devopsy.kinsta.cloud

Tag: devops

  • Transitioning Legacy Systems to DevOps: The XDR Support Paradigm

    Transitioning Legacy Systems to DevOps: The XDR Support Paradigm

    In today’s rapid and evolving technological landscape, DevOps emerges as a frontrunner in development methodologies, promising agility, efficiency and continuous delivery. While companies are keen to leverage its benefits, the transition from legacy systems can be fraught with challenges. Integrating extended detection and response (XDR) while making this transition can serve as a powerful tool to manage these challenges. In this article, we will delve deep into the potential pitfalls of this transition and explore the remedial measures with the aid of XDR solutions.

    The Legacy System Quagmire

    Legacy systems are often monolithic, siloed and heavily dependent on older technologies. According to a report by Forrester, a staggering 72% of corporate architectures still run on legacy systems. These systems, while operational, often lack the flexibility and scalability required for a DevOps-driven environment. They can become roadblocks, slowing down processes and becoming vulnerable points of failure.

    The Inherent Challenges

    Scalability Concerns: DevOps emphasizes scalability. However, legacy systems weren’t built with this level of dynamism in mind. Most legacy systems face scalability issues when introduced to DevOps practices.

    Security Vulnerabilities: The older tech stacks are more vulnerable to modern-day threats. Without continuous updates, these systems become an attacker’s paradise. This is where XDR solutions come into play, offering real-time threat detection and response across the infrastructure.

    Integration Hurdles: Legacy systems often struggle to integrate with modern tools used in DevOps, from container orchestration tools like Kubernetes to configuration management tools like Ansible.

    XDR to the Rescue

    Holistic Threat Detection: XDR’s primary strength lies in its comprehensive threat detection across networks, clouds, endpoints and applications. This compensates for the potential vulnerabilities of legacy systems, providing a safety net.

    Real-time Responses: XDR doesn’t just detect threats; it actively responds to neutralize them. As per a study by IDC, integrating XDR can reduce incident response times by up to 50%, ensuring that any security breach on legacy components is swiftly managed.

    Forensic Capabilities: Understanding the root cause of issues is paramount. XDR solutions come with forensic capabilities, allowing teams to understand the “how” and “why” behind incidents, an essential feature when dealing with older systems that may lack documentation.

    Migrating With Minimal Friction

    Training and Skill Development: One of the most underrated challenges is the lack of skills in managing and migrating legacy systems. Investing in training and reskilling can bridge this gap. A recent survey by DevOps Institute revealed that 72% of organizations that prioritized skill development found transitions, including to DevOps, smoother and more efficient.

    Pilot Programs: Before a full-blown transition, it’s wise to run pilot programs. This involves migrating a smaller, non-critical segment of the legacy system and integrating XDR support. The lessons learned here can guide the broader migration.

    Continuous Monitoring: Post-migration is not the time to be complacent. Using XDR’s monitoring capabilities, teams should continuously oversee the newly migrated segments for any anomalies or performance issues.

    Infrastructure-as-Code (IaC) and Legacy Systems

    Bridging the Gap with Automation: DevOps heavily leans on the principle of automation, and infrastructure-as-code (IaC) plays a pivotal role in achieving this. However, legacy systems often don’t align seamlessly with IaC principles. By using IaC platforms like Terraform or CloudFormation, organizations can create templates for legacy systems, making them more malleable for DevOps operations.

    Ensuring Compliance: One challenge with legacy systems is maintaining compliance, especially when regulatory standards evolve. IaC, when coupled with XDR’s continuous monitoring, can ensure that these older systems remain compliant. According to a 2021 report by Puppet, companies that adopted IaC methodologies witnessed a 60% faster compliance update implementation time on their legacy systems.

    Cultural Shifts and Organizational Dynamics

    The Human Element: While the technical challenges of migration are tangible, the human element is often overlooked. Employees used to legacy systems might resist transitioning to DevOps practices, fearing obsolescence or due to comfort with older methods.

    Change Management: Addressing this requires an organized change management strategy. Workshops, seminars and constant communication can assuage fears and foster a culture of continuous learning. A study by the Harvard Business Review highlighted that organizations that integrated change management strategies had a higher success rate in technical transitions, including shifts to DevOps models.

    Collaborative Approach: It’s essential to foster a collaborative environment where both legacy system experts and DevOps enthusiasts work together. XDR’s collaborative tools can be instrumental here, providing a platform where teams can discuss XDR solutions, share insights and collaboratively respond to threats.

    Conclusion

    Transitioning from legacy systems to a DevOps model is not a mere technical challenge; it’s a transformative journey for the entire organization. The challenges, while significant, are not insurmountable, especially with tools like XDR providing support. By understanding the pitfalls and leveraging advanced solutions, businesses can ensure a smooth transition and harness the power of DevOps to its fullest.

  • Policy-Based Governance: Modernizing Authorization for the Enterprise

    Policy-Based Governance: Modernizing Authorization for the Enterprise

    As we’ve moved away from on-prem environments to cloud-based ones and policy managed via code, we’ve inadvertently changed the way the infrastructure is managed. The infrastructure that used to be managed by IT/security teams has now shifted to being managed by the engineering operations teams. With that shift, engineering teams have found themselves now in control of new environments, including the access management part. This new change of control and management of access in the privileged environment requires a new approach to the solutions we use for privileged access governance.

    Shift to Cloud-Based Environments

    Cloud adoption has grown rapidly in the past few years, and while many organizations were already moving to the cloud from on-prem, the COVID-19 pandemic accelerated the transition, causing remote working to become the norm and changing the way responsibilities are managed.

    These organizations found they needed to adopt new ways of working to be able to support and provide critical services to off-site workers. As a result, more than 90% of organizations have adopted some form of cloud-based infrastructure, changing the way they work.

    The cloud is prevailing for tech stacks that need to elevate access governance over the cloud infrastructure. In fact, a whopping 94% of enterprises report they are using cloud services today, and 75% say security is a top concern.

    Cloud Shift, Worldwide, 2019 – 2025

    “The shift to the cloud has only accelerated over the past two years due to COVID-19, as organizations responded to a new business and social dynamic,” said Michael Warrilow, research vice president at Gartner.

    Shift to a Fast-Paced, Customer-Value Mindset

    Since switching to a new agile working environment, customers have gotten used to expecting very fast service. Instead of having a change management process that deploys to production once every few weeks, it’s now common to deploy hundreds of times to production every day.

    Access: required more often, more dynamically.

    Dynamic Changes: With the shift to the agile pace in which the new company works, access has been complicated a hundredfold. (Being able to support customers, fix bugs and being able to move very quickly. Needs to support these hundreds of changes to the customer’s product in their hand.

    Scale of Access has Increased: With the shift to cloud-based infrastructure, it’s now possible to create 5000 databases, for example, with a click of a button. All of them require access management because each one requires an understanding of who needs access and in what way.

    New Solutions are Needed

    The cloud providers, AWS, Azure and GCP, are now the way that people work. And most resources, whether databases or the machines that are running are doing so in the cloud environment. However, companies that shifted to the cloud are dissatisfied with what their legacy PAM solutions can provide them for the cloud resources. These solutions are ill-suited for their new needs. Instead, they need a solution that is agile and scalable to match their new working environment.

    With a perceived lack of options, companies are choosing to have the engineering team put in time to create new solutions. These solutions are meant to provide the following benefits: A frictionless way to gain permissions, an easy way to integrate with the tools their team uses today, and instead of manual provisioning and manual access requests, these solutions should automate those tasks.

    They are creating these in a variety of ways, including the following examples:

    1. Dev Portals: Dev portals are internal developer portals that act as a storehouse of information for the developers in a company. They work by centralizing the data in an internal portal, which offers developers and managers the ability to construct a system that facilitates monitoring of the software ecosystem, receiving alerts about alterations, and visually representing interconnected details. This portal additionally simplifies the task of overseeing diverse facets of the software development lifecycle, encompassing aspects like microservices interconnections, ownership statuses, dependencies, resource allocation, and beyond. By utilizing these, companies are delivering and gaining permissions to cloud environments.

    2. Slack Bots: Many times, a developer will need elevated access to resources or databases, so they need an easy way to request and be granted the permissions they need when they need them.

    3. Terraform Automation: Terraform is an infrastructure-as-code tool that lets you build, change, and version cloud and on-prem resources safely and efficiently. Terraform policies and infrastructure code/policies could also be leveraged to define access control policies. The way it works is that engineers or support teams are required to request a change in the Terraform code in order to gain the access they need. If there is a Terraform code in place describing the policy of groups to role access to the relevant infrastructure they would like to gain access to, they can request that access by requesting a Terraform code change. They would need to understand which parts of the code to change and submit a “pull request” to make the desired change which will resolve in the permissions change. Once a Terraform code change is approved, it changes the policy of access to that infrastructure which will change the permissions that the group has.

    (a) Static: The Terraform state is static, meaning that permissions granted in this way are essentially meant to be long-lasting, standing permissions. In addition, these permissions are usually granted to a role and are very coarse-grained(not granularly defined, as they need to suit the whole role’s needs). This creates an environment of over-privileges. That is why, in many cases where the permissions should be granted to a specific task or person or for a limited time, teams choose to use one-off permission granting to manually grant the permissions. However, these one-off permissions create drifts in the environment essentially if they are not revoked. And since there is no process in place for these one-off just-in-time (JIT) permissions, the revocation needs to also be manual and needs to be remembered to be done.

    (b) “Drifts”: (A drift in Terraform is when the environment isn’t accurately reflected in the Terraform policy). Therefore, by changing the access outside of Terraform, you’re creating a drift because it doesn’t match up correctly, making it a poor answer to one-off permissions. For anything needing high privileges, you want it to be dynamic instead of remaining as part of Terraform.

    4. Jira Service Management With Your Cloud Provider: Integrating Jira with GCP (as a cloud provider example), allows the IT security team the ability to automate servicing access tickets by creating forms where teams can request a certain role or be granted access to a certain project.

    5. Manual JIT Cloud Role: This is a method that allows self-serve permissions to engineers to get the permissions they need on demand. The role needs to be predefined, and essentially, as long as the engineer is part of the group, he or she will be able to gain those privileges on-demand. This reduces the standing privileges in the environment at any given point; however, roles granted are usually for a generic cloud admin rather than being granular, as it’s hard to determine ahead of time exactly what permissions will be needed.

    Problems With DIY Solutions

    Because of the lack of the right tool, companies are spending expensive resources creating their own solutions for access control. Team members know they need something that leverages cloud capabilities rather than their PAM for on-premises. These solutions are time-consuming; companies need to build then maintain the infrastructure. They are also very limited in what they can do since they are not the company’s main concern.

    Policy-Based Governance

    With the growing adoption of cloud-based tools, it’s more important than ever that companies learn to adapt. Engineers have now found themselves in control of permissions in new environments they were not looking to manage. It’s clear that a new approach to access governance is necessary.

    The quickest and most sure-fire way to do this is by utilizing tools deliberately made for the new cloud-based era that address the challenge in a way that is suited to the new, fast-paced, dynamic cloud environment. These new solutions are policy-based and leverage the fact that with the shift to new ways of managing infrastructure, it’s necessary to give the IT and Operations teams more control, granularity and automation.

    Platforms such as Apono for cloud-based permissions management allow organizations to benefit from centralized permissions management, enforce dynamic role-based access controls, monitor and audit privileged user activities and mitigate security risks effectively.

    Final Thoughts

    In addition to the shift we have witnessed in the control of the environment and access governance, organizations are also shifting to delegating more ownership to the engineers themselves. This means providing engineering teams more control of their code — all the way to production. However, with that comes the need to give them only granular control over the environment and provide access management to their specific scope without creating a burden on the teams in managing/granting the access. This leads the operations teams to search for solutions to address the need for access management delegation in the environment. New solutions and platforms addressing the new environment’s privileged permissions management challenge need to take that into consideration as well.

    As a final note, I believe the shift in mindset occurred a few years ago already. However, we are only now realizing some of its effects. One of them is the shift in privileged access challenges and all that comes with that. Seeing the many internal solutions that small-to-large companies have created for themselves gives us an understanding of how this evolved market isn’t being addressed with the same platforms that have existed for well over a decade.

  • Evolution of Toil Identification and Mitigation – A Classic SRE Challenge

    Evolution of Toil Identification and Mitigation – A Classic SRE Challenge

    Ingo Averdunk, leading architecture and solutions for cloud service management and site reliability engineering for IBM Cloud, contributed to this article.

    Christine White, architect at IBM Consulting, also contributed to this article.

    When many people think of operations, they often associate it with manual, labor-intensive work. This is because operations traditionally encompass a wide range of activities that involve managing and executing tasks related to producing, distributing and maintaining goods and services. Information technology (IT) is not different: Manual work has traditionally been a significant component of various tasks and processes. Examples are server maintenance, software installation, troubleshooting and network configuration, which historically required manual intervention and human expertise. Much of the work is inherently repetitive by nature, time-consuming and prone to errors. We call this work “toil”, the kind of work tied to running a production service that tends to be manual, repetitive, automatable, tactical, devoid of enduring value and that scales linearly as a service grows.

    Toil

    This toil has always been there. And toil leads to burnout, increased errors and reduced scalability. Only how we have addressed it has changed. While toil remains, toil mitigation has evolved.

    In the beginning, toil was addressed through labor arbitrage. Between 2000 and 2010, we leveraged labor arbitrage to reduce the cost of manual work by outsourcing and out-tasking activities to near-shore and later off-shore countries. When there is a problem, the person who can solve it steps up. Then, to manage it better, we started to do workload or work item management. You have systems, like prioritized backlogs, and we started putting a structure on how we solve these problems. But still, it was all resource-based; everything was resource-intensive. And that is why the engineers became more of the subject matter experts (SME). Being a subject matter expert took time, and being critical to operations became exhausting. And the repetitive manual activities required took time and effort they didn’t have.

    As issues became repetitive, we started thinking about scripting. We evolved from a complete labor arbitrage mentality to writing spot scripts. Engineers began to use scripts to solve a small part of the most oppressive problem.

    Tools started to be developed to enable and automate some of the work. Organizations purchased tools, and education of the tools commenced. It became important to be an SME in your area and an SME of the tools in your area. However, the tools were specific to certain functions and features that they provided, and the software development life cycle (SDLC) had a lot of nuances that many of the tools did not capture. So even when the tools helped, it created this mass of white spaces that could not be integrated easily. Tools were very format-specific and didn’t have the flexibility end to end integration. Newer problems emerged as formats were different, speeds were different, and other differences manifested. Even as this initial approach was an improvement over the labor arbitrage mentality and reduced the amount of manual effort required, it often resulted in complex and brittle systems that were difficult to maintain and debug.

    Automation

    From 2015 to 2020, automation became more prevalent. On one side, the cost levers of labor arbitrage were reduced, and due to low skill, quality suffered. Thanks to technological advancements, automation in various flavors became a viable alternative: Runbook automation, robotic process automation, etc.

    Aligning tool isolation with SDLC nuances required an end-to-end automation philosophy. We need to do automation across the board. Toil elimination was a goal of end-to-end automation, and that obsession led to SRE thinking and the principle of capping operational load. An SRE spends only 50% of his time on operational activities, and the other 50% on “engineering” (improvements): Identification and elimination of problem areas, improving and integrating tooling, etc. And thus, the genesis of the next wave of reliability engineering principles began.

    Parallel to this, adopting DevOps and Agile practices as well as cloud computing enabled IT teams to define and manage infrastructure and application configurations through code, allowing for automated provisioning and scaling. Applications are designed for reliability and instrumented for observability, providing insights into the production use. Traditional organizational barriers are broken down and teams cooperate across the entire SDLC with a shared goal that balances velocity and quality.

    The new focus on leveraging cloud-native technologies and practices to build scalable, flexible and resilient systems allowed SRE teams to adopt cloud-native technologies such as Kubernetes, containers and serverless computing to build systems that are designed to be highly available, fault-tolerant and easy to maintain.

    Toil mitigation and the SDLC lifecycle have evolved from reactive handling of problems to a proactive approach to operations, where alerting is maturing, and other sophisticated instrumentation is implemented. A proactive approach underlined the importance of observability and monitoring. SRE teams are using observability tools such as Prometheus, Grafana and New Relic to monitor their systems in real-time. The ability to identify potential issues before they become incidents emerges.

    AI Infusion

    The next step is being more prescriptive and predictive. That calls for a good bit of AI infusion. We have historical knowledge management systems that will tell us what went wrong, how it went wrong, how it got resolved and how long it took. We can augment that knowledge base with publicly available and curated knowledge.

    Starting in 2020, AI and machine learning are being used in IT for tasks such as anomaly detection, predictive maintenance, risk assessment and automated incident response, reducing the manual effort required for monitoring and maintaining systems.

    AI systems can learn from all this and not only predict issues, but when issues happen, they also auto-resolve. An extension of AI, Generative AI, will disrupt this event more. Prediction of a problem will also lead to the generation of executable runbooks and playbooks, which are agnostic to specific types of automation or automation tools. New challenges such as data quality, model training and explainability have arisen simultaneously and human intervention is still required until we can reach a certain comfort level. There will need to be a manual effort to look at Generative AI proposed solutions, approve them and prove the solutions in lower-level environments, before applying them into production environments.

    In the next 18 – 36 months, we will see that morph into risk-taking, where Generative AI will become digital operations. If we look at an Ops team or a SRE team, there are different personas, and we firmly believe that those personas will be assisted by an AI bot. These bots will participate in daily standups, and they will learn. They will participate in issue-resolution processes, and they will learn. They will start participating in retrospectives, and they will learn. This “AI persona” will become a digital worker in a highly integrated digital team. The manual tasks that once were solved through AI are now becoming complex decision-making processes. We are entering the 4th wave of automation improvements using Generative AI.

    Be Prepared

    Just as the invention of fire, tires, the printing press, the telephone and the lightbulb, what we do with this advancement will greatly shape our future. We must be prepared. And the best way to be prepared is to upskill our practitioners with newer technology skills like AI. The upskilling needs to include all the core and peripheral benefits and concerns to help them manage and improve upon the current day. We need to clarify roles amongst practitioners so there is not an “I’m being replaced by a bot” mentality, and most importantly being open-minded about the possibilities, building on those possibilities and embracing risk rather than fighting it!

  • How to Navigate App Modernization Strategies for the Cloud

    How to Navigate App Modernization Strategies for the Cloud

    In today’s fast-paced digital landscape, organizations continuously seek ways to innovate and enhance customer experiences while optimizing efficiency. One of the most transformative strategies is modernizing applications for the cloud. App modernization isn’t just a technology upgrade. It’s a holistic business transformation. The cloud’s elasticity and scalability present opportunities to create deeper customer engagement, foster seamless partner integration and unlock valuable data insights. However, this requires a thoughtful approach that aligns with business goals and technological aspirations.

    Let’s look at some best practices for modernizing apps for the cloud and navigating potential challenges.

    The App Modernization Process

    It’s important to determine whether the cost of migration will lead to better efficiency and customer engagement that will yield an increase in return on investment (ROI). This approach allows businesses to address current issues and future-proof their data stacks.

    After assessing the cost and ROI considerations, organizations need to follow a systematic process to successfully modernize an app for the cloud. The first step is to evaluate the existing app’s strengths, weaknesses and areas for improvement. It’s crucial to assess an app’s compatibility with cloud platforms and set clear modernization goals.

    Following the assessment, design a cloud-native architecture that aligns with the cloud’s scalability, elasticity and distributed nature. This may involve rearchitecting the app to include such cloud-native components as microservices, containers, or serverless computing. Once the architecture is in place, migrate the app’s data from on-premises or legacy databases to cloud-based storage solutions to ensure data availability, scalability and ease of management in the cloud environment.

    The next step involves rewriting or modifying the app’s codebase to take advantage of cloud services and APIs. Specific modules or functionalities may need to be re-engineered to enhance performance, scalability and integration capabilities. Perform comprehensive automated load and security testing to verify reliability and performance in the cloud.

    Once the modernized app has been tested, deploy it on the selected cloud platform. Implement monitoring and logging mechanisms to gain insights into the app’s behavior, performance and user experience so it operates effectively in the cloud environment.

    Navigating App Modernization Challenges

    While the benefits of app modernization are significant, challenges can arise during the process.

    One of these hurdles involves dealing with legacy constraints. Legacy applications often have complex architectures that complicate modernization. Careful planning is required to update outdated technology stacks and move from a tightly coupled system to a loosely coupled one.

    Another obstacle pertains to data migration complexities. Transferring large amounts of data from on-premises systems to the cloud demands meticulous planning and execution to maintain data integrity, security and minimal downtime.

    Integration presents another potential difficulty. Modernized apps often need to integrate with other systems, both on-premises and in the cloud. Many cloud vendors offer tools and programs to aid in migrating data. These, coupled with credits to be used as apps are migrated, can significantly reduce costs.

    Security and compliance are critical considerations when moving to the cloud. Cloud environments require robust security measures to protect sensitive data, and organizations must comply with relevant regulations and industry standards.

    Effective change management strategies are another aspect to consider to ensure a smooth transition for users and stakeholders. App modernization is a business process modernization representing a significant change in business operations.

    In-House Vs. Outsourced App Modernization

    Choosing between in-house and outsourced app modernization depends on several factors.

    First, if an organization has skilled developers with cloud expertise, handling app modernization in-house is a viable option. On the other hand, if a team lacks the necessary skills, it may be more practical to outsource the task to a cloud service provider or a specialized development partner. It should be noted that if the development work is outsourced, it’s important to keep the app business owners involved to ensure that the modernized solutions yield the expected benefits.

    Another consideration is the availability of resources, such as time, budget and staffing. App modernization projects require dedicated efforts, and outsourcing becomes appealing if internal resources are limited. By entrusting the project to external professionals, organizations can ensure it receives the attention and resources needed without burdening the internal team.

    Evaluating whether app modernization aligns with an organization’s core competencies is also essential. If the application is not directly related to the core business, outsourcing can free up internal resources and redirect them toward more strategic initiatives. This allows an organization to concentrate on areas in which it has a competitive advantage and expertise while still benefiting from the app modernization expertise of external partners.

    Pioneering the Future

    Modernizing an app for the cloud is a transformative journey that holds the potential to enhance customer experiences, optimize processes and unlock valuable insights. Navigating challenges through meticulous planning, robust testing and adherence to standards are crucial, whether opting for in-house expertise or external collaboration. By embracing cloud-native principles and using advanced technologies, organizations can position themselves at the forefront of innovation, ensuring lasting success in the dynamic landscape of the digital era.

  • How to Alleviate Your Observability Challenges

    How to Alleviate Your Observability Challenges

    Observability is becoming a keystone of contemporary DevOps practices. Even departments that weren’t traditionally a part of DevOps are seeing the benefits of being brought under the auspices of observability teams. In 2023, however, organizations are finding that the road to adoption is bumpier than expected. Here are seven of the biggest challenges DevOps teams face with observability and some suggestions for mitigating them.

    Increasing MTTR

    MTTR, or mean time to recovery, is the time it takes to get a system back up and running after an outage or bug. A longer MTTR means more downtime and poorer service for clients. Worryingly, the DevOps Pulse Report indicates that the average MTTR is increasing. This year, 73% of its respondents reported an MTTR of multiple hours; last year’s figure was only 64%.

    MTTR often results from an inability to diagnose incidents due to data siloes impairing observability. This can be improved by an observability platform that allows engineers to see the big picture.

    Costs of Telemetry Data

    Along with increasing MTTR, many organizations have to deal with costs incurred by high telemetry data volumes. It’s a big problem. An IDC survey of 200 companies found that 53% of respondents highlighted the costs of storing log data.

    Much of the problem is due to an outmoded tiered pricing model. Many vendors charge per GB of data, so if your data volumes fluctuate, so will your data costs. They also have obscure pricing models, meaning many organizations find it difficult to know what they’re paying for. At Coralogix, we’ve created a new business model that is one-third of the cost of standard log storage solutions.

    Tool Sprawl

    To understand why this is a challenge, we need to answer the question: What is observability? Effective observability requires integrating data from every aspect of your application. Because many organizations implement monitoring with multiple tools, they suffer from tool sprawl. This has the effect of siloing telemetry data, making it harder to correlate data and extract insights into system performance.

    There are a range of options for mitigating tool sprawl, such as thoroughly evaluating a tool’s costs and benefits before including it in your DevOps strategy. The most effective solution is a “single pane of glass” tool that combines insights synoptically on a single dashboard.

    Kubernetes Complexity

    Elastic reports that organizations are increasingly turning to cloud-based solutions such as Kubernetes for their DevOps. Kubernetes can supercharge organizations with its ability to dynamically scale infrastructure as needed, eliminating the cost overheads of dedicated servers.

    However, Kubernetes is complex and comes with its own set of challenges. Kubernetes’ scalable architecture comes from containerization, a paradigm in which applications are hosted in objects called containers. This means that developing in Kubernetes requires the ability to work with a lot of spinning plates.

    A good way to combat this is better training in organizations. Additionally, breaking down silos allows different teams to transfer knowledge.

    Security Challenges

    Kubernetes’ popularity brings security challenges. These can include privilege escalation, where a user manages to gain privileges such as write access, and security misconfigurations, where developers forget to change the non-secure default configurations.

    There are several strategies for mitigating Kubernetes security risks. These include scoping roles to particular namespaces, using service meshes, and enhancing security with Coralogix’s Kubernetes Operator.

    Beyond Kubernetes, there’s the larger issue of integrating security into an observability strategy, which is becoming a challenge for an increasing number of IT businesses. To counter this, more and more businesses are starting to incorporate observability and security monitoring under a single umbrella. Solutions such as infrastructure and application metrics can enhance security and monitoring.

    Scaling Platforms

    To meet the challenges posed by rising data costs and increasing cloud complexity, businesses are turning to open-source solutions. These come with their own challenges, however. According to the DevOps Pulse Report, around 30% of enterprises surveyed had problems with infrastructure management, scaling and upgrading relevant components. Because many open-source platforms require specialized knowledge to maintain, businesses have trouble sourcing the skills and expertise for them.

    Tools such as OpenTelemetry can make scaling easier by integrating with platforms such as Coralogix.

    Troubleshooting Data Pipeline Performance

    Implementing observability requires having a reliable and high-performance pipeline for telemetry data. However, organizations using open-source platforms often have trouble monitoring and troubleshooting the performance of their data pipeline. This can impair observability as telemetry data is of lower quality.

    Data engineer Abraham Alcantara suggests ten key steps to troubleshoot data pipelines successfully. These include identifying data pipeline software and infrastructure, reproducing and isolating issues and automating issue scenarios. Another strategy is to apply machine learning, such as Coralogix uses.

  • FAQs for Software Testing: All the Background You Need

    FAQs for Software Testing: All the Background You Need

    No matter what type of application you and your team are building, testing is a critical step in the software development life cycle (SDLC) that can not be overlooked. In this article, I’ll answer frequently asked questions about software testing, why software testing matters and suggest best practices to help ensure your application is ready to delight customers by the time it is deployed.

    Why Do We Need Testing?

    Software testing involves evaluating an application to see if it meets the requirements and functions it was built for. The process includes identifying defects, errors and bugs, along with flaws in user experience. Software testing is so critical because finding and fixing these bugs and issues in the development process means the end product reaching customers works properly and reliably and delivers a high-quality user experience. Additionally, testing during the development process makes bugs easier and less expensive to fix as opposed to making fixes after an application has been deployed.

    Overview of Testing Types

    Let’s quickly define the common types of software testing:

    ● Unit Testing: This process evaluates individual components of software to ensure they are working properly.
    ● System Testing: This involves ensuring an entire application or system meets requirements and functions as it should, end to end.
    ● Integration Testing: This looks at whether or not different modules of an application or system work properly together.
    ● Regression Testing: This process revisits previously tested components of software to make sure they work correctly after a change has been made.
    ● Acceptance Testing: This process determines whether an application meets the needs of end users and is ready to be deployed.
    ● Usability Testing: This process assesses how user-friendly a customer will find the product.

    Creating the Plan

    Any solid software testing plan is going to incorporate multiple testing approaches, including manual, automated, user experience, exploratory and non-functional testing. You’ll start by defining the scope for what parts of your application, integrations and functionality you need to test for. Then, you can decide what type of testing methods are needed for each and document these to ensure consistency.

    You’ll want to combine code coverage and test coverage to ensure the code for your application is tested thoroughly. The type of tests you prioritize will vary based on different products, and your testing strategy will certainly evolve and grow over time.

    Measuring Your Strategy for Thoroughness and Effectiveness

    To ensure your testing strategy is thorough and effective, you will need to make sure to test for code, device coverage and all aspects of user experience. The bottom line for the program should be delivering value to the business by making sure the application works as intended for users. To be effective, testing should take place before an application reaches end users, so issues and bugs can be identified and fixed before deployment. To be thorough, testing strategies must be balanced, end-to-end, and look at functionality, usability, accessibility and more.

    Effective software testing can be measured in a few ways, including:

    ● Code Coverage: The percentage of code that is tested.
    ● Test Case Effectiveness: The percentage of test cases that found defects or bugs.
    ● Time to Detect and Fix Defects: The time it takes to find and fix issues in an application.
    ● Defect Density: The number of defects found per unit of code.

    Improving Your Testing Approach

    Your software testing program should be evolving and improving over time and can improve by:
    ● Including test-driven development
    ● Leveraging a shift-left approach
    ● Using automation where possible
    ● Benchmarking QA for current projects versus previous ones
    ● Conducting a cost-benefit analysis

    Testing Best Practices

    While the below list is not exhaustive, here are some top best practices to keep in mind as you build out a software testing practice at your organization:
    ● Incorporate the voice and feedback of your customer into product design
    ● Make accessibility a priority
    ● Test throughout the entire SDLC, including in pre-production, after changes are made to code, and in-sprint testing
    ● Maintain a matrix for the different devices using your application
    ● Prioritize high-quality user experience
    ● Automate repetitive tests where possible
    ● Review and adjust testing processes regularly, as needed
    ● Track and report results to analyze trends and identify areas for improvement
    ● Balance between leveraging different testing types

    Your organization’s software testing strategy directly impacts the quality of the end application you deliver to your customers. Poor usability, accessibility, and reliability of an application can frustrate users and send them to competitors, while high-quality user experiences and application performance can boost adoption and keep customers happy and loyal to your brand. With so much competition, taking the time to build out, adjust and prioritize software testing is critical for modern businesses.