Fix several links to use native references

Author: koletzilla

docs/integrations/data-ingestion/etl-tools/dbt/features-and-configurations.md

@@ -126,8 +126,8 @@ without `on cluster` clause for this model.
 #### Read-after-write Consistency {#read-after-write-consistency}
 
 dbt relies on a read-after-insert consistency model. This is not compatible with ClickHouse clusters that have more than one replica if you cannot guarantee that all operations will go to the same replica. You may not encounter problems in your day-to-day usage of dbt, but there are some strategies depending on your cluster to have this guarantee in place:
-- If you are using a ClickHouse Cloud cluster, you only need to set `select_sequential_consistency: 1` in your profile's `custom_settings` property. You can find more information about this setting [here](https://clickhouse.com/docs/operations/settings/settings#select_sequential_consistency).
-- If you are using a self-hosted cluster, make sure all dbt requests are sent to the same ClickHouse replica. If you have a load balancer on top of it, try using some `replica aware routing`/`sticky sessions` mechanism to be able to always reach the same replica. Adding the setting `select_sequential_consistency = 1` in clusters outside ClickHouse Cloud is [not recommended](https://clickhouse.com/docs/operations/settings/settings#select_sequential_consistency).
+- If you are using a ClickHouse Cloud cluster, you only need to set `select_sequential_consistency: 1` in your profile's `custom_settings` property. You can find more information about this setting [here](/operations/settings/settings#select_sequential_consistency).
+- If you are using a self-hosted cluster, make sure all dbt requests are sent to the same ClickHouse replica. If you have a load balancer on top of it, try using some `replica aware routing`/`sticky sessions` mechanism to be able to always reach the same replica. Adding the setting `select_sequential_consistency = 1` in clusters outside ClickHouse Cloud is [not recommended](/operations/settings/settings#select_sequential_consistency).
 
 ## General information about features {#general-information-about-features}
 
@@ -269,7 +269,7 @@ group by event_type
 
 ### Materialization: view {#materialization-view}
 
-A dbt model can be created as a [ClickHouse view](https://clickhouse.com/docs/en/sql-reference/table-functions/view/)
+A dbt model can be created as a [ClickHouse view](/sql-reference/table-functions/view/)
 and configured using the following syntax:
 
 Project File (`dbt_project.yml`):
@@ -286,7 +286,7 @@ Or config block (`models/<model_name>.sql`):
 
 ### Materialization: table {#materialization-table}
 
-A dbt model can be created as a [ClickHouse table](https://clickhouse.com/docs/en/operations/system-tables/tables/) and
+A dbt model can be created as a [ClickHouse table](/operations/system-tables/tables/) and
 configured using the following syntax:
 
 Project File (`dbt_project.yml`):
@@ -500,7 +500,7 @@ If you prefer not to preload historical data during MV creation, you can disable
 
 #### Refreshable Materialized Views {#refreshable-materialized-views}
 
-To use [Refreshable Materialized View](https://clickhouse.com/docs/en/materialized-view/refreshable-materialized-view),
+To use [Refreshable Materialized View](/materialized-view/refreshable-materialized-view),
 please adjust the following configs as needed in your MV model (all these configs are supposed to be set inside a
 refreshable config object):
 
@@ -711,7 +711,7 @@ keys used to populate the parameters of the S3 table function:
 | structure             | The column structure of the data in bucket, as a list of name/datatype pairs, such as `['id UInt32', 'date DateTime', 'value String']`  If not provided ClickHouse will infer the structure. |
 | aws_access_key_id     | The S3 access key id.                                                                                                                                                                        |
 | aws_secret_access_key | The S3 secret key.                                                                                                                                                                           |
-| role_arn              | The ARN of a ClickhouseAccess IAM role to use to securely access the S3 objects. See this [documentation](https://clickhouse.com/docs/en/cloud/security/secure-s3) for more information.     |
+| role_arn              | The ARN of a ClickhouseAccess IAM role to use to securely access the S3 objects. See this [documentation](/cloud/data-sources/secure-s3) for more information.     |
 | compression           | The compression method used with the S3 objects.  If not provided ClickHouse will attempt to determine compression based on the file name.                                                   |
 
 See
@@ -738,9 +738,9 @@ dbt Core v1.10 introduced catalog integration support, which allows adapters to
 
 ClickHouse recently added native support for Apache Iceberg tables and data catalogs. Most of the features are still `experimental`, but you can already use them if you use a recent ClickHouse version.
 
-* You can use ClickHouse to **query Iceberg tables stored in object storage** (S3, Azure Blob Storage, Google Cloud Storage) using the [Iceberg table engine](https://clickhouse.com/docs/en/engines/table-engines/integrations/iceberg) and [iceberg table function](https://clickhouse.com/docs/en/sql-reference/table-functions/iceberg).
+* You can use ClickHouse to **query Iceberg tables stored in object storage** (S3, Azure Blob Storage, Google Cloud Storage) using the [Iceberg table engine](/engines/table-engines/integrations/iceberg) and [iceberg table function](/sql-reference/table-functions/iceberg).
 
-* Additionally, ClickHouse provides the [DataLakeCatalog database engine](https://clickhouse.com/docs/engines/database-engines/datalakecatalog), which enables **connection to external data catalogs** including AWS Glue Catalog, Databricks Unity Catalog, Hive Metastore, and REST Catalogs. This allows you to query open table format data (Iceberg, Delta Lake) directly from external catalogs without data duplication.
+* Additionally, ClickHouse provides the [DataLakeCatalog database engine](/engines/database-engines/datalakecatalog), which enables **connection to external data catalogs** including AWS Glue Catalog, Databricks Unity Catalog, Hive Metastore, and REST Catalogs. This allows you to query open table format data (Iceberg, Delta Lake) directly from external catalogs without data duplication.
 
 ### Workarounds for Working with Iceberg and Catalogs {#workarounds-iceberg-catalogs}