Why BigQuery, and the pricing trap
Welcome! I'm Param. This is the GCP analytics course in the learnwithparam data engineering track. We are going to take a real e-commerce dataset, model it in dbt, host the runner on Cloud Run, schedule with Airflow, and ship the whole stack on every push to main. Everything grounds in a real companion project: dbt models, a Cloud Run runner, Airflow DAGs, and Terraform you will read and reason about piece by piece.
BigQuery wins three things. Storage and compute are decoupled, so you only pay for what you query. Joins on terabyte-scale tables run in seconds without tuning. The SQL surface is rich enough for analytics, with arrays, structs, and window functions that hold their own against Snowflake.
BigQuery pricing models
Three guardrails. Set a maximum bytes billed per query at the project level. Add table-level partitioning so filters prune before scanning. Use the BigQuery cost estimator in the console for any query that touches a fact table. Together they make a runaway scan visible before it bills.
Quiz: Quiz
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