Docs - Transform data in your warehouse

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class: subtitle, middle, center
## _How is knowledge documented and maintained at your company?_

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# Why is documentation important?

- Empowers the writer to refer back to their thinking
- Empowers the reader to find their own answers
- Increases velocity of onboarding
- Reduces for ad hoc, repeated communication

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<h1>Docs | Focus</h1>

<li> Explain how to add descriptions to models and sources </li>

<li> Configure doc blocks </li>

<li> Generate and view documentation in dbt Cloud </li>

<li> Build intuition for the value of documenting your dbt project </li>

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# Our favorite metaphor

* Analytics engineers are librarians.
* Documentation is the card catalog.
* The goal is to empower analyst-researchers.
--

* _The librarian and the card catalog do not replace each other—they reinforce and enable each other!_

### Analytics is the organization of an organization's information.

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# Our strong opinions!
.denser-text[
* Code is _surface area_. Testing and documentation are _coverage_.
* Untested, undocumented code _cannot be trusted_. In some sense, it is worse than having no code at all.
* Reliable documentation is _key_ to scaling a data team.¹
* The generation of data documentation should be automated.
* Data documentation should include both:
    * Inferred information from the codebase/database
    * Manual inputs ("this field means that")
* The process to update documentation _must_ be part of the same workflow as the transformations it seeks to describe. If the two are separate, documentation will _always_ lag behind.
]

???
- Slide setup: From dbt Viewpoint: "Bad data can lead to bad analyses, and bad analyses can lead to bad decisions. Any code that generates data or analysis should be reviewed and tested."
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## dbt docs

???
not docs.getdbt.com, though those are also great

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### Adding descriptions
.dense-text[
Descriptions can be added to models, sources, and columns (in your existing `.yml` files)
]

```yml
version: 2

models:
  - name: stg_orders
    description: One record per order
    columns:
      - name: order_id
        tests:
          - unique
          - not_null

- name: status
        description: "{{ doc('order_status') }}"
        tests:
          - accepted_values:
              values: ['placed', 'shipped', 'completed', 'return_pending', 'returned']

- name: amount
        description: Amount in USD
```
]
]

```yml
version: 2

sources:
  - name: jaffle_shop
    description: A replica of the postgres database
    database: raw
    tables:
      - name: orders
        description: One record per order
        columns:
          - name: id
            tests:
              - unique
              - not_null

- name: status
            description: "{{ doc('order_status') }}"
            tests:
              - accepted_values:
                  values: ['placed', 'shipped', 'completed', 'return_pending', 'returned']

```

]
]
???
Demo in dbt cloud. 
--
.dense-text[
What's the `{{ doc('order_status') }}`?
]

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### `docs` blocks

`docs` blocks allow you to write long-form descriptions.

Docs blocks are added to `.md` files in your `models/` directory.

| status         | definition                                                 |
|----------------|------------------------------------------------------------|
| placed         | Order placed but not yet shipped                           |
| shipped        | Order has been shipped but hasn't yet been delivered       |
| completed      | Order has been received by customers                       |
| return_pending | Customer has indicated they would like to return this item |
| returned       | Item has been returned                                     |

{% enddocs %}

```
]

???
- Docs blocks are great for special formatting but also allow for more modular documentation. A given .md file can be referenced and used in multiple descriptions without having to copy and paste any text.

???
Demo Time:

Regular docs:
- "Let's see that in action!"
- add regular model and/or column descriptions to your yml file
- run `dbt docs generate`
- show where those end up in the docs

Doc blocks:
- add a doc block file `order_status.md`
- paste in the above markdown code ensuring to add newline white spaces
- add description: `"{{ doc('order_status') }}"`
- run `dbt docs generate`
- within docs, find the doc block and talk about it
- talk through all the other data shown (metadata, columns, types)
- talk through DAG / lineage graph and model selection syntax

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# Generating documentation
.denser-text[
When dbt generates documentation it takes into account:
* Everything it knows about your project:
    * Descriptions (ofc!)
    * Model dependencies
    * Model SQL
    * Sources
    * Tests
* Additional things about your warehouse (from the information schema)
    * Column names and types (even if they aren't documented!)
    * Table stats

This information is used to populate a documentation website.

For example: [dbt Learn demonstration project](https://cloud.getdbt.com/accounts/1951/jobs/5297/docs/#!/overview), [our MRR playbook](https://www.getdbt.com/mrr-playbook/#!/model/model.acme.mrr), [GitLab's dbt project](https://dbt.gitlabdata.com/).
]
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## How do I generate docs?

* Generating documentation in dbt is as simple as running a command.
* Run `dbt docs generate`
* Docs page will be created and saved in the `view docs` link in the IDE

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## dbt Cloud - Development
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]

]
???
- When using the CLI, `dbt docs serve` is the command to generate documentation
- No need to run the 'dbt serve' command in dbt cloud in order to view dbt docs.
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## dbt Cloud - Deployment
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# What should I document?

For example:
* Sources:
    - How the source data came to be in the warehouse
    - Known caveats
    - The definitions of poorly named fields and tables
* Final models:
    - The grain of the model. Potentially a description of how the model was built.
    - Column definitions, especially for columns that include business logic
* Intermediate models
    - As required
]
???
- Consider the intended audience for the documentation. Are the docs primarily for your team/department or are they intended for a wider, less technical stakeholder group?
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## Checkpoint

- **Share out:** What are some elements of dbt documentation that you are excited to implement and why?

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# Knowledge check

You should be able to:
* Add descriptions to your dbt project
* Generate documentation for your project

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# Zoom Out

<li> Distributed dbt Learn norms</li>

<li> Who is an analytics engineer?</li>

<li> dbt Fundamentals</li>

<li> Working Group 1</li>

<li> dbt Project Design</li>

<li> Working Group 2</li>

<li> Testing</li>

<li> Sources</li>

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<li class="orange"> Deployment</li>

<li class="grey">Working Group 3</li>

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