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.

Delta Lake is an open source storage layer that sits on top of existing data lake file storage, such AWS S3, Azure Data Lake Storage, or HDFS. It uses versioned Apache Parquet files to store data, and a transaction log to keep track of commits, to provide capabilities like ACID transactions, data versioning, and audit history.

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.

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.

To create a Delta table, you can use existing Apache Spark SQL code and change the format from parquet, csv, or json to delta. Once you have a Delta table, you can write data into it using Apache Spark's Structured Streaming API. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table. Databricks provides quickstart documentation that explains the whole process.

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.

Delta Lake on Databricks 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, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. Others choose a data lake, like Amazon S3. 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 Panoply, and To S3.