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Viadot

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Documentation: https://dyvenia.github.io/viadot/

Source Code: https://github.com/dyvenia/viadot

A simple data ingestion library to guide data flows from some places to other places.

Getting Data from a Source

Viadot supports several API and RDBMS sources, private and public. Currently, we support the UK Carbon Intensity public API and base the examples on it.

from viadot.sources.uk_carbon_intensity import UKCarbonIntensity
ukci = UKCarbonIntensity()
ukci.query("/intensity")
df = ukci.to_df()
df

Output:

from to forecast actual index
0 2021-08-10T11:00Z 2021-08-10T11:30Z 211 216 moderate

The above df is a python pandas DataFrame object. The above df contains data downloaded from viadot from the Carbon Intensity UK API.

Loading Data to a Source

Depending on the source, viadot provides different methods of uploading data. For instance, for SQL sources, this would be bulk inserts. For data lake sources, it would be a file upload. We also provide ready-made pipelines including data validation steps using Great Expectations.

An example of loading data into SQLite from a pandas DataFrame using the SQLiteInsert Prefect task:

from viadot.tasks import SQLiteInsert

insert_task = SQLiteInsert()
insert_task.run(table_name=TABLE_NAME, dtypes=dtypes, db_path=database_path, df=df, if_exists="replace")

Set up

Note: If you're running on Unix, after cloning the repo, you may need to grant executable privileges to the update.sh and run.sh scripts:

sudo chmod +x viadot/docker/update.sh && \
sudo chmod +x viadot/docker/run.sh

a) user

Clone the main branch, enter the docker folder, and set up the environment:

git clone https://github.com/dyvenia/viadot.git && \
cd viadot/docker && \
./update.sh

Run the enviroment:

./run.sh

b) developer

Clone the dev branch, enter the docker folder, and set up the environment:

git clone -b dev https://github.com/dyvenia/viadot.git && \
cd viadot/docker && \
./update.sh -t dev

Run the enviroment:

./run.sh -t dev

Install the library in development mode (repeat for the viadot_jupyter_lab container if needed):

docker exec -it viadot_testing pip install -e . --user

Running tests

To run tests, log into the container and run pytest:

docker exec -it viadot_testing bash
pytest

Running flows locally

You can run the example flows from the terminal:

docker exec -it viadot_testing bash
FLOW_NAME=hello_world; python -m viadot.examples.$FLOW_NAME

However, when developing, the easiest way is to use the provided Jupyter Lab container available in the browser at http://localhost:9000/.

How to contribute

  1. Fork repository if you do not have write access
  2. Set up locally
  3. Test your changes with pytest
  4. Submit a PR. The PR should contain the following:
  • new/changed functionality
  • tests for the changes
  • changes added to CHANGELOG.md
  • any other relevant resources updated (esp. viadot/docs)

Please follow the standards and best practices used within the library (eg. when adding tasks, see how other tasks are constructed, etc.). For any questions, please reach out to us here on GitHub.

Style guidelines

  • the code should be formatted with Black using default settings (easiest way is to use the VSCode extension)
  • commit messages should:
    • begin with an emoji
    • start with one of the following verbs, capitalized, immediately after the summary emoji: "Added", "Updated", "Removed", "Fixed", "Renamed", and, sporadically, other ones, such as "Upgraded", "Downgraded", or whatever you find relevant for your particular situation
    • contain a useful description of what the commit is doing