This post helps you with loading your data from Delighted to PostgreSQL. If you are looking to get analytics-ready data without the manual hassle you can integrate Delighted to PostgreSQL with Blendo, so you can focus on what matters, getting value out of your business data.
Access your data on Delighted
The first step in loading your Delighted data to any kind of data warehouse solution, is to access your data and start extracting it.
Using the REST API that Delighted offers you can programatically interact with your account in order to gain access to your NPS Survey Data. By doing so you can:
- Retrieve and list all survey responses
- Check new submissions and any updates to existing surveys.
- List subscribed and unsubscribed people
- List people whose emails have bounced
You can also retrieve some basic aggregated metrics for any user-defined time period such as the average score of all your surveys or of a specific trend or client.
In addition to the above, the things that you have to keep in mind when dealing with the Delighted API, are:
- Rate limits. In order to guarantee a high quality of service to all users of the API, Delighted may rate limit requests in certain usage scenarios. However, with normal API usage it is unlikely to experience rate limits.
- Authentication. You can authenticate to Delighted using a private API key that is linked to your account. All API requests must be made over HTTPS and are authenticated via HTTP Basic Auth.
- Pagination. API endpoints that return a collection of items are always paginated.
Delighted is a web app that allows to quickly and easily send surveys to your customers while aiming to showcase your brand and improve the quality and quantity of the received feedback. The different types of surveys that can be launched via Delighted include the following:
- Web surveys: Feedback can be gathered directly from your website without having to collect any email address.
- Email surveys: Surveys are delivered to the customers’ emails.
- SMS surveys: Feedback can be gathered using text messages.
Apart from the above, Delighted allows offers nice features regarding the analysis and the reporting of the collected feedback. You can create dashboards in order to get a bird’s eye view on your realtime data and also filter your responses by promoters, passives or detractors as well any other property you may have passed with them.
Transform and prepare your Delighted data for PostgreSQL
After you have accessed your data on Delighted, you will have to transform it based on two main factors,
- The limitations of the database that the data will be loaded onto
- The type of analysis that you plan to perform
Each system has specific limitations on the data types and data structures that it supports. If for example you want to push data into Google BigQuery, then you can send nested data like JSON directly.
Also, you have to choose the right data types. Again, depending on the system that you will send the data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database.
Also, you have to consider that the reports you’ll get from Delighted are like CSV files in terms of their structure and you need to somehow identify what and how to map to a table into your database.
Each table is a collection of columns with a predefined data type like an integer or VARCHAR. PostgreSQL, like any other SQL database supports a wide range of different data types.
A typical strategy for loading data from Delighted to a Postgres database is to create a schema where you will map each API endpoint to a table. Each key inside the Delighted API endpoint response should be mapped to a column of that table and you should ensure the right conversion to a Postgres compatible data type.
Load data from Delighted to PostgreSQL
For example, if you an endpoint from Delighted returns a value as String, you should convert it into a VARCHAR with a predefined max size or TEXT data type. tables can then be created on your database using the CREATE SQL statement.
Once you have defined your schema and you have created your tables with the proper data types, you can start loading data into your database.
The preferred way of adding larger datasets into a PostgreSQL database is by using the COPY command. COPY is copying data from a file on a file system that is accessible by the Postgres instance, in this way much larger datasets can be inserted into the database in less time. COPY requires physical access to a file system in order to load data.
Nowadays, with the cloud-based, fully managed databases, getting direct access to a file system is not always possible. If this is the case and you cannot use a COPY statement, then another option is to use PREPARE together with INSERT, to end up with optimized and more performant INSERT queries.
Updating your Delighted data on PostgreSQL
As you will be generating more data on Delighted, you will need to update your older data on PostgreSQL. This includes new records together with updates to older records that for any reason have been updated on Delighted.
You will need to periodically check Delighted for new data and repeat the process that has been described previously while updating your currently available data if needed. Updating an already existing row on a PostgreSQL table is achieved by creating UPDATE statements.
Another issue that you need to take care of is the identification and removal of any duplicate records on your database. Either because Delighted does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database.
In general, ensuring the quality of the data that is inserted in your database is a big and difficult issue and PostgreSQL features like TRANSACTIONS can help tremendously, although they do not solve the problem in the general case.
The best way to load data from Delighted to PostgreSQL
So far we just scraped the surface of what you can do with PostgreSQL and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources.
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