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By Rudderstack Team

How to load data from the Zendesk Chat to Redshift

This post helps you with syncing your customer support data from Zendesk Chat to Redshift. Suppose you are looking to get analytics-ready data without the manual hassle. In that case, you can integrate Zendesk Chat to Redshift with RudderStack, so you can focus on what matters, getting value out of the analysis of your customer support data.

Access your data on Zendesk Chat

The first step in loading your Zendesk Chat data to any data warehouse solution is to access your data and start extracting it.

Zendesk Chat offers a rich and well-defined API that belongs to the Representational State Transfer (REST) category. Using it, you can perform RESTful operations such as reading, modifying, adding, and deleting your helpdesk data, thus allowing you to interact with your account programmatically.

Among the 10 provided resources, you can find information about Accounts, Agents, Visitors, Chats, Shortcuts, Triggers, Bans, Departments, Goals, Skills and Roles.

In addition to the above, the things that you have to keep in mind when dealing with the Zendesk Chat API are:

  • Rate limits. The API is rate limited, i.e., it only allows a certain number of requests per minute.
  • Authentication.If the Zendesk Chat account is created in Zendesk Support, the user must authenticate with an OAuth access token. If a stand-alone Chat account is used, either a basic authentication or an OAuth access token can be used.
  • Paging and dealing with big amount of data.

About Zendesk Chat

Zendesk Chat is a live chat solution that helps businesses increase sales conversion by engaging important leads on their websites.

While using Zendesk Chat as your live chat you can anticipate customer questions and offer help when—and where—they need it most using chat. This way the agents can help more customers in less time, which means happier customers more of the time.

In more details by using Zendesk Chat, you can:

  • Display chats and agents metrics
  • Create and display a real-time dashboard
  • Monitor a specific department
  • Predict or estimate the capacity and other derived metrics

Transform and prepare your Zendesk Chat Data

After you have accessed your data on Zendesk Chat, you will have to transform it based on two main factors,

  1. The limitations of the database that the data will be loaded onto
  2. 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 you want to push data into Google BigQuery, you can send nested data like JSON directly.

Of course, this is not an option when dealing with tabular data stores, like Microsoft SQL Serven. Instead, you will have to flatten out your data, just as in the case of JSON, before loading into the database.

Also, you have to choose the right data types. Again, depending on the system you 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. Zendesk Chat has a very limited set of available data types which means that your work to do these mappings is much straightforward but equally important with any other case of a data source.

Due to the rich and complex data model that Zendesk Chat follows, some of the provided resources might have to be flattened out and be pushed in more that one table.

Transform and prepare Zendesk Chat data for Amazon Redshift

Redshift is built around industry-standard SQL with added functionality to manage very large data sets and high-performance analysis. So, to load any data into it, you will have to follow its data model, a typical relational database model. 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 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 and 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 that 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 Zendesk Chat data into Amazon Redshift

To upload your data to Amazon S3 you will have to use the AWS REST API. The first task that you have to perform is to create a bucket, you do that by executing an HTTP PUT on the 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 reference for the Bucket operations on Amazon AWS documentation.

After you have created your bucket you can start sending data to Amazon S3, using again the same API but by 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 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 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 is able to read multiple files in simultaneously and it automatically distributes the workload to the cluster nodes and performs the load in parallel.

The best way to load data from Zendesk Chat to Redshift

So far we just scraped the surface of what you can do with Redshift and how to load data into it. Things can get even more complicated if you want to integrate data coming from different sources.

Are you striving to achieve results right now?

Instead of writing, hosting, and maintaining a flexible data infrastructure use RudderStack that can handle everything automatically for you.

RudderStack, with one click, integrates with sources or services, creates analytics-ready data, and syncs your Zendesk Chat to Redshift right away.

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