[Ready] Remove FAQ documentation content, fix links

docs/conf.py

@@ -215,13 +215,13 @@
     "https://eternallybored.org/misc/wget/",
     "https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table.from_pandas",
     "https://www.oracle.com/java/technologies/javase-downloads.html",  # "forbidden" url
-    "https://towardsdatascience.com/the-importance-of-layered-thinking-in-data-engineering-a09f685edc71",
-    "https://medium.com/quantumblack/beyond-the-notebook-and-into-the-data-science-framework-revolution-a7fd364ab9c4",
     "https://www.java.com/en/download/help/download_options.html",  # "403 Client Error: Forbidden for url"
     # "anchor not found" but it's a valid selector for code examples
     "https://docs.delta.io/latest/delta-update.html#language-python",
     "https://github.com/kedro-org/kedro/blob/main/kedro/framework/project/default_logging.yml",
+    "https://github.com/kedro-org/kedro/blob/main/README.md#the-humans-behind-kedro",  # "anchor not found" but is valid
     "https://opensource.org/license/apache2-0-php/",
+    "https://docs.github.com/en/rest/overview/other-authentication-methods#via-username-and-password"
 ]
 
 # retry before render a link broken (fix for "too many requests")

docs/source/contribution/technical_steering_committee.md

@@ -1,8 +1,11 @@
 # Join the Technical Steering Committee
 
-The term "Technical Steering Committee (TSC)" describes the group of people that maintain Kedro. The TSC has many responsibilities surrounding the project's future development; you can read about our duties in our [Technical Charter](https://github.com/kedro-org/kedro/blob/main/kedro_technical_charter.pdf). We accept new members on the TSC as this fuels Kedro's continued development. You can see [a list of Kedro's current and past maintainers](../faq/faq.md#who-maintains-kedro).
+The term "Technical Steering Committee" (TSC) describes the group of Kedro maintainers.
+
+The TSC is responsible for the project's future development; you can read about our duties in our [Technical Charter](https://github.com/kedro-org/kedro/blob/main/kedro_technical_charter.pdf). We accept new members into the TSC to fuel Kedro's continued development. We list [Kedro's current and past maintainers](https://github.com/kedro-org/kedro/blob/main/README.md#the-humans-behind-kedro) on GitHub.
+
+In this section, we describe:
 
-In this section, we detail:
 - [Responsibilities of a maintainer](#responsibilities-of-a-maintainer)
 - [Requirements to become a maintainer](#requirements-to-become-a-maintainer)
 - [Application process](#application-process)
@@ -59,4 +62,6 @@ GitHub discussions is used to host votes on issues, proposals and changes affect
 
 ### Adding or removing maintainers
 
-Adding or removing maintainers requires a pull request against [our team list](../faq/faq.md#who-maintains-kedro). The decision is made based on TSC members votes in that pull request. Additions and removals of maintainers require **a 2/3 majority**.
+The decision to add or remove a maintainer is made based on TSC members votes in that pull request. Additions and removals of maintainers require **a 2/3 majority**.
+
+The act of adding or removing maintainers onto the list requires a pull request against [Kedro's README.md file](https://github.com/kedro-org/kedro/blob/main/README.md#the-humans-behind-kedro).

docs/source/deployment/aws_step_functions.md

@@ -387,4 +387,4 @@ Generally speaking, the [limitations on AWS Lambda](https://docs.aws.amazon.com/
 
 ## Final thought
 
-One major benefit of running a Kedro pipeline in a serverless computing platform is the ability to take advantage of other services from the same provider. For example, AWS has recently announced a [Feature Store for SageMaker](https://aws.amazon.com/sagemaker/feature-store/). We could easily use it as the Features layer in [Kedro's Data Engineering convention](../faq/faq.md#what-is-data-engineering-convention).
+One major benefit of running a Kedro pipeline in a serverless computing platform is the ability to take advantage of other services from the same provider, such as the use of the AWS [feature store for SageMaker](https://aws.amazon.com/sagemaker/feature-store/) to store features data.

docs/source/development/commands_reference.md

@@ -316,7 +316,7 @@ Call the `run()` method of the `KedroSession` defined in `kedro.framework.sessio
 kedro run
 ```
 
-`KedroContext` can be extended in `run.py` (`src/<package_name>/run.py`). In order to use the extended `KedroContext`, you need to set `context_path` in the [`pyproject.toml` configuration file](../faq/architecture_overview.md#kedro-project).
+`KedroContext` can be extended in `run.py` (`src/<package_name>/run.py`). In order to use the extended `KedroContext`, you need to set `context_path` in the `pyproject.toml` configuration file.
 
 #### Modifying a `kedro run`
 

docs/source/extend_kedro/plugins.md

@@ -219,7 +219,7 @@ Your plugin needs to have an [Apache 2.0 compatible license](https://www.apache.
 - [kedro-great](https://github.com/tamsanh/kedro-great), by [Tam-Sanh Nguyen](https://github.com/tamsanh), integrates Kedro with [Great Expectations](https://greatexpectations.io), enabling catalog-based expectation generation and data validation on pipeline run
 - [Kedro-Accelerator](https://github.com/deepyaman/kedro-accelerator), by [Deepyaman Datta](https://github.com/deepyaman), speeds up pipelines by parallelizing I/O in the background
 - [kedro-dataframe-dropin](https://github.com/mzjp2/kedro-dataframe-dropin), by [Zain Patel](https://github.com/mzjp2), lets you swap out pandas datasets for modin or RAPIDs equivalents for specialised use to speed up your workflows (e.g on GPUs)
-- [kedro-mlflow](https://github.com/Galileo-Galilei/kedro-mlflow), by [Yolan Honoré-Rougé](https://github.com/galileo-galilei) and [Takieddine Kadiri](https://github.com/takikadiri), facilitates [MLflow](https://www.mlflow.org/) integration inside Kedro projects while enforcing [Kedro's principles](../faq/faq.md#what-are-the-primary-advantages-of-kedro). Its main features are modular configuration, automatic parameters tracking, datasets versioning, Kedro pipelines packaging and serving and automatic synchronization between training and inference pipelines for high reproducibility of machine learning experiments and ease of deployment. A tutorial is provided in the [kedro-mlflow-tutorial repo](https://github.com/Galileo-Galilei/kedro-mlflow-tutorial). You can find more information in the [kedro-mlflow documentation](https://kedro-mlflow.readthedocs.io/en/stable/).
+- [kedro-mlflow](https://github.com/Galileo-Galilei/kedro-mlflow), by [Yolan Honoré-Rougé](https://github.com/galileo-galilei) and [Takieddine Kadiri](https://github.com/takikadiri), facilitates [MLflow](https://www.mlflow.org/) integration within a Kedro project. Its main features are modular configuration, automatic parameters tracking, datasets versioning, Kedro pipelines packaging and serving and automatic synchronization between training and inference pipelines for high reproducibility of machine learning experiments and ease of deployment. A tutorial is provided in the [kedro-mlflow-tutorial repo](https://github.com/Galileo-Galilei/kedro-mlflow-tutorial). You can find more information in the [kedro-mlflow documentation](https://kedro-mlflow.readthedocs.io/en/stable/).
 - [Kedro-Neptune](https://github.com/neptune-ai/kedro-neptune), by [Jakub Czakon](https://github.com/jakubczakon) and [Rafał Jankowski](https://github.com/Raalsky), lets you have all the benefits of a nicely organized Kedro pipeline with Neptune: a powerful user interface built for ML metadata management. It lets you browse and filter pipeline executions, compare nodes and pipelines on metrics and parameters, and visualize pipeline metadata like learning curves, node outputs, and charts. For more information, tutorials and videos, go to the [Kedro-Neptune documentation](https://docs.neptune.ai/integrations-and-supported-tools/automation-pipelines/kedro).
 - [kedro-dolt](https://www.dolthub.com/blog/2021-06-16-kedro-dolt-plugin/), by [Max Hoffman](https://github.com/max-hoffman) and [Oscar Batori](https://github.com/oscarbatori), allows you to expand the data versioning abilities of data scientists and engineers
 - [kedro-kubeflow](https://github.com/getindata/kedro-kubeflow), by [GetInData](https://github.com/getindata), lets you run and schedule pipelines on Kubernetes clusters using [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/overview/)