Remove manifest.json
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How to Make Your Own Google Chrome Extension?

Workfall

Inside this directory, we’ll create a file named ‘manifest.json’ This file will play a crucial role in defining the metadata, permissions, and settings for our extension. Enter a file name and save the file as manifest.json. Add the following code to our manifest.json file. Let’s create the index.html file.

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Data Engineering Weekly #141

Data Engineering Weekly

The blog narrates how to schedule dbt jobs in Airflow by parsing dbt's manifest.json file, and auto construct Airflow tasks. The first one that caught my eye is Astronomer published a blog post Introducing Cosmos 1.0: the best way to run dbt Core in Airflow.

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Data Engineering Weekly #143

Data Engineering Weekly

The integration story is a repeating design pattern where an adopted code parses the manifest.json file and constructs dynamic DAGs in Airflow. [link] Instacart: Adopting dbt as the Data Transformation Tool at Instacart Instacart writes about its adoption of dbt as the data transformation tool.

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How to identify your business-critical data

Towards Data Science

If not, you can also use the manifest.json file that dbt produces as part of the artifacts at each invocation and the depends_on property for each node to loop through all your models and count the total number of models that depend on them. These are typically models that everything else depends on such as users, orders or transactions.

BI 80
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How to Create a React Native Portal with Examples

Knowledge Hut

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dbt multi-project collaboration

Christophe Blefari

Once you have dbt build the core project a manifest.json will be generated and tables will be created in the database. On the finance project, with dbt-loom install— pip install dbt-loom — you need to declare the core project as a dependant manifest.

Project 264
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Building An “Amazon.com” For Your Data Products

Monte Carlo

target/manifest.json --run-results./target/run_results.json To implement this, the data product team can run the dbt data quality test as part of their data pipeline and upload the results to Monte Carlo with a simple CLI command. > > dbt test > montecarlo import dbt-run --manifest./target/manifest.json