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clear cache in the right places in preprocess docs Bring back links, redirects, reorder Fix caching issue on historic docs Fix some broken links (will need cleanup)
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CONTRIBUTING.md

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## Table of Contents
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1. [Getting Started](#getting-started)
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1. [Getting Started](#intro)
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2. [Submitting Changes](#submitting-changes)
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3. [Adding or updating core dependencies](#adding-or-updating-core-dependencies)
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4. [Linting](#linting)

docs/website/docs/build-a-pipeline-tutorial.md

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# Building data pipelines with `dlt`, from basic to advanced
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This in-depth overview will take you through the main areas of pipelining with `dlt`. Go to the
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related pages you are instead looking for the [quickstart](getting-started.md), or the
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[walkthroughs](walkthroughs).
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related pages you are instead looking for the [quickstart](./intro.md).
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## Why build pipelines with `dlt`?
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For example, let's consider a scenario where you want to load a list of objects into a DuckDB table
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named "three". With `dlt`, you can create a pipeline and run it with just a few lines of code:
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1. [Create a pipeline](walkthroughs/create-a-pipeline.md) to the [destination](dlt-ecosystem/destinations).
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1. Give this pipeline data and [run it](walkthroughs/run-a-pipeline.md).
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1. [Create a pipeline](./walkthroughs/create-a-pipeline.md) to the [destination](dlt-ecosystem/destinations).
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1. Give this pipeline data and [run it](./walkthroughs/run-a-pipeline.md).
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```py
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import dlt
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adjustments to the schema as needed. You can then import the modified schema files back into `dlt` to
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use them in your pipeline.
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Read more: [Adjust a schema docs.](walkthroughs/adjust-a-schema.md)
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Read more: [Adjust a schema docs.](./walkthroughs/adjust-a-schema.md)
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## Governance Support in `dlt` Pipelines
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predefined schemas, pipelines maintain data integrity and facilitate standardized data handling
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practices.
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Read more: [Adjust a schema docs.](walkthroughs/adjust-a-schema.md)
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Read more: [Adjust a schema docs.](./walkthroughs/adjust-a-schema.md)
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### Schema evolution
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docs/website/docs/dlt-ecosystem/destinations/bigquery.md

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my_resource = bigquery_adapter(my_resource, partition="partition_column_name")
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```
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Refer to the [full API specification](../../api_reference/destinations/impl/bigquery/bigquery_adapter.md) for more details.
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Refer to the [full API specification](../../api_reference/destinations/impl/bigquery/bigquery_adapter) for more details.
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<!--@@@DLT_TUBA bigquery-->
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docs/website/docs/dlt-ecosystem/verified-sources/filesystem/basic.md

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### Prerequisites
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Please make sure the `dlt` library is installed. Refer to the [installation guide](../../../getting-started).
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Please make sure the `dlt` library is installed. Refer to the [installation guide](../../../intro).
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### Initialize the filesystem source
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docs/website/docs/dlt-ecosystem/verified-sources/index.md

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### Core sources
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<DocCardList items={useCurrentSidebarCategory().items.filter(
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item => item.label === '30+ SQL Databases' || item.label === 'REST APIs' || item.label === 'Filesystem & buckets'
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item => item.label === '30+ SQL Databases' || item.label === 'REST APIs' || item.label === 'Filesystem & cloud storage'
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### Verified sources
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<DocCardList items={useCurrentSidebarCategory().items.filter(
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item => item.label !== '30+ SQL Databases' && item.label !== 'REST API generic source'&& item.label !== 'Filesystem & buckets'
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item => item.label !== '30+ SQL Databases' && item.label !== 'REST APIs' && item.label !== 'Filesystem & cloud storage'
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)} />
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### What's the difference between core and verified sources?

docs/website/docs/dlt-ecosystem/verified-sources/rest_api/basic.md

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### Prerequisites
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Please make sure the `dlt` library is installed. Refer to the [installation guide](../../../getting-started).
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Please make sure the `dlt` library is installed. Refer to the [installation guide](../../../intro).
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### Initialize the REST API source
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- `include_from_parent`: A list of fields from the parent resource to be included in the resource output. See the [resource relationships](#include-fields-from-the-parent-resource) section for more details.
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- `selected`: A flag to indicate if the resource is selected for loading. This could be useful when you want to load data only from child resources and not from the parent resource.
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You can also pass additional resource parameters that will be used to configure the dlt resource. See [dlt resource API reference](../../../api_reference/extract/decorators.md#resource) for more details.
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You can also pass additional resource parameters that will be used to configure the dlt resource. See [dlt resource API reference](../../../api_reference/extract/decorators#resource) for more details.
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### Endpoint configuration
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docs/website/docs/general-usage/schema.md

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Variant columns are generated by a normalizer when it encounters data item with type that cannot be
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coerced in existing column. Please see our [`coerce_row`](https://github.com/dlt-hub/dlt/blob/7d9baf1b8fdf2813bcf7f1afe5bb3558993305ca/dlt/common/schema/schema.py#L205) if you are interested to see how internally it works.
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Let's consider our [getting started](../getting-started#quick-start) example with slightly different approach,
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Let's consider our [getting started](../intro) example with slightly different approach,
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where `id` is an integer type at the beginning
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```py

docs/website/docs/getting-started.md

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docs/website/docs/tutorial/load-data-from-an-api.md

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### Explore the data
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To allow sneak peek and basic discovery you can take advantage of [built-in integration with Strealmit](reference/command-line-interface#show-tables-and-data-in-the-destination):
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To allow sneak peek and basic discovery you can take advantage of [built-in integration with Strealmit](../reference/command-line-interface#show-tables-and-data-in-the-destination):
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```sh
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dlt pipeline quick_start show
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## What’s next
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Congratulations on completing the tutorial! You've come a long way since the [getting started](../getting-started) guide. By now, you've mastered loading data from various GitHub API endpoints, organizing resources into sources, managing secrets securely, and creating reusable sources. You can use these skills to build your own pipelines and load data from any source.
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Congratulations on completing the tutorial! You've come a long way since the [getting started](../intro) guide. By now, you've mastered loading data from various GitHub API endpoints, organizing resources into sources, managing secrets securely, and creating reusable sources. You can use these skills to build your own pipelines and load data from any source.
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Interested in learning more? Here are some suggestions:
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1. You've been running your pipelines locally. Learn how to [deploy and run them in the cloud](../walkthroughs/deploy-a-pipeline/).

docs/website/netlify.toml

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