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How to load data from Google Sheets to PostgreSQL

Access your data on Google Sheets

The first step in loading your Google Sheets data to any data warehouse solution is accessing your data and extracting it.

Google Sheets offers a REST API that you can use to interact with your account programmatically. Due to the nature of Google Sheets, there is no specific set of tables extracted, but each sheet of every 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 being used, the Google Sheets API has a rate limit per project and user.
  2. Authentication: You authenticate on Google Sheets using either OAuth or the application's API key.
  3. Paging and dealing with a large amount of data: Platforms like Google Sheets dealing with clickstream data tend to generate a lot of data, like events on your web properties.
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Transform and prepare your Google Sheets Data for PostgreSQL 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 you will load the data into
  2. The type of analysis that you want to perform

Each system has specific limitations on the data types and data structures that it supports. If, for example, you want to push the data into Google BigQuery, then you can send nested data in a JSON format directly. However, you must keep in mind that the data you get from Google Sheets is in the form of a tabular report, just like a CSV.

Of course, when dealing with tabular data stores like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data before loading it into your database.

Another consideration is that you have to choose the right data types. Again, depending on the system you will send the 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 your queries' expressivity 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, meaning that your work to do these mappings is much easier and straightforward.

Each table is a collection of columns with a predefined data type as an integer or VARCHAR. PostgreSQL, like any other SQL database, supports a wide range of different data types.

A typical strategy for loading data from Google Sheets to PostgreSQL is to create a schema where you will map each API endpoint to a table. You should map each key inside the Google Sheets API endpoint response to a column of that table, and you should ensure the right conversion to a Postgres-compatible data type.

Export data from Google Sheets to PostgreSQL

If an endpoint from Google Sheets returns a value as String, you should convert it into a VARCHAR with a predefined maximum size or TEXT data type. Tables can then be created on your database using the CREATE SQL statement.

Once you have defined your schema and created your tables with the proper data types, you can start loading data into your database.

The preferred way of adding larger datasets into a PostgreSQL database is by using the COPY command. COPY is copying data from a file on a file system accessible by the Postgres instance. In this way, you can insert much larger datasets into the database in less time.

Note, however, that the COPY command requires physical access to a file system to load data. With cloud-based, fully managed databases these days, getting direct access to a file system is not always possible.

If this is the case and you cannot use a COPY statement, then another option is to use PREPARE and INSERT statements together to end up with optimized and more performant INSERT queries.

Updating your Google Sheets data on PostgreSQL

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

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

Another issue that you need to take care of is identifying and removing any duplicate records on your database. Google Sheets does not have a mechanism to identify new and updated records. Also, errors in your data pipelines might introduce duplicate records in your database.

In general, ensuring the quality of the data inserted in your database is a big and difficult issue, and PostgreSQL features like TRANSACTIONS can help tremendously. However, they do not solve the problem in the general case.

The best way to load data from Google Sheets to PostgreSQL

So far, we just scraped the surface of what can be done with PostgreSQL and how to ingest data into it. How you proceed depends on the data you want to load, the service from which it is coming, and your use-case requirements. Things can get even more complicated if you want to integrate data coming from different sources.

Instead of writing, hosting, and maintaining a flexible data infrastructure, a possible alternative is to use a product like RudderStack to handle this kind of problem for you automatically.

You can 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 your Google Sheets data into PostgreSQL and start generating insights from your data.

Sign Up For Free And Start Sending Data

Test out our event stream, ELT, and reverse-ETL pipelines. Use our HTTP source to send data in less than 5 minutes, or install one of our 12 SDKs in your website or app.

Don't want to go through the pain of direct integration?

RudderStack's Google Sheets integration

makes it easy to send data from Google Sheets to PostgreSQL.