Wootric provides tools businesses use to monitor and improve customer happiness through the use of Net Promoter Score (NPS), CSAT, and Customer Effort Score metrics.

Snowflake is a cloud-based data warehouse service that runs on Amazon Web Services using EC2 and S3 instances. Snowflake is designed to be fast, flexible, and easy to work with. For instance, for query processing, Snowflake creates virtual warehouses that run on separate compute clusters, so querying one virtual warehouse doesn't slow down the others.

Wootric provides a REST API that lets you get information about users and segments, responses, metrics, and other elements. If, for example, you wanted to retrieve a list of all end users, you could call GET /v1/end_users. This API call takes five optional parameters that let you filter and limit the results returned.

The Wootric API returns data in JSON format. For example, the result of a call to retrieve a list of end users might look like this.

[
  {
    "id": 1,
    "created_at" : "2017-12-01 18:36:59",
    "updated_at" : "2017-12-01 18:36:59",
    "email": "nps@example.com",
    "last_surveyed": null,
    "external_created_at": null,
    "user_id": 16,
    "page_views_count" : 1,
    "properties": {"plan": "Small Business", "product": "Example"}
  },
  {
    "id": 2,
    "created_at" : "2017-12-01 18:36:59",
    "updated_at" : "2017-12-04 12:43:44",
    "email": "nps2@example.com",
    "last_surveyed": null,
    "external_created_at": null,
    "user_id": 16,
    "page_views_count" : 3,
    "properties": {"plan": "Enterprise", "product": "The Company"}
  }
]

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Wootric's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. This means you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

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 help you with the task of loading your data. If you're not loading a lot of data, look into the data loading wizard in the Snowflake web UI, but for many organizations, the limitations on that tool will make it a non-starter as a reliable ETL solution. Instead:

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

You can copy 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 Wootric.

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