Continuous delivery is a software engineering discipline that allows organizations to consistently develop and release quality software at a rapid pace. It is achieved by automating the software development lifecycle, enabling tasks such as development, testing and release to occur concurrently and continuously.
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: Meta launches its much-leaked Threads microblogging app. A spinoff from Instagram, it’s already reached 30 million signups at the time of writing—even before it’s available in the EU. But how is Meta’s DevOps team making it scale? (more…)
Someone once said, “Life is better at the lake.” That may be true in the physical world, but when it comes to using data lakes for observability, that’s not typically the case.
Just about every organization in every industry has a digital presence, whether it’s a website, an app or another digital service. This makes observability a universal need; it helps teams monitor software performance, optimize performance, detect potential security issues and quickly monitor for bugs if and when they occur. These responsibilities typically fall on engineering teams and require as close to real-time response as possible.
Some organizations also have business intelligence (BI) needs, aimed at generating insights from multiple data sources for business decision-making. These responsibilities typically fall on business decision-makers; they are not real-time decisions, but rather made by looking back over long time periods.
Companies that go all-in on a data lake strategy may ignore the needs of the engineering teams working on the real-time mission-critical aspects of the business and force all of the company’s data, queries, dashboards, etc. into a data lake solution. That doesn’t actually meet the needs of the engineering teams. The result? Engineering teams use unsanctioned solutions, fracturing the company’s data strategy from the ground up.
By using a standards-based observability solution built on open source InfluxDB 3.0, companies can give their engineers the observability tools they need while preserving their data lake strategy.
Here are three main challenges that teams come across when using data lakes for time-series, and how to solve the problem with InfluxDB 3.0.
Problem One: Inefficient and Slow Ingest
Data sources generate time-series data at an incredibly high rate, easily producing hundreds or thousands of data points every second; some sources even do so as quickly as every nanosecond. Data lakes can store massive volumes of data, but they aren’t optimized to quickly ingest and make it readable on a millisecond time scale.
Data lake ingest jobs can be measured in minutes or even hours, which is far too late for any meaningful interventions that can prevent catastrophes such as downtime. Customers will notice the outage before the data is even ingested into the data lake, much less acted upon.
Problem Two: Time-Series Life Cycle
Full-fidelity data is necessary for real-time analytics and observability, but the value of the full-fidelity data fades over time. Storing the full-fidelity data in a data lake may be convenient, but it is costly and not necessary for typical BI purposes.
InfluxDB is designed for the time-series data life cycle. This life cycle entails:
1. The leading edge of data. That is, data written in the last two hours or so. This is the data that is most likely to be queried, so the full fidelity data is kept in memory ready to query instantly.
2. Compacted data. That is, data older than the leading edge that is still likely to be queried and requires full fidelity is compacted off to disk in object store, which still permits fast querying but does not incur the costs of storing data in memory.
3. Retention policies. That is, data that has lost its value in its full fidelity form. For example, for observability data, data older than a few weeks can be aggregated into a much smaller data footprint. Then, this aggregated data is ingested into the company’s data lake while the full fidelity data is automatically dropped from InfluxDB 3.0.
Without such a system, it is necessary to store the full fidelity data in the data lake and incur those massive costs without a reasonable way to manage the life cycle of the data. With such a system, the correct amount of observability data can be ingested into your data lake to support your BI needs.
Problem Three: Slower Query Performance
Engineers can experience a high degree of frustration when they are troubleshooting an issue in real-time, but their dashboards won’t load or their queries won’t return in a reasonable amount of time. Data lakes simply are not designed to produce fast results for observability, and so query performance is a primary complaint of engineers trying to use a data lake for observability. Waiting minutes for a query to return when you are running a BI report may be annoying, but it could spell extended downtime for an engineering team.
Fortunately, InfluxDB is built on the Apache Arrow standards, which means that it can be directly queried by your engineering team. Via protocols such as Delta Sharing and Iceberg, anyone can query into InfluxDB 3.0 from their data lake and join the real-time observability data stored in InfluxDB 3.0 with the data lake data. This is akin to having your cake and eating it too.
If you are trying to use a data lake strategy to fulfill your engineering team’s observability needs, you are going to frustrate those teams because the data lake is not designed for data ingestion, the data life cycle, or the query needs of those teams. This will cause those teams to create rogue solutions that are inconsistent with your company’s data lake strategy.
By providing those teams with open source InfluxDB 3.0, engineering teams will have a first-class observability platform that is designed to interoperate sensibly with your company’s data lake. This enables the critical mission of your observability platform while maintaining your overall data lake strategy for your BI needs.
Platform engineering is emerging as a way of evolving DevOps, with 94% of respondents to the recent State of DevOps report agreeing that the practice helps their organization to reap the benefits of DevOps. The business arguments for platform engineering are clear: In that same report, respondents stated that platform engineering improves efficiency (59%), speeds up delivery time (58%) and improves workflow and process standards (57%).
But in addition to the huge benefits organizations are now realizing in terms of business value, platform engineering is also pretty crucial for a business’s long-term viability. And by that, I mean that platform engineering can be inherently sustainable.
First, it’s important to note that ‘sustainability’ here doesn’t just mean ‘green.’ Rather, sustainability means that an organization is able to meet the needs of the present without compromising the ability of future generations to meet their needs, too. These are needs that come in three different varieties: People, planet and profits.
This means that to be truly sustainable, platform engineering needs to deliver lasting and stable returns on things like return on investment year-over-year, improved awareness of and reduction in an organization’s carbon footprint and better governance and optimization through monitoring and analytics.
Any approach which supports business growth while simultaneously empowering employees and being kind to the planet is the dream for pretty much every organization. But how exactly can departments and wider organizations unlock all of these benefits? The answer is: There’s no magic bullet.
Sustainable platform engineering is about treating your platform as an iterative cycle: It isn’t the actual creation of the tool that matters, as much as it is the maintenance, upkeep and usability of the platform as your business moves forward. This, naturally, will take time, effort and money—all of which, it’s safe to say in the current climate, are in short supply. But it is crucial that businesses act strategically, looking for areas for improvement and paths that will all add up to a more sustainable business in the long term.
These paths can be divided into three main areas of focus, and the first is GitOps or infrastructure-as-code (IaC).
A tweak that every organization should start making is using Git as a single source of truth for deployments, enabling developers to work faster by empowering them to use their own code without hands-on involvement from ops teams. This could be combined with optimized infrastructure, moving toward IaC to enable faster and more efficient deployments.
These principles form the foundation of sustainable platform engineering as they protect profits, allowing the business to keep moving forward and investing in the other areas crucial for a sustainable operation. Combining IaC with GitOps ensures speed and simplicity while maintaining a high-quality end product.
Creating an optimized infrastructure is important, but just as important is empowering employees to feel good about the work that they are doing. That’s why implementing a self-service tool alongside an optimized UX is the second area of focus. This removes the pressure on those in the tech department to be infrastructure experts while erasing the need for endless tickets to be raised for issues.
By freeing up DevOps engineers and platform experts to spend more time doing what organizations need them to do, businesses can keep innovating to maintain an edge over their competitors. Self-service capabilities combined with an accessible UX are a useful combination for a much-improved developer experience (DevX).
Finally, it’s important to keep in mind that self-service isn’t just going to benefit end-users; it also has major upsides for improved governance, as well as keeping control of cloud spending and carbon footprint impact. Optimizing and iterating your platform engineering tool means that a business can keep up with the fast pace of the global tech market while also continuing to implement the latest tweaks and upgrades. It all adds up and builds on the organization’s platform engineering solution.
If an organization follows the above three paths, it is well on its way to a more sustainable business powered by platform engineering. By focusing on targeted, achievable projects to build a solid foundation, organizations can achieve big benefits for people, the planet and profit.
From an economic perspective, sustainable platform engineering ensures legacy infrastructure is modernized, silos are broken down and production is accelerated, all ensuring the business doesn’t risk lagging behind competitors. A stable and secure return on investment is key to sustainability in the medium to long term, and sustainable platform engineering ensures a business is able to weather future challenges and keep a healthy profit margin.
As we all know, underpinning any great business is the people who work there. Techs get stressed by having to interact with infrastructure they know nothing about, DevOps and specialists get stressed when those techs desperately raise tickets in an attempt to get help, and platform engineers get stressed when the weight of providing a solution to these problems rests squarely on their shoulders. Implementing a UX-strong self-service portal relieves the pressure, allowing employees to focus on delivering real value to the business through innovation. A better DevX ultimately leads to better business outcomes.
And, of course, it’s ultimately no good having a successful business if the planet we all share continues to suffer from the effects of the climate emergency. Just like a business that hemorrhages money is unsustainable, so is a planet that wastes its finite resources. By getting control of cloud usage and controlling cloud climate impact, businesses will not only be empowering their people and improving their bottom line, but they will also be making a tangible difference to our shared environment, supporting our planet as it recovers from years of climate neglect.
Platform engineering offers lots of tangible benefits for businesses, but sustainable platform engineering can be even better, not just for a business, but employees and our shared environment. It might just be time to upgrade DevOps and start thinking about building a sustainable organization that supports profit, people and the planet.
We are all familiar with DevOps as a newer approach to software development. The concept is designed to enable developers and IT operations teams to work together. Platform engineering, on the other hand, is a more mature, older approach that has become new again.
Platform engineering, at its core, is the concept of productizing your internal development platforms to make it easier for developers to work. It is an emerging technology approach that can help accelerate the delivery of applications and the pace at which they produce business value. More recently, companies have grown their complex software architectures which have created silos for the teams that manage these various architectures. There may be multiple teams with various specialized skill sets and a single DevOps engineer who takes care of the continuous integration (CI) jobs to deploy and release to production.
Now let’s imagine that you have 10 squads that focus on building and maintaining different applications. Now all of these teams are building and operating 10 different platforms to run their applications, which creates inefficiency across the teams and reinforces silos. In this scenario, teams may spend half of their time building and maintaining the platforms instead of delivering business value. This creates a large cognitive load on developers.
This model becomes very hard to scale. Even though you have these squads managing their own infrastructure and platforms, you may find gaps because there is no centralized standard. So how do platform engineering concepts help with these complex landscapes and boost efficiency?
Platform engineers take all of the tools used to run an application across your various teams and standardize their usage. This enables self-service capabilities for your engineering organizations. Platform engineering can help centralize how you are deploying and running your applications across a complex software landscape. This includes not only the standardization of tools being used but also documentation and processes.
Establishing platform teams requires a consulting mindset and thinking about what resources, services and components can be centralized to better serve the rest of the organization. Some may refer to this as the center of excellence (COE) model, or simply IT for IT. This boosts efficiency and cuts down the time to delivery by providing a central set of tools and platforms for your engineering organization. Many may see platform engineering as a cost center, but the ROI is higher-quality releases, a standardized way of developing and delivering software and shorter delivery times.
In my consulting days, I worked for firms that focused on delivery for the Salesforce platform. We often had complex projects that we needed to deliver for large enterprise customers who had multiple applications integrating into their CRM. Each project team often had a different way of delivering and developing based on the customer’s needs. This created specialized skill sets, but we had no standard way of developing and delivering. Investing in a platform engineering team to support the delivery organization can add immense benefits for large consulting firms, enabling them to standardize the tools and platforms being used to run and deliver applications at scale.
Having a centralized place for your developers to go for process, documentation and tools to help them build and run applications can increase the speed at which they deliver. The efficiencies gained in having a smoother self-service flow for your developers and cutting down the delivery time are worth the investment. It brings significant long-term value that the customer will experience long after the engagement is completed, which can greatly impact future considerations for your firm.
IT services firms should find ways to align and centralize their technologies with platform engineering concepts. You may not be able to exactly build out an internal developer platform, but adopting some of the core concepts will help you be more efficient as an organization. For example, if you are an IT services firm specializing in delivering applications on the Salesforce platform, look for extensible DevOps platforms that can support your development organizations.
Also, look for ways to enable a self-service workflow to make it easier for your developers to work and scale. This may include investing in a platform engineering function to support the rest of the delivery organization. By establishing platform engineering concepts, you will be able to cut down the delivery time and create consistency in how you deliver and deploy applications for your customers.
Mendix, a subsidiary of Siemens, today updated its namesake low-code application development platform to add support for DevOps workflows and make it simpler for developers to compose applications and embed artificial intelligence (AI) capabilities.
Sheryl Koenigsberg, vice president of product marketing for Mendix, said Mendix 10 makes it possible to use Git repositories for version control along with Mendix Pipelines, currently in beta. Mendix Pipelines is a continuous integration/continuous delivery (CI/CD) environment that will be embedded in the low-code platform. In addition, Mendix is adding support for Webhooks that can be used to integrate Mendix with existing CI/CD platforms.
Mendix has also added a Solutions Kit, now generally available, to make it possible to customize applications in a way that ensures those changes will be preserved as application environments are upgraded.
There is also now a Portfolio Manager tool to track projects and a connector for integration with the Jira project management software from Atlassian.
Later this year, Mendix will also make generally available Adaptation Insights to provide insights into application extensions.
In the future, developers will be able to leverage an extensibility framework to tailor the Mendix Studio Pro integrated development environment (IDE) by adding their own plugins, wizards, editors and third-party integrations. Later this year, Mendix 10 will also add, in beta, a version of its Studio Pro IDE for MacOS.
The Mendix approach to composability is also being extended to incorporate AI. The company is adding Mendix Assist, a bot that generates validation logic for data entry. This module is the second AI capability that Mendix has developed after previously previewing Mendix Chat, a tool based on a large language model created using Mendix data that will be integrated into the Mendix integrated development environment (IDE) later this year. Mendix Assist will be incorporated into Mendix Chat.
Mendix is also developing additional LLMs for creating applications and will as part of its commitment to composability plans to make it possible to integrate third-party AI services and LLMs into the Mendix Runtime to create “smart apps.” AI models will be converted to the Open Neural Network Exchange (ONNX) format, an open standard for machine learning models, to provide that capability.
In addition, Mendix is adding support for event-driven architectures via Business Events and an Event Broker, to build more complex applications.
Other capabilities being added to Mendix 10 include updated REST and database connectors, support for visual data filters, rich properties and design controls, a Start from Spreadsheet tool, end user survey tools, a PDF document generator and a revamped set of governance and risk assessment tools.
Finally, Mendix is adding a Terraform module to make it simpler to deploy its low-code platform on the Amazon Elastic Kubernetes Service (Amazon EKS) in addition to providing a reference implementation for building private clouds to develop applications using the Mendix Cloud service.
As AI-infused low-code applications are developed faster, it’s not clear to what degree organizations are prepared to embrace the inevitable disruption that will ensue. However, the one thing that is clear is that the DevOps workflows used to deploy these applications at scale will soon be put to the ultimate test.
Noname Security today made generally available an update to a tool for testing application programming interface (API) security that promises to make it easier for DevOps teams to ensure APIs are secure.
Filip Verloy, a field CTO for Noname Security, said Active Testing V2 is purposely designed to make it simpler to integrate API testing within a larger DevSecOps workflow.
Version 2 of Active Testing, for example, provides additional integrations with continuous integration/continuous deployment (CI/CD) platforms along with automated API discovery tools.
Other capabilities included in the latest release are tools to automate API response inspection, troubleshooting, classification and vulnerability discovery.
Finally, Noname Security has also added support for GraphQL-based APIs alongside REST APIs.
In total, there are now more than 160 API security tests that can be employed on an API testing tool that Noname Security developed to enable it to build Active Testing, said Verloy. In effect, Active Testing is an extension of that core API testing tool, he noted.
The biggest issue DevSecOps teams encounter when testing API security is that most legacy tools are not designed to provide any visibility into the business logic used to construct APIs. As such, there is a need for API security tools that can be easily integrated within DevSecOps workflows to discover issues before APIs are exposed in a production environment, he noted.
While a lot of progress has been made toward securing software supply chains, APIs have generally not received enough attention. Developers typically create APIs, but they don’t typically assume responsibility for managing and securing them. As the number of APIs within organizations continues to proliferate, there is a growing need to centralize API management and security.
Historically, developers have assumed that cybersecurity teams were making sure APIs are secure. Cybersecurity teams, conversely, assumed that the developers that created APIs would be responsible for securing them. DevSecOps best practices make it possible to now extend responsibility for security further left toward developers in a way that enables cybersecurity teams to define the policies and controls that should be used, noted Verloy.
It may a while before every organization masters the all the nuances of DevSecOps, but as pending legislation that requires organizations to better secure software supply chains becomes law it’s only a matter of time before organizations revisit API security—if they have not done so already.
In the meantime, cybercriminals have already discovered that insecure APIs provide a convenient mechanism for exfiltrating data that they can then sell or hold for ransom. The number of rogue and zombie APIs that are unknown or that have been forgotten has become a major cybersecurity issue. Ideally, DevSecOps teams would not only test the security of APIs before they are deployed but would also address a significant amount of technical API security debt that has already accumulated.
One way or another, API security is going to become a significant issue in the weeks and months ahead. The issue now is how to address it before it gets any worse.
System Initiative (SI) today made available in private beta a platform that creates simulations of IT environments. The platform leverages a built-in framework to generate the code needed to update an application environment.
Fresh from raising $18 million in funding, SI CEO Adam Jacob said the ultimate goal is to provide a level of abstraction for DevOps environment that eliminates much of the complexity that creates bottlenecks in DevOps workflows.
For all intents and purposes, DevOps is fundamentally broken; there are too many manual processes that need to be performed, he added. The SI platform represents an ambitious effort to reimagine how DevOps processes are constructed and maintained, said Jacob.
At the core of that effort are models of IT environments created by the SI platform that are accessed via visual interface. The automation framework built into the platform then uses the relationships between models to infer configuration dynamically and then automatically generate TypeScript code that a DevOps team can apply to automate a task.
Feedback loops are also provided in real time to provide insight into the viability of a configuration and eliminate the need for no state files to track and no ‘plan’ versus ‘apply’ stages.
As more organizations realize how dependent they are on software to drive digital processes, many of them are looking to improve the productivity of developers and the DevOps teams that support them. After a wave of initial progress, many DevOps teams find themselves supporting DevOps workflows using custom code that varies widely in quality. SI is making a case for a digital twin platform that generates a set of code that can be applied more consistently, rather than relying on a DevOps engineer to write code.
Ultimately, the goal is to launch a new DevOps era to eliminate most of the silos that slow down application development and deployment, noted Jacob. In fact, one of the reasons few organizations today fully automated DevOps across the application development and deployment process is that it’s simply too difficult to achieve using legacy tools and DevOps platforms, he added.
A recent survey conducted by the Continuous Delivery Foundation (CDF), for example, found only 22% of respondents are using CI and CD approaches to automate all building, testing and deployment of code in a production environment. Instead, many organizations are using a combination of DevOps and waterfall processes to manage application development and deployment.
It’s not clear how many organizations are willing to fundamentally change the way they have implemented DevOps. But at a time when more organizations are moving to centralize the management of DevOps, many of them are evaluating their options, at the very least. The issue, of course, is that multiple DevOps teams within the same organization have committed to disparate platforms. Enticing them to centralize on a common platform will require not just a compelling technical advantage but also a significant amount of political capital.
Cycloid today added a Bootstrap Stacks capability that provides DevOps teams with the ability to build Git templates or use ones created by the company to define cloud computing environments.
The capability is accessed via a portal added to the Cycloid platform for automating software delivery. It also makes it simpler for DevOps teams to reuse templates that are compatible with Terraform or Kubernetes environments as needed.
In the months ahead, Cycloid plans to release Headers, a metadata tool that automatically populates project parameters in display pages and a project management tool to provide a unified view for each development environment, including continuous integration/continuous delivery (CI/CD) pipelines, documentation, automation, logs, cloud costs and carbon emissions.
Cycloid founder Benjamin Brial said the templates provide a layer of abstraction that makes it simpler to implement GitOps workflows. As a best practice, GitOps enables DevOps teams to unify the management of code to provision IT infrastructure and software delivery. However, providing access to a Git repository alone is insufficient, said Brial. DevOps teams need access to a set of templates that automate the process and eliminate what is otherwise a complex set of tasks, he noted.
Easily discovered templates also eliminate organizations’ tendency to create redundant automations simply because a development team was unable to find an existing framework that another team already created, added Brial.
That approach also enables DevOps teams to more easily embrace platform engineering principles to centralize the management of DevOps using a Bootstrap Service catalog embedded within the Cycloid platform, said Brial. That capability makes it possible to enforce GitOps policies in a way the developers will have to respect because the automation frameworks are readily available, he added.
It’s not clear whether organizations are embracing platform engineering. Historically, adoption of DevOps workflows in most organizations has tended to flow from the bottom up. As a result, it’s fairly common for multiple application development teams within an organization to have adopted multiple DevOps platforms to manage workflows. Advocates for platform engineering are making a case for eliminating many of those redundant platforms in favor of a corporate standard.
At the core of any approach is the assumption that centralization will reduce costs by eliminating redundancy. Issues can arise, however, when centralization results in loss of flexibility. Many of the teams that adopt DevOps want to be able to add and replace tools as they see fit. If there is a corporate standard that narrows their tool options, it’s just a matter of time before those teams start to look for ways around the corporate standard.
Each organization will need to strike a balance between flexibility and the need to bring some order to DevOps workflows. In the meantime, however, the pace at which multiple software development initiatives are being launched is likely to force a platform engineering conversation that, in many cases, many organizations have postponed longer than anyone cares to admit.
Harness today added a raft of generative artificial intelligence (AI) capabilities to its continuous integration/continuous delivery (CI/CD) platform as part of a larger effort to reduce the level of manual toil that today conspires to create DevOps bottlenecks.
The added generative AI capabilities include an AI Development Assistant (AIDA), which analyzes log files and correlates error messages with known issues. It also includes the ability to use natural language to define policies for governing cloud assets and costs and an ability to automatically identify security vulnerabilities and generate code fixes.
Other generative AI capabilities that will be added to AIDA over the next three to six months include automated code reviews along with tools for creating pipelines and launching chaos engineering experiments.
Harness CEO Jyoti Bansal said these are the first of a series of generative AI capabilities that will be applied across the entire software development life cycle (SDLC) using large language models (LLMs) that the company has trained using publicly available sources. AIDA, at its core, provides a tool for creating custom prompts for launching queries against LLMs and a vector database that Harness has incorporated into its platform.
That approach means no customer data was used to create the model and none of the data used will find its way into a generative AI platform that is available to anyone, said Bansal. As a result, organizations don’t need to be concerned they light run afoul of licensing issues because proprietary code was inadvertently included in an LLM, he added.
Most of the existing generative AI capabilities that have been brought to market thus far are designed primarily to improve developer productivity, noted Bansal. Harness is now bringing the benefit of generative AI to DevOps teams that today rely on too many manual processes to manage the automations they build on behalf of application development teams, he added.
Harness is making AIDA available at no extra charge at a time when many DevOps teams will soon be evaluating the AI capabilities provided to them. Most DevOps teams are not going to want to manage workflows without being able to rely on AI to reduce toil and eliminate bottlenecks. Overall, Harness is forecasting generative AI will drive a 30% to 50% increase in DevOps productivity. In addition, Harness is forecasting there will be a 50% to 75% reduction in the time spent remediating vulnerabilities.
Bansal said that as that process occurs, Harness is betting more organizations will move to replace legacy CI/CD platform with more modern alternatives.
Previously, Harness has invested in machine learning algorithms to automate a range of DevOps tasks. Generative AI, in combination with machine learning, will transform DevOps workflows in ways that will ultimately improve both the rate at which applications can be built and their overall quality.
It’s not clear whether organizations might be willing to replace CI/CD platforms that they have been customizing for decades, but at the very least, that option is now more widely considered than ever.
Imagine this scenario: After investing countless hours in developing your software, it is finally ready for deployment. Now, you must select the appropriate software deployment tools to guarantee a seamless release.
This article will guide you through the concept of software deployment and discuss various tools that cater to different needs—from dedicated deployment tools to comprehensive CI/CD pipeline solutions and GitOps approaches. By understanding these categories and their benefits, you’ll be well-prepared to make an informed decision on which tool is the best fit for your organization’s needs.
Software deployment plays a crucial role in the software development life cycle. It involves packaging, distributing and installing an application on target systems or environments, aiming to make your software accessible and usable for end-users seamlessly.
In today’s fast-paced world, companies must be able to deliver applications rapidly and reliably. A well-planned software deployment strategy ensures that new features reach users promptly while minimizing downtime and errors during updates.
When selecting the ideal software deployment tool, consider the following factors:
Bamboo by Atlassian is a robust tool that integrates seamlessly with other Atlassian products, such as Jira and Bitbucket.
It offers first-class support for multiple languages and platforms while providing built-in deployment projects for smooth releases.
GitLab CI/CD, an integral part of GitLab, simplifies pipeline management using YAML configuration files stored within your project repository.
This approach enables easy collaboration among team members while ensuring consistent build environments across all stages of development.
Codefresh is a cloud-native solution designed specifically for Kubernetes deployments, It provides a modern interface to manage pipelines visually, along with advanced real-time debugging capabilities.
Jenkins was originally conceived as a continuous integration (CI) tool, but many organizations use it mainly for deployment. It is an open source automation server that provides support for building and deploying software projects, as well as to schedule and run tasks at regular intervals. Learn more about Jenkins in this in-depth blog post.
Harness is an intelligent software delivery platform that simplifies deployments through automation and machine learning. Their continuous delivery-as-a-service (CDaaS) approach helps you deploy faster while minimizing risks and errors.
If you’re using Amazon Web Services (AWS), AWS CodeDeploy might be a perfect fit for your needs. This completely managed service automates code deployments to any instance or serverless Lambda function in AWS, guaranteeing consistent results every time.
An open source project backed by Netflix and Google, Spinnaker offers powerful multi-cloud continuous delivery capabilities for deploying applications at scale across various cloud providers like AWS, GCP, Azure, Kubernetes clusters, etc.
Argo CD is a declarative, Kubernetes-native continuous delivery tool that uses Git repositories as the source of truth for application definitions. It simplifies rollbacks, provides audit trails and enforces consistency across environments.
Flux CD, another Kubernetes-focused option, automates deployments by monitoring your code repository for changes and applying them to your cluster accordingly. Bonus: It supports multi-tenancy and integrates with Helm charts.
Lastly, Weave GitOps Core offers an open source foundation for managing Kubernetes applications using the power of GitOps workflows. This versatile tool can be extended to fit various use cases while maintaining simplicity in its core functionality.
There are many software deployment tools available that can help automate the software delivery process, reduce errors and improve collaboration between development and operations teams. From open source tools like Jenkins to cloud-native solutions like AWS CodeDeploy, each tool has its own strengths and features that make it suitable for different use cases.
When selecting a software deployment tool, it is important to consider factors such as compatibility, automation, scalability, security, monitoring, deployment environment compatibility, integration with existing tools and support and documentation. By carefully evaluating these factors and choosing the right tool for your specific needs, you can ensure that your software deployment process is efficient, reliable and successful.