Just like all data modeling, consistency and standardization is key when determining when and what to cast. BI tools require certain fields to be specific data typesĪ key thing to remember when you’re casting data is the user experience in your end BI tool: are business users expecting customer_id to be filtered on 1 or '1'? What is more intuitive for them? If one id field is an integer, all id fields should be integers.Differences in needs or miscommunication from backend developers. This typically happens for a few reasons: Syntax CAST (expr AS typename) Where typename is any of: INT64, NUMERIC, BIGNUMERIC, FLOAT64, BOOL, STRING, BYTES, DATE, DATETIME, TIME, TIMESTAMP, ARRAY, STRUCT You can read more about each type here. But what are the scenarios folks run into that call for these conversions? At their core, these conversions need to happen because raw source data doesn’t match the analytics or business use case. The CAST function allows you to convert between different Data Types in BigQuery. It also works to cast as a string: SELECT cast (SUM (totals.totalTransactionRevenue) 0.20 as string) AS COGS. Produces a concatenation of the elements in an array as a STRING value. You can use the new numeric/decimal value to get the format you want: SELECT cast (SUM (totals.totalTransactionRevenue) 0.20 as numeric) AS COGS. Reverses the order of elements in an array. Roboquery converts this function and lot. Try our Free Online Converter for Bigquery. Concatenates one or more arrays with the same element type into a single array. converts a string to a number CAST(1210.73 AS NUMERIC) Result: 1210.73. You know at one point you’re going to need to cast a column to a different data type. Produces an array with one element for each row in a subquery. You may also see the CAST function replaced with a double colon (::), followed by the data type to convert to cast(order_id as string) is the same thing as order_id::string in most data warehouses. In addition, the syntax to cast is the same across all of them using the CAST function. Google BigQuery, Amazon Redshift, Snowflake, Postgres, and Databricks all support the ability to cast columns and data to different types. SQL CAST function syntax in Snowflake, Databricks, BigQuery, and Redshift A few reasons for that: data cleanup and standardization, such as aliasing, casting, and lower or upper casing, should ideally happen in staging models to create downstream uniformity and improve downstream performance. Hot Network Questions At what speed information moves through the atoms of a rigid object Quicksearch: include the Organizations Nickname syntax coloring does not work for flex file Short story about. However, the order_id and customer_id fields are now strings, meaning you could easily concat different string variables to them.Ĭasting columns to their appropriate types typically happens in our dbt project’s staging models. Let’s be clear: the resulting data from this query looks exactly the same as the upstream orders model. After running this query, the orders table will look a little something like this: order_id
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