increase productivity allowing SRE and DevOps to become proactive and focus on addressing more complex failure scenarios.

September 28, 2023
Generative AI’s Impact on Developers
There is a growing belief in the developer community that future software development will be performed by machines rather than humans by 2040. Software development will undergo a radical change with the combination of machine learning, artificial intelligence, natural language processing and code generation that draws from large language models (LLMs). Most organizations believe that there will be a 30-40% improvement in overall developer productivity with AI. While these arguments have some merit, I do not believe developers will be fully replaced. Instead, I believe generative AI can augment developers to support faster development and higher-quality code.

I’ll address the impact of generative AI on the development community under three pillars:
- Automation and productivity
- Quality engineering and compliance
- Ways of working
Automation and Productivity
- There will be a focus on increased automation all the way from business planning to operations of applications.
- LLMs can help provide better alignment of user stories to business requirements. In fact, one of the best use cases for generative AI during planning phases is to auto-generate user stories from business requirements documents.
- Since ambiguity of requirements or guesswork is taken out of the equation, one can expect a clearer “definition of done” through the auto-creation of acceptance criteria.
- In a typical development cycle, 15%-20% of coding defects are attributed to improperly defined requirements. Generative AI augmentation can result in significant reduction of those defects.
- Generative AI augmentation can help developers with better planning and estimation of work. Rather than relying on personal experience or home-grown estimation models, LLMs can better predict the complexity of work and developers and continually learn and adapt through multiple development sprints.
- AI-augmented code creation can allow developers to focus on solving complex business problems and creative thinking rather than worrying about repetitive code generation. Over the last decade or so, the perception of software development as a creative pursuit has been a dying phenomenon. With AI, I think that more and more younger developers will be attracted to the field. AI will put the “fun” back in coding.
- AI-assisted DevOps and continuous integration will further accelerate deployments of code so developers can focus more on solving complex business problems. Deployment failures due to human errors can be drastically reduced.
- Elaborating on the above, newer and less experienced developers can also generate higher-quality code with AI-augmentation, leading to better overall consistency of code in large programs.
- Overall, from a development standpoint, I think AI augmentation will free up 30% of developers’ time to work on enhancing user experience and other value-added tasks.
Quality Engineering and Compliance
- In a hybrid cloud world, solutions will become more distributed than ever, making system architecture more complex.
- LLMs can assist in regulating design documents and architecture work products to conform to industry/corporate standards and guidelines. In essence, LLMs can act as virtual Architecture Review Boards. In a typical development life cycle, architecture/design reviews and approvals make up 5%-8% of work and augmenting the process with generative AI capabilities can cut that time in half.
- Ssecurity compliance for cloud-based solutions is imperative. LLMs can assist in ensuring such compliance very early on in the development life cycle, leading to more predictable deployments and timely program delivery.
- Generative AI-augmented test case creation can optimize the number of test cases needed to support the development while increasing the overall code coverage for quality.
- For greenfield development, there can be a 20%-40% increase in overall code quality through automatic bug-fixing embedded within a DevOps pipeline. Developers can focus on bug avoidance rather than bug fixes.
- AI-augmented test data generation can increase testing cycles by 25%. Proper test data availability has been a big pain point for quality engineers for decades.
Ways of Working
- Developers will have to shift their focus to understand how to use the right AI models and prompts to generate documents, code, test cases etc. rapidly. This is a change from writing everything from scratch. Picking the right model for the right use case is very important from an overall efficacy standpoint while also remaining cognizant of the trustworthiness, security and data lineage of the models. This is why I don’t think developers can simply be fully replaced (at least not in the near future).
- Generative AI is not magic. It does what it is trained to do based on the examples it is provided and how well it is prompted. Developers will need to quickly learn and adapt to this new paradigm.
- This requires different sets of skills and training for developers. It is also incumbent on management to ensure developer enablement happens so they can succeed in this new world. Simply having an AI technology strategy without a developer enablement strategy means a failed overall strategy. The outcomes will not be fruitful.
AI-enabled development is here to stay, despite the nervousness in the developer community. Of course, there are some good reasons to be nervous. Generative AI, unlike many other revolutionary technologies that have emerged in the recent past, doesn’t just enable work but creates “things.” And there is a school of thought that believes this creation will displace developers. It won’t. Generative AI-augmented development will provide developers with a pathway to higher creative thinking, which really is one of the fundamental aspects of software development.

My message to the development community: Don’t fear generative AI; embrace, learn about and from and enjoy creating with generative AI.
September 27, 2023
Grafana Labs Partner Spotlight Video

What are the Grafana offerings for modernizing applications on AWS?

September 26, 2023