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Cloud Data WarehouseIntermediate → AdvancedNEW
BigQuery for Data Engineers
Master Google BigQuery — serverless Dremel architecture, partitioning and clustering, advanced SQL analytics (UNNEST, scripting, BigQuery ML), Python pipelines with the BQ client, cost optimization, and integration with Airflow and dbt.
4.9rating0 students3h 30m total3 lessons
What you'll learn
Understand BigQuery's Dremel/Colossus architecture and serverless execution
Design tables with DATE/DATETIME partitioning and multi-column clustering
Write BigQuery SQL: UNNEST arrays, STRUCT access, ROLLUP, scripting, stored procedures
Use window functions with ROWS vs RANGE and APPROX_COUNT_DISTINCT for scale
Build Python ETL pipelines with schema enforcement, dry-run cost checks, and Storage API reads
Integrate BigQuery with Airflow using BigQueryInsertJobOperator and dbt with BQ adapter
Monitor costs with INFORMATION_SCHEMA and require_partition_filter
Final Project
Build a Python ETL pipeline loading API data to BigQuery with schema enforcement and a cohort retention analysis, orchestrated with Airflow
Curriculum
3 lessons · 3h 30mCourse Info
Lessons3 lessons
Total time3h 30m
LevelIntermediate → Advanced
Students0
Rating4.9 / 5.0