How to load data from Zendesk to Redshift

Blendo Team

This post will help you with syncing your Zendesk 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 Zendesk to Amazon Redshift by using Blendo, where the whole process will be handled by Blendo and you can focus on what matters, the analysis of your data.

Extract your data from Zendesk

Zendesk APIs are not specific to pulling data, Zendesk provides more than a hundred different APIs for you to integrate with. So you can easily manage your users, enhance your team’s productivity and create seamless integrations. You can create integrations or even enrich Zendesk with data from external sources. Zendesk API is a RESTful API that can be accessed through HTTP.

As a RESTful API, interacting with it can be achieved by using tools like CURL or Postman or by using HTTP clients for your favorite language or framework.

A few suggestions:

Additionally, Zendesk offers a number of SDKs and libraries so you can access the API from your framework of choice without having to deal with the technicalities of HTTP. API clients are available for the following languages:

You will need more time to read this post than integrating Zendesk to Redshift.

Effortlessly Sync All Your Zendesk Data to Redshift

Zendesk API Authentication

Zendesk’s API is an SSL-only API, regardless of how your account is configured. You must be a verified user to make API requests. You can authorize against the API using either basic authentication with your email address and password, with your email address and an API token, or with an OAuth access token.

Zendesk rate limiting

The API is rate limited. It only allows a certain number of requests per minute depending on your plan and the endpoint. Zendesk reserves the right to adjust the rate limit for given endpoints to provide a high quality of service for all clients. The current limits are the following:

Plan Requests per minute
Essential 10
Team 200
Professional 400
Enterprise 700
High Volume API Add-On (Professional or Enterprise) 2500

 

Pagination

By default, most list endpoints return a maximum of 100 records per page. You can change the number of records on a per-request basis by passing a per_page parameter in the request URL parameters. Example: per_page=50. However, you can’t exceed 100 records per page on most endpoints.

When the response exceeds the per-page maximum, you can paginate through the records by incrementing the page parameter. Example: page=3. List results include next_page and previous_page URLs in the response body for easier navigation:

{
  "users": [ ... ],
  "count": 1234,
  "next_page": "https://account.zendesk.com/api/v2/users.json?page=2",
  "previous_page": null
}

Endpoints and available resources

The Zendesk REST API exposes a large number of resources and endpoints that allows the user to interact with the platform in every possible way. Thus it is possible to create new applications on top of the Zendesk platform, integrate external systems with it and of course pull data out of the platform. The most important resources are the following:

  • The tickets that your customers create through Zendesk.
  • Ticket events. Changes that have occurred to the tickets.
  • Organizations.
  • Users.
  • Ticket metrics. These are metrics related to your tickets.
  • Data related to the Net Promoter Score.
  • Articles

Let’s assume that we want to pull all the tickets we have on Zendesk. To do that we need to perform a GET request to the appropriate end-point, like this: GET /api/v2/incremental/tickets.json?start_time=1332034771

curl https://{subdomain}.zendesk.com/api/v2/incremental/tickets.json?start_time=1332034771 \
  -v -u {email_address}:{password}

And a sample response:

Status: 200 OK

{
  "end_time": 1383685952,
  "next_page": "https://{subdomain}.zendesk.com/api/v2/incremental/tickets.json?start_time=1383685952",
  "count": 1,
  "tickets": [
    {
      "url": "https://{subdomain}.zendesk.com/api/v2/tickets/1.json",
      "id": 2,
      "created_at": "2012-02-02T04:31:29Z",
      "generated_timestamp": 1390362285
      ...
     },
     ...
  ]
}

A complete ticket object might contain the following fields:

ame Type Read-only Mandatory Comment
id integer yes no Automatically assigned when creating tickets
url string yes no The API url of this ticket
external_id string no no An id you can use to link Zendesk tickets to local records
type string no no The type of this ticket, i.e. “problem”, “incident”, “question” or “task”
subject string no no The value of the subject field for this ticket
raw_subject string no no The dynamic content placeholder, if present, or the “subject” value, if not. See Dynamic Content
description string yes no The first comment on the ticket
priority string no no Priority, defines the urgency with which the ticket should be addressed: “urgent”, “high”, “normal”, “low”
status string no no The state of the ticket, “new”, “open”, “pending”, “hold”, “solved”, “closed”
recipient string no no The original recipient e-mail address of the ticket
requester_id integer no yes The user who requested this ticket
submitter_id integer no no The user who submitted the ticket; The submitter always becomes the author of the first comment on the ticket.
assignee_id integer no no What agent is currently assigned to the ticket
organization_id integer yes no The organization of the requester
group_id integer no no The group this ticket is assigned to
collaborator_ids array no no Who are currently CC’ed on the ticket
forum_topic_id integer no no The topic this ticket originated from, if any
problem_id integer no no The problem this incident is linked to, if any
has_incidents boolean yes no Is true of this ticket has been marked as a problem, false otherwise
due_at date no no If this is a ticket of type “task” it has a due date. Due date format uses ISO 8601 format.
tags array no no The array of tags applied to this ticket
via Via yes no This object explains how the ticket was created
custom_fields array no no The custom fields of the ticket
satisfaction_rating object yes no The satisfaction rating of the ticket, if it exists, or the state of satisfaction, ‘offered’ or ‘unoffered’
sharing_agreement_ids array yes no The ids of the sharing agreements used for this ticket
followup_ids array yes no The ids of the followups created from this ticket – only applicable for closed tickets
ticket_form_id integer no no The id of the ticket form to render for this ticket – only applicable for enterprise accounts
brand_id integer no no The id of the brand this ticket is associated with – only applicable for enterprise accounts
created_at date yes no When this record was created
updated_at date yes no When this record last got updated

The results of the Zendesk API is always in JSON format. The API offers you the opportunity to get very granular data about your accounting activities and use it for analytics and reporting purposes.

About Zendesk

Zendesk provides a cloud-based customer service platform, that includes ticketing, self-service options, and customer support features. Zendesk focuses on improving the communication between your customers and your company. It brings all your customer communication into one place. The supported communication channels are:

  • Mail. Zendesk helps to organise all the emails you receive from your customers.
  • Social. You can connect your Facebook and Twitter accounts with Zendesk.
  • Voice. Take customer calls from within Zendesk.
  • Chat. Zopim Chat allows you to communicate with your customers from within your product.

Zendesk’s help desk software helps streamline customer support with time-saving tools like triggers and automations. And it’s intuitive, built with the experience of customer service and support desk agents in mind. Some important features of the ticketing system that Zendesk offers, are:

  • It helps to Solve tickets better with teammates through a collaborative environment where information can be shared.
  • Get access to relevant information across teams. Zendesk comes with an internal Knowledge Base that allows agents to quickly refer to information and processes without losing their place.
  • Make everyone inside your company a support agent. With light agents, everyone inside your company can view tickets and make private comments.

Additionally, Zendesk offers a suite of analytics tools that will help you to get closer to your user through data. With these tools you can:

  • Gain visibility into customers interactions
  • Measure your team’s performance
  • See the business impacts of great service

It is possible to track a large number of metrics related to your customers, support teams and your business.

But in case that you would like to run some more engaged analysis with your Zendesk data, or fuse the customer support related data with data from other sources like your transactional database and logs, Zendesk exposes a rich ecosystem of APIs and tools that you can use to access and pull your data among other functionalities.

Prepare your Zendesk Data for Amazon Redshift

Amazon Redshift is built around industry-standard SQL with added functionality to manage very large datasets 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 datatypes that are supported by Redshift, currently the datatypes that are supported are the following:

  • SMALLINT
  • INTEGER
  • BIGINT
  • DECIMAL
  • REAL
  • DOUBLE PRECISION
  • BOOLEAN
  • CHAR
  • VARCHAR
  • DATE
  • TIMESTAMP

As your data are 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 is 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 datasources that are supported as input by Redshift, these are the following:

About Amazon Redshift

Amazon Redshift is one of the most popular data warehousing solutions which is part of the Amazon Web Services (AWS) ecosystem. It is a petabyte scale, fully managed data warehouse as a service solution that runs on the cloud. It is SQL based and you can communicate with it as you would do with PostgreSQL, actually you can use the same driver although it would be better to use the drivers recommended by Amazon. You can connect either through JDBC or ODBC connections.

Load data from Zendesk to Redshift

The first step to load your Zendesk data to Redshift, is to put them in a source that Redshift can pull it from. As it was mentioned earlier there are three main data sources supported, Amazon S3, Amazon DynamoDB and Amazon Kinesis Firehose, with Firehose being the most recent addition as a way to insert data into 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 but also 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 again the same AWS REST 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.

DynamoDB imports data again from S3, it adds another step between S3 and Amazon Redshift so if you don’t need it for other reasons you can avoid it.

Amazon Kinesis Firehose is the latest addition as a way to insert data into Redshift and offers a real-time streaming approach into data importing. The necessary steps for adding data to Redshift through Kinesis Firehose are the following:

  1. create a delivery stream
  2. add data to the stream

whenever you add new data to the stream, Kinesis takes care of adding these data to S3 or Redshift, again going through S3, in this case, is redundant if your goal is to move your data to Redshift. The execution of the previous two steps can be performed either through the REST API or through your favorite library just as in the previous two cases.

The difference here is that for pushing your data into the stream you’ll be using a Kinesis Agent.

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.

insert into category_stage values
(12, 'Concerts', 'Comedy', 'All stand-up comedy performances');

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 is able to read multiple files in simultaneously and it automatically distributes the workload to the cluster nodes and performs the load in parallel. As a command COPY is quite flexible and allows for many different ways of using it, depending on your  use case. Performing a COPY on Amazon S3 is as simple as the following command:

copy listing
from 's3://mybucket/data/listing/'
credentials 'aws_access_key_id=;aws_secret_access_key=';

For more examples on how to invoke a COPY command, you can check the COPY examples page on Amazon Redshift documentation. As in the INSERT case, the way to perform the COPY command is by connecting to your Amazon Redshift instance using a JDBC or ODBC connection and then invoke the commands you want using the SQL Reference from Amazon Redshift documentation.

The best way to load data from Zendesk 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 Blendo that can handle this kind of problems automatically for you.

Blendo integrates with multiple sources or services like databases, CRM, email campaigns, analytics and more. Quickly and safely move all your data from Zendesk to Redshift and start generating insights from your data.

Help your customer support and executive team take ownership of the insights that live in your Zendesk customer support platform.

Blendo is the easiest way to automate powerful data integrations.

Try Blendo free for 14 days. No credit card required.