Merge branch 'main' of https://github.com/stackabletech/airflow-operator

 into fix/remove-remaining-airflowdb-references

docs/modules/airflow/pages/getting_started/installation.adoc

@@ -25,14 +25,14 @@ WARNING: Do not use this setup in production! Supported databases and versions a
 
 There are 2 ways to run Stackable Operators
 
-1. Using xref:stackablectl::index.adoc[]
+1. Using xref:management:stackablectl:index.adoc[]
 
 2. Using Helm
 
 === stackablectl
 
 stackablectl is the command line tool to interact with Stackable operators and our recommended way to install Operators.
-Follow the xref:stackablectl::installation.adoc[installation steps] for your platform.
+Follow the xref:management:stackablectl:installation.adoc[installation steps] for your platform.
 
 After you have installed stackablectl run the following command to install all Operators necessary for Airflow:
 
@@ -49,7 +49,8 @@ The tool will show
 [INFO ] Installing airflow operator
 ----
 
-TIP: Consult the xref:stackablectl::quickstart.adoc[] to learn more about how to use stackablectl. For example, you can use the `-k` flag to create a Kubernetes cluster with link:https://kind.sigs.k8s.io/[kind].
+TIP: Consult the xref:management:stackablectl:quickstart.adoc[] to learn more about how to use stackablectl. For
+example, you can use the `--cluster kind` flag to create a Kubernetes cluster with link:https://kind.sigs.k8s.io/[kind].
 
 === Helm
 
@@ -65,8 +66,11 @@ Then install the Stackable Operators:
 include::example$getting_started/code/getting_started.sh[tag=helm-install-operators]
 ----
 
-Helm will deploy the Operators in a Kubernetes Deployment and apply the CRDs for the Airflow cluster (as well as the CRDs for the required operators). You are now ready to deploy Apache Airflow in Kubernetes.
+Helm will deploy the Operators in a Kubernetes Deployment and apply the CRDs for the Airflow cluster (as well as the
+CRDs for the required operators). You are now ready to deploy Apache Airflow in Kubernetes.
 
 == What's next
 
-xref:getting_started/first_steps.adoc[Set up an Airflow cluster] and its dependencies and xref:getting_started/first_steps.adoc#_verify_that_it_works[verify that it works] by inspecting and running an example DAG.
+xref:getting_started/first_steps.adoc[Set up an Airflow cluster] and its dependencies and
+xref:getting_started/first_steps.adoc#_verify_that_it_works[verify that it works] by inspecting and running an example
+DAG.

docs/modules/airflow/pages/index.adoc

@@ -2,12 +2,18 @@
 :description: The Stackable Operator for Apache Airflow is a Kubernetes operator that can manage Apache Airflow clusters. Learn about its features, resources, dependencies and demos, and see the list of supported Airflow versions.
 :keywords: Stackable Operator, Apache Airflow, Kubernetes, k8s, operator, engineer, big data, metadata, job pipeline, scheduler, workflow, ETL
 
+:k8s-crs: https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/
+
 The Stackable Operator for Apache Airflow manages https://airflow.apache.org/[Apache Airflow] instances on Kubernetes.
-Apache Airflow is an open-source application for creating, scheduling, and monitoring workflows. Workflows are defined as code, with tasks that can be run on a variety of platforms, including Hadoop, Spark, and Kubernetes itself. Airflow is a popular choice to orchestrate ETL workflows and data pipelines.
+Apache Airflow is an open-source application for creating, scheduling, and monitoring workflows. Workflows are defined
+as code, with tasks that can be run on a variety of platforms, including Hadoop, Spark, and Kubernetes itself. Airflow
+is a popular choice to orchestrate ETL workflows and data pipelines.
 
 == Getting started
 
-Get started using Airflow with the Stackable Operator by following the xref:getting_started/index.adoc[] guide. It guides you through installing the Operator alongside a PostgreSQL database and Redis instance, connecting to your Airflow instance and running your first workflow.
+Get started using Airflow with the Stackable Operator by following the xref:getting_started/index.adoc[] guide. It
+guides you through installing the Operator alongside a PostgreSQL database and Redis instance, connecting to your
+Airflow instance and running your first workflow.
 
 === Custom resources
 
@@ -58,7 +64,11 @@ image::airflow_overview.drawio.svg[A diagram depicting the Kubernetes resources
 
 The diagram above depicts all the Kubernetes resources created by the operator, and how they relate to each other.
 
-For every xref:concepts:roles-and-role-groups.adoc#_role_groups[role group] you define, the Operator creates a StatefulSet with the amount of replicas defined in the RoleGroup. Every Pod in the StatefulSet has two containers: the main container running Airflow and a sidecar container gathering metrics for xref:operators:monitoring.adoc[]. The Operator creates a Service per role group as well as a single service for the whole `webserver` role called `<clustername>-webserver`.
+For every xref:concepts:roles-and-role-groups.adoc#_role_groups[role group] you define, the Operator creates a
+StatefulSet with the amount of replicas defined in the RoleGroup. Every Pod in the StatefulSet has two containers: the
+main container running Airflow and a sidecar container gathering metrics for xref:operators:monitoring.adoc[]. The
+Operator creates a Service per role group as well as a single service for the whole `webserver` role called
+`<clustername>-webserver`.
 
 Additionally, a ConfigMap is created for each RoleGroup. These ConfigMaps contain two files, `log_config.py` and `webserver_config.py`, which contain logging and general Airflow configuration respectively.
 
@@ -70,11 +80,14 @@ NOTE: Redis is only needed if the executors have been set to `spec.celeryExecuto
 
 == Using custom workflows/DAGs
 
-https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html[Direct acyclic graphs (DAGs) of tasks] are the core entities you will use in Airflow. Have a look at the page on xref:usage-guide/mounting-dags.adoc[] to learn about the different ways of loading your custom DAGs into Airflow.
+https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html[Direct acyclic graphs (DAGs) of tasks] are
+the core entities you will use in Airflow. Have a look at the page on xref:usage-guide/mounting-dags.adoc[] to learn
+about the different ways of loading your custom DAGs into Airflow.
 
 == Demo
 
-You can install the xref:stackablectl::demos/airflow-scheduled-job.adoc[] demo and explore an Airflow installation, as well as how it interacts with xref:spark-k8s:index.adoc[Apache Spark].
+You can install the xref:demos:airflow-scheduled-job.adoc[] demo and explore an Airflow installation, as
+well as how it interacts with xref:spark-k8s:index.adoc[Apache Spark].
 
 == Supported Versions
 

tests/templates/kuttl/logging/airflow-vector-aggregator-values.yaml.j2

@@ -92,10 +92,6 @@ customConfig:
       condition: >-
         .pod == "airflow-scheduler-custom-log-config-0" &&
         .container == "vector"
-    filteredAutomaticLogConfigInitDb:
-      type: filter
-      inputs: [vector]
-      condition: .container == "airflow-init-db"
     filteredInvalidEvents:
       type: filter
       inputs: [vector]