Salesforce, a cloud-based software-as-a-service platform, is the most popular CRM application in use today. Salesforce is amazingly customizable, has tons of integration functionality, and includes almost too many bells and whistles to count. Companies can use it to do everything from managing account planning to time management and team collaboration.

Snowflake is a cloud-based data warehouse that's fast, flexible, and easy to work with. It runs on Amazon Web Services EC2 and S3 instances, and separates compute and storage resources, enabling users to scale the two independently and pay only for resources used. Snowflake can natively load and optimize both structured and semi-structured data and make it available via SQL. It provides native support for JSON, Avro, XML, and Parquet data, and can provide access to the same data for multiple workgroups or workloads simultaneously with no contention roadblocks or performance degradation.

Step one is to get all of that data out of Salesforce. Salesforce provides many APIs for its products that can deliver data on accounts, leads, tasks, and more. You can find a list of APIs on one of the company's 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 our purposes, we'll use the REST API with SOQL (Salesforce Object Query Language), but the same data is available using other protocols, including streaming for real-time receipt of data.

The Salesforce Rest API can return JSON- or XML-formatted data depending on your preference. Here's 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"
        }, 

        ...

    ]
}

Depending on your data structures, you may need to prepare your data before loading. Check the supported data types for Snowflake and make sure that your data maps neatly to them.

Note that you won't need to define a schema in advance when loading JSON or XML data into Snowflake.

Snowflake's documentation outlines a Data Loading Overview that can lead you through the task of loading your data. If you're not loading a lot of data, Snowflake's data loading wizard may be helpful, but for many organizations, its limitations make it unacceptable. Instead, you can:

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

You can copy data from your local drive or from Amazon S3. Snowflake lets you make a virtual warehouse that can power the insertion 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 Salesforce.

And remember, as with any code, once you write it, you have to maintain it. If Salesforce 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.

Snowflake 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 Microsoft Azure Synapse Analytics, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. 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 Panoply, To Azure Synapse Analytics, To S3, and To Delta Lake.