#Airflow api free
Open-source community-Airflow is free and has a large community of active users.Ease of use-you only need a little python knowledge to get started.Airflow can run anything-it is completely agnostic to what you are running. Airflow uses Python to create workflows that can be easily scheduled and monitored.
#Airflow api software
First developed by Airbnb, it is now under the Apache Software Foundation.
Request your personalized demo here from Pipekit.Apache Airflow is an open-source platform for authoring, scheduling and monitoring data and computing workflows. Put Argo Workflows to work on your data today. The added benefit with Argo Workflows was that the tasks were defined as containers, so if Canva needed to move to another tool in the future (and the tool ran containers), it would be a relatively straightforward migration path. Why Canva chose Argo Workflows over AirflowĬanva found Airflow and Argo Workflows to be capable tools because they could both support their workflows, do live UI and logging updates, and timeouts and retries.īut they did not like Airflow's deployment, so chose Argo Workflows over Airflow.Īrgo Workflows had a better deployment story for their DAGs via an API and command-line tool, and the DAGs were more declarative. Argo Workflows also required Kubernetes which increased the complexity somewhat, but if Kubernetes was the answer, Argo Workflows was a lighter weight orchestrator to implement.The amount of YAML required could make the management and templating messy very quickly.The main issues they found with Argo Workflows: They realized there was a lot of excitement around the Kubernetes executor for Airflow, but wondered why they would not simply use Kubernetes directly instead.They found that they almost certainly needed to run the database and the scheduler on separate machines, and had to monitor the Web UI, the scheduler and the executors (using the celery executer added to the complexity). The installation was complicated with a lot of dependencies and moving parts.This meant that the DAG that was deployed was not necessarily the DAG that ran. Overly complex or dynamic DAGs - basic conditional logic and loops were enough.Locked into only using Python for their data pipelines.Have reproducibility (be deterministic).Support existing data workloads at Canva (mostly Apache Spark).The evaluation criteriaĪs part of the evaluation criteria, they needed it to: They did the setup themselves on AWS to get a better understanding of the various components and dependencies of both projects. The Canva data team had limited time to evaluate the tools in the market and decided to conduct a comprehensive proof of concept between Argo Workflows and Airflow to see which one would work for them.įor their proof of concept, they set up both Airflow and Argo Workflows and implemented an existing, simple, realistic data pipeline. These issues were the reason why they explored alternative workflow tools.
The tool was not well known and had little community support or knowledge around it.They had to develop custom workarounds for their scheduling system.Installing custom software was slow and awkward.They had limitations in the types of EC2 instances they could run in the IWC data pipeline.Even though it was a complicated tool, it solved a complicated problem reliably for them.Īs the team grew in scale (and ambition) they started running into limitations with AWS data pipelines. Initially, when Canva had a very small data team, they used AWS data pipelines that worked well for them. Generate recommendations for their user templates.Improve the search relevance in their media library.Context and ProblemĬanva uses workflows (orchestrated and repeatable patterns of activity) to: You can view Greg’s talk here on YouTube. Because of time constrictions, they only conducted a comprehensive proof of concept between Argo and Airflow to see which one would work for them.īelow we summarize the talk delivered by Greg Roodt from Canva where he shared the evaluation process and his experience with using Argo so far. When the data team ran into various limitations and issues with their existing workflow system, they searched for a new, effective workflow system. Their application allows users to use templates, images and videos to create designs. Airflow for Kubernetes-native WorkflowsĬanva gave a talk at Data Council explaining why they chose Argo Workflows instead of Airflow for orchestrating their Spark data jobs.Ĭanva is a Sydney-based startup on a mission to empower the world to design.
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