Google BigQuery learning path: everything to study, topic by topic

Understand BigQuery's serverless architecture, pricing model, key differentiators, and core use cases in modern data analytics.

Explore BigQuery's distributed architecture, storage layers, query engine, and how data flows through the system.

Learn to organize data using datasets, tables, views, and materialized views for optimal performance and maintainability.

Understand key performance metrics, monitoring tools, and foundational optimization principles in BigQuery.

Master BigQuery's SQL dialect, data types, operators, and basic query structure aligned with ANSI SQL standards.

Combine data from multiple tables using various join types, subqueries, and set operations efficiently.

Summarize data using GROUP BY, aggregate functions, HAVING clauses, and advanced grouping patterns.

Apply proven techniques to reduce bytes scanned, improve execution speed, and optimize slot utilization.

Analyze query execution plans, understand bottlenecks, and use diagnostic tools to optimize performance.

Master advanced performance optimization including slot utilization analysis, query plan interpretation, BI Engine acceleration, materialized view strategies, and caching mechanisms.

Load data from various sources: batch (Cloud Storage, databases), streaming (Pub/Sub, Dataflow), and real-time APIs.

Design and implement end-to-end ETL pipelines using SQL, Dataflow, and orchestration tools like Composer.

Implement data quality checks, handle invalid records, and ensure data integrity throughout the pipeline.

Design scalable schemas using normalization, denormalization, and nested structures; manage schema changes safely.

Implement efficient incremental loading patterns to handle large-scale data updates without full table reloads.

Implement real-time data pipelines using streaming inserts, BigQuery Storage Write API, change data capture patterns, and Pub/Sub subscriptions for continuous ingestion.

Design integration architectures with Dataflow, Dataproc, Cloud Composer orchestration, Pub/Sub event-driven pipelines, and connected sheets for business users.

Use window functions to perform ranking, running totals, lag/lead analysis, and advanced analytical queries.

Master Common Table Expressions (CTEs) and recursive queries for hierarchical data, graph traversal, and complex analytics.

Create reusable functions using SQL or JavaScript for complex business logic, transformations, and custom metrics.

Apply sophisticated SQL patterns: pivoting, unpivoting, string operations, JSON handling, and edge-case optimization.

Perform spatial analysis using GEOGRAPHY data type, spatial functions (ST_DISTANCE, ST_CONTAINS, ST_INTERSECTION), GIS operations at scale, and geospatial visualization.

Introduction to BQML: create and evaluate ML models directly in BigQuery without moving data or writing ML code.

Build regression and classification models for forecasting, customer segmentation, and business intelligence.

Apply ARIMA and ARIMA+ models to forecast trends, detect seasonality, and analyze time-dependent data.

Design and implement cohort analysis, funnel analysis, retention metrics, and advanced KPI calculations.

Implement real-time analytics using streaming inserts, snapshot tables, and low-latency query optimization.

Master advanced BQML capabilities including CREATE MODEL syntax variations, feature engineering in SQL, model export, hyperparameter tuning, and production deployment patterns.

Leverage BigQuery for AI/ML use cases including vector search, embedding generation, Vertex AI integration, feature stores, and ML pipeline orchestration.

Manage access using Google Cloud IAM roles, dataset-level permissions, and column/row-level security policies.

Protect data using encryption at rest, encryption in transit, Customer-Managed Encryption Keys (CMEK), and data masking.

Enable audit logging, track data access, generate compliance reports, and implement data retention policies.

Minimize BigQuery costs through slot reservations, smart partitioning, caching strategies, and usage monitoring.

Implement comprehensive cost management strategies including slot reservations vs on-demand pricing, query cost estimation, budget alerts, and organizational cost allocation.

Establish data governance frameworks using column-level security, policy tags, Data Catalog integration, data lineage tracking, and organizational IAM policies.

Enable secure data sharing using Analytics Hub, authorized views, cross-project dataset access, data clean rooms, and shared dataset management.

Manage BigQuery at organizational scale including capacity planning, monitoring dashboards, audit log analysis, reservation management, and operational best practices.