BigQuery also supports DATETIME_TRUNC and TIMESTAMP_TRUNC functions to support truncation of more granular date/time types. In Snowflake and Databricks, you can use the DATE_TRUNC function using the following syntax:Ī note on BigQuery: BigQuery’s DATE_TRUNC function supports the truncation of date types, whereas Snowflake, Redshift, and Databricks’ can be a date or timestamp data type. The DATE_TRUNC function in Snowflake and Databricks There may be some minor differences between the argument order for DATE_TRUNC across data warehouses, but the functionality very much remains the same.īelow, we’ll outline some of the slight differences in the implementation between some of the data warehouses. Most, if not all, modern cloud data warehouses support some type of the DATE_TRUNC function. The DATE_TRUNC function can be used in SELECT statements and WHERE clauses. The date part: This is the days/months/weeks/years (level) you want your field to be truncated out to.How to use the DATE_TRUNC function įor the DATE_TRUNC function, there are two arguments you must pass in: Instead, DATE_TRUNC is your standard kitchen knife-it’s simple and efficient, and you almost never start cooking (data modeling) without it. However, the DATE_TRUNC function isn’t your swiss army knife–it’s not able to do magic or solve all of your problems (we’re looking at you star). Overall, it’s a great function to use to help you aggregate your data into specific date parts while keeping a date format. This can make date/time fields easier to read, as well as help perform cleaner time-based analyses. Using the DATE_TRUNC function, you can truncate to the weeks, months, years, or other date parts for a date or time field. Wordy, wordy, wordy! What does this really mean? If you were to truncate out to its month, it would return (the first day of the month). The DATE_TRUNC function will truncate a date or time to the first instance of a given date part. To do that, you’re going to need a handy dandy function that helps you round out date or time fields. However, you’re likely looking at your data at a somewhat zoomed-out level-weekly, monthly, or even yearly. having data at a more granular level always allows you to zoom in. Timestamps > dates, daily data > weekly data, etc. In general, data people prefer the more granular over the less granular.
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