Google BigQuery is a powerful platform designed for analyzing vast amounts of data. To make the most out of it, you need to understand some effective methods for querying data. Here are a few recommendations:

1. Structure Your SQL Query #

Effectively structuring your SQL query can enhance your BigQuery performance. It's recommended to break down complex queries into smaller ones, which can potentially reduce computational demands.

# Example
SELECT column1, column2
FROM `project.dataset.table`
WHERE column1 = "value";

2. Utilize Partitioning and Clustering #

BigQuery supports table partitioning and clustering, which help to improve the efficiency of data management and reduce query costs. Partitioning divides a table into segments, while clustering organizes data based on specific column values.

3. Use Approximate Aggregate Functions #

BigQuery provides approximate aggregation functions such as APPROX_COUNT_DISTINCT and APPROX_TOP_COUNT, which offer faster performance than their exact counterparts at the cost of a small precision trade-off.

4. Take Advantage of Caching #

BigQuery automatically caches query results for 24 hours. Re-running the same query within this period can retrieve results faster and won't incur additional costs.

5. Optimize Data Types #

Using appropriate data types can make your queries more efficient. For instance, instead of storing date and time information as strings, use BigQuery's native DATE and TIMESTAMP data types.

Remember, the key to efficient querying in BigQuery is understanding your data and the way it's structured. Knowing when and how to apply these best practices can significantly enhance your BigQuery performance.