Yes, you can transform data from a delimited file when loading it into Snowflake. Snowflake provides various options for transforming data during the loading process, including:
Data Transformation Using SQL: You can use SQL commands to transform data during the loading process. For example, you can use SQL commands to modify data values, split columns, concatenate columns, and more.
Pre-Processing Using External Tools: You can use external tools such as Python or PowerShell to preprocess the delimited file before loading it into Snowflake. This can include performing data transformations, filtering data, and converting data types.
Transformations Using Snowflake’s COPY Command: Snowflake’s COPY command includes several options for transforming data during the loading process. For example, you can use the COPY command to specify a custom delimiter, skip header rows, and set null and default values.
Transformations Using Snowpipe: Snowpipe is Snowflake’s continuous data ingestion service that enables you to load data into Snowflake in real-time. You can use Snowpipe to perform data transformations during the loading process, including filtering, cleaning, and formatting data.
In summary, there are several options available to transform data from a delimited file when loading it into Snowflake. Depending on your specific use case and requirements, you can choose the option that best suits your needs.
You can specify these transformations using the COPY INTO command, which allows you to specify a variety of loading options. For example, to skip the first row of a CSV file and convert a column to a different data type, you can use the following command:
COPY INTO my_table
FILE_FORMAT = (FORMAT_NAME = my_format)
SKIP_HEADER = 1
ON_ERROR = ‘CONTINUE’
In this example, **`my_table`** is the name of the table you want to load the data into, **`@my_stage/my_file.csv`** is the location of your CSV file in your Snowflake external stage, **`my_format`** is the name of the file format you defined in Step 2. The `SKIP_HEADER` parameter skips the first row of the file, and the `(id, name, age:int)` clause specifies the columns to load and their data types. The `age:int` clause converts the `age` column to an integer data type.