This post helps you with loading your data from Enchant to Snowflake. If you are looking to get analytics-ready data without the manual hassle, you can integrate Enchant to Snowflake with RudderStack, so you can focus on what matters, getting value out of your customer support data.
Access your data on Enchant
The first step in loading your Enchant data to any kind of data warehouse solution is to access your data and start extracting it.
Enchant offers a REST API built on pragmatic RESTful design principles that you can use to programmatically interact with your account.
From the available endpoints, you can retrieve the following information:
- Tickets: All user requests are tracked as tickets. Tickets contain one or more messages
- Messages: Messages include the replies and notes associated with the tickets
- Attachments: Attachments are associated with messages. After uploading an attachment, a message must be created using the attachment id. An attachment can be associated with only one message.
- Users: Details about your help desk operators.
- Customers: Details about the customers associated with at least one ticket.
- Contacts: Email addresses and Twitter accounts are represented as contacts on a customer.
In addition to the above, the things that you have to keep in mind when dealing with the Enchant API, are:
- Rate limits. The API is rate limited to 100 credits per minute for an entire help desk across all endpoints, users, and tokens. A request is typically worth 1 credit.
- Authentication. Requests to the Enchant API are authenticated using access tokens.
- Pagination. Requests for collections can return between 0 and 100 results. All endpoints are limited to 10 results by default. However, not all endpoints support pagination.
Enchant is a customer support software that focuses on the support needs of small or medium-sized companies. It is a simpler and much cheaper alternative for other help desk software like Zendesk, primarily focusing on e-mail integration.
While using Enchant as your ticketing platform you can easily keep track of all ongoing tickets as well as manage all the support-related communication across all channels. You can also produce various helpdesk reports in order to better understand your team’s performance, gauge your customers’ level of satisfaction and gain important insight regarding possible improvements.
Apart from all the above, Enchant also offers a knowledge base space in order to allow customers to help themselves regarding the most frequently asked questions while enabling the live chat in your website or in your app you can improve your customer experience by resolving issues on the spot.
Transform and prepare your Enchant Data for Snowflake
After you have accessed your data on Enchant, 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.
Of course, when you are dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data, just as in the case of JSON, before loading it into the database.
Also, you have to choose the right data types. Again, depending on the system that you will send every 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. Enchant has a very limited set of available data types which means that your work to do these mappings is much easier and straightforward, but nonetheless equally important with any other case of a data source.
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 is also supported. With Snowflake, it is possible to load data directly 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. But these types are not that useful for data warehouse use cases.
A typical strategy for loading data from Enchant to Snowflake is to create a schema where you will map each API endpoint to a table.
Each key inside the Enchant 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 any data types from Enchant API might change, you will adapt your database tables accordingly. There’s no such thing as automatic data type casting.
After you have a complete and well-defined data model or schema for Snowflake, you can move forward and start loading your data into the database.
Load data from Enchant to Snowflake
Usually, data is loaded into Snowflake in a bulk way, using the COPY INTO command. Files containing 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 each data directly into a service like Amazon S3, from where Snowflake can access data directly.
Updating your Enchant data on Snowflake
As you will be generating more data on Enchant, 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 Enchant.
You will need to periodically check Enchant 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 Enchant 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.