How to load data from Enchant to Redshift
This post will help you with syncing your Enchant data to Amazon Redshift. By doing this, you will be able to perform advanced analytics on a system that is designed for this kind of data, like Amazon Redshift. Alternatively, you can simplify the process of syncing data from Enchant to Amazon Redshift by using RudderStack, where the whole process will be handled by RudderStack and you can focus on what matters, the analysis of your customer support data.
Access your data on Enchant
The first step in loading your Enchant data to any 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 interact with your account programmatically.
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 endpoint, 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 self-claimed to be a simpler and cheaper alternative for other help desk software like Zendesk, primarily focusing on email integration.
While using Enchant as your ticketing platform, you can easily keep track of all ongoing tickets and manage all the support-related communication across all channels. You can also produce various helpdesk reports to better understand your team’s performance, gauge your customers’ level of satisfaction, and gain important insight into possible improvements.
Apart from all the above, Enchant also offers a knowledge base space to allow customers to help themselves regarding the most frequently asked questions while enabling the live chat on 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 Amazon Redshift
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 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.
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 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. 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 equally important with any other data source case.
Transform and prepare your Enchant data for Amazon Redshift
Amazon Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, in order to load your data into it, you will have to follow its data model, which is a typical relational database model. The data you extract from your data source should be mapped into tables and columns where you can consider the table as a map to the resource you want to store and columns the attributes of that resource.
Also, each attribute should adhere to the data types that are supported by Redshift.
As your data is probably coming in a representation like JSON that supports a much smaller range of data types you have to be really careful about what data you feed into Redshift. Make sure that you have mapped your types into one of the datatypes that are supported by Redshift.
Designing a Schema for Redshift and mapping the data from your data source to it is a process that you should take seriously as it can both affect the performance of your cluster and the questions you can answer. It’s always a good idea to have in your mind the best practices that Amazon has published regarding the design of a Redshift database. When you have concluded on the design of your database, you need to load your data on one of the data sources that are supported as input by Redshift, these are the following:
Load your Enchant data into Amazon Redshift
To upload your data to Amazon S3, you will have to use the AWS REST API. As we see again, APIs play an important role in both the extraction and the loading of data into our data warehouse. The first task that you have to perform is to create a bucket. You do that by executing an HTTP PUT on the Amazon AWS REST API endpoints for S3.
You can do this by using a tool like CURL or Postman. Or use the libraries provided by Amazon for your favorite language. You can find more information by reading the API reference for the Bucket operations on Amazon AWS documentation.
After you have created your bucket, you can start sending your data to Amazon S3, using the same AWS REST API but using the endpoints for Object operations. As in the Bucket case you can either access the HTTP endpoints directly or use the library of your preference.
Amazon Redshift supports two methods for loading data into it. The first one is by invoking an INSERT command. You can connect to your Amazon Redshift instance with your client using either a JDBC or ODBC connection, and then you perform an INSERT command for your data.
The way you invoke the INSERT command is the same as you would do with any other SQL database. For more information, you can check the INSERT examples page on the Amazon Redshift documentation.
Redshift is not designed for INSERT-like operations. On the contrary, the most efficient way of loading data into it is by doing bulk uploads using a COPY command.
You can perform a COPY command for data that lives as flat files on S3 or from an Amazon DynamoDB table. When you perform COPY commands, Redshift can read multiple files simultaneously and automatically distributes the workload to the cluster nodes and performs the load in parallel.
The best way to load data from Enchant to Amazon Redshift and possible alternatives
So far, we just scraped the surface of what can be done with Amazon Redshift 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 Enchant to Redshift and start generating insights from your data.
Sign Up For Free And Start Sending Data
Test out our event stream, ELT, and reverse-ETL pipelines. Use our HTTP source to send data in less than 5 minutes, or install one of our 12 SDKs in your website or app.