Earlier this month, I had the opportunity to deliver a dedicated training session on data manipulation with BigQuery SQL at the Swiss Marketplace Group (SMG) — one of the leading digital marketplace platforms in Switzerland. The goal: empower analysts and data practitioners with the skills needed to efficiently query, transform, and operationalize large datasets using Google BigQuery.

This wasn’t just another SQL 101 course. It was a deep-dive into practical data engineering with BigQuery, using real-world e-commerce datasets and scenarios drawn from SMG’s operations. In this post, I’ll walk you through the key technical concepts we covered, the hands-on exercises, and why BigQuery is an essential part of any modern cloud data stack.

Why BigQuery?

Google BigQuery is a serverless, highly scalable, and cost-effective cloud data warehouse built for fast SQL analytics on massive datasets.

Key features that make BigQuery stand out:

• Distributed MPP engine that handles petabyte-scale data with ease
• Standard SQL support with extensions for analytical needs
• Automatic optimization (no need to manage indexes or partitions manually)
• Native integration with Google Cloud ecosystem (e.g., Looker, Vertex AI, GCS)
• BI Engine acceleration for sub-second dashboards

At SMG, with large volumes of marketplace data generated daily (listings, user interactions, pricing history, etc.), BigQuery provides the performance and flexibility to run complex analytics workloads at scale.

Training Agenda: From SQL Basics to Complex Data Transformations

Our session was designed to bring participants with diverse skill levels to a common ground, and then guide them through advanced transformations and optimizations. Here’s what we covered:

✅ 1. Data Manipulation in BigQuery SQL

This was the core of the training. We worked through real-world examples to apply:

• SELECT with window functions (ROW_NUMBER, RANK, LAG, LEAD)
• Advanced JOIN types and anti-joins to detect changes or duplicates
• Nested and repeated fields: querying and flattening semi-structured data (UNNEST, WITH OFFSET)
• Using ARRAY and STRUCT to model hierarchical data
• MERGE statements for upserts and incremental loads

Use case tackled: Identifying first-time users who became paying customers within 7 days, across millions of records — using PARTITION BY, FILTER, and DATE_DIFF.

✅ 2. Common Table Expressions (CTEs) and Query Refactoring

We emphasized readability and modularity:

• Chaining CTEs for step-by-step transformations
• Materializing intermediate logic for debugging
• Using temporary tables for large-stage processing

Participants learned how to move away from deeply nested queries and instead modularize logic using CTEs and views—essential for production-grade analytics pipelines.

✅ 3. Data Quality, Validations & Edge Case Handling

• Handling missing values using IFNULL, COALESCE
• Validating schema assumptions with INFORMATION_SCHEMA
• Applying assertions and sanity checks using CASE and COUNT(*)

We simulated a schema drift issue where the device_type column had new unexpected values, and participants had to write logic to classify or isolate invalid rows.

✅ 4. Performance Optimization

BigQuery is powerful, but not free. So we concluded with:

• Avoiding SELECT * in production
• How materialized views and scheduled queries reduce compute costs
• Caching behavior and query reuse
• Testing performance using APPROX_QUANTILES for distribution analysis
• Using table partition decorators (e.g., mytable$20240501) for quick backfills

Interactive, Hands-On Learning

Participants got to apply each concept in a live BigQuery sandbox environment, working with real anonymized datasets from the e-commerce and classifieds domain. The engagement was phenomenal — teams tackled bonus exercises involving product listings, pricing anomalies, and user churn analysis.

The most exciting part? Watching business analysts who had only basic SQL skills begin to write high-performance queries on 10M+ row datasets within a couple of hours.

Final Thoughts

Delivering this session at SMG was a fantastic experience — not just because of the content, but because of the collaborative spirit and curiosity of the participants.

BigQuery SQL is much more than just SQL-on-the-cloud. It’s a gateway to modern cloud-native data engineering. With the right foundations, teams can go far beyond traditional reporting: from real-time behavioral analysis to powering machine learning pipelines.

If your team is moving to Google Cloud or looking to scale up your analytical capabilities, mastering BigQuery is a must.