to Snowflake

This page provides you with instructions on how to extract data from 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 is a CRM that is easy to set up, and easy to administer. Some of the big advantages to using are the automation. There is integrated calling along with built-in email automation. You can also integrate easily with tools like Zapier to get even more automation.

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

For starters, you need to get your data out of That can be done by making calls to the REST API. The full documentation for the API can be found here.

To use the REST API, your script needs to make HTTP requests, and parse the response. The API uses JSON as its communication format. The standard HTTP methods like GET, PUT, POST and DELETE are going to be your major tools here.’s API offers access to leads, which is the main building block for data. Using methods outlined in the API documentation, you can retrieve the data you’d like to move to your data warehouse.

Sample data

When you query the API, it will return JSON formatted data. Below is an example response from the leads endpoint.

    "has_more": false,
    "data": [
            "id": "stat_1ZdiZqcSIkoGVnNOyxiEY58eTGQmFNG3LPlEVQ4V7Nk",
            "label": "Potential"
            "id": "stat_2ZdiZqcSIkoGVnNOyxiEY58eTGQmFNG3LPlEVQ4V7Nk",
            "label": "Bad Fit"
            "id": "stat_3ZdiZqcSIkoGVnNOyxiEY58eTGQmFNG3LPlEVQ4V7Nk",
            "label": "Qualified"
            "id": "stat_8ZdiZqcSIkoGVnNOyxiEY58eTGQmFNG3LPlEVQ4V7Nk",
            "label": "Not Serious"

Preparing data

Now that you’ve got JSON, you need to map all those data fields into a schema that can be inserted into your destination database. This means that, for each value in the response, you need to identify a predefined data type (i.e. INTEGER, DATETIME, etc.) and build a table that can receive them.

Check out the Stitch Documentation to get a good sense of what fields and data types will be provided by each endpoint. Once you have identified all of the columns you will want to insert, go ahead and build a destination table that will receive all of this data.

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 data up to date

Ok, you’ve built a script that requests data from and moves it into your data warehouse. What happens next week when you need to access the most recent leads? It’s also important to consider the situation where an entry in your destination needs to be updated to a new value. This functionality is crucial to your script actually being useful down the line. The last thing to do is set your script up as a cron job or continuous loop to keep pulling new data as it appears.

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 data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.