[docs] Update lifecycle warnings for integrations (dagster-io#27635)

## Summary & Motivation

Update warnings for all integrations changed in this [PR
stack](dagster-io#26865) for the API
Lifecycle project.

## How I Tested These Changes

BK and docs preview

Author: maximearmstrong

docs/docs/guides/build/external-pipelines/aws-emr-containers-pipeline.md

@@ -4,6 +4,10 @@ description: "Learn to integrate Dagster Pipes with AWS EMR Containers to launch
 sidebar_position: 300
 ---
 
+import Preview from '../../../partials/\_Preview.md';
+
+<Preview />
+
 This tutorial gives a short overview on how to use [Dagster Pipes](/guides/build/external-pipelines/) with [AWS EMR on EKS](https://aws.amazon.com/emr/features/eks/) (the corresponding AWS API is called `emr-containers`).
 
 The [dagster-aws](/api/python-api/libraries/dagster-aws) integration library provides the <PyObject section="libraries" object="pipes.PipesEMRContainersClient" module="dagster_aws" /> resource, which can be used to launch EMR jobs from Dagster assets and ops. Dagster can receive regular events such as logs, asset checks, or asset materializations from jobs launched with this client. Using it requires minimal code changes to your EMR jobs.

docs/docs/integrations/libraries/airbyte/airbyte-cloud.md

@@ -19,11 +19,9 @@ sidebar_custom_props:
   logo: images/integrations/airbyte.svg
 ---
 
-:::note
+import Beta from '../../../partials/\_Beta.md';
 
-This integration is currently in **beta**.
-
-:::
+<Beta />
 
 This guide provides instructions for using Dagster with Airbyte Cloud using the `dagster-airbyte` library. Your Airbyte Cloud connection tables can be represented as assets in the Dagster asset graph, allowing you to track lineage and dependencies between Airbyte Cloud assets and data assets you are already modeling in Dagster. You can also use Dagster to orchestrate Airbyte Cloud connections, allowing you to trigger syncs for these on a cadence or based on upstream data changes.
 

docs/docs/integrations/libraries/airlift/index.md

@@ -19,6 +19,10 @@ sidebar_custom_props:
   logo: images/integrations/airflow.svg
 ---
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 Airlift is a toolkit for integrating Dagster and Airflow. Using `dagster-airflift`, you can:
 
 - Observe Airflow instances from within Dagster

docs/docs/integrations/libraries/aws/cloudwatch.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/aws-cloudwatch.svg
 ---
 
+import Deprecated from '../../../partials/\_Deprecated.md';
+
+<Deprecated />
+
 This integration allows you to send Dagster logs to AWS CloudWatch, enabling centralized logging and monitoring of your Dagster jobs. By using AWS CloudWatch, you can take advantage of its powerful log management features, such as real-time log monitoring, log retention policies, and alerting capabilities.
 
 Using this integration, you can configure your Dagster jobs to log directly to AWS CloudWatch, making it easier to track and debug your workflows. This is particularly useful for production environments where centralized logging is essential for maintaining observability and operational efficiency.

docs/docs/integrations/libraries/aws/ecr.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/aws-ecr.svg
 ---
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 This integration allows you to connect to AWS Elastic Container Registry (ECR). It provides resources to interact with AWS ECR, enabling you to manage your container images.
 
 Using this integration, you can seamlessly integrate AWS ECR into your Dagster pipelines, making it easier to manage and deploy containerized applications.

docs/docs/integrations/libraries/aws/secretsmanager.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/aws-secretsmanager.svg
 ---
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 This integration allows you to manage, retrieve, and rotate credentials, API keys, and other secrets using [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/).
 
 ### Installation

docs/docs/integrations/libraries/aws/ssm.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/aws-ssm.svg
 ---
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 The Dagster AWS Systems Manager (SSM) Parameter Store integration allows you to manage and retrieve parameters stored in AWS SSM Parameter Store directly within your Dagster pipelines. This integration provides resources to fetch parameters by name, tags, or paths, and optionally set them as environment variables for your operations.
 
 ### Installation

docs/docs/integrations/libraries/datadog.md

@@ -19,6 +19,10 @@ sidebar_custom_props:
   logo: images/integrations/datadog.svg
 ---
 
+import Beta from '../../partials/\_Beta.md';
+
+<Beta />
+
 While Dagster provides comprehensive monitoring and observability of the pipelines it orchestrates, many teams look to centralize all their monitoring across apps, processes and infrastructure using Datadog's 'Cloud Monitoring as a Service'. The `dagster-datadog` integration allows you to publish metrics to Datadog from within Dagster ops.
 
 ### Installation

docs/docs/integrations/libraries/dbt/dbt-cloud.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/dbt/dbt.svg
 ---
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 Dagster allows you to run dbt Cloud jobs alongside other technologies. You can schedule them to run as a step in a larger pipeline and manage them as a data asset.
 
 ### Installation

docs/docs/integrations/libraries/dbt/reference.md

@@ -434,8 +434,6 @@ Dagster supports fetching additional metadata at dbt execution time to attach as
 
 :::note
 
-**Emitting row count data for dbt models is currently an experimental feature.**
-
 To use this feature, you'll need to be on at least `dagster>=0.17.6` and `dagster-dbt>=0.23.6`.
 
 :::
@@ -452,8 +450,6 @@ Once your dbt models have been materialized, you can view the row count data in
 
 :::note
 
-**Emitting column-level metadata for dbt models is currently an experimental feature.**
-
 To use this feature, you'll need to be on at least `dagster>=1.8.0` and `dagster-dbt>=0.24.0`.
 
 :::

docs/docs/integrations/libraries/dlt/index.md

@@ -21,12 +21,6 @@ sidebar_custom_props:
 
 This integration allows you to use [dlt](https://dlthub.com/) to easily ingest and replicate data between systems through Dagster.
 
-:::note
-
-This integration is currently **experimental**.
-
-:::
-
 ### Installation
 
 ```bash

docs/docs/integrations/libraries/dlt/using-dlt-with-dagster.md

@@ -3,12 +3,6 @@ title: "Using dlt with Dagster"
 description: Ingest data with ease using Dagster and dlt
 ---
 
-:::
-
-This feature is considered **experimental**
-
-:::
-
 The [data load tool (dlt)](https://dlthub.com/) open-source library defines a standardized approach for creating data pipelines that load often messy data sources into well-structured data sets. It offers many advanced features, such as:
 
 - Handling connection secrets
@@ -244,7 +238,7 @@ For example, let's say we have defined a set of dlt assets named `thinkific_asse
 
 ### Using partitions in your dlt assets
 
-While still an experimental feature, it is possible to use partitions within your dlt assets. However, it should be noted that this may result in concurrency related issues as state is managed by dlt. For this reason, it is recommended to set concurrency limits for your partitioned dlt assets. See the [Limiting concurrency in data pipelines](/guides/operate/managing-concurrency) guide for more details.
+It is possible to use partitions within your dlt assets. However, it should be noted that this may result in concurrency related issues as state is managed by dlt. For this reason, it is recommended to set concurrency limits for your partitioned dlt assets. See the [Limiting concurrency in data pipelines](/guides/operate/managing-concurrency) guide for more details.
 
 That said, here is an example of using static named partitions from a dlt source.
 

docs/docs/integrations/libraries/embedded-elt.md

@@ -24,11 +24,6 @@ This package includes two integrations:
 - [Sling](https://slingdata.io) provides a simple way to sync data between databases and file systems.
 - [data Load Tool (dlt)](https://dlthub.com) easily loads data from external systems and APIs.
 
-:::note
-
-This integration is currently **experimental**.
-
-:::
 
 ## Installation
 

docs/docs/integrations/libraries/fivetran.md

@@ -19,11 +19,9 @@ sidebar_custom_props:
   logo: images/integrations/fivetran.svg
 ---
 
-:::note
+import Beta from '../../partials/\_Beta.md';
 
-This integration is currently in **beta**.
-
-:::
+<Beta />
 
 This guide provides instructions for using Dagster with Fivetran using the `dagster-fivetran` library. Your Fivetran connector tables can be represented as assets in the Dagster asset graph, allowing you to track lineage and dependencies between Fivetran assets and data assets you are already modeling in Dagster. You can also use Dagster to orchestrate Fivetran connectors, allowing you to trigger syncs for these on a cadence or based on upstream data changes.
 

docs/docs/integrations/libraries/gcp/bigquery/index.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/gcp-bigquery.svg
 ---
 
+import Beta from '../../../../partials/\_Beta.md';
+
+<Beta />
+
 The Google Cloud Platform BigQuery integration allows data engineers to easily query and store data in the BigQuery data warehouse through the use of the `BigQueryResource`.
 
 ### Installation

docs/docs/integrations/libraries/gcp/bigquery/using-bigquery-with-dagster.md

@@ -4,6 +4,10 @@ description: Store your Dagster assets in BigQuery
 sidebar_position: 100
 ---
 
+import Beta from '../../../../partials/\_Beta.md';
+
+<Beta />
+
 This tutorial focuses on creating and interacting with BigQuery tables using Dagster's [asset definitions](/guides/build/assets/defining-assets).
 
 The `dagster-gcp` library provides two ways to interact with BigQuery tables:

docs/docs/integrations/libraries/gcp/dataproc.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/gcp-dataproc.svg
 ---
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 Using this integration, you can manage and interact with Google Cloud Platform's Dataproc service directly from Dagster. This integration allows you to create, manage, and delete Dataproc clusters, and submit and monitor jobs on these clusters.
 
 ### Installation

docs/docs/integrations/libraries/github.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/github.svg
 ---
 
+import Deprecated from '../../partials/\_Deprecated.md';
+
+<Deprecated />
+
 This library provides an integration with _[GitHub Apps](https://docs.github.com/en/developers/apps/getting-started-with-apps/about-apps)_ by providing a thin wrapper on the GitHub v4 GraphQL API. This allows for automating operations within your GitHub repositories and with the tighter permissions scopes that GitHub Apps allow for vs using a personal token.
 
 ### Installation

docs/docs/integrations/libraries/jupyter/index.md

@@ -19,6 +19,10 @@ sidebar_custom_props:
   logo: images/integrations/jupyter.svg
 ---
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 Dagstermill eliminates the tedious "productionization" of Jupyter notebooks.
 
 Using the Dagstermill library enables you to:

docs/docs/integrations/libraries/looker/index.md

@@ -19,6 +19,10 @@ sidebar_custom_props:
   logo: images/integrations/looker.svg
 ---
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 Dagster allows you to represent your Looker project as assets, alongside other your other technologies like dbt and Sling. This allows you to see how your Looker assets are connected to your other data assets, and how changes to other data assets might impact your Looker project.
 
 ### Installation

docs/docs/integrations/libraries/looker/using-looker-with-dagster.md

@@ -3,12 +3,9 @@ title: "Using Looker with Dagster"
 description: Represent your Looker assets in Dagster
 ---
 
-:::
-
-This feature is considered **experimental**
-
-:::
+import Beta from '../../../partials/\_Beta.md';
 
+<Beta />
 
 This guide provides instructions for using Dagster with Looker using the `dagster-looker` library. Your Looker assets, such as views, explores, and dashboards, can be represented in the Dagster asset graph, allowing you to track lineage and dependencies between Looker assets. You can also use Dagster to orchestrate Looker PDTs, allowing you to trigger refreshes of these materialized tables on a cadence or based on upstream data changes.
 

docs/docs/integrations/libraries/openai/using-openai-with-dagster.md

@@ -3,12 +3,6 @@ title: "OpenAI & Dagster"
 description: "The dagster-openai library provides the ability to build OpenAI pipelines with Dagster and log OpenAI API usage metadata in Dagster Insights."
 ---
 
-:::note
-
-This feature is considered **experimental**
-
-:::
-
 The `dagster-openai` library allows you to build OpenAI pipelines with Dagster and log OpenAI API usage metadata in [Dagster Insights](/dagster-plus/features/insights).
 
 Using this library's <PyObject section="libraries" module="dagster_openai" object="OpenAIResource" />, you can easily interact with the [OpenAI REST API](https://platform.openai.com/docs/introduction) via the [OpenAI Python API](https://github.com/openai/openai-python).

docs/docs/integrations/libraries/pandas/index.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/pandas.svg
 ---
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 Perform data validation, emit summary statistics, and enable reliable DataFrame serialization/deserialization. The dagster_pandas library provides you with the utilities for implementing validation on Pandas DataFrames. The Dagster type system generates documentation of your DataFrame constraints and makes it accessible in the Dagster UI.
 
 ### Installation

docs/docs/integrations/libraries/pandas/using-pandas-with-dagster.md

@@ -5,6 +5,10 @@ description: "The dagster-pandas library provides the ability to perform data va
 
 # Pandas & Dagster
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 :::note
 
 This page describes the `dagster-pandas` library, which is used for performing data validation. To simply use pandas with Dagster, start with the [Dagster Quickstart](/getting-started/quickstart)

docs/docs/integrations/libraries/pandera/index.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/pandera.svg
 ---
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 The `dagster-pandera` integration library provides an API for generating Dagster Types from [Pandera DataFrame schemas](https://pandera.readthedocs.io/en/stable/dataframe_schemas.html).
 
 Like all Dagster types, Dagster-Pandera-generated types can be used to annotate op inputs and outputs. This provides runtime type-checking with rich error reporting and allows Dagster UI to display information about a DataFrame's structure.

docs/docs/integrations/libraries/pandera/using-pandera-with-dagster.md

@@ -5,6 +5,10 @@ description: Generate Dagster types for dataframes with Pandera.
 
 # Pandera & Dagster
 
+import Beta from '../../../partials/\_Beta.md';
+
+<Beta />
+
 The `dagster-pandera` integration library provides an API for generating [Dagster Types](/api/python-api/types) from [Pandera](https://github.com/pandera-dev/pandera) dataframe schemas. Like all Dagster types, `dagster-pandera`-generated types can be used to annotate [op](/guides/build/ops) inputs and outputs.
 
 Using Pandera with Dagster allows you to:

docs/docs/integrations/libraries/powerbi/index.md

@@ -17,13 +17,11 @@ sidebar_custom_props:
   logo: images/integrations/powerbi.svg
 ---
 
-This guide provides instructions for using Dagster with Power BI using the `dagster-powerbi` library. Your Power BI assets, such as semantic models, data sources, reports, and dashboards, can be represented in the Dagster asset graph, allowing you to track lineage and dependencies between Power BI assets and upstream data assets you are already modeling in Dagster. You can also use Dagster to orchestrate Power BI semantic models, allowing you to trigger refreshes of these models on a cadence or based on upstream data changes.
-
-:::note
+import Beta from '../../../partials/\_Beta.md';
 
-This integration is currently **experimental**.
+<Beta />
 
-:::
+This guide provides instructions for using Dagster with Power BI using the `dagster-powerbi` library. Your Power BI assets, such as semantic models, data sources, reports, and dashboards, can be represented in the Dagster asset graph, allowing you to track lineage and dependencies between Power BI assets and upstream data assets you are already modeling in Dagster. You can also use Dagster to orchestrate Power BI semantic models, allowing you to trigger refreshes of these models on a cadence or based on upstream data changes.
 
 
 ## What you'll learn

docs/docs/integrations/libraries/powerbi/using-powerbi-with-dagster.md

@@ -3,11 +3,9 @@ title: "Using Power BI with Dagster"
 description: Represent your Power BI assets in Dagster
 ---
 
-:::note
+import Beta from '../../../partials/\_Beta.md';
 
-This feature is considered **experimental**
-
-:::
+<Beta />
 
 This guide provides instructions for using Dagster with Power BI using the [`dagster-powerbi`](/api/python-api/libraries/dagster-powerbi) library. Your Power BI assets, such as semantic models, data sources, reports, and dashboards, can be represented in the Dagster asset graph, allowing you to track lineage and dependencies between Power BI assets and upstream data assets you are already modeling in Dagster. You can also use Dagster to orchestrate Power BI semantic models, allowing you to trigger refreshes of these models on a cadence or based on upstream data changes.
 

docs/docs/integrations/libraries/prometheus.md

@@ -18,6 +18,10 @@ sidebar_custom_props:
   logo: images/integrations/prometheus.svg
 ---
 
+import Beta from '../../partials/\_Beta.md';
+
+<Beta />
+
 This integration allows you to push metrics to the Prometheus gateway from within a Dagster pipeline.
 
 ### Installation

docs/docs/integrations/libraries/sigma/index.md

@@ -17,14 +17,11 @@ sidebar_custom_props:
   logo: images/integrations/sigma.svg
 ---
 
-This guide provides instructions for using Dagster with Sigma using the `dagster-sigma` library. Your Sigma assets, including datasets and workbooks, can be represented in the Dagster asset graph, allowing you to track lineage and dependencies between Sigma assets and upstream data assets you are already modeling in Dagster.
-
-:::note
+import Beta from '../../../partials/\_Beta.md';
 
-This integration is currently **experimental**.
-
-:::
+<Beta />
 
+This guide provides instructions for using Dagster with Sigma using the `dagster-sigma` library. Your Sigma assets, including datasets and workbooks, can be represented in the Dagster asset graph, allowing you to track lineage and dependencies between Sigma assets and upstream data assets you are already modeling in Dagster.
 
 ## What you'll learn
 

docs/docs/integrations/libraries/sigma/using-sigma-with-dagster.md

@@ -3,11 +3,9 @@ title: "Using Sigma with Dagster"
 description: Represent your Sigma assets in Dagster
 ---
 
-:::note
+import Beta from '../../../partials/\_Beta.md';
 
-This feature is considered **experimental**
-
-:::
+<Beta />
 
 This guide provides instructions for using Dagster with Sigma using the [`dagster-sigma`](/api/python-api/libraries/dagster-sigma) library. Your Sigma assets, including datasets and workbooks, can be represented in the Dagster asset graph, allowing you to track lineage and dependencies between Sigma assets and upstream data assets you are already modeling in Dagster.