ETL Processing on Google Cloud Using Dataflow and BigQuery
In this lab you build several Data Pipelines that ingest data from a publicly available dataset into BigQuery, using these Google Cloud services:
- Cloud Storage
You will create your own Data Pipeline, including the design considerations, as well as implementation details, to ensure that your prototype meets the requirements. Be sure to open the python files and read the comments when instructed to.
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Create a Cloud Storage Bucket
Copy Files to Your Bucket
Create the BigQuery Dataset (name: lake)
Build a Data Ingestion Dataflow Pipeline
Build a Data Transformation Dataflow Pipeline
Build a Data Enrichment Dataflow Pipeline
Build a Data lake to Mart Dataflow Pipeline