Salesforce to Snowflake

This page provides you with instructions on how to extract data from Salesforce and load it into Snowflake. (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 Salesforce?

Salesforce is the CRM to rule them all. It's part of the cloud platform which encompasses a huge variety offerings not limited to just CRM. The Salesforce CRM is amazingly customizable, has tons of integration functionality, and includes almost too many bells and whistles to count. Companies can do everything from managing account planning to time management and team collaboration.

About Snowflake

Snowflake is a data warehouse solution that is entirely cloud based. It's a managed service. If you don't want to deal with hardware, software, or upkeep for a data warehouse you're going to love Snowflake. It runs on the wicked fast Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be flexible and easy to work with where other relational databases are not. One example of this is the query execution. Snowflake creates virtual warehouses where query processing takes place. These virtual warehouses run on separate compute clusters, so querying one of these virtual warehouses doesn't slow down the others. If you have ever had to wait for a query to complete, you know the value of speed and efficiency for query processing.

Getting data out of Salesforce

Step one is to get all of that precious data out of Salesforce. For our purposes here, we'll focus on CRM and customer datasets. There are a great many Salesforce API's available for their various products. There is a list on one of their helpdesk posts with some direction on when and how to use each API.

By looking through that post, you can get an idea of which API makes the most sense for your use case. For this post, we'll discuss the REST API and show some examples. Keep in mind that the same data is available using other protocols (including streaming for real-time receipt of data).

SOQL (Salesforce Object Query Language) is what you will need to write for this project. Using SOQL, you'll have access to records such as accounts, leads, tasks, and many more.

Sample Salesforce data

The Salesforce Rest API can return JSON or XML formatted data depending on your preference. Here is what a sample response might look like in JSON format:

    "done" : true,
    "totalSize" : 14,
    "records" : 
            "attributes" : 
                "type" : "Account",    
                "url" : "/services/data/v20.0/sobjects/Account/001D000000IRFmaIAH"  
            "Name" : "Test 1"
            "attributes" : 
                "type" : "Account",    
                "url" : "/services/data/v20.0/sobjects/Account/001D000000IomazIAB"  
            "Name" : "Test 2"



Preparing data for Snowflake

Depending on the structure that you data is in, you may need to prepare it for loading. Take a look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them. If you have a lot of data, you should compress it. Gzip, bzip2, Brotli, Zstandard v0.8 and deflate/raw deflate compression types are all supported.

One important thing to note here is that you don't need to define a schema in advance when loading JSON data into Snowflake. Onward to loading!

Loading data into Snowflake

There is a good reference for this step in the Data Loading Overview section of the Snowflake documentation. If there isn’t much data that you’re trying to load, then you might be able to use the data loading wizard in the Snowflake web UI. Chances are, the limitations on that tool will make it a non-starter as a reliable ETL solution. There two main steps to getting data into Snowflake:

  • Use the PUT command to stage files
  • Use the COPY INTO table command to load prepared data into the awaiting table from the prior step.

For the COPY step, you’ll have the option of copying from your local drive, or from Amazon S3. One of Snowflakes’ slick features lets you to make a virtual warehouse that will power the insertion process.

Keeping Salesforce data up to date

So now what? You have a script that pulls data from Salesforce and loads it into a data warehouse. It's time to plan for when you add new custom fields and need to change your database structure to add them. The key is to build your script in such a way that it can identify incremental updates to your data. This is where functionality like the Salesforce streaming API can come in handy.

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 solve this problem automatically. With just a few clicks, Stitch starts extracting your Salesforce data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.