This post will help you with syncing your Freshdesk 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 Freshdesk to 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.
Access your data on Freshdesk
The first step in loading your Freshdesk data to any kind of data warehouse solution is to access your data and start extracting it.
Freshdesk offers a rich and well-defined API (here) that belongs to the Representational State Transfer (REST) category. Using it you can perform RESTful operations such as reading, modifying, adding and deleting of your helpdesk data, thus allowing you to programmatically interact with your account.
Among the 18 provided resources, you can find information about Tickets and Conversations, Agents, Companies, Surveys and Satisfaction Ratings and many more.
In addition to the above, the things that you have to keep in mind when dealing with the Freshdesk’s API, are:
- Rate limits. Depending on the chosen plan and API version that is being used, there is a rate limit in calls per hour.
- Authentication. You authenticate on Freshdesk using a key.
- Paging and dealing with a big amount of data. Platforms like Freshdesk that are dealing with clickstream data tend to generate a lot of data, like events on your web properties.
Freshdesk is a SaaS customer support platform released by Freshworks that integrates traditional support channels such as email, phone and LiveChat with social channels like Facebook or Twitter.
While using Freshdesk 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.
Prepare your Freshdesk Data for Amazon Redshift
After you have accessed the data on Freshdesk, 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. 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 the data, just as in the case of JSON, before loading into the database.
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 data into it, you will have to follow its data model which is a typical relational database model. The data you extract from a 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.
As 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 are supported by Redshift.
Designing a Schema for Redshift and mapping the data from a 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 any data on one of the data sources that are supported as input by Redshift, these are the following:
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.
Freshdesk 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.
Due to the rich and complex data model that Freshdesk follows, some of the provided resources might have to be flattened out and be pushed in more that one table.
How to Load Data from Freshdesk to Redshift
To upload any 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 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 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.
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 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 Freshdesk 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 Freshdesk to Redshift and start generating insights.