Extract data from gcs and load to bigquery using dataflow and python

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to extract data from google cloud storage (gcs) and load it into bigquery using dataflow in python, you can follow the steps outlined below:

1. set up your google cloud environment:
make sure you have a google cloud platform account and have created a project.
enable the necessary apis: dataflow, bigquery, and cloud storage.
install the necessary libraries by running `pip install apache-beam[gcp]`.

2. create a dataflow pipeline in python to read data from gcs and load it into bigquery:



3. replace `'your-project-id'`, `'your-bucket'`, `'input-data.txt'`, `'your_dataset.your_table'`, and the schema with your actual project id, bucket name, input file path, bigquery table, and schema respectively.

4. run the dataflow pipeline:
run the pipeline by executing the python script in your local environment or in a cloud-based environment like google cloud shell.
monitor the job progress in the google cloud console.

5. verify the data:
once the dataflow job completes successfully, check your bigquery table to ensure that the data has been loaded correctly.

by following these steps and customizing the code example with your specific project details and data parsing logic, you can extract data from gcs and load it into bigquery using dataflow in python.

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