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Based on our record, Apache Airflow seems to be a lot more popular than DevSpace (for Kubernetes and Docker). While we know about 68 links to Apache Airflow, we've tracked only 3 mentions of DevSpace (for Kubernetes and Docker). We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
DevSpace is very similar to Skaffold in terms of features, with the added benefits of a dedicated UI and a two-way file sync. The UI gives your team an overview of the stack and easy access to logs. At the same time, the file synchronization feature makes their development process faster by letting them directly change code from a running container. - Source: dev.to / about 2 years ago
DevSpace is an open-source developer tool for Kubernetes that lets you develop and deploy cloud-native software faster. It is a client-only CLI tool that runs on your machine and works with any Kubernetes cluster. You can use it to automate image building and deployments, to develop software directly inside Kubernetes and to streamline workflows across your team as well as across dev, staging and production. - Source: dev.to / about 2 years ago
And speaking of cycle times, the Loft team has also built DevSpace, a developer workflow tool for engineers working with Kubernetes clusters. Have you ever waited around for a new container to build so you can see if your changes work? Or even worse, for a CI pipeline to run integration tests? With DevSpace you can hot reload your app in the running container as you make changes. It's super cool and it's open... - Source: dev.to / about 3 years ago
Instead of the custom orchestrator I used, a proper orchestration tool should replace it like Apache Airflow, Dagster, ..., etc. - Source: dev.to / 3 days ago
An integral part of an ML project is data acquisition and data transformation into the required format. This involves creating ETL (extract, transform, load) pipelines and running them periodically. Airflow is an open source platform that helps engineers create and manage complex data pipelines. Furthermore, the support for Python programming language makes it easy for ML teams to adopt Airflow. - Source: dev.to / 14 days ago
Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities. - Source: dev.to / about 1 month ago
For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules. - Source: dev.to / 4 months ago
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows. Source: 8 months ago
Okteto - Development platform for Kubernetes applications.
ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.
Telepresence - Telepresence is an open source tool that lets you develop and debug your Kubernetes services...
Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.
Garden.io - Cloud native & Kubernetes testing done right
Make.com - Tool for workflow automation (Former Integromat)