How to load data from Delighted to Google BigQuery
This post will help you with syncing your Delighted data to Google BigQuery. By doing this you will be able to perform advanced analytics on a system that is designed for this kind of data payloads, like Google BigQuery. Alternatively, you can simplify the process of syncing data from Delighted to Google BigQuery by using RudderStack, where the whole process will be handled by RudderStack and you can focus on what matters, the exploration and the analysis of your net promoter score (NPS) data in order to get actionable feedback for your product and business.
More specifically, by doing so you can easily move beyond traditional analysis and monitor how your NPS fluctuates over time and per person. You can also correlate your Delighted data with your support tickets and find out how your net promoter score is affected by the tickets your customers are reporting.
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 programmatically 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 you 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 real-time data and also filter your responses by promoters, passives or detractors as well as any other property you may have passed with them.
Transform and prepare your Delighted data for Google BigQuery
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.
Load data from Delighted to Google BigQuery
If you want to load Delighted data to Google BigQuery, you have to use one of the following supported data sources.
- Google Cloud Storage
- Sent data directly to BigQuery with a POST request
- Google Cloud Datastore Backup
- Streaming insert
- App Engine log files
- Cloud Storage logs
From the above list of sources, 5 and 6 are not applicable in our case.
For Google Cloud Storage, you first have to load your data into it, there are a few options on how to do this, for example, you can use the console directly as it is described here and do not forget to follow the best practices.
Another option is to post your data through the JSON API, as we see again APIs play an important role in both the extraction but also the loading of data into our data warehouse. In its simplest case, it’s just a matter of one HTTP POST request using a tool like CURL or Postman.
After you have loaded your data into Google Cloud Storage, you have to create a Load Job for BigQuery to actually load the data into it, this Job should point to the source data in Cloud Storage that have to be imported, this happens by providing source URIs that point to the appropriate objects.
The best way to load data from Delighted to Google BigQuery and possible alternatives
So far we just scraped the surface of what can be done with Google BigQuery and how to load data into it. The way to proceed relies heavily on the data you want to load, from which service they are coming from, and the requirements of your use case.
Things can get even more complicated if you want to integrate data coming from different sources. A possible alternative, instead of writing, hosting, and maintaining a flexible data infrastructure, is to use a product like RudderStack that can handle this kind of problem automatically for you.
RudderStack integrates with multiple sources or services like databases, CRM, email campaigns, analytics, and more. Quickly and safely move all your data from Delighted into Google BigQuery and start generating insights from your data.
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