Data Quality Assurance Stack

[ThaliaBarrera]

Ensuring Data Quality with Airbyte, dbt, and Great Expectations.

Extract data using Airbyte, transform it using dbt, and utilize Great Expectations to validate data quality, ensuring the reliability and accuracy of your analytical data.

How to get started:

• Check out the README and the Contribution instructions in this repo.
• Check one of the existing quickstarts and observe their structure.
• (Optional, but highly recommended) Implement one of the quickstarts by following the instructions in their README. I recommend starting with E-commerce analytics in BigQuery

[gauriimaheshwarii]

Hey, @ThaliaBarrera, I would like to work on this. Can you please assign it to me?

[ThaliaBarrera]

Hi @gauriimaheshwarii! Sure, I have assigned it to you.

[ThaliaBarrera]

Hi @gauriimaheshwarii, this message is just to remind you that according to the Hackathon rules you have 5 days to submit progress on the issue. I’ll give you a couple more days to submit a PR, otherwise I’ll have to mark the issue as “To be Claimed” or reassign it.

Thank you for understanding! Let me know if you have any questions.

[1cadumagalhaes]

This is interesting, can I add an orchestrator as Airflow or this is supposed to be standalone?

[ThaliaBarrera]

You can certainly add an orchestrator @1cadumagalhaes! I have assigned the issue to you. Let me know if that’s okay.

[1cadumagalhaes]

Yeah, thanks!

[ThaliaBarrera]

Hi @1cadumagalhaes! are you still working on this? 🙂