![]() ![]() The first items that we will need to create are the Kubernetes resources. To do so, we will need to create and initialise a set of auxiliary resources using YAML configuration files. In this blog series, we will dive deep into Airflow: first, we will show you how to create the essential Kubernetes resources to be able to deploy Apache Airflow on two nodes of the Kubernetes cluster (the installation of the K8s cluster is not in the scope of this article, but if you need help with that, you can check out this blog post!) then, in the second part of the series, we will develop an Airflow DAG file (workflow) and deploy it on the previously installed Airflow service on top of Kubernetes.Īs mentioned above, the objective of this article is to demonstrate how to deploy Airflow on a K8s cluster. This enables users to dynamically create Airflow workers and executors whenever and wherever they need more power, optimising the utilisation of available resources (and the associated costs!). In today’s technological landscape, where resources are precious and often spread thinly across different elements of an enterprise architecture, Airflow also offers scalability and dynamic pipeline generation, by being able to run on top of Kubernetes clusters, allowing us to automatically spin up the workers inside Kubernetes containers. On top of this, it also offers an integrated web UI where users can create, manage and observe workflows and their completion status, ensuring observability and reliability. Airflow is an open-source platform which can help companies to monitor and schedule their daily processes, able to programmatically author, schedule and monitor workflows using Python, and it can be integrated with the most well-known cloud and on-premise systems which provide data storage or data processing. To obtain better control and visibility of what is going on in the environments where these processes are executed, there needs to be a controlling mechanism, usually called a scheduler. ![]() chore: release 8.6.0 by in #559 New Contributorsįull Changelog: airflow-8.5.3.airflow-8.6.These days, data-driven companies usually have a huge number of workflows and tasks running in their environments: these are automated processes which are supporting daily operations and activities for most of their departments, and include a wide variety of tasks, from simple file transfers to complex ETL workloads or infrastructure provisioning.fix: cast user values with toString before b64enc by in #557.feat: add airflow triggerer Deployment by in #555.feat: allow labels on sync and db-migrations Deployments/Jobs by in #467.feat: add ingressClassName value to ingress by in #527.feat: fully support helm templating in extraManifests by in #523.fix: PG_ADVISORY_LOCK are not released in pgbouncer by in #529.fix: allow ingress servicePort to be string or number by in #530.feat: add airflow.clusterDomain value by in #441.fix: only set CONNECTION_CHECK_MAX_COUNT once by in #533.fix: update default to v3.5.0 by in #544.feat: set default pgbouncer.maxClientConnections to 1000 by in #543.fix: replace pgbouncer readinessProbe with startupProbe by in #547.feat: add "task creation check" to scheduler liveness probe by in #549.feat: database passwords with values + username from secret by in #553.feat: allow to specify a list of roles by in #539.fix: set DUMB_INIT_SETSID=0 for celery workers (warm shutdown) by in #550. ![]()
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