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Category: DevOps Onramp

Observability: The Central Watchtower Your All-Seeing IT Needs

If there’s something you need to keep secure, it’s natural to want to keep it sealed away under lock and key. However, even keeping something in a vault, far from prying eyes and ill intent, may not be enough to offer peace of mind. After all, how can you have the peace of mind of knowing it’s safe if you don’t regularly check to ensure it hasn’t bolted, been burgled or broken down?

Now, multiply the volume and value of things being monitored tenfold — and you get a sense of the daily inefficiencies and anxieties IT professionals battle.

Instead of jewels, these teams safeguard, manage and maintain something far more valuable: The data that underpins everything from business transactions to system performance to threat detection to service delivery and beyond. To make matters more complicated, today’s digital ecosystems are more complicated, distributed and disparate than ever before.

Databases are critical to managing a successful IT department — and it’s time to discard the widespread industry’s perception of them as an unknowable black box. To be sure, databases represent the most difficult component of the IT ecosystems to observe, tune, manage and scale. But the database isn’t a Magic 8 ball in terms of its internal processes; We no longer have to shake the mystery toy and accept one of the six answers it’s designed to spit out.

The fact of the matter is that database specialists and IT teams need a clear view of database performance telemetry if they want any hope of maintaining the health, stability and scalability of their services.

Observability Tools

This is where observability tools can play a transformative role. Taking its cues from the all-seeing panopticon watchtower, observability eliminates the dark corners of the database dungeon by providing a comprehensive view across the full cloud-native, on-premises and hybrid technology stacks.

The word panopticon derives from the Greek word panoptes, or “all-seeing.” The panopticon model features a central, lighted guard tower at the center of a circular room — allowing 360-degree lines of sight that allow the guards to observe every surrounding cell from a single, centralized vantage point.

English philosopher Jeremy Bentham had no way of foreseeing the challenges of today’s database professionals when he first conceived of the panopticon in the 1700s. The concept would lay the blueprint for a prison system designed around one core concept: Enabling the minimum number of guards to effectively monitor the maximum number of inmates. Bentham’s system also works as a strategy for ensuring the health and performance of your organization’s critical databases.

The panopticon was designed so that every cell could be monitored from one central point — ensuring not only better and more straightforward security but preserving resources in saved time, manpower and effort expended. Think of your ITOps, database, DataOps and DevOps teams as the wardens of your IT performance. Traditional monitoring can be likened to patrolling a traditional prison: Rows of cells facing out into a hallway that is patrolled on a schedule or used to spot and address misbehavior as it’s noticed.

How can they be sure they’re focusing their efforts in the right place? Or trust that Cell Block A won’t stage a breakout while they’re breaking up an argument in Cell Block B?

In short: They can’t. Odds are, the team is either running from outage to outage in an everlasting firefighting mode or relying on random spot checks for something as vital as the health of our IT systems.

Observability, as opposed to monitoring, puts teams into that central panopticon watchtower, allowing them to not just see everything, but to see it within the context of the bigger picture. This comprehensive visibility and always-on observation enable IT teams to identify critical issues as they occur, even those caused by complex dependencies between the database, operating system, storage subsystem and network.

Database Performance Issues

Furthermore, database performance issues are a starting gun for costly bottlenecks or serious outages that can handcuff your company’s ability to compete or grow. Without complete and precise database monitoring and observability, IT operations struggle to accurately determine an application’s root cause of performance issues. This increases the risk of downtime, data loss and poor customer experiences.

Discovering the root cause of performance issues is quite literally a system-critical undertaking, whether you’re simply running your enterprise, scaling your operations or deploying new code. Observability also offers teams the rare opportunity to get ahead of performance issues since you can see performance anomalies in advance of a service disruption rather than relying on real-time remediation.

With traditional methods, DevOps and IT teams must manually analyze the data presented to them, correlate it to the problem, and locate the error before they can finally begin addressing it. In contrast, observability collects data to provide information on what’s not performing as expected and why. This allows teams to take a proactive approach to solving downtime and bottlenecks and proactively preventing them.

In the software world, that translates directly to more time spent where it matters: Developing new business value within your applications and infrastructures, fueling innovation and exceeding customer expectations.

Given how complicated the modern database has — and continues to — become, arming your enterprise with the right observability solutions can provide a boost to efficiency and performance and pardon your teams from a sentence to time-consuming, mundane and error-prone work.

November 28, 2023
Can Unlocking Joy Unlock Developer Productivity?

Software development has become more complex in the last 10 years with the introduction of the cloud, containerization and microservices – not to mention increased attention to security and governance. All of which falls on developers to manage.

On the surface, this seems like a sensible way to keep engineering organizations lean. But it comes with a price. The increased cognitive load combined with less time for actual coding turns what should be an exciting job into a series of chores.

As CTO of Atlassian, my goal is to build a world-class engineering organization. I want our team of 5,000+ developers to be productive, sure. But more than that, I want them to experience the joy of creation and a sense of pride in their work.

So, my leadership team and I made a strategic bet. We wagered that unlocking developer joy would, in turn, unlock productivity. It’s paying off so far, and I believe it’s an approach that can work for any engineering organization.

Where is the Love?

To be clear, the goal isn’t joy for its own sake. It’s joy in the service of improving productivity across the engineering department. Because it’s not as if developers are inherently lazy, they want to perform at a world-class level. So what’s getting in their way?

Unsurprisingly, our devs knew exactly what was slowing them down: Brittle systems in need of some major TLC, lack of time to fix them and fundamental best practices that had fallen by the wayside. It was papercut after papercut, sapping the energy from even our most enthusiastic engineers.

With that in mind, we looked for ways to remove friction so our devs could stay in their flow state longer. We ultimately arrived at a three-part framework that has proved successful. In concert, we defined baseline metrics for overall developer satisfaction, the time between commit and deploy, pull request cycle time, self-serve documentation and self-serve dependency maintenance. You can’t improve what you don’t measure, after all.

Part One: Provide Awesome Tools

Even before we finalized our baseline metrics, we knew that cycle times for deployments and pull requests were too long. Other issues we identified stemmed from macro-level changes across the industry. Like the fact that increasingly a lot of software is assembled from existing components, we believe this trend will only increase over time.

Most SaaS products, Atlassian’s included, comprise hundreds of microservices, libraries and other components woven together with fresh code. If you need to get up to speed on an unfamiliar microservice, it can take hours to locate the documentation or wait for a colleague several time zones away to come online.

We built Compass to solve this. It removes friction by establishing a self-serve catalog of all software components, including who owns each one, changelogs and health-check metrics. Other work in this area includes introducing new automation in the pull request process, migrating to new test libraries, and external service mocking to enable testing against upstream services within a local dev environment.

We’ve set incremental goals with milestones every few months to maintain a sense of progress and momentum. As of now, we’re tracking toward a 90% reduction in the time it takes for changes in shared services to make their way into individual products.

Part Two: Empower Teams to be the Change They Seek

Our developers were well aware of the issues slowing them down but didn’t feel they had permission to spend time fixing them. So my leadership team, with support from Atlassian’s co-founders, told our development teams to use 10% of their time removing speed bumps.

For example, before our developer joy initiative, less than 12% of the Confluence team’s pull requests reached production within 48 hours. Through a combination of restructuring their repositories, improving test efficiency, and deployment pipeline automation, they bumped that number up to 31% in just one quarter.

Similarly, the Trello team eliminated 24,000 lines of obsolete code and adjusted their branching model to reduce code freezes. Another team increased their deployment frequency by 300% by eliminating manual approvals for low-risk changes.

These are just a few wins we’ve scored by granting teams more latitude and asking them to explicitly set time aside to fix things.

Part Three: Foster an Amazing Engineering Culture

We want Atlassian Engineering to be known for clean code, an ownership mindset when it comes to quality and strong, agile practices.

On the coding side, I thought back to my days of working on the Windows NT operating system. The code was rich with comments and supporting documentation that let me understand it on the very first read. That’s not the case with all of Atlassian’s code, so we’re in the process of modernizing our code base and documentation.

We’re also building a framework for engineering standards. This includes guidance that helps engineers write the right tests at the right time, as well as pulling considerations like security and compliance upstream. We want secure and compliant code from the start so we have fewer rewrites on nearly complete features.

An amazing engineering culture also creates space for developers to learn, experiment and share. To that end, we’ve run hackathons focused on developer productivity and started a monthly internal newsletter showcasing the progress we’ve made. We’re also strengthening our “demo culture” with a weekly showcase where teams present projects they’re working on, exchange ideas and generally catalyze innovation.

Continuous Improvement for the Win

Developer joy and its side effect, developer productivity, is now part of the everyday conversation within Atlassian engineering. It’s also a company-level OKR, complete with funding and a central team that takes on department-wide projects and then rolls them out to the teams.

A year in, our teams are noticeably stronger, moving faster and have more room to test out new tech. (We’re especially excited about our experiments with AI tools like coding assistants and all the opportunities to build on top of our AI platform.) And we can see this in the data. At the start of this initiative, only 50% of our developers reported being satisfied with their work environment. Now, our internal surveys show that number hovers around 70%.

A code base is like a human body; if you take care of it, it’ll take care of you. If you neglect the upkeep, however, eventually, it’s going to break in very painful ways. This isn’t just a project – it’s a lifestyle. There will always be a next step to take. I couldn’t be happier that we’re on the path toward greater developer joy and have come this far in such a short time.

November 27, 2023
A DevOps Thanksgiving

As we head into Thanksgiving and the start of holiday season here in the U.S., I wanted to reach out to readers, visitors and friends around the world with a message: In spite of things that make us all sad, there is much to be thankful for this year.

Hopefully, you are not living in an active war zone. Living in peace is something we used to take for granted. It is hard to concentrate on developing code and things like CI/CD, observability and vulnerabilities when you are worried about bombs, drones and gunshots around you. I never used to feel grateful for living a life that allowed me to focus on my work, building a better company and doing more business. But I realize this year how thankful I am to be able to pursue my livelihood in peace. While news of war, death and destruction is never far from our ears, it seems, for most of us, our ears and eyes are as close as it gets. For this, let us be thankful.

While this past year has seen so many layoffs and cutbacks, especially in the tech industry, we should be thankful for earning our livelihoods in an industry that is still so dynamic. We work in an industry that continues to lead the world with bold new innovations and cutting-edge technologies. Even for our friends who were impacted by the cutbacks and layoffs, most have found new jobs and greater opportunities to explore and grow. The past few years have seen such huge growth that some contraction, I guess, is to be expected. But overall, the tech industry is still chugging along. For this, let us be thankful.

What about DevOps, you ask? Well, let us be thankful for DevOps, which continues to evolve and adapt to the opportunities in the market. With innovations in platform engineering, cloud-native technology, GitOps and more, DevOps has proven resilient and adaptable in absorbing these innovations to continue allowing us to develop faster, better, higher-quality code. I am excited about some of the AI-powered innovations that seem on the cusp of once again accelerating our development and deployment choices. This coming year, I believe DevOps will continue to be a major force in IT departments. For this, let us be thankful.

Another trend I have watched develop this year is a return to a semblance of normalcy. We emerged from the pandemic and are returning to offices (well, some of us, anyway). We are going to in-person events and even dusting off our business wear. I think some of the traditional IT frameworks, like ITSM, project management, etc., will freshen up as they combine with new trends like generative AI, DevOps and more. This should bring a new spark to some of the more mature areas of IT and will revitalize IT across the board. For this, let us be thankful.

Finally, let us be thankful that you are reading this in good health. There are many biotech breakthroughs on the horizon that can make our lives so much better. This past year, I have personally undergone my own technology-powered transformation and am healthier and fitter than I have been in a long time. I hope you take time this holiday season to concentrate on your own personal wellness and physical condition. A life well lived requires you to live well. There are so many options available to help you. For this, let us be thankful.

From all of us here at Techstrong Group to all of our friends around the world, happy Thanksgiving and best wishes for this holiday season.

November 22, 2023
FinOps Foundation Previews FOCUS to Help Optimize Cloud Costs

The FinOps Foundation has made available a preview of version 1.0 of a FinOps Open Cost and Usage Specification (FOCUS) that promises to normalize cost and usage data to make it simpler to optimize cloud computing costs.

Udam Dewaraja, chairperson for the FOCUS project, said normalizing schema and terminology for cost and usage data in a vendor-neutral way will make it simpler for IT teams to understand the true cost of cloud computing services and enable them to make comparisons based on value more easily.

The most recent release of the specification adds support for both software-as-a-service (SaaS) applications along with open source data converter implementations and data validators. It also enables use cases for discount analysis, unit pricing, allowing rate transparency and detailed usage analysis while enabling the overlying business context information using tags and resource metadata.

FOCUS 1.0 will also include a SQL-compatible library, with more than forty commonly performed FinOps use case examples tied to FOCUS data outputs curated by experienced FinOps practitioners.

The FOCUS project has the support of Amazon Web Services (AWS), Microsoft, Google and Oracle along with organizations such as CapitalOne, Walmart, Workday and Goldman Sachs that consume cloud services.

Today, many organizations find it challenging to assess the true cost of cloud computing services. That lack of transparency often results in fewer workloads being moved to the cloud simply because no one is certain what their monthly billing cost might be, noted Dewaraja.

Many organizations today engage consultants to analyze existing and potential cloud computing costs that can vary widely from one month to the next. FOCUS should make it simpler for IT leaders to make informed cost decisions as application workloads are being deployed in highly dynamic IT environments, noted Dewaraja. Ultimately, the goal is to bake cost analysis capabilities directly into DevOps workflows, he added.

It’s not clear how quickly organizations are embracing FinOps as a best practice for containing cloud computing costs, but in the face of ongoing economic headwinds, pressure on IT teams to reduce the total cost of IT is rising. In many cases, developers have been allowed to provision cloud resources on demand with minimal supervision, resulting in spiraling costs as more workloads are deployed in cloud computing environments.

As IT organizations increasingly employ multiple cloud platforms to run different classes of workloads, understanding the true cost of choosing to use one cloud over another is becoming more important. At the same time, IT organizations have a greater appreciation for the need to move workloads between platforms that might enable them to reduce costs. Just because an application workload was created in the cloud doesn’t mean that workload should be deployed there. The challenge IT organizations arguably face going forward will be integrating streams of financial data into DevOps processes to make more economically informed decisions about how to allocate their cloud resources in real-time.

November 22, 2023
What the Future Holds for FinOps and DevOps

FinOps, or financial operations, is a cultural practice that brings financial accountability to the variable spend model of the cloud. It’s a strategic approach combining systems, best practices and culture to increase an organization’s ability to understand cloud costs and make informed decisions.

In essence, FinOps is a framework for managing cloud operating expenses (OpEx). It’s about bringing together technology, business and finance professionals to ensure that companies can get the most out of their cloud investments. The goal is to provide businesses with a better understanding of their cloud usage and costs, enabling them to make strategic decisions about resource allocation and cost management.

FinOps bridges the gap between finance and operations. It’s not just about monitoring costs; it’s about understanding why those costs occur, how they relate to business value and what can be done to optimize them. It’s a comprehensive, cross-functional and continuous approach to managing cloud expenses, ensuring that businesses can maximize their return on investment (ROI) in the cloud.

What Does FinOps Mean for DevOps Teams?

Traditionally, DevOps has been focused on improving the speed, efficiency and quality of software delivery. But with the advent of cloud computing, cost has become a crucial factor. The dynamic nature of the cloud, with its pay-as-you-go model, means that costs can quickly spiral out of control if not properly managed. This is where FinOps comes in.

Budget Responsibility

As a DevOps team member, you’re likely used to thinking in terms of speed, performance, and quality. With FinOps, you need to add cost to that equation. You’re not just responsible for delivering high-quality software quickly; you’re also accountable for the cost of that delivery. This requires a shift in mindset, moving from a project-based budgeting approach to a product-based one.

In a project-based approach, you have a fixed budget for a specific project. Once the project is finished, so is the budget. In a product-based approach, on the other hand, you’re responsible for the ongoing costs of a product throughout its life cycle. This means you need to consider not just the cost of development, but also the cost of maintenance, support, and continuous improvement.

Resource Optimization

In the context of FinOps, resource optimization is all about making sure you’re getting the most bang for your buck. You want to ensure that you’re using your cloud resources as efficiently as possible, minimizing waste and maximizing value.

Resource optimization can involve a variety of strategies, from rightsizing instances to eliminating idle resources. But it’s not just about cutting costs; it’s also about aligning costs with value. You need to understand the value that each resource brings to your organization and allocate costs accordingly. This requires a deep understanding of your cloud usage and a commitment to continuous monitoring and improvement.

Collaboration With Finance

One of the key aspects of FinOps is the collaboration between DevOps and finance. You’re no longer working in silos; you’re part of a cross-functional team working towards a common goal. This requires open communication, mutual understanding, and shared responsibility.

As a DevOps team member, you need to understand the financial implications of your decisions. You need to be able to articulate the value of your work in financial terms, demonstrating how your efforts contribute to the organization’s bottom line. At the same time, you need to be open to feedback and guidance from your finance colleagues, learning from their expertise and incorporating their insights into your decision-making process.

Performance Metrics

Performance metrics are crucial in the world of FinOps. They provide the data you need to understand your cloud usage, monitor your costs and make informed decisions. But these aren’t just any metrics; they’re metrics that align with your business goals and reflect the value of your cloud investments.

These metrics might include cost per workload, cost per user or cost per unit of output. The key is to choose metrics that make sense for your organization, providing meaningful insights that can guide your resource allocation and cost management efforts. And remember, these metrics need to be regularly reviewed and updated to reflect changes in your cloud usage and business needs.

Automation of Cost Controls

Automation is a key tool in the FinOps toolkit. It can help you streamline your cost management processes, reduce manual effort and increase accuracy. From automated alerts to automated remediation, there are many ways automation can improve your FinOps efforts.

With automated alerts, you can be notified when your cloud costs exceed a certain threshold. This allows you to respond quickly, addressing issues before they become major problems. With automated remediation, you can take this a step further, automatically adjusting your cloud resources in response to changes in demand or performance.

Future FinOps Trends

The year 2024 promises to be an important one for FinOps, with several trends poised to redefine the landscape:

1. AI Algorithms for Cost Analysis and Prediction

The use of AI algorithms for real-time cost analysis and predictions is predicted to be a major trend in 2024. AI enables the automation of complex financial calculations and projections, allowing for more accurate and timely decision-making. AI is already not just analyzing and predicting costs but also providing actionable insights to optimize these costs.

The power of AI in FinOps lies in its ability to process massive amounts of data quickly and accurately. It can analyze past and current financial data, identify patterns, and predict future trends. This means that instead of reacting to financial changes after they happen, you can be proactive and make strategic decisions based on accurate predictions.

The use of AI in FinOps will also enhance risk management. By predicting potential financial risks, AI can help organizations take preventive measures and mitigate the impact of these risks. The integration of AI in FinOps is a trend you cannot afford to ignore if you aim for financial operations excellence.

2. Empowering Teams to Manage Their Own Cloud Costs

Traditionally, financial operations and cost management have been centralized functions. However, the advent of cloud computing and the shift toward decentralized work models have resulted in a new trend—empowering individual teams to manage their own cloud costs. In 2024, we can expect this trend to gain further momentum.

In a cloud environment, costs are often directly linked to usage. This means that the teams using the cloud resources are in the best position to control these costs. By empowering these teams to manage their own cloud costs, organizations can promote accountability, encourage resource optimization, and ultimately achieve cost efficiency.

This approach requires a shift in mindset and the development of new skills. Teams need to understand the financial implications of their actions and make cost-effective decisions. They also need tools and processes to monitor their cloud usage and manage their costs effectively.

3. Managing Costs Across Multi-Cloud and Hybrid Cloud Deployments

The use of multiple cloud providers and the coexistence of cloud and on-premises data centers have added a new layer of complexity to financial operations. Managing costs across these diverse environments is a major challenge, and it’s another trend that is expected to shape FinOps in 2024.

With multiple cloud providers, each with its own pricing model and billing system, cost management can be a daunting task. Add to this the costs of maintaining on-premises data centers, and the complexity multiplies. In 2024, organizations will need to adopt strategies and tools that enable them to manage these costs effectively.

One of these strategies is the use of cost management platforms that provide a unified view of costs across multiple environments. These platforms can track and analyze costs, allocate them to the appropriate departments or projects, and provide insights for cost optimization. They can also automate cost reporting and forecasting, easing the workload of financial teams.

4. Emphasizing the Security Aspects of FinOps

As financial operations become more complex, the risk of abnormal spending patterns or unauthorized activities increases. Identifying and addressing these issues promptly is critical to prevent financial losses and maintain financial integrity. In 2024, the identification of abnormal spending patterns or unauthorized activities will be a key focus area in FinOps.

Tools and technologies that enable real-time monitoring and anomaly detection will play a crucial role in this regard. These tools can analyze financial data, detect deviations from normal patterns, and alert you to potential issues. They can also help you trace these anomalies back to their source, facilitating investigation and resolution.

Moreover, organizations will need to strengthen their internal controls and governance mechanisms. They will need to define clear responsibilities and accountabilities, establish robust approval processes and enforce strict access controls. Regular audits and reviews will also be essential to ensure compliance and detect any irregularities.

5. Measuring and Optimizing for Environmental Impact

The environmental impact of financial operations is a concern that cannot be overlooked. As organizations strive to be more sustainable, measuring and optimizing for environmental impact is becoming a key aspect of FinOps. This trend is expected to gain prominence in 2024.

The environmental impact of financial operations is largely tied to energy consumption. In a cloud environment, for instance, the energy consumed by data centers contributes to carbon emissions. Therefore, optimizing cloud usage not only reduces costs but also reduces environmental impact.

To measure and optimize for environmental impact, organizations will need to track their energy consumption, calculate their carbon footprint, and implement strategies to reduce it. These strategies can include using energy-efficient technologies, optimizing resource usage and sourcing renewable energy.

Conclusion

In conclusion, FinOps is a dynamic and evolving field. The trends and predictions outlined above provide a roadmap for achieving financial operations excellence in 2024. By harnessing the power of AI, empowering teams to manage their own cloud costs, managing costs across diverse environments, emphasizing security in FinOps and optimizing for environmental impact, you can take your FinOps program to the next level.

November 16, 2023
A Practical Guide to Mitigating DevOps Backlog

Every company aspires to get the first-mover advantage in its respective industry. This strategy allows companies to establish a robust brand presence and offer strong product offerings in the market. However, at times, this may also lead early-stage startups to prioritize pushing product features to market without adequate consideration toward stabilizing and maintaining the product. The above-mentioned approach may help win business and keep customers satisfied; achieving excellence in DevOps still remains an elusive goal. From operational issues to the lack of focus on streamlining DevOps, several factors add up to the already existing pile of DevOps backlog.

According to the Puppet DevOps report 2023, nearly 80% of organizations remain in the middle of their DevOps journey, experiencing varying levels of success within the team level but not across the entire organization. And, one of the areas that could hinder an organization’s success is the accumulating pile of DevOps backlog.

What is DevOps Backlog?

In typical software development teams, the primary focus is often on crafting functional and groundbreaking solutions, which is understandable given the constant drive to meet strict deadlines and deliver exceptional products. However, this relentless cycle of prioritizing outputs can inadvertently relegate the development process to a lower priority, resulting in the accumulation of tasks and issues contributing to the DevOps backlog.

DevOps Backlog consists of user stories, bug fixes, technical tasks, or any other work required in supporting and sustaining the development, deployment and maintenance of a product. It is dynamic and changes with time as and when new tickets get added to the list.

Key Factors Fuelling DevOps Backlog

Managing a substantial DevOps backlog can be one of the most complex challenges for companies to overcome. Within the CI/CD pipeline and the collaboration of multiple teams, intricate system architectures and more, several potential drivers contribute to the growth of the DevOps backlog. Let’s explore some of them:

Relying on a Small DevOps Team: In many cases, DevOps teams consist of a limited number of specialists who act as the lynchpin, holding various processes together. Larger development teams often rely on these smaller DevOps teams, resulting in dependencies that lead to blocked pipelines and delayed handovers, further adding to the DevOps backlog.

Architecture Changes: As development progresses and new services are introduced, architectures naturally evolve and change. This constant evolution necessitates new tooling and infrastructure requirements. Adapting to these changes and aligning new tools and frameworks with the evolving architecture contribute to the pile of the DevOps backlog.

Adapting to Changing Business Requirements: Business requirements are dynamic and evolve over time, adding complexity to the DevOps backlog. For instance, if your organization operates as a software-as-a-service (SaaS) provider or in the healthcare industry, you may need to establish new environments tailored to specific countries, regions, or regulatory frameworks. Managing these new environments and ensuring compliance with relevant regulations adds to the workload of the DevOps team and adds to the DevOps backlog stockpile.

Ticket Overflow: In many scenarios, developers do not have direct access to DevOps toolchains, resulting in an abundance of developer tickets that need to be processed by the DevOps team. This flood of tickets significantly reduces the bandwidth and efficiency of the DevOps team, impeding their progress in addressing the backlog.

Mitigating DevOps Backlog: Prioritizing Essential Strategies

While collaborating with companies to establish DevOps standardization, we initially assumed that the DevOps backlog challenge was limited to a select few. However, we were surprised to discover that even well-established companies in the later stages of product development struggle with significant DevOps backlogs without strategic interventions. Our conversations with over 200 organizations convinced us that the DevOps backlog is a prevalent issue faced by most companies today.

A Lack of Focus on Proactive Approach

The symbiotic relationship between business growth and technological advancements is undeniable. As time progresses, the architecture inevitably becomes more intricate, resulting in a growing number of tasks and challenges within the DevOps backlog. The complexities surrounding DevOps resonate with numerous companies, yet the proactive approach to addressing them remains an issue.

At our organization, we firmly believe that discussions revolving around DevOps stories are vital for organizations aiming to tackle this critical issue head-on. Now, let’s delve into the top 10 stories that software development teams can incorporate into their backlog to strike a balance between prioritizing software development and implementing essential process improvements.

1. Automating Provisioning and Configuration of Resources and Infrastructure
Proper provisioning and configuration of resources and infrastructure are vital for software development and deployment teams. By automating these processes and leveraging the appropriate toolchains and metrics, teams can streamline resource management, fostering consistent and efficient deployment practices. This, in turn, leads to enhanced productivity and improved overall development efficiency.

2. Application Life Cycle Management
Application life cycle management encompasses the comprehensive management of an application throughout its entire life cycle, from development to deployment and beyond. Teams that engage in discussions surrounding these stories take the necessary steps to streamline the entire life cycle. By leveraging toolchains and implementing proficient workflows, these teams achieve heightened productivity and maintain consistency across the application’s lifespan.

3. Release Management and Strategies
Stories related to release management and strategies go beyond mere release scheduling. They encompass crucial aspects such as zero downtime deployments and the implementation of effective rollback strategies. By prioritizing these stories, development teams can ensure smooth and seamless deployments, minimizing disruptions for end-users and optimizing the user experience.

4. Observability
Observability is the key to implementing effective monitoring practices for applications. This involves setting up alerts, capturing relevant metrics and configuring dashboards to track application performance and resource utilization. Prioritizing observability empowers teams to proactively identify and resolve any issues within the DevOps pipeline, ensuring a stable and reliable environment for continuous improvement.

5. Database Management
Database management entails activities such as upgrading and migrating databases, implementing scheduled backups and defining restoration policies. A robust approach to database management ensures the stability and reliability of the data infrastructure, mitigating the risks of data loss or downtime. By prioritizing this story, teams can safeguard their valuable data assets and maintain operational continuity.

6. Access and Permissions
Secure access and permissions are essential for developers across different environments, including testing, staging and production. This story emphasizes the importance of maintaining a transparent audit trail, identifying and rectifying permission leaks and ensuring compliance with security standards. Prioritizing access and permissions not only promotes a secure development environment but also minimizes the risk of data breaches or unauthorized access.

7. Environment Creation and Management
Establishing and managing environments is a complex and time-consuming process that requires collaboration among various stakeholders. From development to quality assurance (QA), pre-production and production environments, meticulous configuration and provisioning are paramount. By addressing this crucial need, teams can ensure well-prepared environments, minimizing discrepancies that may arise during the development and testing stages.

8. Security and Compliance
Security and compliance are fundamental elements that should be considered when designing and implementing an architecture. Adhering to the standards and regulations defined by the geographical countries of deployment is crucial. Neglecting this aspect could result in substantial fines, vulnerabilities and the need for extensive damage control in the future, wasting valuable resources. Therefore, prioritizing security and compliance is imperative for long-term success.

9. Cloud Cost Visibility and Optimization
The dynamic nature of cloud computing necessitates efficient monitoring and optimization of costs, striking a delicate balance between frugality and system performance. Teams must possess comprehensive visibility into cloud cost usage to identify areas of potential savings. By scrutinizing resource allocation, assessing utilization patterns and implementing cost management strategies, organizations can minimize expenditure without compromising the functionality and stability of their applications.

10. Exploring New Tools and Frameworks
It is imperative for organizations to continually explore new tools and frameworks that help elevate software development practices. By implementing zero-trust networks and network segmentation, teams can fortify their security measures, bolster network resilience, and proactively mitigate potential risks. By remaining at the forefront of technological advancements, teams harness the power of these transformative tools and frameworks to optimize overall efficiency and propel innovation to new heights.

Intervene Now to Tackle DevOps Backlog

Prioritizing product features over process can lead to complex issues in DevOps backlog. Solving this problem requires a major shift in mindset and a proactive approach toward mitigating DevOps backlog. Whether your DevOps backlog is in the initial stage or has reached a later stage where it is getting more complex, the time to intervene is now.

November 6, 2023
Software Deployment Security: Risks and Best Practices

In an era where software is at the heart of every business, deploying applications securely and efficiently has never been more critical.

From cloud-based systems that manage sensitive customer data to mobile apps that drive consumer engagement, software deployments come with a wide array of security challenges. Missteps in the deployment process can lead to disastrous consequences, ranging from data breaches to system outages.

This article covers the risks involved in software deployment and provides best practices to mitigate these dangers effectively.

What is Software Deployment?

Software deployment refers to all the processes involved in making a software system available for use. It’s a broad term that includes various stages like installation, configuration, testing and optimization. In simple terms, software deployment is the bridge that connects software developers with the end-users, allowing them to use the software product.

The primary goal of software deployment is to make a software application ready for use in a specific environment, be it a test environment, a staging environment or a production environment. It starts with installing the software system, which involves copying and configuring the software application on the servers where it will run. It then proceeds to the configuration of the software system, which involves setting the system parameters to meet the specific needs of the environment it will be running in.

Software deployment doesn’t end once the software system is up and running. It also includes monitoring and optimizing the software system to ensure it runs smoothly and efficiently. Moreover, it involves the implementation of updates and patches to fix bugs and enhance the software’s performance.

Risks in Software Deployment

Like any other process in the SDLC, software deployment comes with a set of risks that can potentially disrupt your software development projects:

Code Tampering

One of the risks in software deployment is code tampering. This happens when unauthorized individuals access the software code and alter it for malicious purposes. Code tampering can lead to the introduction of bugs, the loss of functionality or even the creation of security vulnerabilities.

To mitigate the risk of code tampering, it’s important to implement access controls that limit who can access your software code. Additionally, you should use version control systems that track changes made to your software code, allowing you to identify and rectify any unauthorized changes.

Third-Party Vulnerabilities

Third-party vulnerabilities are another risk in software deployment. These are vulnerabilities in third-party libraries and components used in your software application. If these vulnerabilities are exploited, they can compromise your software application.

To mitigate the risk of third-party vulnerabilities, it’s important to keep your third-party libraries and components up-to-date. Furthermore, you should regularly audit your third-party libraries and components for vulnerabilities and take the necessary steps to patch them.

Configuration Errors

Configuration errors are one of the most common risks in software deployment. These are errors in the configuration of the software system that can lead to the system not functioning as intended.

To mitigate the risk of configuration errors, it’s important to have a thorough testing process. This should include both functional testing to ensure the system works as intended and configuration testing to ensure the system’s configuration is correct.

Inadequate Environment Isolation

Inadequate environment isolation is another risk in software deployment. This happens when the test, staging and production environments are not properly isolated, leading to issues like data leakage or the unintentional deployment of untested code to the production environment.

To mitigate this risk, you should ensure that your test, staging, and production environments are properly isolated. This includes having separate servers for each environment and implementing access controls to prevent unauthorized access.

Lack of Monitoring

Lastly, the lack of monitoring is a risk in software deployment. Without proper monitoring, it’s difficult to identify and rectify issues in your software system.

To mitigate this risk, you should implement a robust monitoring system that allows you to track the performance of your software system. This should include error tracking to identify and rectify issues and performance monitoring to ensure your software system runs efficiently.

Common Deployment Strategies and Their Security Implications

Blue-Green Deployment

Blue-Green Deployment is a release management strategy designed to reduce downtime and risk by running two identical production environments, known as Blue and Green. At any time, only one of these environments is live, with the live environment serving all production traffic.

The primary security implication of the Blue-Green deployment is the risk of data inconsistency during the switchover. If not properly managed, sensitive data could be exposed, lost or corrupted. Furthermore, because two environments are maintained, security measures must be duplicated, potentially leading to inconsistencies and vulnerabilities if not properly managed.

Canary Deployment

Canary deployment is a strategy where new software versions are gradually rolled out to a small subset of users before being deployed to the entire infrastructure. This strategy allows teams to test and monitor the performance of the new release in a live environment with less risk.

Canary deployment can potentially expose new software versions to a smaller user base, potentially exposing vulnerabilities before a full-scale release. If a vulnerability is exploited during this stage, it could lead to a security breach affecting a subset of users. There is also the risk of vulnerabilities or malware “leaking” from the canary environment to the main production environment.

Feature Toggles

Feature toggles, also known as feature flags, allow developers to enable or disable features in a live environment. This means that new features can be deployed into the production environment but kept hidden from users until they are ready for release.

The main security risk with feature toggles is that if they are not correctly implemented, they can potentially expose unfinished or untested features to end-users, leading to potential vulnerabilities being exposed.

Rolling Deployment

Rolling deployment is a strategy where the new version of software is gradually deployed across all servers or nodes in the infrastructure, one after the other. This strategy ensures that there is always a version of the software available to users.

The primary security implication with rolling deployment is that there will be a period where different versions of the software are running concurrently. This could lead to potential vulnerabilities if the newer version has security improvements that the older version lacks.

A/B Testing Deployment

A/B testing deployment is a strategy where two versions of the software are run concurrently, with some users directed to the new version and others to the old version. This allows teams to test the performance and user acceptance of the new version before a full release.

The primary security implication of A/B testing deployment is similar to that of rolling deployment: different versions of the software running concurrently could potentially expose vulnerabilities. Additionally, directing users to different versions could potentially expose them to different levels of risk.

7 Best Practices for Secure Software Deployment

1. Code Reviews

Code reviews, also known as peer reviews, enable developers to learn from each other, enhance code quality and ensure adherence to coding standards. It’s a practice where multiple eyes scrutinize the code, spotting potential sources of vulnerabilities that a single developer might overlook.

However, code reviews are not just about hunting for bugs. They also foster knowledge sharing and collaboration amongst team members. A thorough code review process can help in identifying reusable code, reducing code complexity and increasing overall code efficiency.

2. Automated Security Scans

Automated security scans are another essential tool in your arsenal for secure software deployment. They help in identifying vulnerabilities in your code, such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF) that attackers can exploit.

Implementing automated security scans in your deployment process can save you a significant amount of time and resources. It allows you to detect and fix vulnerabilities before the software is deployed, reducing the potential for security breaches.

3. Environment Hardening

Environment hardening involves taking measures to secure your system against potential threats by reducing its attack surface. It includes practices like removing unnecessary software, services, and users, setting up firewalls and configuring system permissions.

One of the most critical aspects of environment hardening is system patching. Timely patching ensures that your system is not vulnerable to known security flaws.

Moreover, environment hardening also includes measures like regular system audits and continuous monitoring. It’s not a one-time process but an ongoing effort to ensure the security of your software deployment.

4. Principle of Least Privilege

The principle of least privilege (PoLP) is a crucial principle in secure software deployment. It stipulates that a user or a process should only have the minimum privileges necessary to perform its function. This limits the potential damage in a security breach.

Implementing PoLP requires a clear understanding of your system and the roles and responsibilities of each user or process. Regular audits can help ensure that PoLP is being followed and that unnecessary privileges are revoked promptly.

5. Secure Configuration Management

Secure configuration management is an essential practice in ensuring the security of your software deployment. It involves maintaining a standard, secure configuration for your system and regularly checking and updating it to address evolving threats.

The first step in secure configuration management is establishing a baseline configuration that adheres to industry best practices. This configuration should then be applied to all systems.

Regular audits should be conducted to ensure systems adhere to the baseline configuration. Any deviations should be promptly identified and rectified.

6. Immutable Deployments

In an immutable deployment, once a version of the software is deployed, it is never modified—any changes cause a new version of the software to be deployed. This approach reduces the risk of configuration drift and makes your deployments more predictable and reliable.

While implementing immutable deployments may require a shift in your deployment practices, the security and reliability benefits are significant. It can also help simplify the deployment process and make it more efficient.

Mastering the art of software deployment is a journey that involves the careful implementation and continuous improvement of various practices. While the road may seem long and winding, the destination—a secure and efficient software deployment process—is well worth the effort.

November 2, 2023
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.

October 30, 2023
How to Build a Data Platform for Self-Service, Ad-Hoc Analytics

In a digital world, data is often the differentiator between success and failure. Whether it’s defending against cybersecurity threats, improving application performance, or resolving full-blown outages, data is critical to the modern DevOps team.

In fact, there are plenty of situations where DevOps engineers, SRE teams and other observability-focused departments need to explore reams of data rapidly and flexibly. An SRE trying to figure out the root cause of latency may need to dissect the movement of data across endpoints to find the malfunctioning one. Another engineer may need to compare historical and current performance to find anomalous behavior. Lastly, a DevOps engineer may have to dissect user performance metrics to understand the scope of a slowdown—whether it is global or regional.

Every use case requires dashboards that can provide detailed data through a wide variety of visualizations, including choropleth maps, stack areas, pie charts and bar graphs. Ideally, a dashboard will enable teams to isolate dimensions, apply filters, and dive into data for deeper insights.

Unfortunately, not all dashboards can support this type of fast, flexible analysis. Many still utilize outdated technologies, which lack the flexibility, agility and scalability required to seamlessly explore data. Many were also designed without the urgency that today’s data requires—after all, in previous years, most uses for dashboards (such as internal reporting) were not time-sensitive.

Other dashboards are constrained by templates, which offer a finite array of widgets, tools and drill-down capabilities. These dashboards may feel unwieldy and sluggish, unsuited for a rapidly evolving situation like an application outage.

Requirements

To truly fulfill the promise of self-service, ad-hoc analytics across huge datasets, teams need to work in real-time—and so they need a database capable of timely responses.

When an application goes down, an SRE may not know what to look for and, hence, needs to quickly comb through lots of data. While some dashboards rely on workarounds for faster queries, such as pre-aggregations, precomputing, or rollups, this is not possible in this instance because the SRE simply won’t know what to look for. After all, they can’t necessarily predict what will go wrong, and even if they work on assumptions from previous failures, this current issue could be much different.

Therefore, dashboards must provide plenty of functionalities and visualizations. Users should be able to filter data by time, isolate variables like location, and zoom into specific time intervals with a few clicks. Dashboards should also accommodate diverse data types, including intricate parent-child relationships and nested columns.

Further, dashboards must offer a depth of insight. An online streaming media platform may need to assess user metrics (such as latency or load times) across different devices, operating systems, and regions to fine-tune performance. A cloud provider has to monitor their physical hardware for high temperatures, slowed network switches or devices, and other anomalies.

Because data is now so important to success, many more people across a company, including data scientists, product managers and external users, themselves require data-driven insights. In these situations, dashboards must handle the increased user traffic and query activity, maintaining fast responses even under load. This is especially important considering that a single-user operation (such as a zoom) will require multiple queries on the backend to execute.

A database also must scale seamlessly. If an organization’s environment generates millions of events an hour, that equates to billions of events per day or week—challenging for any database to ingest, store, analyze and query. In fact, many databases cannot successfully provide fast response times while managing large datasets and high query volume. As an example, transactional databases (OLTP) can often query rapidly but cannot execute analytics at scale, while analytical databases (OLAP) can analyze massive volumes of data but not at speed.

Apache Druid for Independent, Ad-hoc Data Exploration

This is where open source Apache Druid comes in. Combining the scale and advanced analytics of an OLAP database with the speed of an OLTP database, Druid offers swift, real-time data exploration.

Upon ingesting data, Druid makes it immediately available for querying and analysis, removing the need to first batch or aggregate the data in some way. In addition, Druid natively integrates with streaming technologies like Apache Kafka and Amazon Kinesis, removing the need for workarounds or connectors.

Druid powers interactive visualizations, providing millisecond response times, enabling more versatile exploration, expanding the range of available dimensions and filters, and even maintaining subsecond speeds in the face of surging user and query volumes.

Druid’s unique architecture also enables easy scaling. By separating key duties among separate node types—data nodes for storage, master nodes for data ingestion and availability, and query nodes for executing queries and returning results via the scatter/gather method—Druid ensures that nodes can be independently scaled based on need. Afterward, Druid also automatically rebalances traffic to ensure consistent performance.

Salesforce: A Druid Success Story

Salesforce pioneered the customer relationship management (CRM) space, serves 150,000 customers worldwide and earns billions in annual revenue.

The Edge Intelligence Team is the division of Salesforce that tackles the massive task of ingesting, processing, filtering, aggregating, and querying the entirety of their log data — anywhere from billions to trillions of lines daily. Each minute, Salesforce ingests 200 million metrics, while each day, Salesforce processes five billion daily events globally. In total, Salesforce accumulates dozens of petabytes of data in their transactional store, five petabytes of logs in their data centers, and almost 200 petabytes in their Hadoop storage.

Salesforce teams use Druid to unlock real-time insights into product performance and user experiences, diving into large datasets instantly. Anyone from engineers to account executives can query a wide variety of dimensions, filters and aggregations to better understand trends, troubleshoot any issues that arise and set strategies for the future.

By using Druid’s compaction abilities, Salesforce also decreased the number of Druid rows by 82%, leading to an overall reduction of their storage footprint by 47% and accelerating their query response times by 30%.

October 19, 2023
5 Effective Git Workflows to Streamline Your Development Process

In the fast-paced world of software development, efficient collaboration and version control are vital for successful project management. Git, a distributed version control system, offers various workflows to facilitate seamless collaboration among team members. Each workflow is designed to address specific development needs and enhance productivity. In this blog post, we will explore five popular Git workflows and discuss their significance in the software development process.

Centralized Workflow

The centralized workflow is a simple and straightforward approach commonly used in small teams or projects. In this workflow, a central repository acts as a single source of truth for the project. Developers clone the repository, make changes in their local branches and push their changes to the central repository for integration. This workflow is easy to understand and implement, making it ideal for beginners. It promotes a linear development process, making it easier to track changes and maintain code consistency.

Feature Branch Workflow

The feature branch workflow is widely adopted in larger development teams. It enables parallel development and promotes the isolation of work. Each feature or task in this workflow is given a specific branch. Developers work on their features, branch off from the main branch, and submit updates to the remote repository. Once the feature is complete, it undergoes code review and is merged back into the main branch. This workflow allows for better collaboration as developers can work independently on separate branches without interfering with each other’s progress.

Gitflow Workflow

The gitflow workflow is a robust and well-defined branching model that is particularly useful for projects with multiple release cycles and long-term maintenance. It incorporates two main branches: The “master” branch for stable releases and the “develop” branch for ongoing development. Developers create feature branches from the “develop” branch, work on their features, and merge them back into “develop” for integration testing. Once the code in “develop” is ready, it is merged into the “master” branch for a new release. This workflow ensures a structured and controlled release process, making it easier to manage versioning and hotfixes.

Forking Workflow

The forking workflow is commonly used in open source projects and promotes collaboration among multiple contributors. In this workflow, each developer creates a personal fork of the main repository. They make changes in their forked repository, create a pull request to propose changes and undergo a review process. The changes can then be merged into the main repository by the project maintainers. This workflow allows for a clear separation of duties and provides a controlled mechanism for accepting contributions from external developers.

Release Flow Workflow

The release flow workflow is well-suited for projects that require frequent releases and hotfixes. It combines the concepts of the feature branch and gitflow workflows. The main difference lies in the way releases are managed. In this workflow, a dedicated release branch is created for each release. Once the release is ready, it is merged into both the “master” and “develop” branches. This workflow allows for efficient bug fixes and maintenance, ensuring that critical updates are quickly propagated to the main development branch.

Why Are These Workflows Needed?

Efficient Collaboration: Git workflows provide structure and guidelines for collaboration, enabling developers to work in parallel without conflicts. They facilitate seamless communication and code integration, fostering teamwork and productivity.

Code Consistency and Quality: By following established workflows, developers can ensure code consistency and maintain high-quality standards. Workflows enable code review processes, catching potential issues early and reducing the risk of introducing bugs into the main codebase.

Traceability and Version Control: Git workflows provide a clear history of changes, allowing developers to trace back and understand the evolution of the codebase. They enable easy rollbacks and provide a reliable version control system, safeguarding against accidental data loss.

Streamlined Releases and Hotfixes: Workflows like gitflow and release flow are specifically designed to streamline the release process, making it easier to manage versioning, hotfixes, and long-term maintenance. These workflows ensure that stable releases are delivered promptly and critical updates are quickly deployed.

Conclusion

Git workflows are powerful tools that help streamline the software development process. Whether you’re a small team or a large enterprise, adopting the right workflow can significantly improve collaboration, code quality, and overall productivity. From the centralized simplicity to the structured gitflow model, each workflow serves a specific purpose and caters to different project requirements. By leveraging the appropriate workflow, developers can navigate the complexities of version control and empower their teams to deliver exceptional software.

October 13, 2023