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‎modules/concepts/pages/opa.adoc

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== How it works
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// How it is deployed
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OPA is run by the xref:opa::index.adoc[Stackable OPA operator]. OPA is deployed with the OpaCluster resource, from which the operator creates a DaemonSet to run an OPA instance on every node of the cluster. Because of this, every Pod making policy requests will always make the request locally, minimizing latency and network traffic.
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OPA is run by the xref:0.10@opa::index.adoc[Stackable OPA operator]. OPA is deployed with the OpaCluster resource, from which the operator creates a DaemonSet to run an OPA instance on every node of the cluster. Because of this, every Pod making policy requests will always make the request locally, minimizing latency and network traffic.
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=== Define policies
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== Further reading
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Read more about the xref:opa::index.adoc[]. Read more about product integration with OPA for these products:
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Read more about the xref:0.10@opa::index.adoc[]. Read more about product integration with OPA for these products:
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* xref:0.6@trino::usage.adoc#_authorization[Trino]
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* xref:kafka::usage.adoc[Kafka]
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* xref:druid::usage.adoc#_using_open_policy_agent_opa_for_authorization[Druid]
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* xref:0.7@kafka::usage.adoc[Kafka]
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* xref:0.7@druid::usage.adoc#_using_open_policy_agent_opa_for_authorization[Druid]
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You can also have a look at the xref:contributor:opa_configuration.adoc[implementation guidelines for OPA authorizers] or learn more about the xref:service_discovery.adoc[service discovery mechanism] used across the platform.

‎modules/concepts/pages/pvc.adoc

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== Operator usage
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=== Airflow
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The xref:airflow::index.adoc[Airflow operator] can use a xref:airflow::usage.adoc#_via_persistentvolumeclaim[PersistentVolumeClaim for externally-loaded airflow jobs], known as Direct Acyclic Graphs (DAGs), as shown in this https://github.com/stackabletech/airflow-operator/blob/main/examples/simple-airflow-cluster-dags-pvc.yaml[example]. Airflow expects that all components (webserver, scheduler and workers) have access to the DAG folder, so the PersistentVolumeClaim either has to be accessible with the `ReadWriteMany` access mode or node selection should be declared to ensure all components run on the same node.
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The xref:0.5@airflow::index.adoc[Airflow operator] can use a xref:0.5@airflow::usage.adoc#_via_persistentvolumeclaim[PersistentVolumeClaim for externally-loaded airflow jobs], known as Direct Acyclic Graphs (DAGs), as shown in this https://github.com/stackabletech/airflow-operator/blob/main/examples/simple-airflow-cluster-dags-pvc.yaml[example]. Airflow expects that all components (webserver, scheduler and workers) have access to the DAG folder, so the PersistentVolumeClaim either has to be accessible with the `ReadWriteMany` access mode or node selection should be declared to ensure all components run on the same node.
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=== Spark-k8s
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Users of the xref:spark-k8s::index.adoc[Spark-k8s operator] have a variety of ways to manage SparkApplication dependencies, one of which is to xref:spark-k8s::usage.adoc#_pyspark_externally_located_dataset_artifact_available_via_pvcvolume_mount[mount resources on a PersistentVolumeClaim]. An example is shown https://github.com/stackabletech/spark-k8s-operator/blob/main/examples/ny-tlc-report.yaml[here].
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Users of the xref:0.5@spark-k8s::index.adoc[Spark-k8s operator] have a variety of ways to manage SparkApplication dependencies, one of which is to xref:0.5@spark-k8s::usage.adoc#_pyspark_externally_located_dataset_artifact_available_via_pvcvolume_mount[mount resources on a PersistentVolumeClaim]. An example is shown https://github.com/stackabletech/spark-k8s-operator/blob/main/examples/ny-tlc-report.yaml[here].
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== Further reading
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‎modules/concepts/pages/s3.adoc

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// -------------- Intro ----------------
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Many of the tools on the Stackable platform integrate with S3 storage in some way.
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For example Druid can xref:druid::usage.adoc#_s3_for_ingestion[ingest data from S3] and also xref:druid::usage.adoc##_s3_deep_storage[use S3 as a backend for deep storage], Spark can use an xref:spark-k8s::usage.adoc#_s3_bucket_specification[S3 bucket] to store application files and data.
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For example Druid can xref:0.7@druid::usage.adoc#_s3_for_ingestion[ingest data from S3] and also xref:0.7@druid::usage.adoc##_s3_deep_storage[use S3 as a backend for deep storage], Spark can use an xref:0.5@spark-k8s::usage.adoc#_s3_bucket_specification[S3 bucket] to store application files and data.
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== S3Connection and S3Bucket
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// introducing the objects

‎modules/concepts/pages/service_discovery.adoc

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Consult discovery ConfigMap documentation for specific products:
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* xref:druid::discovery.adoc[Apache Druid]
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* xref:hdfs::discovery.adoc[Apache Hadoop HDFS]
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* xref:hive::discovery.adoc[Apache Hive]
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* xref:kafka::discovery.adoc[Apache Kafka]
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* xref:opa::discovery.adoc[OPA]
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* xref:zookeeper::discovery.adoc[Apache ZooKeeper]
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* xref:0.7@druid::discovery.adoc[Apache Druid]
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* xref:0.5@hdfs::discovery.adoc[Apache Hadoop HDFS]
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* xref:0.7@hive::discovery.adoc[Apache Hive]
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* xref:0.7@kafka::discovery.adoc[Apache Kafka]
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* xref:0.10@opa::discovery.adoc[OPA]
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* xref:0.11@zookeeper::discovery.adoc[Apache ZooKeeper]

‎modules/operators/pages/supported_versions.adoc

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== Apache Airflow
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include::airflow::partial$supported-versions.adoc[]
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include::0.5@airflow::partial$supported-versions.adoc[]
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== Apache Druid
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include::druid::partial$supported-versions.adoc[]
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include::0.7@druid::partial$supported-versions.adoc[]
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== Apache HBase
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include::hbase::partial$supported-versions.adoc[]
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include::0.4@hbase::partial$supported-versions.adoc[]
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== Apache HDFS
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include::hdfs::partial$supported-versions.adoc[]
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include::0.5@hdfs::partial$supported-versions.adoc[]
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== Apache Hive
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include::hive::partial$supported-versions.adoc[]
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include::0.7@hive::partial$supported-versions.adoc[]
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== Apache Kafka
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include::kafka::partial$supported-versions.adoc[]
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include::0.7@kafka::partial$supported-versions.adoc[]
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== Apache NiFi
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include::nifi::partial$supported-versions.adoc[]
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include::0.7@nifi::partial$supported-versions.adoc[]
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== Open Policy Agent (OPA)
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include::opa::partial$supported-versions.adoc[]
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include::0.10@opa::partial$supported-versions.adoc[]
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== Apache Spark on Kubernetes
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include::spark-k8s::partial$supported-versions.adoc[]
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include::0.5@spark-k8s::partial$supported-versions.adoc[]
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== Apache Superset
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include::superset::partial$supported-versions.adoc[]
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include::0.6@superset::partial$supported-versions.adoc[]
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== Apache ZooKeeper
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== Trino
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include::zookeeper::partial$supported-versions.adoc[]
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include::0.6@trino::partial$supported-versions.adoc[]
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== Trino
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== Apache ZooKeeper
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include::trino::partial$supported-versions.adoc[]
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include::0.11@zookeeper::partial$supported-versions.adoc[]

‎modules/operators/partials/operator_doc_links.adoc

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** xref:airflow::index.adoc[Apache Airflow]
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** xref:druid::index.adoc[Apache Druid]
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** xref:hbase::index.adoc[Apache HBase]
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** xref:hdfs::index.adoc[Apache Hadoop HDFS]
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** xref:hive::index.adoc[Apache Hive]
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** xref:kafka::index.adoc[Apache Kafka]
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** xref:nifi::index.adoc[Apache NiFi]
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** xref:spark-k8s::index.adoc[Apache Spark on K8S]
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** xref:superset::index.adoc[Apache Superset]
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** xref:trino::index.adoc[Trino]
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** xref:zookeeper::index.adoc[Apache ZooKeeper]
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** xref:opa::index.adoc[OpenPolicyAgent]
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** xref:commons-operator::index.adoc[Commons]
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** xref:secret-operator::index.adoc[Secret]
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** xref:0.5@airflow::index.adoc[Apache Airflow]
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** xref:0.7@druid::index.adoc[Apache Druid]
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** xref:0.4@hbase::index.adoc[Apache HBase]
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** xref:0.5@hdfs::index.adoc[Apache Hadoop HDFS]
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** xref:0.7@hive::index.adoc[Apache Hive]
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** xref:0.7@kafka::index.adoc[Apache Kafka]
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** xref:0.7@nifi::index.adoc[Apache NiFi]
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** xref:0.5@spark-k8s::index.adoc[Apache Spark on K8S]
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** xref:0.6@superset::index.adoc[Apache Superset]
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** xref:0.6@trino::index.adoc[Trino]
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** xref:0.11@zookeeper::index.adoc[Apache ZooKeeper]
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** xref:0.10@opa::index.adoc[OpenPolicyAgent]
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** xref:0.3@commons-operator::index.adoc[Commons]
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** xref:0.5@secret-operator::index.adoc[Secret]

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