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.)

Pulling Data Out of Delighted

The first step of getting Delighted data into Redshift is actually pulling that data off of Delighted’s servers. This is possible using the Delighted REST API, which is available to all Delighted customers. Full API documentation can be accessed here.

Survey data from Delighted can be accessed programmatically via various methods. Lets focus on the REST API for it’s ability to retrieve all of your historical data, in addition to new data. Delighted’s API can give you access to useful data endpoints like metrics and survey_responses. Using methods outlined in the Delighted API documentation, you can retrieve the data you’d like to load into Redshift.

Sample Delighted Data

After you are able to successfully query the Delighted API, it returns JSON formatted data. Take a look at an example response:

  "id": "2",
  "person": "1",
  "score": 5,
  "comment": null,
  "permalink": "",
  "created_at": 1495548677

Preparing Delighted Data for Redshift

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

The Delighted API documentation can help you define what fields and data types will be provided by each endpoint. Once you have identified all of the columns you will want to insert, use the CREATE TABLE statement in Redshift to build a table that will receive all of this data.

Inserting Delighted Data into Redshift

It may seem like the easiest way to add your data is to build tried-and-true INSERT statements that add data to a Redshift table row-by-row. If you have been writing SQL for a while, you will be tempted to take this route. It will work, however it isn’t the most efficient way to go.

Redshift actually offers some good documentation for how to best bulk insert data into new tables. The COPY command is particularly useful for this task, as it allows you to insert multiple rows without needing to build individual INSERT statements for each row.

If you cannot use COPY, it might help to use PREPARE to create a prepared INSERT statement, and then use EXECUTE as many times as required. This can help you avoid the overhead of constantly planning and parsing the INSERT statement.

Keeping Data Up-To-Date

Great! You’ve built a script that pulls data from Delighted and moves it into Redshift.  What happens on Monday when you have 23 new surveys from the weekend?

The key is to build your script in such a way that it can also identify incremental updates to your data. Some API’s include fields like created_at that allow you to quickly identify records that are new since your last update (or since the newest record you’ve copied into Redshift). You can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

Other Data Warehouse Options

Redshift is totally awesome, but sometimes you need to start smaller, or optimize for different things. In this case, many people choose to get started with Postgres, which is an open source RDBMS that uses nearly identical SQL syntax to Redshift. If you’re interested in seeing the relevant steps for loading this data into Postgres, check out Delighted to Postgres

Easier and Faster Alternatives

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.