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  • Red Hat Unveils Developer Hub to Boost Dev Productivity

    Red Hat Unveils Developer Hub to Boost Dev Productivity

    At its Red Hat Summit conference today, Red Hat unveiled its Red Hat Developer Hub, a developer portal that provides access to an opinionated set of development tools based on the open source Backstage project.

    The portal pulls together a set of curated tools and resources so that developers encounter less friction and avoid the complexity of numerous optional tools that span disparate vendors. The goal is to increase developer productivity while reducing the volume of optional tools and information that must be sifted through to actually deliver production code.

    Red Hat Developer Hub aims to streamline the development process and allow organizations to maximize existing resources and developer skills while reducing sprawl, said Chris Wright, CTO and SVP for global engineering at Red Hat. This will help organizations maintain application development velocity and drive a consistent user experience for developers based on tools and skills they’re already familiar with, Wright told attendees.

    Rather than building and maintaining their own internal developer portal, Red Hat is making a case for using Developer Hub to deliver a platform approach to addressing the complexity and sprawl DevOps teams face. Red Hat Developer Hub is, in essence, an opinionated and optimized version of Backstage that aims to deliver improved developer experiences across environments, including Kubernetes and container application platforms such as Red Hat OpenShift.

    Developer Hub will offer a single pane of glass to view all available developer tools and resources, developer self-service capabilities and security and compliance guardrails for cloud-native application development that comply with enterprise-wide best practices, according to the company.

    Additionally, Red Hat Developer Hub includes a set of ‘golden path’ templates—pre-defined, pre-architected and supported templates for further accelerating application development.

    The developer hub is based on the Cloud Native Computing Foundation (CNCF) incubating open source project Backstage, originally developed by Spotify and donated to the CNCF in 2020. Backstage is now being advanced by contributions from multiple vendors, including Red Hat.

    Backstage has been gaining traction in cloud-native application environments that are especially challenging to develop using multiple tools. There’s a lot more focus on developer productivity, which Backstage addresses by creating a catalog of blueprints that developers can readily consume rather than requiring them to build these capabilities themselves multiple times over. The goal is to create scaffolds that developers can consistently reuse across multiple application development projects.

    Red Hat has also developed Red Hat Plug-ins for Backstage, a bundle of six plug-ins that integrate various key systems into Backstage, helping to extend the project’s functionality, ensure a consistent user experience and improve workflow efficiency. The plugins can be used across all Backstage installations, providing even more flexibility for developers across platforms and Kubernetes distributions and include:

    • Application Topology for Kubernetes, which enables developers to visualize real-time status of application and infrastructure workloads deployed to any Kubernetes target, including Red Hat OpenShift, with greater consistency.
    • Multi-Cluster View with Open Cluster Manager (OCM), which provides a view into clusters from Open Cluster Manager’s MultiClusterHub and MultiCluster Engine in Backstage.
    • Container Image Registry for Quay, which improves the integration and speed of interactions with Quay registries by providing a view into container image details. This includes security vulnerabilities (CVEs) associated with deployed images.
    • Pipelines with Tekton, which gives users a view into the details of all Tekton pipeline runs and their respective statuses across all services.
    • Authentication and Authorization with Keycloak, which enables platform engineers to load users and groups from Keycloak into Backstage, allowing the use of multiple authentication providers to be applied to Backstage entities.
    • GitOps with Argo CD, which helps track the health and status of the Argo CD status for services within Backstage.

    Red Hat said additional plugins are in the works. Red Hat Developer Hub will be generally available later this year, but Red Hat is offering a ‘test drive’ through the upstream IDP project Janus.

     

  • Red Hat Adds Generative AI and Event-Driven Capabilities to Ansible

    Red Hat Adds Generative AI and Event-Driven Capabilities to Ansible

    Red Hat today unveiled a technical preview of Ansible Lightspeed with IBM Watson Code Assistant, a generative artificial intelligence (AI) service specifically trained on domain-specific data collected via the Ansible automation platform.

    At the same time, Red Hat is also making available an event-driven capability within version 2.4 of the Ansible platform that enables runbooks and other tasks to automatically execute when received from a third-party tool or application.

    Both capabilities were announced at the Red Hat Summit and are part of an ongoing Red Hat effort to democratize IT automation.

    First previewed within the Project Wisdom initiative launched by Red Hat and IBM Research last year, Ansible Lightspeed IBM Watson Code Assistant is based on a large language model (LLM) that was developed using Ansible data. Watson Code Assistant, currently in technical preview, is a service that automatically generates suggestions to improve code as developers create an application. IBM promised to make IBM Watson Code Assistant available later this year.

    Red Hat is now applying that generative AI capability to the YAML code that DevOps teams need to create Ansible playbooks. IT teams can create a simple natural language prompt that will translate their domain expertise into YAML code for creating or editing Ansible Playbooks. Red Hat and IBM, however, will not automatically use the code from an organization to fine-tune their AI model without express consent.

    Rich Henshall, director of product management for Red Hat Ansible, said as IT environments become more complex, it’s clear many are hitting a ceiling in terms of what can be automated using legacy custom scripts. Ansible Lightspeed IBM Watson Code Assistant, along with an event-driven capability, will make it much simpler to automate the management of IT environments at scale using a declarative framework, he added.

    Ansible, for example, can now receive events from third-party tools, determine next steps and consistently initiate an automated response using Ansible playbooks. Ansible can also consume events from third-party monitoring, observability and IT analytics tools from providers such as Cisco ThousandEyes, CyberArk, Dynatrace, F5, IBM Instana, IBM Turbonomic, Palo Alto Networks and Zabbix with more to come, noted Henshall. Red Hat is also making available supplementary content for Red Hat OpenShift, Red Hat Insights, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform and ServiceNow, in addition to enabling IT teams to create their own custom integrations.

    At this juncture, it’s not clear why more IT processes have not been automated using Ansible or other frameworks. Many DevOps teams continue to rely on custom scripts that typically don’t scale and are generally poorly documented. Any time the individual who wrote those scripts leaves an organization, it’s incredibly difficult—if not impossible—for the original scripts to be maintained, noted Henshall.

    One way or another, it’s clear that a more scalable approach to IT automation is required, especially with the rise of more complex cloud-native application environments. It’s no longer economically viable for most organizations to rely on legacy processes that require a small army of IT professionals to maintain.

  • Red Hat Moves to Secure Software Supply Chains

    Red Hat Moves to Secure Software Supply Chains

    Red Hat today announced a portfolio of cloud services designed to better secure software supply chains. The expanded portfolio includes Red Hat Trusted Application Pipeline to secure continuous integration/continuous delivery (CI/CD) workflows and Red Hat Trusted Content offerings to better secure software components.

    Announced at the Red Hat Summit, the Red Hat Trusted Software Supply Chain combines a suite of curated open source tools into a cloud service managed by Red Hat. The Red Hat Trusted Application Pipeline service is based on sigstore, an emerging de facto standard for cryptographically signing code to ensure immutability that Red Hat helped drive, while Red Hat Trusted Content provides access to a set of software components that Red Hat has vetted.

    Red Hat Trusted Software Supply Chain also provides access to existing tools such as Quay, a registry for containers, and the Advanced Cluster Security (ACS) platform for securing Kubernetes clusters.

    Red Hat Trusted Software Supply Chain is scheduled to be made available in preview in the next few weeks, and will also track the provenance of components. In addition, the service will monitor emerging threats to software components and alert users of new and emerging risks. The service will also surface recommendations for remediating vulnerabilities it identifies based on best practices defined by Red Hat.

    Finally, Red Hat Trusted Application Pipeline also promises to make it simpler to automate the deployment of secure code with a few clicks within the context of an integrated continuous integration/continuous delivery (CI/CD) pipeline. DevOps team can also import Git repositories and configure container-native continuous build, test and deployment pipelines, inspect source code and transitive dependencies, autogenerate software bill of materials (SBOMs) within builds and verify and promote container images. An enterprise contract policy engine can consistently enforce compliance with the Supply Chain Levels for Software Artifacts (SLSA) framework.

    Finding ways to implement DevSecOps workflows is a major challenge for any organization. Red Hat is making a case for a managed cloud service based on a collection of security software and services that a DevOps team would otherwise have to implement themselves.

    Tasked with securing increasingly complex software development environments, it’s not clear whether DevOps teams will opt to rely on a cloud service, but given the general lack of security expertise among software development teams, the fastest way to secure a software supply chain for most organizations is to rely on a service.

    Regardless of approach, of course, DevSecOps will soon become a standard element of application development workflows. Once that occurs, it’s not clear whether DevOps will simply become DevSecOps or whether security will be assumed to be an integrated element of any set of DevOps best practices.

    Sarwar Raza, vice president and general manager for cloud services at Red Hat, said regardless of approach, it’s clear that implicit trust is no longer good enough. Organizations of all sizes will need to be able to demonstrate that they have maintained a chain of custody for the software components used to build an application, he noted.

    One way or another, the overall security of downstream applications should eventually improve as the processes used to construct software are increasingly locked down.

  • 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:

    LinkedIn

    Samsung Electronics America
    Mountain View, California
    Lead Site Reliability Engineer (SRE) – DevOps Specialist
    $180,000 to $210,000

    Indeed.com

    Federal Reserve Bank of New York
    New York, New York
    DevOps Engineer
    $178,100 to $207,900

    CareerBuilder.com

    SAIC
    Chantilly, Virginia
    DevOps Advisor
    $130,000 to $200,000

    Dice.com

    Saicon Consultants
    Alpharetta, Georgia
    DevOps Engineer
    $110,000 to $140,000

    Simply Hired.com

    Hewlett-Packard Enterprise
    Fort Collins, Colorado
    DevOps Automation Engineer
    $109,400 – $251,500

  • Despite DevOps, Software Supply Chain Security Challenges Persist

    Despite DevOps, Software Supply Chain Security Challenges Persist

    A survey of 397 IT, cybersecurity and application development professionals conducted by Enterprise Strategy Group (ESG) found that while most respondents work for organizations that have adopted DevOps practices, multiple software supply chain security issues and DevSecOps concerns have yet to be addressed.

    The survey, conducted on behalf of Data Theorem, a provider of a platform for securing application programming interfaces (APIs), the survey also found 82% of respondents have adopted DevOps, with 51% reporting they make extensive use of DevOps workflows (51%). Nearly three-quarters (73%) are deploying code weekly and 31% are deploying code daily.

    However, securing DevOps workflows remains a work in progress, with 41% reporting security teams lack visibility and control of development processes. Other issues include new builds deployed to production with misconfigurations, vulnerabilities and other security issues (40%), developers skipping security processes (39%), software released without going through security checks and/or testing (38%), lack of security process consistency across different development teams (38%), security team can’t keep pace with release cadences (34%) and developers’ reluctance to work with security (29%).

    API security is also becoming especially challenging, with 92% of respondents dealing with at least one API security incident in the last 12 months. Well over half (57%) have dealt with multiple API security issues in the last 12 months.

    On the plus side, the survey also found that more than half (54%) of teams responsible for securing APIs are involved with development as soon as or before the APIs are published. A full 97% said developers either have a good (22%) or high level (71%) of API security knowledge. A total of 89% reported their organization provides formal API security training to their development teams. More than three-quarters (78%) can remediate an API vulnerability within a day, and 39% reported they can do so within hours. A total of 60% reported a dedicated budget has been specifically allocated to API security, with another 30% identifying it as a discrete item within their cybersecurity budget. Two-thirds (66%) said spending on API security would significantly increase in the next 12 to 18 months.

    The use of APIs is only going to increase in the months and years ahead. Well over one-third (36%) of respondents are using APIs across all their applications, with 50% expecting this to be the case in two years. Another 44% reported most of their applications are using APIs., with 32% expecting this to be the case in two years. More than three-quarters (76%) of organizations reported that they have an average of 26 APIs per application and 53% have more than a quarter of their APIs facing the internet.

    More than a third (35%) are updating APIs daily while another 40% do so weekly. A full 61% are using APIs to connect microservices.

    Data Theorem COO Doug Dooley said those APIs are now being targeted by cybercriminals that routinely test the weaknesses of every API after each deployment and update. The most common types of API attacks reported by survey respondents included exposure of data (32%), account takeover (31%), denial of service (DoS) attack (31%), attack on misconfigured API (30%), fake account creation (28%), API injection attack (28%), ransomware (27%), data breach (26%) and content scraping (26%).

    Despite these issues, however, nearly three-quarters (74%) of survey respondents said they believe their organizations have a robust API security program in place, with 22% reporting they have some process in place. Organizations are using a mix of approaches including API security tools (59%), web application firewalls (WAFs) (57%) and API gateways (50%), distributed DoS mitigation (48%) and bot management solutions (42%).

    Challenges included managing multiple tools (29%), visibility into and control of APIs (28%), inventorying APIs (27%), inconsistent adoption of APIs (27%), API security testing (27%), data governance/exposure (26%), discovering and remediating misconfigured APIs (26%), keeping pace with threats (26%) and accurately tracking API usage (25%).

    Significant API security concerns included API authentication issues (41%), account takeovers (39%), identifying and tracking third-party APIs (38%), using tools not purpose-built for security (38%), shadow/undiscovered APIs (38%), abusive behavior bypassing app security tools (38%), denial of service attacks (38%), data governance and/or data exposure issues as a result of insecure APIs (37%), abusive behavior bypassing bot management (37%) and outdated/unneeded APIs, also known as zombie APIs. (33%).

    The type of vulnerabilities that are of the greatest concern are sensitive data exposure (34%), access control vulnerabilities (31%), API business logic flaws (31%) and DDoS attacks (30%).

    More than 79% of respondents, however, are relying on manual testing and review processes to ensure APIs don’t expose sensitive data (79%), with 63% having automated alerting capability for when sensitive data is exposed.

    Overall, it’s apparent that more resources as being applied to securing software supply chains in general and APIs specifically. However, given the amount of software being built and deployed, it may be a while before organizations see the return on those investments.

  • OpenAI to go Open Source — Elon Musk was a ‘Huge Idiot’ ¦ Mojo Risin’

    OpenAI to go Open Source — Elon Musk was a ‘Huge Idiot’ ¦ Mojo Risin’

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

    This week: Sources say it’s an open source future for OpenAI, and improving AI perf with Mojo. (more…)

  • ServiceNow Adds Observability Platform to SaaS Portfolio

    ServiceNow Adds Observability Platform to SaaS Portfolio

    At its Knowledge 2023 conference, ServiceNow launched an observability service that runs on its software-as-a-service (SaaS) platform.

    Based on the observability platform the company gained with its acquisition of Lightstep, the ServiceNow Cloud Observability platform makes it possible to collect and analyze data collected from software running on infrastructure and also from workflows created using ServiceNow applications.

    Pablo Stern, senior vice president and general manager for technology workflows products at ServiceNow, said the goal is to make it simpler to investigate IT issues involving applications and workflows that have multiple dependencies. That makes it possible to quickly diagnose an issue, determine the root cause and then dispatch the right site reliability engineering (SRE) team to troubleshoot it, he added.

    ServiceNow Cloud Observability uses both open source OpenTelemety agent software to collect logs, metrics and traces in addition to a log management tool that ServiceNow gained last year with its acquisition of Era Software.

    In addition, ServiceNow is employing a Service Graph Connector that makes it simpler to pull data from, for example, OpenTelemetry and Kubernetes clusters into the ServiceNow IT Operations Management (ITOM) without requiring any additional tooling. ServiceNow is also making available its Unified Query Language to make it possible for IT teams to query the data residing in ServiceNow Cloud Observability.

    As application environments become more complex, the need for entire IT teams to have access to an observability platform is growing. While observability has always been a core tenet of DevOps best practices, Stern said it’s clear that these capabilities also need to be made available to IT administrators. In fact, as observability becomes more democratized, it will drive the rise of services operations that will enable IT teams to be better aligned around specific processes, he added.

    Most IT organizations today employ a wide range of tools that enable them to monitor a set of pre-defined metrics. Observability platforms, in contrast, enable IT teams to collect data from systems in a way that can be investigated using a query tool.

    It’s still early days as far as the adoption of observability platforms is concerned, but as the collection of logs, metrics and traces becomes more unified, an opportunity to better converge IT practices based on DevOps principles and the ITIL framework that many IT administrators rely on to manage IT will emerge.

    The challenge, regardless of what methodology is used, is that IT teams need to be able to frame the queries they use to discover the root cause of an IT issue before any potential disruption occurs. In the long term, however, it’s expected that machine learning algorithms will leverage the data collected by observability platforms to automatically identify those issues.

    Regardless of how an IT issue arises, the metric that matters most is how quickly it can be resolved—no matter who in the IT team is tasked with resolving it. The issue is finding a way to provide IT teams with better tools to achieve that goal.

  • Boomi Brings Generative AI to Integration Platform

    Boomi Brings Generative AI to Integration Platform

    Boomi announced today it is applying generative artificial intelligence (AI) to enable organizations to automate integrations via a natural language interface.

    Boomi CEO Steve Lucas said Boomi AI makes it much simpler for professional and citizen developers alike to create integrations at scale using generative AI platforms to invoke the low-code engine that Boomi already provides to create integrations between thousands of applications.

    Those generative AI capabilities will complement a wide range of AI capabilities based on machine learning algorithms that are already embedded in the Boomi platform, he added.

    Generative AI’s Impact on Integration and Automation – Steve Lucas, Boomi from Techstrong.TV on Vimeo.

    Boomi has been widely used to drive business process integration via an integration platform-as-a-service (iPaaS) environment it delivers as a managed service. The low-code platform the company created significantly reduces the need to rely on professional developers to write procedural code using Java, for example, to create those integrations.

    Generative AI capabilities make it possible for almost anyone of any skill level to create those integrations across multiple types of application programming interfaces (APIs), noted Lucas.

    That capability also makes it possible for Boomi to notify end users when, for example, a data attribute in an application has changed and ask them if they would like to apply that change across their entire application environment.

    In addition, Boomi AI can also interpret the intent of a prompt to better orchestrate processes in addition to providing an audit history in natural language that explains how decisions were made and what outcomes occurred as a result. As part of that effort, Boomi algorithms are also trained to avoid biases and unfairness with established ethical guidelines.

    Boomi’s AI engine is trained using anonymized data collected from approximately 20,000 customers that have created more than 200 million integrations using the Boomi platform. Those integrations have been exposed to third-party large language models (LLMs) that the company is using to train Boomi AI, said Lucas. Boomi is not storing any customer data to train its AI models.
    DevOps teams, of course, spend hours creating integrations that should be much simpler to create, test and validate with the rise of generative AI. In fact, the number of integrations being created to drive a wide range of digital business transformation initiatives should substantially increase.

    In the meantime, DevOps teams would be well-advised to start evaluating which tasks they manually perform today that could instead be automated by a generative AI platform. Many of the routine integrations that DevOps teams create today using low-level APIs will be increasingly automated by end users. The ultimate goal should be to identify more complex tasks that previously DevOps teams would not have attempted simply because there wasn’t enough time.

    It’s still too early to determine what impact generative AI will have on the way IT is managed, but DevOps teams that are typically committed to automating as many processes as possible should be at the forefront of adoption. The challenge is going to be simply keeping track of the rate of change that might soon be enabled by these platforms.

  • SapientAI Emerges From Stealth to Apply AI to App Testing

    SapientAI Emerges From Stealth to Apply AI to App Testing

    SapientAI emerged from stealth today to automate the writing of test code using a combination of generative artificial intelligence (AI), machine learning algorithms and data science.

    Fresh from raising $5 million in seed funding, SapientAI CEO Rishi Singh said the goal is to leverage AI to push application testing further left using an AI Test Coder the company built.

    The concept of AI Test Coder is similar to the co-pilot capabilities that GitHub and Google are providing to help developers write better code faster, but also goes beyond generative AI to add deeper analysis and contextual understanding to the tests it automatically generates, noted Singh.

    In the longer term, the ability to use AI to create tests will result in the deployment of more secure, higher-quality applications because there will be no need to short-shrift application testing processes in the interest of meeting deadlines, added Singh.

    Prior to the rise of AI, building application tests has historically been prone to error. AI Test Coder is designed to be a plug-in for an integrated development environment that will leverage the large language models (LLMs) created by OpenAI and cloud service providers to employ natural language to create tests based on an analysis of the application environment created using a combination of machine learning algorithms and data science techniques, said Singh.

    As part of the effort, AI Test Coder instantly inspects all software code to understand the software testing environment, including any incorrect assumptions that stem from errors and bugs, before generating any test code. It then leverages code intelligence to understand existing code and automatically identify and detect patterns to anticipate any new software issue that may arise. AI Test Coder also locates where code changes will have an effect before any test code is created. This eliminates time that would otherwise be wasted on manually debugging code.

    Once those steps are completed, Test Coder then generates more accurate tests without relying on the current trial-and-error approach to testing that can slow application development, said Singh.

    In effect, AI Test Coder provides each developer with their own assistant dedicated to managing testing tasks that ultimately improve quality assurance, including removing many of the vulnerabilities that create application security issues that have become the bane of software supply chains.

    It’s too early to determine precisely what impact AI will have on application development and deployment, but many of the manual tasks that create a sense of drudgery are about to be eliminated. That means the overall pace at which applications can be built and deployed is about to be greatly accelerated. DevOps teams are likely to find themselves pushing more code simultaneously through pipelines than ever.

    Naturally, AI will soon be applied to not just writing code and tests faster, but also DevOps workflows. The issue now is determining which of the tasks that today require manual intervention will be eliminated in a way that enables DevOps teams to focus more of their time and effort on more complex challenges.

  • Nobl9 Adds Tools to Make SLO Queries More Efficient

    Nobl9 Adds Tools to Make SLO Queries More Efficient

    At its SLOconf event today, Nobl9 added a Query Tester tool that validates whether queries made against the data its service level objectives (SLO) management platform collects will deliver meaningful results. Nobl9’s SLO management platform collects data in real-time from data sources such as New Relic, Datadog and Dynatrace to help customers meet SLOs.

    In addition, a Query Checker tool will now automatically put any new SLO into a testing state to automatically validate that the query against that SLO will run correctly.

    The company has also improved the precision of its calculations to provide DevOps teams with more accurate, reliable and actionable insights into the performance of services and added a Metrics Health Notifier toolset that provides insights into anomalous data being collected from various sources as well as what data might be missing. Nobl9 has also added a Custom Query Delay tool that gives DevOps teams more control over when data is pulled along with access to data source logs.

    Kit Merker, chief growth officer for Nobl9, said the overall goal is to improve the overall resiliency of its platform for managing SLOs.

    Finally, Nobl9 also announced that its platform is now available for the Google Cloud Platform (GCP) in addition to existing support for Amazon Web Services (AWS). As part of that GCP support, Nobl9 made available an educational site that provides example of how DevOps team can take advantage of the large language models (LLMs) that Google is making to improve SLOs or generate an SLO that is automatically converted into a set of YAML files.

    A survey of 314 IT professionals conducted by Dimensional Insight on behalf of Nobl9 found 69% have implemented SLOs, with more than three-quarters of those respondents crediting SLOs for preventing business disruptions. A total of 80% also noted their organization has an increased focus on system reliability, with just over a quarter (27%) saying SLOs saved their organizations a half million dollars or more. A full 95% said SLOs are also driving better business decisions.

    However, 97% also reported it is difficult to manage SLOs because of a lack of reporting, lack of application and environment support and interoperability issues.

    Nobl9 is trying to spur greater adoption of SLOs by making available an open source SLO specification that defines a common interface for constructing SLOs across a Git-based workflow. Nobl9’s platform based on that specification has now been integrated with 24 data sources, said Merker.

    SLOs, of course, are not a new idea. They have been employed 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. SLOs-as-code are intended to make it simpler to gather the metrics that confirm whether service levels are being achieved. Those SLOs are then tracked and integrated across any set of application services a DevOps team cares to track.