Module 1: BigQuery Architecture and Fundamentals

• Google BigQuery overview: serverless, petabyte-scale data warehouse
• BigQuery architecture: Dremel execution engine, Colossus storage, and Jupiter network
• BigQuery pricing: on-demand, capacity, and BigQuery Omni
• Navigating BigQuery console: datasets, tables, and query editor

Module 2: BigQuery SQL Essentials

• Standard SQL in BigQuery: SELECT, FROM, WHERE, GROUP BY, and HAVING
• BigQuery-specific functions: DATE, TIMESTAMP, ARRAY, and STRUCT types
• Wildcard table queries for querying multiple tables efficiently
• BigQuery scripting: variables, loops, and procedural SQL

Module 3: Advanced SQL in BigQuery

• Window functions: OVER, PARTITION BY, ORDER BY, and frame clauses
• Analytical functions: ROW_NUMBER, RANK, LAG, LEAD, and NTILE
• WITH clause and nested CTEs for complex query organisation
• PIVOT and UNPIVOT for reshaping analytical datasets

Module 4: BigQuery Data Ingestion and Loading

• Batch loading: CSV, JSON, Avro, Parquet, and ORC from GCS
• Streaming inserts: real-time data ingestion with BigQuery Storage Write API
• BigQuery Data Transfer Service: scheduled imports from SaaS sources
• Federated queries: querying Cloud Storage, Bigtable, and Cloud SQL directly

Module 5: BigQuery Performance and Cost Optimisation

• Partitioning: ingestion-time, column-based, and integer range partitioning
• Clustering: automatic and manual clustering for query acceleration
• Materialised views for pre-computed aggregations
• Query optimisation: avoiding SELECT *, reducing data scanned, and slot usage

Module 6: BigQuery ML

• BigQuery ML overview: training ML models with SQL
• Supported model types: linear regression, logistic regression, k-means, and ARIMA
• CREATE MODEL, EVALUATE, and PREDICT statements
• Importing TensorFlow and XGBoost models into BigQuery ML

Module 7: BigQuery Security and Governance

• IAM roles: dataset, table, and row-level access control
• Column-level security with policy tags and data masking
• VPC Service Controls for data exfiltration prevention
• BigQuery audit logs and Data Catalog integration

Module 8: Capstone Project and Assessment

• BigQuery data warehouse design and SQL analytics project
• Performance optimisation: partitioning and clustering implementation
• BigQuery ML model training and prediction exercise
• Final assessment and course certification

Experience in the Industry Learn from Google Cloud-certified data engineers and analysts who have built BigQuery data warehouses, analytics pipelines, and ML models for enterprises on Google Cloud Platform processing petabytes of data.

Backgrounds at the Top Our BigQuery trainers have delivered GCP data platform projects at technology companies, media organisations, and retail enterprises where BigQuery is the central analytical data store.

Clear & Effective Teaching BigQuery architecture, Standard SQL, advanced analytics, data ingestion, performance optimisation, BigQuery ML, security, and governance are explained with real-world data warehouse and analytics examples.

Hands-On Learning Focus Students write advanced BigQuery SQL, optimise query performance with partitioning and clustering, build BigQuery ML models, and configure access controls through structured hands-on lab exercises.

Up-to-Date Knowledge Trainers keep content current with the latest BigQuery releases, BigQuery Omni multi-cloud capabilities, Duet AI for BigQuery, and evolving Google Cloud data platform best practices.