Delighted to Redshift

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

Delighted provides a service that businesses use to gather feedback from customers. It lets companies send single-question surveys to customers through email, SMS, or the web, and uses Net Promoter Score (NPS) to maximize response rates and feedback quality.

Getting data out of Delighted

Delighted exposes its data through a REST API, and via webhooks for survey responses created and updated. The API calls are simple; for example, the call to get a listing of survey responses is GET /v1/survey_responses.json.

Sample Delighted data

Delighted sends the information it returns in JSON format. Each JSON object may contain more than a dozen attributes, which you have to parse before loading the data into your data warehouse. Here’s an example of what data might look like for survey responses:

[
  {
    "id": "1",
    "person": "10",
    "survey_type": "nps",
    "score": 0,
    "comment": null,
    "permalink": "https://delighted.com/r/2jo3B7Gak9q37XkuHrGLGAbCdevemcx8",
    "created_at": 1713009880,
    "updated_at": null,
    "person_properties": { "purchase_experience": "Retail Store", "country": "USA" },
    "notes": [],
    "tags": []
  },
  {
    "id": "2",
    "person": "11",
    "survey_type": "nps",
    "score": 9,
    "comment": "I loved this app!",
    "permalink": 'https://delighted.com/r/5pFDpmlyC8GUc5oxU6USto5VonSKAqOa',
    "created_at": 1713011680,
    "updated_at": 1713012280,
    "person_properties": null,
    "notes": [
      { "id": "1", "text": "Note 1", "user_email": "foo@bar.com", "created_at": 1713011680 },
      { "id": "2", "text": "Note 2", "user_email": "gyp@sum.com", "created_at": 1713012580 }
    ],
    "tags": []
  },
  ...
]

Preparing Delighted data

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. Delighted'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.

Loading data into Redshift

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

With a table built, you might be tempted to migrate your data (especially if there isn't much of it) by using INSERT statements to add data to your Redshift table row by row. Not so fast! Redshift isn't optimized for inserting data one row at a time. If you have a high volume of data to be inserted, you should load the data into Amazon S3 and use the COPY command to load it into Redshift.

Keeping Delighted 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 Delighted.

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

Redshift is great, but sometimes you need to optimize for different things when you're choosing a data warehouse. Some folks choose to go with Google BigQuery, PostgreSQL, or Snowflake, which are RDBMSes that use similar SQL syntax, or Panoply, which works with Redshift instances. If you're interested in seeing the relevant steps for loading data into one of these platforms, check out To BigQuery, To Postgres, To Snowflake, and To Panoply.

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