BigQuery for Analysts: From Sandbox to Shared Insight

What's inside

1. Level 1: What BigQuery Is and Why It Feels DifferentFree
   Learn what a serverless data warehouse is, why BigQuery keeps storage and compute apart, and what that means for speed and cost. This course is independent of Google and is not endorsed by it.
2. Level 2: Console, Projects, Datasets, Tables and the SandboxFree
   Find your way around the BigQuery console and its hierarchy of project, dataset and table, and set up the free sandbox while knowing its current limits.
3. Level 3: Loading CSV and JSON Into Your Own Tables
   Load a small CSV or newline-delimited JSON file from your computer or from Cloud Storage into a table, and check that the schema and row count are right.
4. Level 4: GoogleSQL Compared With the SQL You Know
   See where GoogleSQL matches standard SQL and where it differs: backtick-quoted names, SELECT * EXCEPT and REPLACE, QUALIFY, SAFE_ functions and case-sensitivity rules.
5. Level 5: Data Types, Dates, Timestamps and Time Zones
   Work with GoogleSQL's data types, especially how DATE, DATETIME and TIMESTAMP differ and how time zones change the answer to questions such as 'sales per day'.
6. Level 6: STRUCT, ARRAY and UNNEST: Nested and Repeated Fields
   Understand why BigQuery tables often store nested and repeated data, and query it with dot notation, UNNEST and array functions.
7. Level 7: Joins and Aggregations at Scale
   Write joins and GROUP BY queries on large public tables that give correct results, and avoid row explosion, skew and double-counting.
8. Level 8: Window Functions for Ranking, Running Totals and Trends
   Use window functions with PARTITION BY, ORDER BY and frame clauses for rankings, running totals, moving averages and period-on-period change.
9. Level 9: Bytes Processed, Dry Runs and the SELECT * Trap
   Learn how BigQuery charges by the columns it scans, why LIMIT and SELECT * do not lower the bill, and how to estimate a query's cost before you run it.
10. Level 10: Query Cache, On-Demand and Capacity Pricing
    Understand when the query cache serves a result free of charge, and how on-demand pricing compares with capacity (editions) pricing, using Google's current pricing pages.
11. Level 11: Partitioning, Clustering and Approximate Aggregates
    Create partitioned and clustered tables so queries read less data, and use approximate aggregate functions when a close answer is good enough and much cheaper.
12. Level 12: Views, Materialized Views, Saved and Scheduled Queries
    Make analysis reusable with logical views, materialized views, saved queries and scheduled queries, and know which of these the sandbox supports today.
13. Level 13: BigQuery ML in Outline
    See how BigQuery ML trains and uses models with SQL statements, using one simple model as an example, and learn what it costs and where it falls short.
14. Level 14: Gemini in BigQuery and Data Canvas: Trust but Verify
    Use Gemini in BigQuery and data canvas to draft and explain SQL, find out which features are generally available and which are in preview, and check generated SQL before you rely on it.
15. Level 15: Sharing Results: IAM Roles, Connected Sheets, Looker Studio
    Share data and findings safely, using the IAM roles analysts commonly need, and deliver results through Connected Sheets and Looker Studio.
16. Level 16: Final Challenge: From Raw File to Shared Insight Exam
    Run a full analysis from start to finish that covers the whole course: load data, query nested fields, keep the cost down, save the result as a reusable view and share it, then check both the answer and the bytes processed.

Access

The first 2 levels are free with a free account. Every level, the podcast edition and the AI tutor come with All Access at £4.99/month or any Creator plan — see pricing.

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