What is Microsoft Power Automate and what are its top alternatives?
Top Alternatives to Microsoft Power Automate
- GitHub Actions
It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want. ...
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...
- Apache Beam
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments. ...
With Camunda, business users collaborate with developers to model and automate end-to-end processes using BPMN-powered flowcharts that run with the speed, scale, and resiliency required to compete in today’s digital-first world ...
It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in. ...
It is an organizational tool that makes life easier. It's a surprisingly powerful way to take notes, make lists, collaborate, brainstorm, plan and generally organize your brain. ...
- Apache Oozie
It is a server-based workflow scheduling system to manage Hadoop jobs. Workflows in it are defined as a collection of control flow and action nodes in a directed acyclic graph. Control flow nodes define the beginning and the end of a workflow as well as a mechanism to control the workflow execution path. ...
Drive process excellence across your organization by connecting people, systems, and data to orchestrate how and when work gets done. ...
Microsoft Power Automate alternatives & related posts
- Integration with GitHub7
- Easy to duplicate a workflow3
- Ready actions in Marketplace3
- Configs stored in .github2
- Docker Support2
- Read actions in Marketplace2
- Active Development Roadmap1
- Lacking [skip ci]5
- Lacking allow failure4
- Lacking job specific badges3
- No ssh login to servers2
- No Deployment Projects1
- No manual launch1
related GitHub Actions posts
I am in the process of evaluating CircleCI, Drone.io, and Github Actions to cover my #CI/ CD needs. I would appreciate your advice on comparative study w.r.t. attributes like language-Inclusive support, code-base integration, performance, cost, maintenance, support, ease of use, ability to deal with big projects, etc. based on actual industry experience.
Thanks in advance!
Hello Everyone, Can some please help me to understand the difference between GitHub Actions And GitLab I have been trying to understand them, but still did not get how exactly they are different.
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Open source6
- Complex workflows5
- Good api3
- Apache project3
- Custom operators3
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
- Observability is not great when the DAGs exceed 2501
related Airflow posts
I am looking for an open-source scheduler tool with cross-functional application dependencies. Some of the tasks I am looking to schedule are as follows:
- Trigger Matillion ETL loads
- Trigger Attunity Replication tasks that have downstream ETL loads
- Trigger Golden gate Replication Tasks
- Shell scripts, wrappers, file watchers
- Event-driven schedules
I have used Airflow in the past, and I know we need to create DAGs for each pipeline. I am not familiar with Jenkins, but I know it works with configuration without much underlying code. I want to evaluate both and appreciate any advise
I am working on a project that grabs a set of input data from AWS S3, pre-processes and divvies it up, spins up 10K batch containers to process the divvied data in parallel on AWS Batch, post-aggregates the data, and pushes it to S3.
I already have software patterns from other projects for Airflow + Batch but have not dealt with the scaling factors of 10k parallel tasks. Airflow is nice since I can look at which tasks failed and retry a task after debugging. But dealing with that many tasks on one Airflow EC2 instance seems like a barrier. Another option would be to have one task that kicks off the 10k containers and monitors it from there.
I have no experience with AWS Step Functions but have heard it's AWS's Airflow. There looks to be plenty of patterns online for Step Functions + Batch. Do Step Functions seem like a good path to check out for my use case? Do you get the same insights on failing jobs / ability to retry tasks as you do with Airflow?
- Unified batch and stream processing2
related Apache Beam posts
I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?
related Camunda posts
- Hadoop Support5
- Open soure1