viadot 0.2.3

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

Viadot

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

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

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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")

Running tests

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

cd viadot/docker
run.sh
docker exec -it viadot_testing bash
pytest

Running flows locally

You can run the example flows from the terminal:

run.sh
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 at http://localhost:9000/.

How to contribute

1. Clone the release branch
2. Pull the docker env by running viadot/docker/update.sh -t dev
3. Run the env with viadot/docker/run.sh
4. Log into the dev container and install in development mode so that viadot will auto-install at each code change:

docker exec -it viadot_testing bash
pip install -e .

5. Edit and test your changes with pytest
6. 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.