Metadata-Version: 2.1
Name: dbt-exposures-crawler
Version: 0.1.0
Summary: Extracts information from different systems and convert them to dbt exposures
Home-page: https://github.com/voi-oss/dbt-exposures-crawler
Author: Voi Technology AB
License: Apache License, Version 2.0
Description: # dbt exposures crawler
        
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        [![Apache License 2.0](https://img.shields.io/github/license/voi-oss/dbt-exposures-crawler)](https://github.com/voi-oss/dbt-exposures-crawler/)
        
        Automates the creation of dbt exposures from different sources. Currently, only Tableau workbooks using Snowflake SQL are
        supported as a source.
        
        > This project is in an ALPHA stage. Internal and external APIs might change between minor versions.
        
        > Please reach out if you try this at your own organization. Feedback is very appreciated, and we
        > would love to hear if you had any issues setting this up at your own.
        
        ## Motivation
        
        [dbt](https://www.getdbt.com/) is an open-source tool to manage data transformations in SQL. It automatically generates
        a documentation portal from your project which includes a dependency lineage graph. It is possible to add external
        downstream dependencies to this graph (such as a dashboard on a Business Intelligence tool) through a dbt feature called
        [exposures](https://docs.getdbt.com/docs/building-a-dbt-project/exposures), which are normally defined through `yaml`
        files.
        
        This project automates the creation of exposures by implementing crawlers to parse the metadata of downstream tools.
        Currently, only Tableau dashboards are supported, but we have plans to include Metabase as well.
        
        A few use cases on how having exposures can help:
        
        * analysts working on a model can use the exposures to perform impact analysis and see which reports might be impacted
          by their changes;
        * report consumers can find their report on dbt and see which models are used and read their documentation;
        * report consumers can find which other reports are using the same models as their favorite reports.
        
        ## How it works
        
        Summary:
        
        1. Retrieve dbt models and sources from `manifest.json`;
        2. Extract metadata (custom SQL and table references) from Tableau workbooks using their GraphQL API);
        3. Try to find occurrences from the dbt models and sources in the Tableau SQL;
        4. Use the Tableau REST API to retrieve additional information about the workbooks (author, project, etc);
        5. Create the dbt exposures (in-memory) and write it back to the `manifest.json`.
        
        More in-depth explanation:
        
        First, you must provide the path to a dbt project [manifest](https://docs.getdbt.com/reference/artifacts/manifest-json).
        The metadata and fully qualified names (database, schema and object name) are extracted from all dbt models and sources
        represented in the manifest. The combination of dbt models and dbt sources will from now on be referred as dbt nodes.
        
        Next, workbook metadata is extracted from Tableau using
        their [Metadata API](https://help.tableau.com/current/api/metadata_api/en-us/index.html), including workbooks that use
        custom SQL queries and workbooks that don't (which are referred in this project as "native SQL" workbooks). Note that
        this API is included in all Tableau licenses (i.e. it does not require the Data Management Add-on), but must
        be [manually enabled](https://help.tableau.com/current/api/metadata_api/en-us/docs/meta_api_start.html#enable)
        if you host your own Tableau Server.
        
        The SQL from the custom SQL workbooks and the table names from the native SQL workbooks are normalized through simple
        heuristics, such as removing quotes and converting the custom SQL to lowercase. Now that both normalized SQL and
        normalized table names from Tableau, and the fully qualified names for the dbt nodes are available, the project tries to
        find the occurrences of the latter in the former.
        
        The result of the above is a mapping of workbooks and which dbt nodes they depend on. For every workbook (with mapped
        dependencies available), extra metadata that was not available in the Metadata API is then retrieved from Tableau by
        using their [REST API](https://help.tableau.com/current/api/rest_api/en-us/REST/rest_api.htm), including when the
        workbook was created, when it was last updated, to which folder it belongs on Tableau and information about the author
        of the workbook.
        
        As a final step, the information above is written back in the provided `manifest.json` in the form of exposures. Note
        that instead of generating `.yaml` files for each exposure, they are written directly on the `manifest.json`.
        
        ## Example
        
        To better understand how the project works, let's take as an example
        the [jaffle_shop](https://github.com/fishtown-analytics/jaffle_shop) dbt sample project. It has, among other models,
        a `customers` and an `orders` model.
        
        Now suppose that you company has 4 workbooks on Tableau:
        
        * Customers workbook: accesses the `customers` dbt model through custom SQL;
        * Company KPIs workbook: accesses both models through custom SQL;
        * Orders workbook: accesses the `orders` model without custom SQL;
        * Unrelated workbook: a workbook that does not use the dbt project but instead has a static data source.
        
        When running this project, you would get the following console output:
        
        <p align="center">
            <a href="https://github.com/voi-oss/dbt-exposures-crawler/blob/main/docs/cli_output.png">
                <img 
                  src="https://github.com/voi-oss/dbt-exposures-crawler/blob/main/docs/cli_output.png?raw=true" 
                  alt="CLI example"
                  width="600px"
                />
            </a>
        </p>
        
        The `manifest.json` that you provided would have 3 new exposures added to it, such as:
        
        <p align="center">
            <a href="https://github.com/voi-oss/dbt-exposures-crawler/blob/main/docs/modified_manifest.png">
                <img 
                  src="https://github.com/voi-oss/dbt-exposures-crawler/blob/main/docs/modified_manifest.png?raw=true" 
                  alt="modified manifest"
                  width="600px"
                />
            </a>
        </p>
        
        Those exposures can then be visualized through your dbt documentation portal, either by finding which exposures are
        downstream dependencies of a certain model:
        
        <p align="center">
            <a href="https://github.com/voi-oss/dbt-exposures-crawler/blob/main/docs/models.gif">
                <img 
                  src="https://github.com/voi-oss/dbt-exposures-crawler/blob/main/docs/models.gif?raw=true" 
                  alt="models in the documentation portal"
                  width="600px"
                />
            </a>
        </p>
        
        Or by doing the inverse. Starting from an exposure, find which models are used on it:
        
        <p align="center">
            <a href="https://github.com/voi-oss/dbt-exposures-crawler/blob/main/docs/exposures.gif">
                <img 
                  src="https://github.com/voi-oss/dbt-exposures-crawler/blob/main/docs/exposures.gif?raw=true" 
                  alt="exposures in the documentation portal"
                  width="600px"
                />
            </a>
        </p>
        
        This example has been taken from the integration tests available in this project. You can read more in the `Testing`
        section below.
        
        ## Assumptions and limitations
        
        * Only custom SQL written on Tableau workbooks using fully qualified names (`DATABASE.SCHEMA.OBJECT`) will be detected;
        * For now, only Tableau workbooks (and not published data sources) are supported. Also, only Snowflake SQL is currently
          supported.
        
        ## Installation
        
        This project requires Python 3.9+. It has only been tested with dbt 0.19, Tableau Server 2021.1 and Snowflake SQL
        dialect.
        
        Over the coming weeks we will have it published on PyPI and you will be able to install it by:
        
        ```shell
        $ pip install dbt-exposures-crawler
        ```
        
        ## Usage
        
        Internally, we use this automation as part of our dbt docs release pipeline. We have a GitHub Action that does the
        following:
        
        1. Clone our dbt repository;
        2. Install dbt and our dependencies;
        3. Run `dbt docs generate` (remember to run it against your production environment);
        4. Run this project (using the `manifest.json` generated from the previous command as input);
        5. Publish the generated documentation portal;
        
        To run this project, we use:
        
        ```shell
        python3 -m exposurescrawler.crawlers.tableau \
                    --manifest-path=~path/to/dbt/target/manifest.json \
                    --ignore-projects Archive \
                    --verbose
        ```
        
        Make sure you check the `.env.example` file to see which environment variables must be defined.
        
        ## Development
        
        Clone the repository and install it in editable mode:
        
        ```shell
        $ pip install -e .
        ```
        
        Before opening a pull request, make sure you run:
        
        * `make lint`: runs `mypy`, `black` and `flake8`;
        * `make test`: runs all tests
        
        ## Architecture
        
        The entry point for the crawlers should be on the `crawlers` module. For now, only Tableau is supported.
        
        The `tableau` module contains all API clients (REST and GraphQL) and models.
        
        The `dbt` module contains a model for representing a dbt exposure and utilities for parsing, interacting and saving dbt
        manifests.
        
        Finally, the `utils` module has functions for logging and string parsing.
        
        ## Testing
        
        For the integration tests, we use a sample `manifest.json` as a fixture. It was manually generated from
        the [jaffle_shop](https://github.com/fishtown-analytics/jaffle_shop), an official dbt sample project.
        
        ```shell
        $ git clone https://github.com/fishtown-analytics/jaffle_shop
        $ cd jaffle_shop
        $ pipenv shell
        $ pip install dbt==0.19.1
        ```
        
        After adding an entry on my dbt profile and then setting the default database on the project to `sample_dbt` on
        the `dbt_project.yaml`:
        
        ```shell
        $ dbt compile --target prod
        ```
        
        The generated `manifest.json` is then prettified and copied to the `tests/_fixtures` folder. I've also manually removed
        the `macros` entries from the file just to make it easier to navigate through it in case of troubleshooting.
        
        ```shell
        $ cat target/manifest.json | jq > $PROJECT_ROOT/tests/_fixtures/manifest.json
        ```
        
        ## Future ideas
        
        * Allow filters to be passed. E.g. only include Tableau workbooks with certain tags;
        * Add support to Tableau published data sources;
        * Include other BI tools to be crawled, such as Metabase.
        
        ## Contributing
        
        We are open and would love to have contributions, both in Pull Requests but also in ideas and feedback. Don't hesitate
        to create an Issue on this repository if you are trying this project in your organization or have anything to share.
        
        ## License
        
        This project is licensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.9, <4
Description-Content-Type: text/markdown
