This page provides you with instructions on how to extract data from Magento and load it into Panoply. (If this manual process sounds onerous, 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.
What is Panoply?
Panoply provides a Smart Cloud Data Warehouse platform that lets users set up a new Amazon Redshift instance in just a few clicks. It uses machine learning algorithms to accomplish complex tasks like schema building, data mining, modeling, scaling, performance tuning, security, and backup. Panoply can import data with no schema, no modeling, and no configuration, and you can use analysis, SQL, and visualization tools on data in Panoply just as you would if you were creating a Redshift data warehouse on your own.
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.
Loading data into Panoply
When you've identified all of the columns you want to insert, use the Reshift CREATE TABLE statement to create a table in your data warehouse to receive all the data.
Once you have a table built, it may seem like the easiest way to replicate your data (especially if there isn't much of it) is to build INSERT statements to add data to your Redshift table row by row. If you have any experience with SQL, this probably will be your first inclination. Think again! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you should load the data into Amazon S3 and then use the COPY command to load it into Redshift.
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.
Other data warehouse options
Panoply is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Amazon Redshift, Google BigQuery, PostgreSQL, Snowflake, or Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax. Others choose a data lake, like Amazon S3 or Delta Lake on Databricks. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To Redshift, To BigQuery, To Postgres, To Snowflake, To Azure SQL Data Warehouse, To S3, and To Delta Lake.
Easier and faster alternatives
If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.
Thankfully, products like Stitch were built to move data from Magento to Panoply automatically. With just a few clicks, Stitch starts extracting your Magento data, structuring it in a way that's optimized for analysis, and inserting that data into your Panoply data warehouse.