Google Sheets to PostgreSQL: 2 Easy Methods

Does your organization store large amounts of data in Google Sheets? Would you prefer your data to be stored in a more secure data storage environment? If this applies to you, then you might want to consider moving your data from Google Sheets to a secure relational database environment like Postgres. This blog will present methods to move data from Google Sheets to PostgreSQL, enabling you to choose the one which best suits your needs.

How to connect Google Sheets to PostgreSQL?

Multiple methods can be used to connect Google Sheets to PostgreSQL and load data easily:

Multiple methods can be used to connect Google Sheets to PostgreSQL and load data easily:

Method 1: Using Hevo to Connect Google Sheets to PostgreSQL

Hevo focuses on two simple steps to move your data from Google Sheets to PostgreSQL:

Configure Source: Connect Hevo with Google Sheets by providing a unique name for your Pipeline, along with details about your authorized Google Sheets account. You can also choose the historical sync duration for your Google Sheets data.

Integrate Data: Complete Google Sheets PostgreSQL migration by providing your destination name, account name, region of your account, database username and password, database and schema name, and the Data Warehouse name.

This concludes the migration process from Google Sheets to Postgres.

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Method 2: Accessing Google APIs using ETL scripts to connect Google Sheets to PostgreSQL

Data stored in Google Sheets can be easily accessed by writing code lines to interact with Google APIs and easily extract data. It requires users to extract data from Google Sheets, convert it into a CSV file and then load the data into PostgreSQL.

This can be implemented using the following steps:

• Step 1: Extracting data from Google Sheets using RESTful APIs
• Step 2: Transforming data into the correct format
• Step 3: Loading Google Sheets data into PostgreSQL

Step 1: Extracting data from Google Sheets using RESTful APIs

Google Sheets provides a REST API, from which you can access your data. More specifically, this is done by making calls to the REST API. It is important to note that Google Sheets is primarily a spreadsheet program, with data being primarily organized into sheets. When extracting the data, it is useful to consider each sheet as representing a table.

To extract data from Google Sheets, you can use the GET command as follows by specifying the desired column range from where you want the data to be exported:

GET https://sheets.googleapis.com/v4/spreadsheets/spreadsheetId/values/Sheet!A1:D5

This will extract data from the specified column range of A1-D5 and generate the following output:

{"range": "Sheet1!A1:D5",   "majorDimension": "ROWS",   "values": [     ["Item", "Cost", "Stocked", "Ship Date"],     ["Wheel", "$20.50", "4", "3/1/2016"],     ["Door", "$15", "2", "3/15/2016"],     ["Engine", "$100", "1", "30/20/2016"],     ["Totals", "$135.5", "7", "3/20/2016"]   ], }

For further information on Google APIs, you can check the official documentation here.

This is how you can extract your data from Google Sheets using the RESTful APIs of Google.

Step 2: Transforming data into the correct format

Before loading the data, you have to make sure that you clean the JSON file to specify the exact columns that you want. The data from Google Sheets is tabular and so will not need any flattening before it is loaded into Postgres. This also means that it may be helpful to convert into CSV format before loading. You also have to ensure that the data types from Google Sheets match their corresponding types in Postgres.

It may also be necessary to create a schema to match each of your tables in Postgres to endpoints from the Google Sheets data. Postgres provides support for a wide variety of data types. Each table consists of columns with a preset data type, such as integer or VARCHAR. PostgreSQL, like every other SQL database, can handle a wide variety of data types.

A popular technique for transferring data from Google Sheets to PostgreSQL is to develop a schema that maps each API endpoint to a database. Each Google Sheets API endpoint response key should be mapped to a table column and converted to a Postgres-compatible data format.

For further information about the supported data types, you can check the official site here

Step 3: Loading Google Sheets data into PostgreSQL

Once you’ve transformed your Google Sheets data into the correct format, you now load it into your PostgreSQL database. Create a staging table and ensure that the table structure matches your data file. You can use the following command to create your staging table:

CREATE TABLE TABLE_NAME (COLUMN_1 TYPE, COLUMN_2 TYPE……)

Use the copy command to load data from your CSV file that contains your Google Sheets data into PostgreSQL as follows:

COPY table_name FROM 'file_name.csv' HEADER CSV DELIMITER ',';

For further information on using the copy command, you can check the official documentation here.

This is how you can write manual ETL scripts and access Google Sheets using its RESTful APIs to connect Google Sheets to PostgreSQL.

Step 4: Updating your Google Sheets data on PostgreSQL

As you generate additional data on Google Sheets, you will need to update your previous PostgreSQL data. This includes both new entries and revisions to existing records that have been modified on Google Sheets for any purpose.

You will need to check Google Sheets on a regular basis for new data and resume the previously stated process, updating your current data as needed. To update an existing row in a PostgreSQL table, use UPDATE statements.

Another issue to address is finding and eliminating duplicate records from your database. Google Sheets fails to provide a feature for identifying new and changed records. Also, faults in your data pipelines may result in duplicate records in your database.

In general, maintaining the quality of data stored in your database is a large and demanding task; however, PostgreSQL capabilities such as TRANSACTIONS can help enormously. However, they do not provide a general solution to the problem. This concludes the process of creating a Google Sheets PostgreSQL integration.

Limitations of migrating data using Google APIs:

• TIme-consuming: You will have to manually write a lot of code under this method. This takes up a lot of time and may not be very helpful in organizations that enforce strict deadlines.
• Requires Constant Maintenance: This method will return inaccurate data in the event that there is a connectivity issue or issues with the Google Sheets API. Consequently, constant monitoring is required just to ensure that you have accurate data
• Difficulty with Data Transformations: It is impossible to perform fast data transformations like currency conversions under this method
• Difficulties with Real-time data: The data captured in this method is at a point in time. You have to write additional code and configure cron jobs to have basic real-time functionality

When using the Google Sheets API, keep the following in mind:

1. Rate limitations: Depending upon the API version utilized, Google Sheets API has a price limit per project and person.
2. Authentication: You can use either OAuth or the app’s API key on Google Sheets.
3. Paging and dealing with enormous amounts of data: Solutions like Google Sheets that deal with clickstream data generate a lot of data, such as web property events.

Use Cases of Google Sheets to PostgreSQL Migration

Some of the real-life use cases of Google Sheets to PostgreSQL Migration are:

• Building Web Applications: PostgreSQL can be used for building various web applications. It provides unique features, like an advanced indexing mechanism, JSON support, and full-text search. This enables developers to create responsive applications.
• Geospatial  Applications: It is suitable for GIS applications as PostgreSQL supports geospatial data through extensions like PostGIS.
• Data Analytics: It has capabilities to do complex querying, and handling large volumes of data. It also supports OLAP(Online Analytical Processing) workflows.
• IoT (Internet of Things): The lightweight nature and flexibility make PostgreSQL suitable for IoT devices in which data storage and management are crucial.

Conclusion

You have learned about 2 methods you can use to connect Google Sheets to PostgreSQL. The manual process requires configurations and is demanding, so check out Hevo, which will do all the hard work for you in a simple intuitive process so that you can focus on what matters, the analysis of your data. 

Visit our Website to Explore Hevo

Want to try Hevo? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. Have a look at our unbeatable Hevo pricing, which will help you choose the right plan for you. You can now transfer data from sources like Google Sheets to your target destination for Free using Hevo!

What are your thoughts on moving data from Google Sheets to PostgreSQL? Let us know in the comments.