Docs: sentence-case section headings

docs/guides/dbt_docs/generating-docs.rst

@@ -121,7 +121,7 @@ Choosing a folder
 All the DbtDocsOperators support specification of a custom folder (prefix) to place documentation in on the target cloud storage. This can be done by
 adding a ``folder_dir`` parameter to the operator definition.
 
-Static Flag
+Static flag
 '''''''''''
 
 All of the DbtDocsOperator accept the ``--static`` flag. To learn more about the static flag, check out the `original PR on dbt-core <https://github.com/dbt-labs/dbt-docs/pull/465>`_.
@@ -149,7 +149,7 @@ The following code snippet shows how to provide this flag with the default jaffl
         dbt_cmd_flags=["--static"],
     )
 
-Custom Callback
+Custom callback
 '''''''''''''''
 
 If you want to run custom code after the docs are generated, you can use the :class:`~cosmos.operators.DbtDocsOperator`. The following code snippet shows how to do this with the default jaffle_shop project:

docs/guides/index.rst

@@ -77,7 +77,7 @@ Specify more details about how Cosmos runs both dbt commands and Airflow Dags. T
    run_dbt/operators/index
    run_dbt/customization/index
 
-Multi-project Setups
+Multi-project setups
 ++++++++++++++++++++
 
 If you have a multi-project architecture where you have multiple dbt projects that reference each others' models, you can set up ``dbt-loom`` with Cosmos to handle cross-project references.

docs/guides/multi_project/multi-project.rst

@@ -1,6 +1,6 @@
 .. _multi-project:
 
-Multi-Project Setups
+Multi-project setups
 --------------------
 
 Cosmos supports multi-project dbt architectures where multiple dbt projects reference each other's models.
@@ -15,7 +15,7 @@ This allows you to:
 
 Cosmos works with dbt-loom out of the box, automatically handling external node references.
 
-How dbt-loom Works
+How dbt-loom works
 ++++++++++++++++++
 
 dbt-loom enables cross-project references by:
@@ -24,7 +24,7 @@ dbt-loom enables cross-project references by:
 2. Injecting the upstream models' metadata into the downstream project's namespace
 3. Allowing cross-project references using the dbt Mesh syntax: ``{{ ref('upstream_project', 'model_name') }}``
 
-How Cosmos Handles dbt-loom
+How Cosmos handles dbt-loom
 +++++++++++++++++++++++++++
 
 When Cosmos parses a dbt project that uses dbt-loom, it encounters two types of nodes:
@@ -62,10 +62,10 @@ The upstream manifest can be generated by running any dbt command that parses th
     # or
     dbt ls       # Lists resources and generates manifest
 
-Configuration Example
+Configuration example
 +++++++++++++++++++++
 
-Project Structure
+Project structure
 '''''''''''''''''
 
 A typical dbt-loom setup has an upstream project and one or more downstream projects:
@@ -92,7 +92,7 @@ A typical dbt-loom setup has an upstream project and one or more downstream proj
         └── models/
             └── fct_revenue.sql      # References upstream models
 
-Upstream Project Configuration
+Upstream project configuration
 ''''''''''''''''''''''''''''''
 
 The upstream project exposes models as ``public`` for cross-project access:
@@ -115,7 +115,7 @@ The upstream project exposes models as ``public`` for cross-project access:
           +materialized: view
           +access: public
 
-Downstream Project Configuration
+Downstream project configuration
 ''''''''''''''''''''''''''''''''
 
 The downstream project configures dbt-loom to read from the upstream manifest.
@@ -335,7 +335,7 @@ To disable automatic asset emission:
         # ...
     )
 
-Cross-Project Sources
+Cross-project sources
 +++++++++++++++++++++
 
 dbt-loom handles **model references** but does not directly support cross-project source references
@@ -397,7 +397,7 @@ If you must reference the same raw table in multiple projects, define the source
 This approach requires keeping source definitions in sync across projects.
 
 
-Cross-Project Macros
+Cross-project macros
 ++++++++++++++++++++
 
 dbt-loom does **not** handle macro sharing. Macros are resolved at compile time within each project.

docs/guides/run_dbt/airflow-worker/async-execution-mode.rst

@@ -36,7 +36,7 @@ We have `observed <https://github.com/astronomer/astronomer-cosmos/pull/1934>`_
 +----------------------------------------------+--------------------------+
 
 
-Getting Started with Airflow Async Mode
+Getting started with Airflow async mode
 +++++++++++++++++++++++++++++++++++++++
 
 This guide walks you through setting up an Astro CLI project and running a Cosmos-based DAG with a deferrable operator, enabling asynchronous task execution in Apache Airflow.
@@ -47,7 +47,7 @@ Prerequisites
 - `Astro CLI <https://www.astronomer.io/docs/astro/cli/install-cli>`_
 - Airflow>=2.9
 
-1. Create Astro-CLI Project
+1. Create Astro-CLI project
 '''''''''''''''''''''''''''
 
 Run the following command in your terminal:
@@ -82,7 +82,7 @@ Edit your Dockerfile to ensure all necessary requirements are included.
     FROM astrocrpublic.azurecr.io/runtime:3.0-2
 
 
-3. Add astronomer-cosmos Dependency
+3. Add astronomer-cosmos dependency
 '''''''''''''''''''''''''''''''''''
 
 In your ``requirements.txt``, add:

docs/guides/run_dbt/airflow-worker/watcher-execution-mode.rst

@@ -1,6 +1,6 @@
 .. _watcher-execution-mode:
 
-Watcher execution mode (Experimental)
+Watcher execution mode (experimental)
 -------------------------------------
 
 With the release of **Cosmos 1.11.0**, we are introducing a powerful new experimental execution mode — ``ExecutionMode.WATCHER`` — designed to drastically reduce dbt pipeline run times in `Apache Airflow® <https://airflow.apache.org/>`_.
@@ -12,7 +12,7 @@ Early benchmarks show that ``ExecutionMode.WATCHER`` can cut total DAG runtime *
 - The ``poke_interval`` and ``timeout`` settings of the ``DbtConsumerWatcherSensor`` operator, which determine the frequency and duration of the sensor's polling.
 
 
-Background: The Problem with the Local Execution Mode in Cosmos
+Background: The problem with the local execution mode in Cosmos
 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 When running dbt via Cosmos using the default ``ExecutionMode.LOCAL``, each dbt model is executed as a separate Airflow task.
@@ -52,7 +52,7 @@ Together, these operators let you:
 - Retry specific models (for resilience)
 
 
-Performance Gains
+Performance gains
 +++++++++++++++++
 
 We used a dbt project developed by Google, the `google/fhir-dbt-analytics <https://github.com/google/fhir-dbt-analytics>`_ project, that interfaces with BigQuery. It contains:
@@ -128,7 +128,7 @@ If you prefer to manage threads through Cosmos profile mappings instead of editi
 
 
 
-Example Usage of ``ExecutionMode.WATCHER``
+Example usage of ``ExecutionMode.WATCHER``
 ++++++++++++++++++++++++++++++++++++++++++
 
 There are two main ways to use the new execution mode in Cosmos — directly within a ``DbtDag``, or embedded as part of a ``DbtTaskGroup`` inside a larger DAG.
@@ -341,7 +341,7 @@ Starting with Cosmos 1.12.0, the ``DbtConsumerWatcherSensor`` supports
 which increases overall task throughput. By default, the sensor now runs in deferrable mode.
 
 
-Known Limitations
+Known limitations
 +++++++++++++++++
 
 Producer task implementation

docs/guides/run_dbt/container/kubernetes.rst

@@ -171,7 +171,7 @@ Enable and trigger a run of the `jaffle_shop_k8s <https://github.com/astronomer/
 
 .. _kubernetes-known-limitations:
 
-Known Limitations
+Known limitations
 +++++++++++++++++
 
 The Kubernetes execution mode has the following limitations:

docs/guides/run_dbt/container/watcher-kubernetes-execution-mode.rst

@@ -1,7 +1,7 @@
 .. _watcher-kubernetes-execution-mode:
 
 
-Watcher Kubernetes execution mode (Experimental)
+Watcher Kubernetes execution mode (experimental)
 ------------------------------------------------
 
 .. versionadded:: 1.13.0
@@ -25,7 +25,7 @@ However, the original ``ExecutionMode.WATCHER`` requires dbt to be installed alo
 For more details on the watcher concept and how it works, please refer to the :ref:`watcher-execution-mode` documentation.
 
 
-How to Use
+How to use
 ++++++++++
 
 Users previously using ``ExecutionMode.KUBERNETES`` can simply replace the ``execution_mode`` to use ``ExecutionMode.WATCHER_KUBERNETES``.
@@ -61,7 +61,7 @@ The following example shows how to configure a ``DbtDag`` with ``ExecutionMode.W
 For the complete setup including Kubernetes secrets, Docker image configuration, and profile setup, refer to the :ref:`kubernetes` documentation.
 
 
-Performance Gains
+Performance gains
 +++++++++++++++++
 
 Early benchmarks using the ``jaffle_shop_watcher_kubernetes`` DAG show significant improvements:
@@ -83,10 +83,10 @@ The performance improvement comes from:
 - Eliminating repeated dbt initialization for each model
 
 
-Known Limitations
+Known limitations
 +++++++++++++++++
 
-Kubernetes Provider Version Compatibility
+Kubernetes provider version compatibility
 '''''''''''''''''''''''''''''''''''''''''
 
 ``ExecutionMode.WATCHER_KUBERNETES`` does not work with older versions of the ``apache-airflow-providers-cncf-kubernetes`` provider (<=10.7.0).
@@ -154,7 +154,7 @@ The  ``operator_args`` must define ``get_logs`` and ``log_events_on_failure``:
     )
 
 
-Other Inherited Limitations
+Other inherited limitations
 '''''''''''''''''''''''''''
 
 The following limitations from ``ExecutionMode.WATCHER`` also apply to ``ExecutionMode.WATCHER_KUBERNETES``:

docs/guides/run_dbt/customization/custom-airflow-properties.rst

@@ -1,11 +1,11 @@
 .. _custom-airflow-properties:
 
-Apache Airflow® Configuration Overrides with Astronomer Cosmos
+Apache Airflow® configuration overrides with Astronomer Cosmos
 ---------------------------------------------------------------
 
 **Astronomer Cosmos** allows you to override Airflow configurations for each dbt task (dbt operator) via the dbt YAML file.
 
-Sample dbt Model YAML
+Sample dbt model YAML
 +++++++++++++++++++++
 
 .. code-block:: yaml

docs/guides/run_dbt/customization/scheduling.rst

@@ -5,7 +5,7 @@ Scheduling
 
 Because Cosmos uses `Apache Airflow® <https://airflow.apache.org/>`_ to power scheduling, you can leverage Airflow's scheduling capabilities to schedule your dbt projects. This includes cron-based scheduling, timetables, and data-aware scheduling. For more info on Airflow's scheduling capabilities, check out the Airflow documentation or check out the `Astronomer documentation <https://docs.astronomer.io/learn/scheduling-in-airflow>`_.
 
-Time-Based Scheduling
+Time-based scheduling
 +++++++++++++++++++++
 
 To schedule a dbt project on a time-based schedule, you can use Airflow's scheduling options. For example, to run a dbt project every day starting on January 1, 2023, you can use the following DAG:
@@ -22,7 +22,7 @@ To schedule a dbt project on a time-based schedule, you can use Airflow's schedu
 
 .. _data-aware-scheduling:
 
-Data-Aware Scheduling
+Data-aware scheduling
 +++++++++++++++++++++
 
 `Apache Airflow® <https://airflow.apache.org/>`_ 2.4 introduced the concept of `scheduling based on Datasets <https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/datasets.html>`_.
@@ -247,7 +247,7 @@ How Dataset Emission Differs by Execution Mode
    emitted URIs by enabling the ``enable_uri_xcom`` setting (see `Emitting Dataset URIs as XCom`_).
 
 
-Known Limitations
+Known limitations
 '''''''''''''''''
 
 Airflow 3.0 and beyond

docs/guides/translate_dbt_to_airflow/managing-sources.rst

@@ -1,6 +1,6 @@
 .. _managing-sources:
 
-Managing Sources
+Managing sources
 ----------------
 
 .. note::
@@ -37,7 +37,7 @@ Example:
     )
 
 
-Source Pruning
+Source pruning
 ++++++++++++++
 
 The ``source_pruning`` is a boolean parameter available in the ``RenderConfig``.
@@ -63,7 +63,7 @@ Example:
 In this example, if the ``jaffle_shop`` project has multiple sources, but only some of them are upstream of the ``customers`` model, Cosmos will only render the necessary sources and prune the rest.
 
 
-on_warning_callback Callback
+on_warning_callback callback
 ++++++++++++++++++++++++++++
 
 The ``on_warning_callback`` is a callback parameter available on the ``DbtSourceLocalOperator``. This callback is triggered when a warning occurs during the execution of the ``dbt source freshness`` command. The callback accepts the task context, which includes additional parameters: test_names and test_results

docs/guides/translate_dbt_to_airflow/testing-behavior.rst

@@ -1,9 +1,9 @@
 .. _testing-behavior:
 
-Testing Behavior
+Testing behavior
 ----------------
 
-Testing Configuration
+Testing configuration
 +++++++++++++++++++++
 
 By default, Cosmos will add a test after each model. This can be overridden using the ``test_behavior`` field in the ``RenderConfig`` object.
@@ -49,7 +49,7 @@ Finally, an example DAG and how it is rendered in the `Apache Airflow® <https:/
 
 .. image:: ../../_static/test_behavior_build.png
 
-Warning Behavior
+Warning behavior
 ++++++++++++++++
 
 .. note::
@@ -113,7 +113,7 @@ When at least one WARN message is present, the function passed to ``on_warning_c
     ``test_results`` context variables, which are specific to test-related warnings.
 
 
-Tests with Multiple Parents
+Tests with multiple parents
 +++++++++++++++++++++++++++
 
 It is common for dbt projects to define tests that rely on multiple upstream models, snapshots or seeds.

docs/index.rst

@@ -35,14 +35,14 @@ simple to manage and orchestrate your dbt workflows using `Apache Airflow® <htt
 time and effort. By automatically turning dbt workflows into Airflow DAGs, Cosmos allows you to focus on building
 high-quality data models without the hassle of managing complex integrations.
 
-What Is Astronomer Cosmos?
+What is Astronomer Cosmos?
 --------------------------
 
 Astronomer Cosmos is an open-source library that bridges Apache Airflow and dbt, allowing you to easily transform your
 dbt projects into Airflow Dags and manage everything seamlessly. With Cosmos, you can write your data transformations
 using dbt and then schedule and orchestrate them with Airflow, making the entire process smooth and straightforward.
 
-Why Should You Use Cosmos?
+Why should you use Cosmos?
 --------------------------
 
 Integrating dbt and Airflow can be complex, but Cosmos simplifies it by seamlessly connecting these
@@ -66,7 +66,7 @@ Check out the Cosmos Fundamentals, get started locally with the Quickstart Guide
 You can also explore more examples in `/dev/dags <https://github.com/astronomer/astronomer-cosmos/tree/main/dev/dags>`_
 or in the `cosmos-demo repo <https://github.com/astronomer/cosmos-demo>`_.
 
-Join the Community
+Join the community
 ------------------
 
 Have questions, need help, or interested in contributing? We welcome all contributions and feedback!

docs/optimize_performance/invocation_mode.rst

@@ -1,6 +1,6 @@
 .. _invocation-mode:
 
-Invocation Modes
+Invocation modes
 ----------------
 .. versionadded:: 1.4