Bunnyshell automates all steps in the release process, from creating servers on multiple clouds (AWS, Azure, Google Cloud, Digital Ocean) to easy provisioning (ready to use apps - install & configure with one click) and one click deployments.
We are helping companies save time and money by standardizing and automating otherwise time consuming, knowledge-dependant or prone to error infrastructure-related tasks.
With Bunnyshell and a few clicks, any developer can:
Migrate easily (from premise to cloud, cloud to cloud) Create servers on multiple clouds Provision & configure applications Deploy with one click and zero downtime (multiple deployments time) Version their work and rollback any time Create dev & test environments on any cloud, version, OS Have automated security updates for all projects
Based on our record, Apache Airflow seems to be a lot more popular than Bunnyshell. While we know about 68 links to Apache Airflow, we've tracked only 2 mentions of Bunnyshell. 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.
Instead of the custom orchestrator I used, a proper orchestration tool should replace it like Apache Airflow, Dagster, ..., etc. - Source: dev.to / 10 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 / 21 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 2 months 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
Https://bunnyshell.com and k8s -- seems like a good way to get going quickly with new projects --. - Source: Hacker News / 8 months ago
With Infrastructure as Code at its current state of maturity, it’s now easier than ever to replicate microservice environments in the cloud. This unlocked a new approach of having a personal production-like cloud environment for every developer, which they can use freely and in isolation. It comes in two flavors - persistent environments, or ephemeral environments created on demand with products like Okteto or... - Source: dev.to / over 1 year ago
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Heroku - Agile deployment platform for Ruby, Node.js, Clojure, Java, Python, and Scala. Setup takes only minutes and deploys are instant through git. Leave tedious server maintenance to Heroku and focus on your code.
ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.
ARGONAUT - Definition, Synonyms, Translations of Argonaut by The Free Dictionary
Make.com - Tool for workflow automation (Former Integromat)
Porter - Heroku that runs in your own cloud