Build real-world SQL skills using BigQuery. Learn how to write, optimize, and debug queries in modern data workflows sha
What you will learn
Learn how to write clean, structured SQL queries using BigQuery and real-world ecommerce datasets.
Understand how to sanity-check AI-generated SQL and identify common logic and performance issues.
Improve prompt engineering by thinking in SQL and communicating clearly with tools like ChatGPT or Copilot.
Build reliable analytics workflows using filters, joins, aggregations, subqueries, and CTEs.
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- Master BigQuery’s Scalability: Learn BigQuery’s architecture for petabyte-scale datasets. Efficiently query massive data volumes, ensuring fast, cost-effective analyses in data-intensive environments.
- Advanced Analytical Functions Demystified: Beyond basic aggregations, explore powerful window functions (e.g.,
ROW_NUMBER,RANK,LEAD,LAG) for complex calculations, trend identification, and deeper insights from data sequences. - Navigate Semi-Structured Data: Effectively query and transform JSON data types within BigQuery, essential for modern API logs and diverse sources, bridging structured and unstructured analytics.
- Optimize for Performance and Cost: Gain practical BigQuery query optimization knowledge: execution plans, partitioning, clustering, and designing cost-efficient queries to manage cloud expenditure.
- Data Preparation for Machine Learning: Use SQL to craft, clean, and engineer features directly within BigQuery, providing high-quality input datasets for ML models and accelerating data science workflows.
- Build Robust Data Models: Learn best practices for structuring data using views and materialized views in BigQuery to create consistent, performant, and easily consumable data models for analytical needs.
- Automate and Schedule Queries: Operationalize SQL workflows by scheduling queries within BigQuery or integrating with orchestration tools, transitioning from ad-hoc analysis to automated data pipelines.
- Leverage BigQuery ML Fundamentals: Introduction to performing machine learning tasks directly within BigQuery using SQL, enabling simple predictive model building and evaluation in your analytical environment.
- Ethical Data Handling & Bias Detection with SQL: Understand how SQL identifies potential biases or privacy concerns within datasets, promoting responsible data practices in the AI era.
- PROS:
- Hands-on BigQuery Expertise: Gain practical, in-demand skills with Google Cloud’s leading data warehouse.
- Future-Proof Your Analytics Career: Equip yourself with modern SQL techniques and an understanding of its synergy with AI.
- Real-World Problem Solving: Apply concepts to realistic scenarios, building confidence in tackling complex business questions.
- Empowerment in the AI Ecosystem: Learn to collaborate effectively with AI tools, critically assess output, and enhance data engineering contributions.
- Efficiency and Optimization Focus: Develop the ability to write efficient and cost-effective SQL queries for large-scale data.
- CONS:
- Assumes Foundational SQL Familiarity: While covering modern aspects, the course moves at a pace that benefits learners with a basic understanding of SQL syntax and concepts.
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