Chapter 3 Scheduled Queries

Scheduled queries are a powerful feature in BigQuery that allow you to automate the execution of SQL queries on a schedule. With scheduled queries, you can specify the frequency, start time, and other settings for running queries on a regular basis. This can be useful for a wide range of use cases, from data ingestion to data processing and analysis.

To create a scheduled query in BigQuery, you can use the Cloud Console like so.

Scheduled queries can be useful for a variety of use cases, such as:

  • Data ingestion: If you’re ingesting data from external sources on a regular basis, you can use scheduled queries to automate the process of moving data from external sources into BigQuery.

  • Data processing: If you’re working with large datasets, you may need to run complex queries that take a long time to execute. By scheduling these queries to run during off-peak hours, you can avoid impacting the performance of other queries and workloads.

  • Data analysis: If you’re running complex analysis on your data, you may need to run multiple queries to get the insights you need. By scheduling these queries to run on a regular basis, you can automate the process of generating reports and insights.

Overall, scheduled queries can be a powerful tool for automating the execution of SQL queries in BigQuery. By specifying the frequency, start time, and other settings for your queries, you can save time and improve the efficiency of your data processing and analysis workflows.

3.0.1 Exercise 2

  1. Create a SQL query that creates a new table in your dataset.
  2. Schedule this query to run in 5 minutes time.

3.1 Partitioned Tables

Partitioning tables in Google BigQuery involves dividing a table into smaller, more manageable partitions based on a specified column or columns. Partitioning tables can help improve query performance and reduce costs by limiting the amount of data that needs to be scanned.

To partition a table in BigQuery, you can use the CREATE TABLE statement with a partitioning clause. For example, the following statement creates a table partitioned by date:

CREATE TABLE my_partitioned_table
PARTITION BY DATE(timestamp)
OPTIONS(
  partition_expiration_days=365,
  description="A partitioned table"
) AS
SELECT *
FROM my_source_table;

In this example, the PARTITION BY clause specifies that the my_partitioned_table should be partitioned by the timestamp column. The OPTIONS clause sets the partition expiration to 365 days and provides a description for the table.

Once the partitioned table is created, queries can be run against individual partitions, rather than the entire table. This can help improve query performance and reduce costs by limiting the amount of data that needs to be scanned. Partitions are currently limited to 4000 partitions and can be done on integer or date columns.

There are several scenarios in which partitioning tables can be particularly useful. For example:

  • When working with large tables that contain many rows, partitioning can help improve query performance by limiting the amount of data that needs to be scanned.
  • When working with time-series data, partitioning by date or time can be particularly useful, as it allows you to quickly and easily query data for specific time periods.
  • When working with tables that are frequently updated, partitioning can help reduce query costs by limiting the amount of data that needs to be scanned.