Salesforce to Panoply

This page provides you with instructions on how to extract data from Salesforce 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 Salesforce?

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.

What is Panoply?

Panoply is a managed data warehouse platform that lets users set up an Amazon Redshift instance in just a few clicks. Complex tasks like schema building, data mining, modeling, scaling, performance tuning, security, and backup are handled by an array of machine learning algorithms. Panoply can import data with no schema, no modeling, and no configuration, and you can use your favorite analysis, SQL, and visualization tools just as you would if you were creating a Redshift data warehouse on your own.

Getting data out of Salesforce

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.

Sample Salesforce 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"
        }, 

        ...

    ]
}

Loading data into Panoply

Once you've identified the columns you want to insert, you can use the Redshift CREATE TABLE statement to set up a table to receive all of the data.

To populate that table, you might be tempted to use INSERT statements to add data to your Redshift table row by row. Don't do that; Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, a better approach is to load the data into Amazon S3 and use the COPY command to migrate it into Redshift.

Keeping Salesforce 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 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.

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, or Snowflake, which are RDBMSes that use similar SQL syntax. 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, and To Snowflake.

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 Panoply data warehouse.