search and replace absolute links

docs/website/docs/dlt-ecosystem/destinations/bigquery.md

@@ -220,7 +220,7 @@ When staging is enabled:
 
 ## Supported Column Hints
 
-BigQuery supports the following [column hints](https://dlthub.com/docs/general-usage/schema#tables-and-columns):
+BigQuery supports the following [column hints](/general-usage/schema#tables-and-columns):
 
 * `partition` - creates a partition with a day granularity on the decorated column (`PARTITION BY DATE`).
   May be used with `datetime`, `date`, and `bigint` data types.

docs/website/docs/dlt-ecosystem/destinations/clickhouse.md

@@ -220,7 +220,7 @@ To enable this, GCS provides an S3
 compatibility mode that emulates the S3 API, allowing ClickHouse to access GCS buckets via its S3 integration.
 
 For detailed instructions on setting up S3-compatible storage with dlt, including AWS S3, MinIO, and Cloudflare R2, refer to
-the [dlt documentation on filesystem destinations](https://dlthub.com/docs/dlt-ecosystem/destinations/filesystem#using-s3-compatible-storage).
+the [dlt documentation on filesystem destinations](/dlt-ecosystem/destinations/filesystem#using-s3-compatible-storage).
 
 To set up GCS staging with HMAC authentication in dlt:
 

docs/website/docs/dlt-ecosystem/destinations/filesystem.md

@@ -414,7 +414,7 @@ disable_compression=true
 
 - To decompress a `gzip` file, you can use tools like `gunzip`. This will convert the compressed file back to its original format, making it readable.
 
-For more details on managing file compression, please visit our documentation on performance optimization: [Disabling and Enabling File Compression](https://dlthub.com/docs/reference/performance#disabling-and-enabling-file-compression).
+For more details on managing file compression, please visit our documentation on performance optimization: [Disabling and Enabling File Compression](/reference/performance#disabling-and-enabling-file-compression).
 
 ## Files layout
 All the files are stored in a single folder with the name of the dataset that you passed to the `run` or `load` methods of the `pipeline`. In our example chess pipeline, it is **chess_players_games_data**.

docs/website/docs/dlt-ecosystem/destinations/snowflake.md

@@ -194,7 +194,7 @@ Which will read, `|` delimited file, without header and will continue on errors.
 Note that we ignore missing columns `ERROR_ON_COLUMN_COUNT_MISMATCH = FALSE` and we will insert NULL into them.
 
 ## Supported column hints
-Snowflake supports the following [column hints](https://dlthub.com/docs/general-usage/schema#tables-and-columns):
+Snowflake supports the following [column hints](/general-usage/schema#tables-and-columns):
 * `cluster` - creates a cluster column(s). Many columns per table are supported and only when a new table is created.
 
 ## Table and column identifiers

docs/website/docs/dlt-ecosystem/destinations/synapse.md

@@ -173,7 +173,7 @@ Possible values:
 
 ## Supported column hints
 
-Synapse supports the following [column hints](https://dlthub.com/docs/general-usage/schema#tables-and-columns):
+Synapse supports the following [column hints](/general-usage/schema#tables-and-columns):
 
 * `primary_key` - creates a `PRIMARY KEY NONCLUSTERED NOT ENFORCED` constraint on the column
 * `unique` - creates a `UNIQUE NOT ENFORCED` constraint on the column

docs/website/docs/dlt-ecosystem/transformations/dbt/dbt_cloud.md

@@ -102,4 +102,4 @@ DBT_CLOUD__ACCOUNT_ID
 DBT_CLOUD__JOB_ID
 ```
 
-For more information, read the [Credentials](https://dlthub.com/docs/general-usage/credentials) documentation.
+For more information, read the [Credentials](/general-usage/credentials) documentation.

docs/website/docs/dlt-ecosystem/verified-sources/amazon_kinesis.md

@@ -15,7 +15,7 @@ streams of data in real time.
 
 Our AWS Kinesis [verified source](https://github.com/dlt-hub/verified-sources/tree/master/sources/kinesis)
 loads messages from Kinesis streams to your preferred
-[destination](https://dlthub.com/docs/dlt-ecosystem/destinations/).
+[destination](/dlt-ecosystem/destinations/).
 
 Resources that can be loaded using this verified source are:
 

docs/website/docs/dlt-ecosystem/verified-sources/google_analytics.md

@@ -329,7 +329,7 @@ verified source.
    ```
 
    > Loads all the data till date in the first run, and then
-   > [incrementally](https://dlthub.com/docs/general-usage/incremental-loading) in subsequent runs.
+   > [incrementally](/general-usage/incremental-loading) in subsequent runs.
 
 1. To load data from a specific start date:
 
@@ -340,7 +340,7 @@ verified source.
    ```
 
    > Loads data starting from the specified date during the first run, and then
-   > [incrementally](https://dlthub.com/docs/general-usage/incremental-loading) in subsequent runs.
+   > [incrementally](/general-usage/incremental-loading) in subsequent runs.
 
 <!--@@@DLT_TUBA google_analytics-->
 

docs/website/docs/dlt-ecosystem/verified-sources/google_sheets.md

@@ -441,11 +441,11 @@ dlt.resource(
 `name`: Denotes the table name, set here as "spreadsheet_info".
 
 `write_disposition`: Dictates how data is loaded to the destination.
-[Read more](https://dlthub.com/docs/general-usage/incremental-loading#the-3-write-dispositions).
+[Read more](/general-usage/incremental-loading#the-3-write-dispositions).
 
 `merge_key`: Parameter is used to specify the column used to identify records for merging. In this
 case,"spreadsheet_id", means that the records will be merged based on the values in this column.
-[Read more](https://dlthub.com/docs/general-usage/incremental-loading#merge-incremental_loading).
+[Read more](/general-usage/incremental-loading#merge-incremental_loading).
 
 ## Customization
 ### Create your own pipeline

docs/website/docs/dlt-ecosystem/verified-sources/jira.md

@@ -190,7 +190,7 @@ above.
 
 1. Configure the pipeline by specifying the pipeline name, destination, and dataset. To read more
    about pipeline configuration, please refer to our documentation
-   [here](https://dlthub.com/docs/general-usage/pipeline):
+   [here](/general-usage/pipeline):
 
     ```py
     pipeline = dlt.pipeline(

docs/website/docs/dlt-ecosystem/verified-sources/matomo.md

@@ -150,7 +150,7 @@ def matomo_reports(
 
 `site_id`: Website's Site ID as per Matomo account.
 
->Note: This is an [incremental](https://dlthub.com/docs/general-usage/incremental-loading) source method and loads the "last_date" from the state of last pipeline run.
+>Note: This is an [incremental](/general-usage/incremental-loading) source method and loads the "last_date" from the state of last pipeline run.
 
 ### Source `matomo_visits`:
 
@@ -183,7 +183,7 @@ def matomo_visits(
 
 `get_live_event_visitors`: Retrieve unique visitor data, defaulting to False.
 
->Note: This is an [incremental](https://dlthub.com/docs/general-usage/incremental-loading) source method and loads the "last_date" from the state of last pipeline run.
+>Note: This is an [incremental](/general-usage/incremental-loading) source method and loads the "last_date" from the state of last pipeline run.
 
 ### Resource `get_last_visits`
 
@@ -214,7 +214,7 @@ def get_last_visits(
 
 `rows_per_page`: Number of rows on each page.
 
->Note: This is an [incremental](https://dlthub.com/docs/general-usage/incremental-loading) resource method and loads the "last_date" from the state of last pipeline run.
+>Note: This is an [incremental](/general-usage/incremental-loading) resource method and loads the "last_date" from the state of last pipeline run.
 
 
 ### Transformer `visitors`

docs/website/docs/dlt-ecosystem/verified-sources/pg_replication.md

@@ -65,10 +65,10 @@ To get started with your data pipeline, follow these steps:
    dlt init pg_replication duckdb
    ```
 
-   It will initialize [the pipeline example](https://github.com/dlt-hub/verified-sources/blob/master/sources/pg_replication_pipeline.py) with a Postgres replication as the [source](https://dlthub.com/docs/general-usage/source) and [DuckDB](https://dlthub.com/docs/dlt-ecosystem/destinations/duckdb) as the [destination](https://dlthub.com/docs/dlt-ecosystem/destinations).
+   It will initialize [the pipeline example](https://github.com/dlt-hub/verified-sources/blob/master/sources/pg_replication_pipeline.py) with a Postgres replication as the [source](/general-usage/source) and [DuckDB](/dlt-ecosystem/destinations/duckdb) as the [destination](/dlt-ecosystem/destinations).
 
 
-2. If you'd like to use a different destination, simply replace `duckdb` with the name of your preferred [destination](https://dlthub.com/docs/dlt-ecosystem/destinations).
+2. If you'd like to use a different destination, simply replace `duckdb` with the name of your preferred [destination](/dlt-ecosystem/destinations).
     
 3. This source uses `sql_database` source, you can init it as follows:
     
@@ -81,7 +81,7 @@ To get started with your data pipeline, follow these steps:
     
 4. After running these two commands, a new directory will be created with the necessary files and configuration settings to get started.
    
-   For more information, read the guide on [how to add a verified source](https://dlthub.com/docs/walkthroughs/add-a-verified-source).
+   For more information, read the guide on [how to add a verified source](/walkthroughs/add-a-verified-source).
 
    :::note
    You can omit the `[sql.sources.credentials]` section in `secrets.toml` as it is not required.
@@ -109,9 +109,9 @@ To get started with your data pipeline, follow these steps:
    sources.pg_replication.credentials="postgresql://[email protected]:port/database"
    ```
 
-3. Finally, follow the instructions in [Destinations](https://dlthub.com/docs/dlt-ecosystem/destinations/) to add credentials for your chosen destination. This will ensure that your data is properly routed.
+3. Finally, follow the instructions in [Destinations](/dlt-ecosystem/destinations/) to add credentials for your chosen destination. This will ensure that your data is properly routed.
 
-For more information, read the [Configuration section.](https://dlthub.com/docs/general-usage/credentials)
+For more information, read the [Configuration section.](/general-usage/credentials)
 
 ## Run the pipeline
 
@@ -130,12 +130,12 @@ For more information, read the [Configuration section.](https://dlthub.com/docs/
    For example, the `pipeline_name` for the above pipeline example is `pg_replication_pipeline`, you may also use any custom name instead.
 
 
-   For more information, read the guide on [how to run a pipeline](https://dlthub.com/docs/walkthroughs/run-a-pipeline).
+   For more information, read the guide on [how to run a pipeline](/walkthroughs/run-a-pipeline).
     
 
 ## Sources and resources
 
-`dlt` works on the principle of [sources](https://dlthub.com/docs/general-usage/source) and [resources](https://dlthub.com/docs/general-usage/resource).
+`dlt` works on the principle of [sources](/general-usage/source) and [resources](/general-usage/resource).
 
 ### Resource `replication_resource`
 

docs/website/docs/dlt-ecosystem/verified-sources/sql_database/advanced.md

@@ -14,7 +14,7 @@ import Header from '../_source-info-header.md';
 
 Efficient data management often requires loading only new or updated data from your SQL databases, rather than reprocessing the entire dataset. This is where incremental loading comes into play.
 
-Incremental loading uses a cursor column (e.g., timestamp or auto-incrementing ID) to load only data newer than a specified initial value, enhancing efficiency by reducing processing time and resource use. Read [here](https://dlthub.com/docs/walkthroughs/sql-incremental-configuration) for more details on incremental loading with `dlt`.
+Incremental loading uses a cursor column (e.g., timestamp or auto-incrementing ID) to load only data newer than a specified initial value, enhancing efficiency by reducing processing time and resource use. Read [here](/walkthroughs/sql-incremental-configuration) for more details on incremental loading with `dlt`.
 
 
 #### How to configure
@@ -46,7 +46,7 @@ certain range.
    print(info)
    ```
     Behind the scene, the loader generates a SQL query filtering rows with `last_modified` values greater than the incremental value. In the first run, this is the initial value (midnight (00:00:00) January 1, 2024). 
-    In subsequent runs, it is the latest value of `last_modified` that `dlt` stores in [state](https://dlthub.com/docs/general-usage/state).
+    In subsequent runs, it is the latest value of `last_modified` that `dlt` stores in [state](/general-usage/state).
 
 **2. Incremental loading with the source `sql_database`**  
     To achieve the same using the `sql_database` source, you would specify your cursor as follows:
@@ -163,9 +163,9 @@ The examples below show how you can set arguments in any of the `.toml` files (`
     database = sql_database()
     ```
 
-You'll be able to configure all the arguments this way (except adapter callback function). [Standard dlt rules apply](https://dlthub.com/docs/general-usage/credentials/configuration#configure-dlt-sources-and-resources).  
+You'll be able to configure all the arguments this way (except adapter callback function). [Standard dlt rules apply](/general-usage/credentials/configuration#configure-dlt-sources-and-resources).  
 
-It is also possible to set these arguments as environment variables [using the proper naming convention](https://dlthub.com/docs/general-usage/credentials/config_providers#toml-vs-environment-variables):
+It is also possible to set these arguments as environment variables [using the proper naming convention](/general-usage/credentials/config_providers#toml-vs-environment-variables):
 ```sh
 SOURCES__SQL_DATABASE__CREDENTIALS="mssql+pyodbc://loader.database.windows.net/dlt_data?trusted_connection=yes&driver=ODBC+Driver+17+for+SQL+Server"
 SOURCES__SQL_DATABASE__BACKEND=pandas

docs/website/docs/dlt-ecosystem/verified-sources/sql_database/configuration.md

@@ -124,7 +124,7 @@ There are several options for adding your connection credentials into your `dlt`
 
 #### 1. Setting them in `secrets.toml` or as environment variables (Recommended)
 
-You can set up credentials using [any method](https://dlthub.com/docs/devel/general-usage/credentials/setup#available-config-providers) supported by `dlt`. We recommend using `.dlt/secrets.toml` or the environment variables. See Step 2 of the [setup](./setup) for how to set credentials inside `secrets.toml`. For more information on passing credentials read [here](https://dlthub.com/docs/devel/general-usage/credentials/setup).   
+You can set up credentials using [any method](/devel/general-usage/credentials/setup#available-config-providers) supported by `dlt`. We recommend using `.dlt/secrets.toml` or the environment variables. See Step 2 of the [setup](./setup) for how to set credentials inside `secrets.toml`. For more information on passing credentials read [here](/devel/general-usage/credentials/setup).   
 
 
 #### 2. Passing them directly in the script 

docs/website/docs/dlt-ecosystem/verified-sources/sql_database/usage.md

@@ -39,7 +39,7 @@ The PyArrow backend does not yield individual rows rather loads chunks of data a
 
 
 Examples:
-1. Pseudonymizing data to hide personally identifiable information (PII) before loading it to the destination. (See [here](https://dlthub.com/docs/general-usage/customising-pipelines/pseudonymizing_columns) for more information on pseudonymizing data with `dlt`)
+1. Pseudonymizing data to hide personally identifiable information (PII) before loading it to the destination. (See [here](/general-usage/customising-pipelines/pseudonymizing_columns) for more information on pseudonymizing data with `dlt`)
 
     ```py
     import hashlib
@@ -92,11 +92,11 @@ Examples:
 
 ## Deploying the sql_database pipeline
 
-You can deploy the `sql_database` pipeline with any of the `dlt` deployment methods, such as [GitHub Actions](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-github-actions), [Airflow](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer), [Dagster](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-dagster) etc. See [here](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline) for a full list of deployment methods.
+You can deploy the `sql_database` pipeline with any of the `dlt` deployment methods, such as [GitHub Actions](/walkthroughs/deploy-a-pipeline/deploy-with-github-actions), [Airflow](/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer), [Dagster](/walkthroughs/deploy-a-pipeline/deploy-with-dagster) etc. See [here](/walkthroughs/deploy-a-pipeline) for a full list of deployment methods.
 
 ### Running on Airflow
 When running on Airflow:
-1. Use the `dlt` [Airflow Helper](../../../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md#2-modify-dag-file) to create tasks from the `sql_database` source. (If you want to run table extraction in parallel, then you can do this by setting `decompose = "parallel-isolated"` when doing the source->DAG conversion. See [here](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer#2-modify-dag-file) for code example.)
+1. Use the `dlt` [Airflow Helper](../../../walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer.md#2-modify-dag-file) to create tasks from the `sql_database` source. (If you want to run table extraction in parallel, then you can do this by setting `decompose = "parallel-isolated"` when doing the source->DAG conversion. See [here](/walkthroughs/deploy-a-pipeline/deploy-with-airflow-composer#2-modify-dag-file) for code example.)
 2. Reflect tables at runtime with `defer_table_reflect` argument.
 3. Set `allow_external_schedulers` to load data using [Airflow intervals](../../../general-usage/incremental-loading.md#using-airflow-schedule-for-backfill-and-incremental-loading).
 

docs/website/docs/dlt-ecosystem/verified-sources/workable.md

@@ -211,7 +211,7 @@ If you wish to create your own pipelines, you can leverage source and resource m
 verified source.
 
 To create your data pipeline using single loading and
-[incremental data loading](https://dlthub.com/docs/general-usage/incremental-loading) (only for the
+[incremental data loading](/general-usage/incremental-loading) (only for the
 **Candidates** endpoint), follow these steps:
 
 1. Configure the pipeline by specifying the pipeline name, destination, and dataset as follows:
@@ -270,10 +270,10 @@ To create your data pipeline using single loading and
 
 1. To use incremental loading for the candidates endpoint, maintain the same pipeline and
    destination dataset names. The pipeline name helps retrieve the
-   [state](https://dlthub.com/docs/general-usage/state) of the last run, essential for incremental
+   [state](/general-usage/state) of the last run, essential for incremental
    data loading. Changing these names might trigger a
-   [“dev_mode”](https://dlthub.com/docs/general-usage/pipeline#do-experiments-with-dev-mode),
+   [“dev_mode”](/general-usage/pipeline#do-experiments-with-dev-mode),
    disrupting metadata tracking for
-   [incremental data loading](https://dlthub.com/docs/general-usage/incremental-loading).
+   [incremental data loading](/general-usage/incremental-loading).
 
 <!--@@@DLT_TUBA workable-->

docs/website/docs/general-usage/data-enrichments/url-parser-data-enrichment.md

@@ -228,7 +228,7 @@ need to register to use this service neither get an API key.
 ### Run the pipeline
 
 1. Install necessary dependencies for the preferred
-   [destination](https://dlthub.com/docs/dlt-ecosystem/destinations/), For example, duckdb:
+   [destination](/dlt-ecosystem/destinations/), For example, duckdb:
 
    ```sh
    pip install "dlt[duckdb]"

docs/website/docs/general-usage/data-enrichments/user_agent_device_data_enrichment.md

@@ -49,8 +49,8 @@ user_device_enrichment/
 ```
 ### 1. Creating resource
 
-   `dlt` works on the principle of [sources](https://dlthub.com/docs/general-usage/source)
-   and [resources.](https://dlthub.com/docs/general-usage/resource)
+   `dlt` works on the principle of [sources](/general-usage/source)
+   and [resources.](/general-usage/resource)
 
    This data resource yields data typical of what many web analytics and
    tracking tools can collect. However, the specifics of what data is collected
@@ -281,7 +281,7 @@ The first step is to register on [SerpAPI](https://serpapi.com/) and obtain the
 ### Run the pipeline
 
 1. Install necessary dependencies for the preferred
-   [destination](https://dlthub.com/docs/dlt-ecosystem/destinations/), For example, duckdb:
+   [destination](/dlt-ecosystem/destinations/), For example, duckdb:
 
    ```sh
    pip install "dlt[duckdb]"