After you have accessed your data on Google Sheets, you will have to transform it based on two main factors:
- The limitations of the database that is going to be used
- The type of analysis that you plan to perform
Each system has specific limitations on the data types and data structures that it supports. If for example you want to push data into Google BigQuery, then you can send nested data like JSON directly, but keep in mind that every data you get from Google Sheets are in the form of a tabular report just like a CSV.
Of course, when you are dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data, just as in the case of JSON, before loading into the database.
Also, you have to choose the right data types. Again, depending on the system that you will send data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database. Google Sheets has a very limited set of available data types which means that your work to do these mappings is much easier and straight forward, but nonetheless equally important with any other case of a data source.
Data in Snowflake is organized around tables with a well-defined set of columns with each one having a specific data type.
Snowflake supports a rich set of data types. It is worth mentioning that a number of semi-structured data types is also supported. With Snowflake, it is possible to load directly data in JSON, Avro, ORC, Parquet, or XML format. Hierarchical data is treated as a first-class citizen, similar to what Google BigQuery offers.
There is also one notable common data type that is not supported by Snowflake. LOB or large object data type is not supported, instead, you should use a BINARY or VARCHAR type instead. But these types are not that useful for data warehouse use cases.
A typical strategy for loading data from the Sheets to Snowflake is to create a schema where you will map each API endpoint to a table.
Each key inside the Google Sheets API endpoint response should be mapped to a column of that table and you should ensure the right conversion to a Snowflake data type.
Of course, you will need to ensure that as data types from the Google Sheets API might change, you will adapt your database tables accordingly, there’s no such thing as automatic data type casting.
After you have a complete and well-defined data model or schema for Snowflake, you can move forward and start loading your data into the database.