How to Move Data from Freshdesk to BigQuery?

Unlock the full potential of your Freshdesk data by integrating it seamlessly with BigQuery. With Hevo’s automated pipeline, get data flowing effortlessly. Watch our 1-minute demo below to see it in action!

Integrating your data from Freshdesk to BigQuery can be a game-changer for any organization. It removes limitations such as scalability constraints, inability to join its data with external systems, and export limits. This integration comes with a lot of additional features as well, but setting up this integration may not be easy for all users. Therefore, in this blog, we have provided you with 2 step-by-step methods on how you can integrate your Freshdesk data to Bigquery with ease.

Methods to connect Freshdesk to BigQuery

Multiple methods can be used to connect Freshdesk to BigQuery and load data easily:

• Method 1: Using Freshdesk APIs to connect Freshdesk to BigQuery
• Method 2: Using Hevo Data to connect Freshdesk to BigQuery

Method 1: Using Freshdesk APIs to connect Freshdesk to BigQuery

Step 1: Extracting Data from Freshdesk

Freshdesk data can be extracted by making calls to its REST API. For example, data on all tickets can be obtained using GET /api/v2/tickets. The data returned will be in JSON format.

Here is some additional information on Freshdesk API.

Step 2: Preparing the Data Extracted from Freshdesk

• Create a schema for your data tables before you load them.
• You must also ensure that the data types in Google BigQuery match the attributes and data types from Freshdesk.
• Here is some additional information on the types and their corresponding Freshdesk attribute names.
• You can also refer to information on the data types supported by BigQuery for a better understanding.

Step 3: Loading Data into Google BigQuery

The data can be uploaded directly from the JSON file to a BigQuery table using the BigQuery GUI. Alternatively, you can follow these steps:

• Load the data into GCS with gsutil.
• Write code through the BigQuery Command Line Interface (bq) to create a table to store your data and specify the schema. This can be done with the bq load command.
• Load the data into your table.

Here is some additional information on loading JSON data through the bq Command Line Interface.

Limitations of Migrating Data Using Custom Code

• Maintenance: This method may result in inaccurate data whenever the Freshdesk API is down or if you have any issues connecting to it.  
• Hard to Perform Data Transformations: It is impossible to perform fast data transformations like standardizing dates, times, etc or currency conversions under this method
• Data Availability Limitations: You have to write a lot of extra to code or configure cron jobs to enable basic real-time functionality with this method
• Labor Intensive and Time Consuming: This method requires you to write a lot of custom code. This is very time-consuming and could become a problem when there are tight deadlines to meet. 

Method 2: Using Hevo Data to connect Freshdesk to BigQuery

Step 1- Configure Freshdesk as a source.

Step 2 – Configure BigQuery as your destination.

Click “Save & Continue” to finish setting up your pipeline.

Conclusion

In this blog, we provided you with two methods that you can use to establish a seamless connection from Freshdesk to BigQuery. Integrating these 2 tools can open up numerous benefits such as advanced analytics and reporting, faster and scalable querying, machine learning applications, and many such benefits  

While both the methods we have provided are efficient,t we recommend using Hevo’s no-code pipeline tool that streamlines the entire process from real-time replication to transformation without needing to write a single line of code

Sign up for a 14-day free trial with Hevo and streamline your data integration. Also, check out Hevo’s pricing page for a better understanding of the plans.

FAQ on Freshdesk to BigQuery