This project is a data pipeline designed to fetch real-time vehicle data from the TransLoc API and ingest it into Google BigQuery. The pipeline is implemented using Apache Beam, a unified stream and batch processing model. It fetches vehicle data from the API, transforms it, and loads it into BigQuery for further analysis.

Before running the pipeline, ensure you have the following dependencies installed:

• Python 3.x
• Apache Beam
• Google Cloud SDK (See installation guide here)
• Other Python dependencies (install using pip install -r requirements.txt)

1. Clone the Repository:

   git clone https://github.com/JonFillip/transloc_api_gcp_pipeline.git
   cd transport-data-pipeline

2. Install Dependencies

   pip install -r requirements.txt
   

3. Setup Google Credentials

   • Follow the instructions here to setup

4. Setup Project, Cloud Storage and BigQuery

   • Ensure that you have a Google Cloud project set with proper permissions given to the admin and service account. See more here
   • Create a BigQuery Table (Sink)
   • Create a Cloud Storage Bucket (Source)
   • Enable Dataflow API
   • Enable Cloud Storage API
   • Enable BigQuery API

5. Configure the Pipeline Open transloc_streaming_pipeline.py and modify the pipeline options like project ID, region, staging location, etc., according to your Google Cloud project.

6. Run the Pipeline Run the following command to execute the data pipeline:

   python transloc_streaming_pipeline.py \
   --project your-project-id \
   --region your-region \
   --staging_location gs://your-staging-bucket \
   --temp_location gs://your-temp-bucket \
   --runner DataflowRunner \
   --table_name your-project-id:your-dataset.your-table \
   

Adjust the parameters according to your project details.# transloc_api_gcp_etl