Alternatives to Nextflow

Fully managed compute platform to deploy and scale containerized applications securely and quickly. You can write code in your favorite languages, including Go, Python, Java Ruby, Node.js and other languages. For a simple developer experience, we abstract away all infrastructure management. It is built upon the open standard Knative which allows for portability of your applications. You can write code the way you want by deploying any container that listens to events or requests. You can create applications in your preferred language with your favorite dependencies, tools, and deploy them within seconds. Cloud Run abstracts away all infrastructure management by automatically scaling up and down from zero almost instantaneously--depending on traffic. Cloud Run only charges for the resources you use. Cloud Run makes app development and deployment easier and more efficient. Cloud Run is fully integrated with Cloud Code and Cloud Build, Cloud Monitoring and Cloud Logging to provide a better developer experience.

Rivery’s ETL platform consolidates, transforms, and manages all of a company’s internal and external data sources in the cloud. Key Features: Pre-built Data Models: Rivery comes with an extensive library of pre-built data models that enable data teams to instantly create powerful data pipelines. Fully managed: A no-code, auto-scalable, and hassle-free platform. Rivery takes care of the back end, allowing teams to spend time on mission-critical priorities rather than maintenance. Multiple Environments: Rivery enables teams to construct and clone custom environments for specific teams or projects. Reverse ETL: Allows companies to automatically send data from cloud warehouses to business applications, marketing clouds, CPD’s, and more.

Portainer Business makes managing containers easy. It is designed to be deployed from the data centre to the edge and works with Docker, Swarm and Kubernetes. It is trusted by more than 500K users. With its super-simple GUI and its comprehensive Kube-compatible API, Portainer Business makes it easy for anyone to deploy and manage container-based applications, triage container-related issues, set up automate Git-based workflows and build CaaS environments that end users love to use. Portainer Business works with all K8s distros and can be deployed on prem and/or in the cloud. It is designed to be used in team environments where there are multiple users and multiple clusters. The product incorporates a range of security features - including RBAC, OAuth integration and logging, which makes it suitable for use in large, complex production environments. For platform managers responsible for delivering a self-service CaaS environment, Portainer includes a suite of features that help control what users can / can't do and significantly reduces the risks associated with running containers in prod. Portainer Business is fully supported and includes a comprehensive onboarding experience that ensures you get up and running.

Amazon Elastic Kubernetes Service (EKS) is a comprehensive Kubernetes management solution that operates entirely under AWS's management. High-profile clients like Intel, Snap, Intuit, GoDaddy, and Autodesk rely on EKS to host their most critical applications, benefiting from its robust security, dependability, and ability to scale efficiently. EKS stands out as the premier platform for running Kubernetes for multiple reasons. One key advantage is the option to deploy EKS clusters using AWS Fargate, which offers serverless computing tailored for containers. This feature eliminates the need to handle server provisioning and management, allows users to allocate and pay for resources on an application-by-application basis, and enhances security through inherent application isolation. Furthermore, EKS seamlessly integrates with various Amazon services, including CloudWatch, Auto Scaling Groups, IAM, and VPC, ensuring an effortless experience for monitoring, scaling, and load balancing applications. This level of integration simplifies operations, enabling developers to focus more on building their applications rather than managing infrastructure.

Amazon Elastic Container Service (ECS) is a comprehensive container orchestration platform that is fully managed. Notable clients like Duolingo, Samsung, GE, and Cook Pad rely on ECS to operate their critical applications due to its robust security, dependability, and ability to scale. There are multiple advantages to utilizing ECS for container management. For one, users can deploy their ECS clusters using AWS Fargate, which provides serverless computing specifically designed for containerized applications. By leveraging Fargate, customers eliminate the need for server provisioning and management, allowing them to allocate costs based on their application's resource needs while enhancing security through inherent application isolation. Additionally, ECS plays a vital role in Amazon’s own infrastructure, powering essential services such as Amazon SageMaker, AWS Batch, Amazon Lex, and the recommendation system for Amazon.com, which demonstrates ECS’s extensive testing and reliability in terms of security and availability. This makes ECS not only a practical option but a proven choice for organizations looking to optimize their container operations efficiently.

Nextflow Tower is an intuitive, centralized command post that facilitates large-scale collaborative data analysis. Tower makes it easy to launch, manage, monitor, and monitor scalable Nextflow data analysis and compute environments both on-premises and on the cloud. Researchers can concentrate on the science that is important and not worry about infrastructure engineering. With predictable, auditable pipeline execution, compliance is made easier. You can also reproduce results with specific data sets or pipeline versions on-demand. Nextflow Tower was developed and supported by Seqera Labs. They are the maintainers and creators of the open-source Nextflow project. Users get high-quality support straight from the source. Tower integrates Nextflow with third-party frameworks, which is a significant advantage. It can help users take advantage of Nextflow's full range of capabilities.

Kubernetes (K8s), an open-source software that automates deployment, scaling and management of containerized apps, is available as an open-source project. It organizes containers that make up an app into logical units, which makes it easy to manage and discover. Kubernetes is based on 15 years of Google's experience in running production workloads. It also incorporates best-of-breed practices and ideas from the community. Kubernetes is built on the same principles that allow Google to run billions upon billions of containers per week. It can scale without increasing your operations team. Kubernetes flexibility allows you to deliver applications consistently and efficiently, no matter how complex they are, whether you're testing locally or working in a global enterprise. Kubernetes is an open-source project that allows you to use hybrid, on-premises, and public cloud infrastructures. This allows you to move workloads where they are most important.

harpoon allows you to deploy any software within seconds using a simple drag-and-drop method. No code is required to deploy production software using our visual Kubernetes tool. Harpoon offers all the features needed to deploy and configure your software using Kubernetes, the industry's leading container orchestrator. All without writing any code. You can easily deploy and configure Kubernetes and autoscale software in the cloud without writing any code. Search and find any commercial or open-source piece of software anywhere on the planet, and deploy it in the cloud instantly with just one click. Harpoon will run scripts to secure your cloud account before running any applications or service. Connect harpoon anywhere to your source code repository and set up an automated deployment pipeline.

Airflow is a community-driven platform designed for the programmatic creation, scheduling, and monitoring of workflows. It features a flexible architecture and employs a message queue to manage a potentially unlimited number of workers. With its capability to scale infinitely, Airflow pipelines are crafted using Python, which enables the dynamic creation of these workflows. This dynamic pipeline generation empowers developers to write code that can instantiate workflows on the fly. You have the ability to easily define custom operators and enhance libraries to meet the specific abstraction level required for your setting. The design of Airflow pipelines is straightforward and clear, incorporating core parametrization through the advanced Jinja templating engine. Gone are the days of obscure command-line instructions or convoluted XML configurations! Airflow allows the use of standard Python features for workflow creation, such as date and time formatting for scheduling and loops for generating tasks dynamically. This ensures that you maintain maximum flexibility while constructing your workflows. Moreover, this adaptability makes Airflow an excellent choice for diverse use cases across various industries.

JFrog Pipelines allows software teams to ship updates quicker by automating DevOps processes in an efficient and secure manner across all their tools and teams. It automates every step of production, including continuous integration (CI), continuous deliveries (CD), infrastructure, and more. Pipelines is natively integrated with the JFrog Platform and is available with both cloud (software-as-a-service) and on-prem subscriptions.

Reactive Programming can be used on the back-end without the use of complex languages or cumbersome frameworks. Triggers, Actors, and Event Collections make it simple to build data pipelines. They also allow you to work with data streams using simple SQL syntax. This makes it easier for users to avoid the complexities of distributed systems development. Extensible data modeling is a key feature. It supports rich semantics, schema definition, and allows for real-world objects to be represented. Data shaping rules and validation on the fly support a variety of formats, including JSON and XML. This allows you to easily define and evolve your schema while keeping up with changing business requirements. If you can describe it, we can query it. Are you familiar with Javascript and SQL? You already know how to use the database engine. No matter what format you use, a powerful query engine allows you to instantly test logic expressions or functions. This speeds up development and simplifies deployment for unmatched data agility.

The Nebula container orchestrator is designed to allow developers and operators to treat IoT devices as distributed Dockerized applications. It acts as a Docker orchestrator for IoT device as well as distributed services such CDN or edge computing. It is open-source and free. Nebula is an open-source project for Docker orchestration. It's designed to manage large clusters at scale by scaling each component of the project as needed. The project's goal is to be a Docker orchestrator for IoT device as well as distributed services like CDN or edge computing. Nebula can simultaneously update tens of thousands IoT devices around the world with one API call. This is in an effort for developers and ops to treat IoT devices like distributed Dockerized applications.

Mesos operates on principles similar to those of the Linux kernel, but it functions at a higher level of abstraction. Its kernel is deployed across all machines, facilitating applications such as Hadoop, Spark, Kafka, and Elasticsearch by offering APIs that manage resources and schedules throughout entire data centers and cloud infrastructures. Additionally, Mesos includes native capabilities for launching containers using Docker and AppC images. It enables both cloud-native and legacy applications to run within the same cluster while allowing for customizable scheduling policies. Users benefit from HTTP APIs designed for the development of new distributed applications, as well as tools for cluster management and monitoring. Furthermore, there is a built-in Web UI that allows users to observe the state of the cluster and navigate through container sandboxes, enhancing overall operability and visibility. This comprehensive approach makes Mesos a versatile option for managing complex application deployments effectively.

It is a simple and flexible task orchestrator that deploys and manages containers and non-containerized apps across on-prem as well as cloud environments. One 35MB binary that can be integrated into existing infrastructure. It is easy to use on-prem and in the cloud with minimal overhead. You can orchestrate any type of application, not just containers. First-class support for Docker and Windows, Java, VMs, VMs, and other technologies. Orchestration benefits can be added to existing services. Zero downtime deployments, increased resilience, higher resource utilization, as well as greater resilience can all be achieved without containerization. Multi-region, multicloud federation - single command Nomad is a single control plane that allows you to deploy applications worldwide to any region. One workflow to deploy to cloud or bare metal environments. Multi-cloud applications can be enabled with ease. Nomad seamlessly integrates with Terraform Consul and Vault for provisioning and service networking. Secrets management is also possible.

Our platform allows you create distributed coding and data science processes with contractors, freelancers, and developers located anywhere. They work on their own devices, while auditing your data and ensuring data security. Strong Network has created a multi-cloud platform we call Virtual Workspace Infrastructure. It allows companies to securely unify their access to their global data science and coding processes via a simple web browser. The VWI platform is an integral component of their DevSecOps process. It doesn't require integration with existing CI/CD pipelines. Process security is focused on data, code, and other critical resources. The platform automates the principles and implementation of Zero-Trust Architecture, protecting the most valuable IP assets of the company.

Test Kitchen offers a test harness that allows you to execute infrastructure code on a single platform or multiple platforms. A driver plugin architecture can be used to run code on different cloud providers and virtualization technology such as Vagrant and Amazon EC2, Microsoft Azure and Google Compute Engine. Many testing frameworks, including Chef InSpec, Serverspec and Bats For Chef Infra workflows are supported. If you include a cookbooks/ directory, Kitchen will know what to do. All Chef-managed community cookbooks use Test Kitchen as the integration testing tool of preference for cookbooks.

Self-service platform for developers to create apps, databases and jobs. Scale up from one workload to hundreds of workloads on compute or GPUs. GitOps, self-service workflows and templates, pipelines and templates that are highly configurable, will accelerate every step, from push to production. With observability tools, backups and restores, rollbacks, and a rollback feature, you can deploy preview, staging and production environments securely. Northflank integrates seamlessly with your preferred tools and can accommodate any technology stack. You can deploy on Northflank’s secure infrastructure, or on your own account. Either way, you will get the same developer experience and have total control over your data, deployment regions, security and cloud expenses. Northflank uses Kubernetes to deliver the best of cloud native without the overhead. Northflank offers a cloud deployment option for maximum simplicity. You can also connect your GKE or EKS to Northflank to get a managed platform in minutes.

Conductor serves as a cloud-based workflow orchestration engine designed to assist Netflix in managing process flows that rely on microservices. It boasts a number of key features, including an efficient distributed server ecosystem that maintains workflow state information. Users can create business processes where individual tasks may be handled by either the same or different microservices. The system utilizes a Directed Acyclic Graph (DAG) for workflow definitions, ensuring that these definitions remain separate from the actual service implementations. It also offers enhanced visibility and traceability for the various process flows involved. A user-friendly interface facilitates the connection of workers responsible for executing tasks within these workflows. Notably, workers are language-agnostic, meaning each microservice can be developed in the programming language best suited for its purposes. Conductor grants users total operational control over workflows, allowing them to pause, resume, restart, retry, or terminate processes as needed. Ultimately, it promotes the reuse of existing microservices, making the onboarding process significantly more straightforward and efficient for developers.

Pliant's solution to IT Process Automation streamlines, secures, and simplifies the way teams build and deploy automation. Pliant will reduce human error, ensure compliance and increase your efficiency. Pliant allows you to integrate existing automation and create new automation using single-pane orchestration. You can ensure compliance with consistent, practical, built-in governance. Pliant has abstracted thousands from vendor APIs to create intelligent actions blocks that allow users to drag-and drop blocks rather than writing lines of code. Citizen developers can create consistent and meaningful automation across platforms and services in minutes. This allows them to maximize the value of the entire technology stack from one platform. We can add APIs in as little as 15 business days. This is an industry-leading timeframe.

AWS Data Pipeline is a cloud-based service that enables the reliable movement and processing of data between various AWS compute and storage solutions, as well as local data sources, according to defined schedules. By utilizing AWS Data Pipeline, users can consistently access their stored data, perform large-scale transformations and processing, and seamlessly transfer the outcomes to AWS services like Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR. This service simplifies the creation of intricate data processing tasks that are robust, repeatable, and highly reliable. Users can rest easy knowing they do not need to manage resource availability, inter-task dependencies, transient failures, or timeouts, nor do they have to establish a system for failure notifications. Furthermore, AWS Data Pipeline empowers users to efficiently move and process data that was previously inaccessible due to being confined in on-premises data silos, thereby enhancing overall data accessibility and utilization. Ultimately, it streamlines the entire workflow, making data handling more efficient and effective.

GlassFlow is an event-driven, serverless data pipeline platform for Python developers. It allows users to build real time data pipelines, without the need for complex infrastructure such as Kafka or Flink. GlassFlow is a platform that allows developers to define data transformations by writing Python functions. GlassFlow manages all the infrastructure, including auto-scaling and low latency. Through its Python SDK, the platform can be integrated with a variety of data sources and destinations including Google Pub/Sub and AWS Kinesis. GlassFlow offers a low-code interface that allows users to quickly create and deploy pipelines. It also has features like serverless function executions, real-time connections to APIs, alerting and reprocessing abilities, etc. The platform is designed for Python developers to make it easier to create and manage event-driven data pipes.

Kestra is a free, open-source orchestrator based on events that simplifies data operations while improving collaboration between engineers and users. Kestra brings Infrastructure as Code to data pipelines. This allows you to build reliable workflows with confidence. The declarative YAML interface allows anyone who wants to benefit from analytics to participate in the creation of the data pipeline. The UI automatically updates the YAML definition whenever you make changes to a work flow via the UI or an API call. The orchestration logic can be defined in code declaratively, even if certain workflow components are modified.

Dagster is the cloud-native open-source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.

Cloud Composer's managed nature with Apache Airflow compatibility allow you to focus on authoring and scheduling your workflows, rather than provisioning resources. Google Cloud products include BigQuery, Dataflow and Dataproc. They also offer integration with Cloud Storage, Cloud Storage, Pub/Sub and AI Platform. This allows users to fully orchestrate their pipeline. You can schedule, author, and monitor all aspects of your workflows using one orchestration tool. This is true regardless of whether your pipeline lives on-premises or in multiple clouds. You can make it easier to move to the cloud, or maintain a hybrid environment with workflows that cross over between the public cloud and on-premises. To create a unified environment, you can create workflows that connect data processing and services across cloud platforms.

Yandex Data Proc creates and configures Spark clusters, Hadoop clusters, and other components based on the size, node capacity and services you select. Zeppelin Notebooks and other web applications can be used to collaborate via a UI Proxy. You have full control over your cluster, with root permissions on each VM. Install your own libraries and applications on clusters running without having to restart. Yandex Data Proc automatically increases or decreases computing resources for compute subclusters according to CPU usage indicators. Data Proc enables you to create managed clusters of Hive, which can reduce failures and losses due to metadata not being available. Save time when building ETL pipelines, pipelines for developing and training models, and describing other iterative processes. Apache Airflow already includes the Data Proc operator.

Experience robust data engineering processes free from the challenges of infrastructure management. By utilizing straightforward, modular Python, you can define intricate streaming, scheduling, and data backfill pipelines with ease. Transition from traditional ETL methods and access your data instantly, regardless of its complexity. Seamlessly blend deep learning and large language models with structured business datasets to enhance decision-making. Improve forecasting accuracy using up-to-date information, eliminate the costs associated with vendor data pre-fetching, and conduct timely queries for online predictions. Test your ideas in Jupyter notebooks before moving them to a live environment. Avoid discrepancies between training and serving data while developing new workflows in mere milliseconds. Monitor all of your data operations in real-time to effortlessly track usage and maintain data integrity. Have full visibility into everything you've processed and the ability to replay data as needed. Easily integrate with existing tools and deploy on your infrastructure, while setting and enforcing withdrawal limits with tailored hold periods. With such capabilities, you can not only enhance productivity but also ensure streamlined operations across your data ecosystem.

Unified platform that is high-performance and can be layered on any existing infrastructure to facilitate seamless onboarding. Metrolink's intuitive design allows any organization to manage its data integration. It provides advanced manipulations that aim to maximize diverse and complex data and refocus human resource to eliminate overhead. Complex, multi-source, streaming data that is constantly changing in use cases. The focus is on core business and not data utilities. Metrolink is a Unified platform which allows organizations to design and manage their data pipes according to their business needs. This is achieved by providing an intuitive UI and advanced manipulations of complex data. It also allows for data privacy and data value enhancement.

TrueFoundry provides data scientists and ML engineers with the fastest framework to support the post-model pipeline. With the best DevOps practices, we enable instant monitored endpoints to models in just 15 minutes! You can save, version, and monitor ML models and artifacts. With one command, you can create an endpoint for your ML Model. WebApps can be created without any frontend knowledge or exposure to other users as per your choice. Social swag! Our mission is to make machine learning fast and scalable, which will bring positive value! TrueFoundry is enabling this transformation by automating parts of the ML pipeline that are automated and empowering ML Developers with the ability to test and launch models quickly and with as much autonomy possible. Our inspiration comes from the products that Platform teams have created in top tech companies such as Facebook, Google, Netflix, and others. These products allow all teams to move faster and deploy and iterate independently.

You can centralize offline data, import files, clean up data, and process it. With one click, export to a live database Streamline data workflows. Your team can access offline data by centralizing it. Dropbase can import offline files. Multiple formats. You can do it however you want. Data can be processed and formatted. Steps for adding, editing, reordering, and deleting data. 1-click exports. Export to database, endpoints or download code in just one click Instant REST API access. Securely query Dropbase data with REST API access keys. You can access data wherever you need it. Combine and process data to create the desired format. No code. Use a spreadsheet interface to process your data pipelines. Each step is tracked. Flexible. You can use a pre-built library of processing functions. You can also create your own. 1-click exports. Export to a database or generate endpoints in just one click Manage databases. Manage databases and credentials.

Apache Brooklyn is a versatile software solution designed for overseeing cloud applications, enabling you to manage your applications seamlessly across various environments, including public clouds, private clouds, and bare metal servers. With Brooklyn, you can create blueprints that outline your application architecture and store them as text files in version control, ensuring your components are automatically configured and integrated across multiple machines. It supports over 20 public cloud platforms as well as Docker containers, allowing for efficient monitoring of critical application metrics while scaling resources to accommodate demand. Additionally, you can easily restart or replace any failed components, and you have the option to view and modify your applications through a user-friendly web console or automate processes via the REST API for greater efficiency. This flexibility offers organizations the ability to streamline their operations and enhance their cloud management capabilities.

Centurion is a deployment tool specifically designed for Docker, facilitating the retrieval of containers from a Docker registry to deploy them across a network of hosts while ensuring the appropriate environment variables, host volume mappings, and port configurations are in place. It inherently supports rolling deployments, simplifying the process of delivering applications to Docker servers within our production infrastructure. The tool operates through a two-stage deployment framework, where the initial build process pushes a container to the registry, followed by Centurion transferring the container from the registry to the Docker fleet. Integration with the registry is managed via the Docker command line tools, allowing compatibility with any existing solutions they support through conventional registry methods. For those unfamiliar with registries, it is advisable to familiarize yourself with their functionality prior to deploying with Centurion. The development of this tool is conducted openly, welcoming community feedback through issues and pull requests, and is actively maintained by a dedicated team at New Relic. Additionally, this collaborative approach ensures continuous improvement and responsiveness to user needs.

Rapidly create applications without the hassle of overseeing virtual machines or learning unfamiliar tools—simply deploy your app in a cloud-based container. By utilizing Azure Container Instances (ACI), your attention can shift towards the creative aspects of application development instead of the underlying infrastructure management. Experience an unmatched level of simplicity and speed in deploying containers to the cloud, achievable with just one command. ACI allows for the quick provisioning of extra compute resources for high-demand workloads as needed. For instance, with the aid of the Virtual Kubelet, you can seamlessly scale your Azure Kubernetes Service (AKS) cluster to accommodate sudden traffic surges. Enjoy the robust security that virtual machines provide for your containerized applications while maintaining the lightweight efficiency of containers. ACI offers hypervisor-level isolation for each container group, ensuring that each container operates independently without kernel sharing, which enhances security and performance. This innovative approach to application deployment simplifies the process, allowing developers to focus on building exceptional software rather than getting bogged down by infrastructure concerns.

Manage, control, secure, and manage the apps, data, and IT that run your business from edge to cloud. HPE Ezmeral accelerates digital transformation initiatives by shifting resources and time from IT operations to innovation. Modernize your apps. Simplify your operations. You can harness data to transform insights into impact. Kubernetes can be deployed at scale in your data center or on the edge. It integrates persistent data storage to allow app modernization on baremetal or VMs. This will accelerate time-to-value. Operationalizing the entire process to build data pipelines will allow you to harness data faster and gain insights. DevOps agility is key to machine learning's lifecycle. This will enable you to deliver a unified data network. Automation and advanced artificial intelligence can increase efficiency and agility in IT Ops. Provide security and control to reduce risk and lower costs. The HPE Ezmeral Container Platform is an enterprise-grade platform that deploys Kubernetes at large scale for a wide variety of uses.

Mirantis Kubernetes Engine (formerly Docker Enterprise) gives you the power to build, run, and scale cloud native applications—the way that works for you. Increase developer efficiency and release frequency while reducing cost. Deploy Kubernetes and Swarm clusters out of the box and manage them via API, CLI, or web interface. Kubernetes, Swarm, or both Different apps—and different teams—have different container orchestration needs. Use Kubernetes, Swarm, or both depending on your specific requirements. Simplified cluster management Get up and running right out of the box—then manage clusters easily and apply updates with zero downtime using a simple web UI, CLI, or API. Integrated role-based access control (RBAC) Fine-grained security access control across your platform ensures effective separation of duties, and helps drive a security strategy built on the principle of least privilege. Identity management Easily integrate with your existing identity management solution and enable two-factor authentication to provide peace of mind that only authorized users are accessing your platform. Mirantis Kubernetes Engine works with Mirantis Container Runtime and Mirantis Secure Registry to provide security compliance.

What is so special about azk azk (Apache 2.0) is open-source software and will remain so. azk is open source software (Apache 2.0) and has a very easy learning curve. Use the same development tools that you use. It takes only a few commands. It takes minutes, not hours or days. azk works by creating very brief recipe files (Azkfile.js), which describe the environments that will be installed and configured. azk runs very fast and your computer will not feel it. It uses containers rather than virtual machines. Containers are similar to virtual machines but with better performance and lower use of physical resources. azk is built using Docker, an open-source engine for managing containers. An Azkfile.js shared by all programmers ensures complete parity between development environments on different machines and reduces bugs during deployment. Are you unsure if all programmers on your team are using the latest version of the development environment

Marathon is a production-grade container orchestration platform that Mesosphere's Datacenter Operating System(DC/OS) as well as Apache Mesos uses. High availability. Marathon runs as an active/passive cluster, with leader election for 100 percent uptime. Multiple container runtimes. Marathon provides first-class support for Mesos containers (using Docker) and Mesos containers using cgroups. Stateful apps. Marathon can bind persistent storage volumes directly to your application. You can run databases such as Postgres and MySQL, and have storage accounted by Mesos. Beautiful and powerful UI. Service Discovery & Load Balancing. Several methods available. Health checks. Use HTTP or TCP checks to assess the health of your application. Subscribe to Events To receive notifications, you will need to provide an HTTP endpoint. This is used to integrate with external load balancers. Metrics. You can query them at /metrics using JSON format. Push them to DataDog, StatsD, and Graphite. Or, you can scrape them with Prometheus.

Container Engine for Kubernetes is an Oracle-managed container orchestration platform that can help you build modern cloud native apps in a shorter time and at a lower cost. Oracle Cloud Infrastructure offers Container Engine for Kubernetes free of charge, running on more efficient and lower-cost compute shapes than most other vendors. Open-source Kubernetes can be used by DevOps engineers for application workload portability, and to simplify operations with automatic updates. With a single click, deploy Kubernetes clusters, including the underlying virtual clouds networks, internet gateways and NAT gateways. Automate Kubernetes operations using web-based REST API or CLI. This includes cluster creation, scaling, operations, and maintenance. Cluster management is free with Oracle Container Engine for Kubernetes. You can easily and quickly upgrade container clusters with zero downtime to keep them current with the latest stable version Kubernetes.

With over 14,000 clusters of managed production, we are leading the charge. This is just the beginning. Operational visibility into Kubernetes-based services, platforms, and applications. Advanced features to monitor, troubleshoot, create alerts, and build custom dashboards. Cluster level, 30-day retention and natural language processing are all available. High security environment for production workloads. Integrate with advanced IBM services such as AI, Watson and Blockchain to extend your app's capabilities. This is done through an automated, standardized, and secure architecture. This includes Kubernetes secrets that can be managed by customers through IBM Cloud™. Key Protect

Helios is a Docker orchestration platform that allows you to deploy and manage containers across a large number of servers. Helios offers a command-line client and an HTTP API to allow you to interact with the containers running on your servers. It keeps track of all events in your cluster, including version changes, deploys, restarts, and restarts. Although the binary release of Helios was designed for Ubuntu 14.04.1LTS, Helios should work on any platform that has Java 8 and Maven 3. To launch a local environment, use helios–solo with a Helios master or agent. Helios is pragmatic. We don't have all the answers, but we do our best to make sure that what we do have is rock-solid. We don't yet have dynamic scheduling or resource limits. It is more important for us to have the CI/CD use case and surrounding tooling firmly established today than ever before. However, dynamic scheduling and composite jobs are all possible in the future.

Alibaba Cloud's Container Service for Kubernetes (ACK) is a comprehensive managed offering that integrates various services, including virtualization, storage, networking, and security, to deliver a high-performance and scalable environment for running containerized applications. As a Kubernetes Certified Service Provider (KCSP), ACK is also certified under the Certified Kubernetes Conformance Program, guaranteeing a reliable Kubernetes experience and enabling workload portability across different environments. This certification ensures that users benefit from a consistent Kubernetes experience while leveraging enterprise-class cloud-native capabilities. Additionally, ACK emphasizes security with end-to-end application protection and fine-grained access control, allowing users to swiftly set up Kubernetes clusters. Moreover, the service facilitates efficient container-based management of applications throughout their entire lifecycle, enhancing operational agility and performance.

You can accelerate your development by using a storage platform that integrates with Kubernetes. While you focus on running your application we ensure that you have the persistent volumes you need to give you the stability and scale you require. Integrating stateful storage into Kubernetes will simplify your app modernization process and increase efficiency. You can run your database or any other persistent workload in a Kubernetes-based environment without worrying about managing the storage layer. Ondat allows you to provide a consistent storage layer across all platforms. We provide persistent volumes that allow you to run your own databases, without having to pay for expensive hosted options. Kubernetes data layer management is yours to take back. Kubernetes-native storage that supports dynamic provisioning. It works exactly as it should. API-driven, tight integration to your containerized applications.

Enhanced operators for enterprise applications feature a comprehensive application graph and declarative integration that caters to both Kubernetes environments and legacy systems. Through Juju operator integration, we can simplify each operator, enabling their composition to form intricate application graph topologies that handle complex scenarios while providing a user-friendly experience with significantly reduced YAML requirements. The UNIX principle of ‘doing one thing well’ is equally applicable in the realm of large-scale operational code, yielding similar advantages in clarity and reusability. The charm of small-scale design is evident here: Juju empowers organizations to implement the operator pattern across their entire infrastructure, including older applications. Model-driven operations lead to substantial savings in maintenance and operational expenses for traditional workloads, all without necessitating a shift to Kubernetes. Once integrated with Juju, legacy applications also gain the ability to operate across multiple cloud environments. Furthermore, the Juju Operator Lifecycle Manager (OLM) uniquely accommodates both containerized and machine-based applications, ensuring smooth interoperability between the two. This innovative approach allows for a more cohesive and efficient management of diverse application ecosystems.

YARN's core concept revolves around decentralizing resource management and job scheduling/monitoring by utilizing distinct daemons for each function. It employs a centralized ResourceManager (RM) alongside individual ApplicationMasters (AM) for each application, which may consist of either a solitary job or a Directed Acyclic Graph (DAG) of jobs. Together, the ResourceManager and NodeManager create the computational framework necessary for data processing. The ResourceManager serves as the main governing body that allocates resources across all applications within the system. Meanwhile, the NodeManager acts as the agent on each machine, handling containers, tracking their resource utilization (such as CPU, memory, disk, and network), and relaying this information to the ResourceManager/Scheduler. Additionally, the ApplicationMaster functions as a specialized library for each application, responsible for negotiating resource allocation with the ResourceManager and collaborating with the NodeManagers to execute and oversee the tasks efficiently. This division of responsibilities enhances the overall efficiency and scalability of the resource management process.

It is the only Kubernetes distribution which is simple, solid, and certified. It works on any infrastructure, including bare metal, on-premises or private clouds, edge, IoT or public clouds. It's free and open source. Zero Friction – k0s dramatically reduces the complexity involved in installing and running Kubernetes. Bootstrapping new kube clusters takes only minutes. Developer friction is zero, making it easy for anyone to get started, even if they don't have any special skills. Zero Deps: k0s comes as a single binary that has no host OS dependencies other than the kernel of the host OS. It works with any OS without additional software packages. Any security vulnerabilities or performance problems can be fixed directly within the k0s Distribution. Zero Cost - K0s is free for commercial or personal use and will always be. The source code can be found on GitHub, under Apache 2 license.

Apache ODE, known as the Orchestration Director Engine, is designed to execute business processes that adhere to the WS-BPEL standard. It effectively communicates with web services by transmitting and receiving messages while also managing data operations and error handling as outlined in your defined processes. This software accommodates both short-lived and long-running process executions, allowing for the orchestration of all services involved in your application. WS-BPEL, or Business Process Execution Language, is an XML-based format that provides various constructs for creating business processes. It outlines essential control structures such as conditions and loops, along with elements for invoking web services and receiving messages. The language depends on WSDL to characterize the interfaces of web services. Furthermore, message structures can be manipulated, enabling the assignment of specific parts or entire messages to variables that can then be utilized for sending additional messages. Additionally, Apache ODE supports both the WS-BPEL 2.0 OASIS standard and the older BPEL4WS 1.1 vendor specification, ensuring compatibility across different versions. This dual support allows developers to transition smoothly between standards while maintaining functionality.

Lightbend offers innovative technology that empowers developers to create applications centered around data, facilitating the development of demanding, globally distributed systems and streaming data pipelines. Businesses across the globe rely on Lightbend to address the complexities associated with real-time, distributed data, which is essential for their most critical business endeavors. The Akka Platform provides essential components that simplify the process for organizations to construct, deploy, and manage large-scale applications that drive digital transformation. By leveraging reactive microservices, companies can significantly speed up their time-to-value while minimizing expenses related to infrastructure and cloud services, all while ensuring resilience against failures and maintaining efficiency at any scale. With built-in features for encryption, data shredding, TLS enforcement, and adherence to GDPR standards, it ensures secure data handling. Additionally, the framework supports rapid development, deployment, and oversight of streaming data pipelines, making it a comprehensive solution for modern data challenges. This versatility positions companies to fully harness the potential of their data, ultimately propelling them forward in an increasingly competitive landscape.

There is no need to compromise between the convenience and power of infrastructure as a code. Kubestack lets you design your Kubernetes platform using an intuitive, graphical user interface. Export your custom stack to Terraform code to ensure reliable provisioning and long-term sustainability. Platforms built with Kubestack Cloud can be exported to a Terraform root Module, which is based on Kubestack framework. Framework modules are all open-source, which reduces the long-term maintenance effort as well as allowing for easy access to future improvements. To efficiently manage changes with your team, adapt the tried-and-trued pull-request and peer review based workflow. You can reduce the amount of bespoke infrastructure code that you need to maintain and save time in the long-term.

Construct your data pipelines akin to assembling Lego blocks using Data Taps. Integrate fresh metrics layers, delve deeper, and conduct inquiries using real-time streaming SQL capabilities. Collaborate with peers, disseminate, and access data on a global scale. Enhance and modify your setup effortlessly. Employ various models and schemas while evolving your schema. Designed for scalability, it leverages the power of AWS Lambda and S3 for optimal performance. This flexibility allows teams to adapt quickly to changing data needs.

Dataplane's goal is to make it faster and easier to create a data mesh. It has robust data pipelines and automated workflows that can be used by businesses and teams of any size. Dataplane is more user-friendly and places a greater emphasis on performance, security, resilience, and scaling.

Implement robust change data capture pipelines for large-scale, real-time data replication effortlessly, without writing a single line of code. Experience the enhanced capabilities of Change Data Capture with Arcion’s distributed CDC solution. Benefit from automatic schema transformations, seamless end-to-end replication, and flexible deployment options. Arcion’s architecture ensures zero data loss, guaranteeing consistent data flow through built-in checkpointing and more, all without the need for custom coding. Say goodbye to worries about scalability and performance as you take advantage of a highly distributed and parallel architecture that achieves data replication speeds up to ten times faster. Minimize DevOps challenges with Arcion Cloud, the sole fully-managed CDC solution available. With features like autoscaling, high availability, and a monitoring console, you can streamline your operations. Furthermore, the platform simplifies and standardizes your data pipeline architecture, facilitating seamless workload migration from on-premises systems to the cloud without any downtime. With such a comprehensive solution, you can focus on leveraging your data rather than managing the complexities of its movement.