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Move "dlt in notebooks" (#2096)
* move notebooks page to run pipelines, move streamlit creds to streamlit page * remove notebooks page from sidebar
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docs/website/docs/dlt-ecosystem/notebooks.md

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docs/website/docs/general-usage/dataset-access/streamlit.md

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Use the pipeline name you defined in your Python code with the `pipeline_name` argument. If you are unsure, you can use the `dlt pipeline --list` command to list all pipelines.
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## Credentials
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`dlt` will look for `secrets.toml` and `config.toml` in the `.dlt` folder.
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If `secrets.toml` are not found, it will use
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`secrets.toml` from `.streamlit` folder.
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If you run locally, maintain your usual `.dlt` folder.
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When running on streamlit cloud, paste the content of `dlt`
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`secrets.toml` into the `streamlit` secrets.
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## Inspecting your data
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You can now inspect the schema and your data. Use the left sidebar to switch between:

docs/website/docs/walkthroughs/run-a-pipeline.md

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[Running in production](../running-in-production/running.md#inspect-and-save-the-load-info-and-trace)
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for more details.
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## 5. Detect and handle problems
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## Run dlt in Notebooks
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### Colab
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You'll need to install `dlt` like any other dependency:
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```sh
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!pip install dlt
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```
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You can configure secrets using **Secrets** sidebar. Just create a variable with the name `secrets.toml` and paste
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the content of the **toml** file from your `.dlt` folder into it. We support `config.toml` variable as well.
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:::note
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`dlt` will not reload the secrets automatically. Please restart your interpreter in Colab options when you add/change
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content of the variables above.
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:::
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## Troubleshooting
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What happens if something goes wrong? In most cases, the `dlt` `run` command raises exceptions. We put a
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lot of effort into making the exception messages easy to understand. Reading them is the first step

docs/website/sidebars.js

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'general-usage/full-loading',
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]
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},
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'dlt-ecosystem/notebooks'
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]
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},
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{

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