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BY RUDDERSTACK TEAM

How to load data from Freshdesk to Snowflake

01. Access Data

  • 01. Access Data
  • 02. Transform and prepare your Freshdesk Data
  • 03. Load Data
  • 04. Update Data
  • 05. Extract Data

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This post will help you with syncing your Freshdesk customer support data to Snowflake. By doing this, you will be able to perform advanced analytics on a system that is designed for this kind of data payloads, like Snowflake. Alternatively, you can simplify the process of syncing data from Freshdesk to Snowflake 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.

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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 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 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 API are:

  1. Rate limits. Depending on the chosen plan and API version that is being used, Freshdesk allows a number of API calls per hour.
  2. Authentication. You authenticate on Freshdesk using an API key.
  3. Paging and dealing with 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.

About Freshdesk

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.

Transform and prepare your Freshdesk Data

After you have accessed your data on Freshdesk, 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 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 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. 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.

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 Freshdesk to Snowflake is to create a schema where you will map each API endpoint to a table.

Each key inside the Freshdesk 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 the data types from the Freshdesk 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 Freshdesk to Snowflake

Usually, data is loaded into Snowflake in a bulk way, using the COPY INTO command. Files containing the 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 the data directly into a service like Amazon S3 from where Snowflake can access the data directly.

Updating your Freshdesk data on Snowflake

As you will be generating more data on Freshdesk, 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 Freshdesk.

You will need to periodically check Freshdesk 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 Freshdesk 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 the data that is inserted into your database is a big and difficult issue.

The best way to load data from Freshdesk to Snowflake

So far, we just scraped the surface of what can be done with Snowflake 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 Freshdesk to Snowflake and start generating insights from your data.

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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.

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