How to load data from the Delighted to Snowflake
Access your data on Delighted
The first step in loading your Delighted data to any kind of data warehouse solution, is to access them 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 of 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 Snowflake
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 is going to be used
- 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 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.
Data in Snowflake is organized around tables with a well-defined set of columns with each one having a specific data type.
Snowflake supports a rich set of data types. It is worth mentioning that a number of semi-structured data types are also supported. With Snowflake, is possible to load directly data in JSON, Avro, ORC, Parquet, or XML format. Hierarchical data is treated as a first-class citizen, similar to what Google BigQuery offers.
There is also one notable common data type that is not supported by Snowflake. LOB or large object data type is not supported, instead, you should use a BINARY or VARCHAR type instead. But these types are not that useful for data warehouse use cases.
A typical strategy for loading data from Delighted to Snowflake 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 Snowflake data type.
Of course, you will need to ensure that as the data types from the Delighted API might change, you will adapt your database tables accordingly, there’s no such thing as automatic data typecasting.
After you have a complete and well-defined data model or schema for Snowflake, you can move forward and start loading every data you have into the database.
Load data from Delighted to Snowflake
Usually, data is loaded into Snowflake in a bulk way, using the COPY INTO command. Files containing the data, usually in JSON format, are stored in a local file system or in Amazon S3 buckets. Then a COPY INTO command is invoked on the Snowflake instance and data is copied into the data warehouse.
The files can be pushed into Snowflake using the PUT command, into a staging environment before the COPY command is invoked.
Another alternative is to upload any data directly into a service like Amazon S3 from where Snowflake can access data directly.
Updating your Delighted data on Snowflake
As you will be generating more data on Delighted, you will need to update your older data on Snowflake. 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 Snowflake 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 data that is inserted into your database is a big and difficult issue.
The best way to load data from Delighted to Snowflake
So far we just scraped the surface of what you can do with Snowflake 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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