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  • Tray.io Automation Framework Leverages AI to Build Integrations

    Tray.io Automation Framework Leverages AI to Build Integrations

    Tray.io today added an automation framework to an integrated platform-as-a-service (iPaaS) service that leverages an existing Merlin artificial intelligence (AI) engine to identify the most efficient way to automate a workflow.

    The Tray Universal Automation Cloud service can be invoked via three distinct application programming interfaces (APIs) to programmatically create those workflows using a Tray Code low-code or Tray Build no-code tool or via a Tray Chat natural language interface. A Connectivity API provides access to more than 600 connectors, while a Trigger API enables developers to drive event-driven processes. An Authentication API provides a method for managing authorizations. The platform also provides access to testing, error handling and debugging tools.

    Tray.io CEO Richard Waldron said that approach provides IT teams with a composable framework for building integrations that leverages a set of large language models (LLMs) that the company has embedded within its Merlin AI engine to enable IT teams to optimize workflows.

    In addition, the underlying platform takes advantage of Kubernetes, Docker containers and serverless computing frameworks to dynamically scale as required, he added. In contrast, rival legacy iPaaS environments are not as elastic, which enables Tray.io to base its pricing on actual usage of a modern IT platform, noted Waldron.

    The overall goal is to provide a single platform that any type of developer, including business professionals, can invoke to automate workflows, said Waldron.

    As the overall codebase that DevOps teams manage continues to increase, the number of required integrations between applications and processes continues to increase exponentially. Rather than relying on lower-level code to create those integrations, an iPaaS provides the means to automate and simplify building integrations and enable developers to spend more time writing the business logic that will drive a new application. Historically, most of the integrations required to drive, for example, a digital business transformation initiative required a significant amount of low-level code to be developed and maintained.

    Tray.io is now making a case for an iPaaS that takes advantage of AI to more easily invoke generative AI capabilities to optimally create those integrations based on workflow attributes.

    Each organization will need to determine how heavily to rely on AI platforms to build integrations, but these advances—alongside generative AI tools that developers have already embraced to build software—are making developers more productive than ever. That accelerated pace of application development will require DevOps teams to automate as many processes as possible. One option for achieving that goal is for organizations to rely more on an iPaaS to enable developers to build integrations in a way that doesn’t necessarily always have to flow through a DevOps pipeline.

    One way or another, thanks to the rise of AI, the way software is built is about to change drastically. The challenge and the opportunity now is to determine how best to reengineer workflows in a way that best aligns with the goals of the organization.

  • Why Data Silos Are Killing Your Developer Productivity

    Why Data Silos Are Killing Your Developer Productivity

    Every company is at risk of fostering data silos. In fact, a study conducted by Forrester Consulting for Airtable across employees in the U.S. and the UK revealed that, on average, knowledge workers spend 30% of their week looking for the information they require to do their job and the difficulty in finding and accessing data is the top reason why they feel disengaged from work. What causes essential data to be inaccessible in the workplace? We will explore the root of modern data silos, why they drain developers’ productivity and how to break away from these silos with better observability in your organization.

    What are Data Silos?

    A data silo is a collection of data held by a group that is not easily or fully accessible by other groups in the same organization.

    Data silos can be a byproduct of organizational structure. When different departments or business units in an organization operate independently, they develop their own way of collecting, storing and managing their data. The processes to access that data from other business units can be cumbersome or even nonexistent. Sometimes, the individuals responsible for the data will even be against sharing it for fear that it would be misused or misunderstood outside of their particular unit. The difficulty in accessing information then leads to independent data collection from other business units and ultimately creates other data silos and multiple sources of truth.

    Data silos can also happen during times of growth for an organization. New tools are necessary to support the growing needs of the business and are added to the list of tools already in use. Over time, this list grows, and systems that were designed to operate in isolation can create data silos by limiting the ability to integrate with other systems. A recent survey of marketing organizations by Emarsys showed that the surveyed organizations are using an average of 91 marketing cloud services. These 91 tools undoubtedly create data silos unless they are fully integrated together or into a central system.

    Why are Data Silos Killing Your Developer Productivity?

    Data silos aren’t only slowing your developers down by limiting their access to the data needed for development. They also put a lot of extra work on their plates. And it goes without saying that this additional time spent on data management reduces the time available for improving your product.

    Management of Redundant Data
    Multiple teams in the organization storing their data separately means increased workloads for developers, who need to update and maintain separate copies of the same data.

    Inconsistent Data Quality
    Data gaps or quality issues happen more frequently since different parts of the organization have their own standards for collecting, cleaning and validating data. This can lead to development delays or errors in the product.

    Time-Consuming Integrations
    Developers have to code a lot of integrations to connect all the different tools your organization uses and keep the data flowing between them.

    Inefficient Collaboration
    When different teams work in silos, the chances of misunderstanding are greater, leading to development delays and misaligned priorities.

    Difficulty in Data Governance
    Maintaining high standards of data privacy, security and compliance is time-consuming when the data is spread out in different silos and can create costly regulatory risks.

    Steps Leaders Can Take to Break Down Data Silos

    Fortunately, there are steps that you can take to break down the data silos in your organization, relieving your developers at the same time. Each step may have its own set of advantages and challenges, so the solutions you choose ultimately depend on your organization’s needs and objectives.

    Create a Unified Data Strategy
    Set clear goals and standards for data collection, storage, management and analysis at the company level. You should also implement data governance and security policies.

    Adjust the Culture
    Foster a culture of collaboration and data sharing across the company by encouraging teams to share data, insights, and best practices across different departments. You can achieve this through regular communication, training and incentives for cross-functional collaboration.

    Establish Cross-Functional Teams
    Bringing together members of different departments to collaborate on data-related projects can prove invaluable. These teams should have a shared understanding of the organization’s data strategy and should be empowered to make decisions about data collection, analysis and sharing.

    Invest in Data Integration and Interoperability
    Invest in tools and technologies that promote data integration and interoperability across different systems and departments. Adopt standard data formats, implement APIs and data connectors or use data integration platforms to make observability collaborative. Make sure that the tools you choose to add to your company have various native integration capabilities to avoid custom integrations as much as possible.

    Congrats, you now know all about data silos and how to break them down! The next step for you is to identify the data silos in your company and put a plan in place to get rid of them. Your employees and coworkers will thank you!

  • Another Certification

    Another Certification

    another-certification

  • Five Great DevOps Job Opportunities

    Five Great DevOps Job Opportunities

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

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

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

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

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

    Dice.com

    R&T Deposit Solutions
    New York, New York
    DevOps Engineer
    $145,000 to $175,000

    Indeed.com

    Caterpillar
    Chicago, Illinois
    DevOps Engineering Manager
    $141,000 to $178,000

    SimplyHired.com

    Takeda Pharmaceutical
    Boston
    Machine Learning Infrastructure/Platform Engineer
    $130,000 to $186,000

    LinkedIn

    M&T Bank
    Wilmington, Delaware
    DevOps Manager
    $123,000 to $206,000

    CareerBuilder.com

    Fidelity Investments
    Westlake, Texas
    DevOps Engineer
    $120,000 to $160,000

  • There’s No Value in Observability Bloat. Let’s Focus on the Essentials

    There’s No Value in Observability Bloat. Let’s Focus on the Essentials

    The observability market paired with cloud security has been predicted to reach $70B by 2026, but that market prediction is a myth, and I’ll tell you why.

    If observability was a grand buffet and every company needed a feast of features and capabilities of this proportion, maybe this prediction would be valid. But most companies don’t need an over-the-top feast. Legacy APM and observability solutions are serving up an all-you-can-eat buffet when the reality is that most companies only need the essential ingredients to satisfy their observability strategy. And the cost for that can be much more modest. The $70B observability market prediction is, therefore, suspect, considering the true needs of most organizations.

    Complex cloud ecosystems, the rise of applications built on microservices and Kubernetes, and the overall scale and complexity of applications have led to exponentially more data being created in the pursuit of observability. Storing and processing all that data is expensive, and as the volume of data balloons, so does the cost.

    And while the complexity of business-critical systems and the volume of data have scaled, so have the overall cost of those legacy tools—in direct proportion. Partially, this is driven by the features and instrumentation added that only contribute to the bloat and valueless telemetry.

    Customers aren’t seeing value in any of this bloat, and for the most part, all that’s been accomplished is increased complexity in deploying and operating the system, with a commensurate lengthening of time to value. Plus, pricing models haven’t significantly changed despite the advancements in the market. The fact is, organizations can’t really say that they have gotten more insight into the overall performance of their mission-critical systems. What they are saying, however, is that these tools are becoming overwhelming to manage and overwhelmingly expensive, and developer productivity is not increasing.

    What organizations need and want are real-time insights into the health and performance of their cloud infrastructure and applications–specifically the confluence of logs, metrics and traces within the context of modern observability. There is little tolerance for software bugs, slow web experiences, crashed apps and other service interruptions, and the responsibility lies on developers, engineers and ITOps teams to quickly resolve production issues before they impact customer experience.

    There is a call for observability, but rather than an explosion of features and noise, what’s being called for is a narrowing of focus.

    Observability Has an Efficiency Problem

    Telemetry data gathered from the distributed components of modern cloud architectures needs to be centralized and correlated for engineers to gain a complete picture of their environments.

    Engineers need a solution with critical capabilities such as dashboarding, querying and alerting, and AI-based analysis and response, and they need the operation and management of the solution to be streamlined.

    What’s important for them to know is that it’s not necessary to spend more to ensure peak performance and visibility as their environmental complexity grows.

    Legacy players experiencing some maturity in the market may have conditioned them to believe this is true—they have set a standard for higher costs correlating with incremental amounts of data, no matter the quality of that data. Companies are being fed the lie that all this data may be relevant and they’ll be doing themselves a disservice by neglecting even one byte of it.

    Data Does Not Inherently Equal Insights

    No doubt, more data is being generated, but most of it is not relevant or valuable to an organization. Observability can be optimized to bring greater value to customers, and that’s where the market is headed.

    Call it “essential observability.” It’s a disruptive vision to propose a re-architected approach to observability, but what engineers need is a new approach making it easier to surface insights from their telemetry data while deprioritizing low-value data. Costs can be reduced by consuming only the data that enables teams to maintain performance and drive smart business decisions.

    Essential observability is cutting out the fluff and only focusing on—and paying for—the impactful data. It’s about arming engineers with meaningful insights so they can quickly resolve production issues and business disruptions. Organizational performance increases without inflating data complexity and resource consumption.

    The Essential Observability Revolution

    Observability tools have evolved beyond the model of linear price increases as complexity and data scales. For companies (especially growth-stage organizations) that want a partner to help them make more informed decisions and keep their costs more reasonable (i.e., stop recklessly spending for useless data), they need to focus on tools offering them the essentials.

    Solutions are available, offering targeted features and automated capabilities out of the box, delivering the insights needed to understand and fine-tune data. These solutions provide continuous support, reduce complexity and accelerate time to value.

    The next revolution in observability isn’t features and functionality but providing insights to make data-driven decisions that keep essential observability cost-effective while not compromising the value derived.

  • Observability Leaders: The Mighty Minority Holding Their Own Against Outages

    Observability Leaders: The Mighty Minority Holding Their Own Against Outages

    The job of IT teams has become much harder thanks to the new realities of hybrid IT, with workflows occurring across on-premises and cloud-based networks and applications. Yet so much rests on your ability to respond to these challenges, all while managing the expectations of your customers and the health of your bottom line. With the growing complexity of ecosystems comes the need for better visibility, both into specific systems and across your entire stack.

    Observability solutions address this by providing visibility across your network, infrastructure, systems, application, database, digital experience and log monitoring.

    In other words, observability enhances your ability to observe your digital ecosystem with more transparency, more effective detection and more intelligent insights. Take, for example, the very timely and pressing issue of outages. On average, the typical business experiences nine brownouts or outages every month, lasting roughly twelve hours each and averaging $13.7M annually, according to new research we conducted at SolarWinds.

    Over the course of the year, that can add up to nearly 1,300 hours, with outages chipping away at your organization’s productivity, reputation and competitive edge.

    It’s not enough to monitor for these outages and address them once they take down your systems. Repeated or prolonged outages signify a gap in the enterprise’s ability to comprehensively monitor and understand its network’s internal state and performance. A robust observability strategy can preemptively detect anomalies and potential issues—before they become full-blown outages.

    According to new data from the 2023 IT Trends Survey released this month by SolarWinds, companies who’ve embraced observability tend to not only lead in detecting, remediating and preventing outages—they’re experiencing better business and IT outcomes, too.

    The technology professionals surveyed shared how observability is helping them achieve better performance, compliance and resilience in digital environments—and how you can follow their lead.

    Observability Drives Better Business Outcomes

    The observability leaders surveyed were 300% more likely to say their organization is doing extremely well with growing revenue and 250% more likely to say the same about their organization’s speed of innovation. Interestingly, they also reported a better employee experience: They were 388% more likely to rate their organization as doing “extremely well” with employee experience and also reported fewer and smaller skill gaps on their teams.

    So, how does embracing observability actually translate into better business outcomes? According to the data, observability leaders are…

    • Investing in top priorities: Data shows organizations using observability solutions to support the priorities most critical to their growth and success: Improve their customer experience (96%), enable faster innovation (71%), reduce time spent solving (71%) and detecting (60%) issues and increase operational efficiency (55%).
    • More automated and integrated: Observability leaders embracing automation and investing in tools that provide enhanced efficiency are 214% more likely to say they are doing extremely well with operational efficiency, 750% more likely to say they are doing extremely well with auto-remediation of complex alerts and 300% better at automatically collecting background diagnostic data for IT support staff.
    • Ahead on IT: The data found that those ahead of the curve on observability are also leading by huge margins when it comes to monitoring, detecting and resolving issues that could otherwise bring the business to a screeching halt. When it comes to IT, they are 233% better at auto-escalation of tickets, 213% better at auto-remediation of simple alerts and 36% better at settling alert levels based on historical behavior.

    How to Reap Observability Benefits in Your Organization

    All told, observability adoption is still in its early days. Only 1% of enterprises are already up and running using observability tools on a regular basis, while another third are undergoing the implementation process.

    That’s good news for other businesses who are ready to reap the rewards—it’s not too late to be an observability pioneer.

    If you’re interested in bringing observability to your own organization, start by addressing a few important factors that can help you sidestep challenges—such as the accelerating pace of change in technology (72%) or insufficient budget (58%)—that might otherwise obfuscate your observability opportunities:

    • Make sure your observability solutions are appropriate for multi-cloud environments: Multi-cloud environments have become standard for many companies because of the many upsides, but they can be challenging for IT teams to manage. Strategically accelerate your organization’s cloud migration with an observability solution designed for complex hybrid and multi-cloud environments​ to save a lot of headaches down the road.
    • Give your teams autonomy—and build toward autonomous operations: Observability brings your enterprise a step closer to autonomous operations. Autonomous operations, where IT systems can govern themselves, providing a proactive approach to addressing issues, rather than reactive, won’t replace your human teams but rather free up their time and brainpower to support the business and drive innovation.
    • Don’t ignore the database: Databases represent the most difficult ecosystems to observe, tune, manage and scale. They require significant computing power, tend to be memory-intensive and likely make up the bulk of your organization’s cloud spending. Tasks crucial to the health and functionality of your business—including troubleshooting, root cause analysis and remediation—simply cannot happen without full, transparent observability across the numerous resources a database needs to engage to do its job.

    Observability helps balance competing priorities and demystify application requirements while increasing your overall DevOps agility, efficiency and speed to market. Observability not only fosters transparency and collaboration but also encourages a system of ongoing review, which benefits the organization, IT teams and—crucially—your customers.

  • Cisco Acquires Splunk to Create Observability Powerhouse

    Cisco Acquires Splunk to Create Observability Powerhouse

    Cisco today revealed it has signed a definitive agreement to acquire Splunk for $28 billion in cash. The deal will add Splunk, one of the most widely-used platforms for managing IT operations, to Cisco’s portfolio, joining the AppDynamics observability platform in addition to a wide range of IT infrastructure equipment.

    Chuck Robbins, CEO of Cisco, told industry analysts the combined entity will be one of the largest software companies in the world. The Splunk and AppDynamics platform creates a comprehensive observability platform spanning applications, networks and hybrid cloud computing environments, Robbins added.

    By combining AppDynamics with the data collected by the Splunk platform, the combined company will be able to provide greater insights into IT operations than any other company, added Robbins.

    Splunk CEO Gary Steele said the acquisition represents the next natural, logical step for Splunk and furthers its long-term partnership with Cisco to keep IT environments secure and resilient. The two companies will also be able to apply artificial intelligence (AI) more effectively to IT operations, he added.

    In its most recent quarter, Splunk reported revenues of $911 million, with cloud revenue accounting for $445 million, or 29%, of the total. Total Splunk revenues are forecasted to be between $3.925 billion and $3.95 billion for fiscal 2024.

    Splunk previously acquired SignalFX, a provider of an observability platform, in 2019. It’s not yet clear how the platform might be integrated into the AppDynamics platform, but Robbins said there is little overlap between the observability platforms the two companies currently provide.

    The Splunk acquisition comes at a time when it’s becoming more apparent that the need for observability extends well beyond DevOps workflows as organizations become more dependent on software.

    Each IT organization will need to determine how long it will wait for the Splunk acquisition to close before investing further in observability. Historically, acquisitions of this scale have been accompanied by price hikes to help fund the merger once the deal is concluded.

    In the meantime, IT and DevOps leaders will likely revisit the way IT is managed in the months ahead. Splunk came to prominence at a time when most workloads were running in on-premises IT environments. While the bulk of workloads continue to run in those environments, many workloads deployed in the cloud are being managed using platforms designed specifically for those environments. Splunk has been making a case for a hybrid cloud computing approach that assumes the management of on-premises and cloud computing environments will become more unified.

    In theory, economic headwinds are driving more organizations in that direction to reduce the total cost of managing IT. But it’s just as feasible to extend cloud tools to on-premises IT environments as it is to extend legacy tools to the cloud. Despite the inevitable outcome, Splunk rivals will spend the better part of the next year coming up with offers to switch platforms both before and after the deal eventually closes.

  • Nobl9 Unfurls Reliability Center for Managing SLOs

    Nobl9 Unfurls Reliability Center for Managing SLOs

    Nobl9 today launched a next-generation platform for managing service level objectives (SLOs) in a way that promised to improve overall reliability.

    Brian Singer, chief product officer for Nobl9, said in addition to enabling DevOps teams to meet SLOs, the Nobl9 Reliability Center also now provides a single source of truth about the state of reliability in an application environment.

    That capability is critical because as DevOps teams add new features to applications, they need insights into how they are impacting the reliability of the software environment, said Singer. No matter how great any new capabilities might be, it won’t matter much if the application environment slows to a crawl or outright crashes, he added.

    Nobl9 Reliability Center addresses that need by providing dashboards and reports to assign reliability scores based on the metrics collected from the SLOs defined. Those scores can then be shared with senior business and IT leaders so they can better understand the actual state of an application environment, noted Singer.

    In addition, the platform will recommend SLOs and policies and can be used to automate runbooks to improve reliability. The goal is to enable DevOps teams to continuously monitor and manage using SLOs via alerts that are received in a way that is timely enough to act on, added Singer.

    That approach enables DevOps teams to make sense of conflicting alerts generated today by multiple monitoring and management tools. A recent survey published by Nobl9 found 72% of respondents are using six or more monitoring and observability tools.

    The Nobl9 platform collects SLOs by tracking workflows through more than 50 integrations with various DevOps tools and platforms.

    SLOs, of course, are not a new idea. They have been used as a metric to track the performance of IT services for decades.

    However, as more microservices-based applications are built and deployed, it’s becoming more challenging to maintain SLOs across applications that have many more dependencies than legacy monolithic applications. Ultimately, each IT team needs to provide some sort of objective benchmark that assesses their overall effectiveness at delivering application services. The Nobl9 platform makes it simpler to track whether SLOs are achieved and maintained.

    That’s crucial because as more organizations depend on software to deliver digital services, any disruption is likely to have a direct impact on profit and revenue. As such, the reliability of application environments has become a major concern. The issue, of course, is that many of those application environments have dependencies on legacy platforms that, in some cases, have been running for years. Each feature and application programming interface (API) added over time conspires to adversely impact performance. In an era where IT teams are now trying to manage distinct services rather than individual applications, latency can significantly slow down response times.

    Each organization will need to determine for themselves how aggressively they need to manage SLOs. But thanks to the rise of digital business transformation, more senior IT and business leaders are focused on them than ever.

  • Harness Launches Open Source Gitness Platform

    Harness Launches Open Source Gitness Platform

    At its unscripted conference today, Harness unveiled a streamlined open source git platform, dubbed Gitness, that is significantly lighter weight than the platforms DevOps teams traditionally rely on to build increasingly complex applications.

    At the same time, the company is adding four additional modules to its continuous integration/continuous delivery (CI/CD) platform to improve developer productivity and make it simple to build and deploy secure applications.

    Finally, Harness is launching an Engineering Excellence Collective, a consortium of 300 senior engineering leaders. The collective is developing a maturity model based on 11 pillars for achieving software engineering excellence; a FinOps Excellence community initiative was also announced to advance best practices for controlling cloud computing costs.

    Harness CEO Jyoti Bansal said the need for a modern open source Git platform—as an alternative to legacy platforms developed a decade ago—has never been more apparent. Gitness provides all the collaborations, performance, analytics and security capabilities DevOps teams require in a streamlined platform that reduces overall bloat by seamlessly integrating, for example, code hosting and automated pipelines, he added.

    Other core capabilities include automated status checks and mandatory reviews, branch protection rules, user access management tools and a semantic search engine that has been infused with generative artificial intelligence (AI) capabilities that invoke multiple large language models (LLMs).

    Collectively, the approach reduces the amount of time spent when attempting to integrate and manage disparate tools on legacy platforms that inevitably lead to the creation of additional bottlenecks, said Bansal. Rather than bolting on additional tools, the time has come to simplify software engineering by migrating to a platform specifically designed to address modern software engineering requirements, he added.

    Designed to be installed using Docker contains in a matter of minutes, DevOps teams can also take advantage of tools to automatically migrate their existing repositories and pipelines from platforms such as GitHub and GitLab.

    Gitness is also at the core of one of the four additional modules that Harness is adding to its portfolio. The Harness Repository is a premium tool, available in beta, that provides governance and compliance-as-code capabilities embedded within the repository.

    The three other modules being added are a Harness Internal Developer Portal based on open source Backstage software, Harness Infrastructure as Code Management that makes it simpler to implement GitOps workflows at scale and Harness Software Supply Chain Assurance to provide visibility into open source software components and manage the creation of software bills of materials (SBOMs).

    It’s not clear whether DevOps teams are ready to rip and replace existing platforms, but the pressure to improve developer productivity has never been higher. Organizations increasingly realize that developers are spending the bulk of their time managing development environments tied to legacy platforms that create bottlenecks and are becoming more costly to manage and sustain.

    Harness, in effect, is now calling for the establishment of an alternative approach based on open source software that promises to streamline software engineering workflows. As applications incorporate microservices, security controls and AI models, they are only going to become more challenging to build and deploy at scale.

  • Innersourcing Open Source Principles in the Enterprise

    Innersourcing Open Source Principles in the Enterprise

    Software development has experienced a seismic shift in recent years, with open source becoming the heartbeat of innovation. A full 90% of enterprise IT leaders say their organization is using open source, according to a Red Hat report. Leading this transformation is GitHub, the ubiquitous platform that not only jumpstarted the open source movement but has also been quietly transforming how enterprises approach development at large.

    But it’s not just tech companies trumpeting the open source creed—academics are on board, too. A paper recently published by the University of Georgia found a hard-to-dismiss correlation between open source innovation and overall business success. “We find a significantly positive association between GitHub activity and future realized net income, suggesting that open innovation is a strong predictor of future earnings,” according to the report.

    I recently met with Chris Reddington, senior manager of developer advocacy at GitHub, to dive into how and why open source is becoming essential for enterprise innovation. According to Reddington, the once-dominant proprietary information mindset is shifting, with open source cultural principles now permeating most companies, a practice also known as innersource. Put simply, open source is now cool in the enterprise.

    Bridging the Open Source-Enterprise Divide

    GitHub’s close ties to the open source space have allowed them to illuminate the parallels between the open source and enterprise worlds. Reddington noted that, nowadays, open source and enterprise aren’t really separate communities but rather intertwined facets of the same software ecosystem. This movement is represented by the open source program office (OSPO) becoming more commonplace in large organizations.

    For an example of innersource in practice, take Porsche, who recently announced the Porsche FOSS Movement to guide internal common values and development principles around open source. We’ve also seen similar large-scale commitments to open source values at Walmart, Spotify, Facebook and others.

    Open source clearly isn’t just for startups—large companies are increasingly seeing an advantage in both using open source software and sharing their own open source components. The growing investment and maturation of OSS initiatives are positive strides, as it’s important to the stability of the free open source software (FOSS) ecosystem as a whole that those using software contribute back to its maintenance, especially to core packages the world depends upon.

    The Power of Innersource

    The study from the University of Georgia brings to light how enterprises increasingly recognize the need to work in sync with their developers and customers to remain competitive. This is a testament to the evolving nature of development—an industry that’s embracing a “team sport” mentality instead of a siloed approach, explained Reddington. And as enterprises align with open source principles, they are discovering the power of innersource, he said. “Innersource is a development methodology where engineers build proprietary software using best practices from large-scale open source projects, like Kubernetes or Microsoft’s Visual Studio Code,” GitHub explained.

    Reddington elaborated on two key elements of this approach:

    • Breaking down silos: The traditional organization-centric structure of projects is being replaced by a more transparent and collaborative approach. Innersource encourages transparency and asynchronous communication, tearing down barriers that previously inhibited efficient collaboration.
    • Engaging responsibly: Reddington emphasized the significance of enterprises giving back to the open source community. Whether through sponsorships, allocating time for engineers to contribute, or open sourcing their projects, companies can foster symbiotic relationships where they benefit from and contribute to the open source realm.

    Essentially, by adopting innersource principles, enterprises foster an environment that promotes collaboration and knowledge sharing, thus enabling innovation to flourish across teams and projects.

    Balancing Open Innovation and Proprietary Software

    One of the most intriguing aspects of this transformation is finding the equilibrium between open innovation and proprietary software. Reddington acknowledged the necessity for businesses to generate profits, which inherently stems from delivering value.

    While reinventing the wheel may be tempting, Reddington highlighted the importance of recognizing existing solutions and incorporating them into ongoing projects. This pragmatic approach maximizes efficiency and enables teams to focus on the unique, value-generating aspects of their work.

    Organizations should also consider what software of their own could be open sourced. From a business perspective, this most likely won’t be the “crown jewels,” said Reddington, but ancillary components that others could benefit from. For example, had it not been for Google open sourcing Kubernetes, Lyft open sourcing Envoy Proxy or Facebook sharing GraphQL, we would not have these standard components for all to use.

    Open Source Movements: Past, Present, and Future

    Over the last decade, we’ve seen significant, more institutional maturation around open source projects. Reddington pointed to the Cloud Native Computing Foundation (CNCF) as a standout. He specifically noted Kubernetes-based Event Driven Autoscaler (KEDA) as a cool project to help scale Kubernetes containers based on the number of events needing to be processed.

    He also underscored the surge of interest in generative AI, evidenced by projects like GitHub Copilot. It will be curious to see how AI assistants incorporate open source at their core, such as open foundational models and community-supported datasets.

    Potential Setbacks To The Open Source and Innersource Movement

    In the dynamic realm of enterprise-scale software development, nurturing innovation can be a complex endeavor. Innersource principles are one way to change the attitude toward development, making it more efficient, collaborative and aligned to the current and future developer experience expectations.

    Yet, this isn’t to say we haven’t had some setbacks recently that could threaten the progress the OSS community has built. For example, HashiCorp recently abandoned its open source license around Terraform in favor of a business source license (BSL). There is also an increasing number of supply chain-related attacks that threaten common open source libraries.

    Gazing Into the Future

    As Reddington aptly summarized, we’re still in the early days of this transformative journey. The developer community’s inclination for experimentation will undoubtedly drive the solutions to the most pressing challenges in open source. And with enterprise adoption on the rise, the future promises more cohesive collaboration, broader adoption of innersource principles and continued innovation driven by open source values.

    Interestingly, we’re seeing more synergy between open source and enterprise domains, which used to feel like separate realms entirely. And it will be interesting to see how businesses continue to balance transparency and proprietary value into the future. No matter what, the lines between them are blurring, enabling companies to harness the power of innersource for enhanced collaboration and innovation.