How to load data from Google Sheets to Snowflake

Access your data on Google Sheets

The first step in loading Google Sheets data you own to any kind of data warehouse solution is to access your data and start extracting it.

Google Sheets offers a REST API that can be used to programmatically interact with your account. Due to the nature of the application there is no specific set of tables that are being extracted but instead each sheet of each spreadsheet is represented as a separate table.

In addition to the above, the things that you have to keep in mind when dealing with the Google Sheets API, are:

1. Rate limits: Depending on the API version that is being used, Google Sheets API has rate limits per project and per user.
2. Authentication: You authenticate on Google Sheets using either OAuth or the application’s API key.
3. Paging and dealing with a big amount of data: Platforms like Google Sheets that are dealing with clickstream data tend to generate a lot of data, like events on your web properties.

Transform and prepare your Google Sheets Data for Snowflake Replication

After you have accessed your data on Google Sheets, you will have to transform it based on two main factors:

1. The limitations of the database that is going to be used
2. 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.

Export data from Google Sheets to Snowflake

Usually, data is loaded into Snowflake in a bulk way, using the COPY INTO command. Files containing data, usually in JSON format, are stored in a local file system or in Amazon S3 buckets. Then a COPY INTO command is invoked on the Snowflake instance and data is copied into a data warehouse.

The files can be pushed into Snowflake using the PUT command, into a staging environment before the COPY command is invoked.

Another alternative is to upload data directly into a service like Amazon S3 from where Snowflake can access every data directly.

Updating your Google Sheets data on Snowflake

As you will be generating more data on Google Sheets, you will need to update your older data on Snowflake. This includes new records together with updates to older records that for any reason have been updated on Google Sheets.

You will need to periodically check Google Sheets for new data and repeat the process that has been described previously while updating your currently available data if needed. Updating an already existing row on a Snowflake table is achieved by creating UPDATE statements.

Another issue that you need to take care of is the identification and removal of any duplicate records on your database. Either because Google Sheets does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database.

In general, ensuring the quality of data that is inserted into your database is a big and difficult issue.

The best way to load data from Google Sheets to Snowflake

So far we just scraped the surface of what can be done with Snowflake and how to ingest data into it. The way to proceed relies heavily on the data you want to load, from which service they are coming from, and the requirements of your use case. Things can get even more complicated if you want to integrate data coming from different sources.

A possible alternative, instead of writing, hosting and maintaining a flexible data infrastructure, is to use a product like RudderStack that can handle this kind of problems automatically for you.

Easily use the Google Sheets connector from RudderStack, along with multiple sources or services like databases, CRM, email campaigns, analytics, and more. Quickly and safely ingest Sheets data into Snowflake and start generating insights from your data.