Magento to Google Data Studio

This page provides you with instructions on how to extract data from Magento and analyze it in Google Data Studio. (If the mechanics of extracting data from Magento seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Magento?

Magento is an open source content management system for ecommerce web sites. It's known for its flexibility and wide adoption across ecommerce businesses of all sizes.

Getting data out of Magento

You can use the Magento API to extract information. In most recent version, Magento offers both REST and SOAP versions of its API. Be warned, however, that historical versions of different Magento API calls could display inconsistent compatibility.

You can also pull data directly from the underlying database. (Using the API is really just doing this via a layer of abstraction.) If you go this route, familiarize yourself with the Magento database structure.

Preparing Magento data

Your Magento data needs to be structured into a schema for your destination database. If you choose to work with the default Magento database structure in your analytical environment, this simply means recreating the tables and fields that you pulled from your Magento API. You can refer to the API docs or use the information_schema tables in those databases to get the information you need.

Keeping Magento data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Magento.

And remember, as with any code, once you write it, you have to maintain it. If Magento modifies its API, or the API sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

From Magento to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Magento data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Magento to Redshift, Magento to BigQuery, and Magento to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Magento data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.