In the video and course examples, you will see the `JSON_EXTRACT` function. While this function is fully supported and works correctly, it is considered **legacy** syntax in BigQuery.

For your own future projects, we recommend using the modern standard functions:

* `JSON_VALUE`: extracts scalar values (like strings or numbers) and removes extra quotes automatically;
* `JSON_QUERY`: extracts complex JSON objects or arrays.

Modern Practice

Your task is to analyze customer shopping patterns while demonstrating your understanding of BigQuery's specialized features.

1. Write a query that uses partition pruning by filtering on the appropriate partition column (`_PARTITIONDATE`);
2. Use `APPROX_COUNT_DISTINCT` to efficiently count unique customers;
3. Include at least one `JSON` extraction from the products field;
4. Group your results in a meaningful way.


To use `_PARTITIONDATE` for partition pruning, ensure the BigQuery table is created with **Partition by: Ingestion time**. Without this setting, the pseudo-column will not exist, and the query will fail.

Note

Download Dataset

Dive into the fundamentals of the BigQuery platform and transform the way you handle cloud data. Master the skills needed to navigate the console, manage projects, and optimize query costs. Get hands-on experience with BigQuery-specific features like array operations, geographic data, and date functions. Explore advanced optimization techniques and learn the secrets of building machine learning models directly within the warehouse using BigQuery ML.

Gain a solid understanding of the BigQuery environment. distinct from traditional databases. Learn to navigate the console, manage projects and datasets, and analyze costs effectively. Build confidence in setting up your workspace for efficient data analysis.

Explore the essentials of the Query Engine to retrieve data efficiently. Master data selection techniques and learn how to analyze specific product metrics. Discover performance features and query testing methods to ensure your analysis is both fast and accurate.

Take your knowledge to the next level by mastering BigQuery’s unique data types. Learn to handle complex array operations and manipulate date and time functions. Explore geographic features to solve location-based challenges like calculating city distances.

Optimize your workflow by managing large tables and improving query performance. Learn the secrets of effective error handling to troubleshoot issues quickly. Master the skills needed to maintain a high-performance database environment.

Apply your knowledge to real-world scenarios. Discover how to build comprehensive reports and integrate BigQuery with external tools. Transform raw data into actionable business insights through practical application and reporting strategies.

Dive into the fundamentals of BigQuery ML. Discover how to create and execute machine learning models using standard SQL without moving data. Gain hands-on experience with model walkthroughs to predict outcomes directly within the platform.

Challenge: BigQuery Data Selection

Task

Challenge: BigQuery Data Selection

Task