So, a Freshdesk user, huh? It’s nice to talk to someone that knows how pivotal effective customer support is to your operation. You focus on providing the right value to your customers through the appropriate communication channels.

Sometimes the key stakeholders in your organization might need a 360-degree view of your data. That’s where you come in. You gotta integrate the support data from Freshdesk with data from other teams so that the analysts and the scientists can keep working smoothly, right? 

So, if you’re looking for a quick, no-nonsense guide to replicate data from Freshdesk to Databricks, you’ve landed on the correct page. Keep reading for 2 easy replication methods.

How to Replicate Data From Freshdesk to Databricks?

To replicate data from Freshdesk to Databricks, you can either use CSV files or a no-code automated solution. We’ll cover replication via CSV files next.

Replicate Data from Freshdesk to Databricks Using CSV Files

Freshdesk being a cloud-based customer service platform, stores data about customers. This data is categorized into 2 components, i.e., Companies and Contact in Freshdesk.

Follow along to replicate data from Freshdesk to Databricks in CSV format:

Step 1: Export CSV Files from Freshdesk 

  • Click on the hamburger icon on the top-left corner of your Freshdesk window.
  • Select the “Companies” or “Contact” option based on which data you want to export.
  • Select the “Export” button.
  • Now, you can select the fields which you want to export. Following that, click the “Export” button. A CSV file containing all the required data will be sent to the email address associated with the Freshdesk account.

Step 2: Import CSV Files into Databricks

  • In the Databricks UI, go to the side navigation bar. Click on the “Data” option. 
  • Now, you need to click on the “Create Table” option.
  • Then drag the required CSV files to the drop zone. Otherwise, you can browse the files in your local system and then upload them.

After uploading the CSV files, your file path will look like this: /FileStore/tables/<fileName>-<integer>.<fileType>

Your file path while importing CSV files from Freshdesk to Databricks
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Step 3: Modify & Access the Data

  • Click on the “Create Table with UI” button.
  • The data has been uploaded to Databricks. You can access the data via the Import & Explore Data section on the landing page.
Import & Explore Data Option
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  • To modify the data, select a cluster and click on the “Preview Table” option.
  • Then change the attributes accordingly and select the “Create Table” option.

This 3-step process using CSV files is a great way to effectively replicate data from Freshdesk to Databricks. It is optimal for the following scenarios:

  • Less Amount of Data: This method is appropriate for you when the number of reports is less. Even the number of rows in each row is not huge.
  • One-Time Data Replication: This method suits your requirements if your business teams need the data only once in a while.
  • No Data Transformation Required: This approach has limited options in terms of data transformation. Hence, it is ideal if the data in your spreadsheets is clean, standardized, and present in an analysis-ready form. 
  • Dedicated Personnel: If your organization has dedicated people who have to perform the manual downloading and uploading of CSV files, then it’s not much of a headache.
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The repetitive import of CSV files from Freshdesk to Databricks makes it cumbersome. This adds to your misery when you have multiple data sources. Just imagine building custom connectors for each source manually. In addition, you would have to manage each data pipeline to ensure no data loss. It also includes you continuously monitoring for any updates on the connector and being on-call to fix pipeline issues anytime. With most of the raw data being unclean and in multiple formats, setting up transformations for all these sources is another challenge.

We know you’d much rather focus on more productive tasks than repeatedly writing custom ETL scripts, downloading, cleaning, and uploading CSV files.

This is where an automated ETL/ELT solution would take away your toil of building and managing pipelines.

Replicate Data from Freshdesk to Databricks Using Hevo

An automated tool is an efficient and economical choice that takes away a huge chunk of repetitive work. It has the following benefits:

  • Allows you to focus on core engineering objectives while your business teams can jump on to reporting without any delays or data dependency on you.
  • Your marketers can effortlessly enrich, filter, aggregate, and segment raw Freshdesk data with just a few clicks.
  • Without technical knowledge, your analysts can seamlessly standardize timezones, convert currencies, or simply aggregate campaign data for faster analysis. 
  • An automated solution provides you with a list of native in-built connectors. No need to build custom ETL connectors for every source you require data from.

For instance, here’s how Hevo, a cloud-based ETL solution makes the data replication from Freshdesk to Databricks ridiculously easy. 

Step 1: Configure Freshdesk as your Source

  • Fill in the required attributes required for configuring Freshdesk as your source.
Configure Freshdesk as source in Hevo
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Note: You can create an API token from your Freshdesk support portal. You can go to the Profile Settings option and access your API token.

Step 2: Configure Databricks as your Destination

Now, you need to configure Databricks as the destination.

Configure Databricks as the destination in Hevo
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All Done to Setup Your ETL Pipeline

After implementing the 2 simple steps, Hevo will take care of building the pipeline for replicating data from Freshdesk to Databricks based on the inputs given by you while configuring the source and the destination.

Then, Hevo will start fetching your data from Freshdesk and load it in Databricks. By default, data for the last 30 days from the time you created the pipeline will be replicated. However, you can also change this number to accommodate your requirements.

After historical data is uploaded, all the new and updated records will be synced with Databricks.

For in-depth knowledge of how a pipeline is built & managed in Hevo, you can also visit the official documentation for Freshdesk as a source and Databricks as a destination.

Hevo’s fault-tolerant architecture ensures that the data is handled securely and consistently with zero data loss. It also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.

The added benefits that you’ll get with Hevo at your service:

  • Fully Managed: You don’t need to dedicate time to building your pipelines. With Hevo’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
  • Data Transformation: Hevo provides a simple interface to cleanse, modify, and transform your data through drag-and-drop features and Python scripts. It can accommodate multiple use cases with its pre-load and post-load transformation capabilities.
  • Faster Insight Generation: Hevo offers near real-time data replication, giving you access to real-time insight generation and faster decision-making. 
  • Schema Management: With Hevo’s auto schema mapping feature, all your mappings will be automatically detected and managed to the destination schema.
  • Scalable Infrastructure: With the increased number of sources and volume of data, Hevo can automatically scale horizontally, handling millions of records per minute with minimal latency.
  • Transparent pricing: You can select your pricing plan based on your requirements. Different plans are clearly put together on its website, along with all the features it supports. You can adjust your credit limits and spend notifications for any increased data flow.
  • Live Support: The support team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Take our 14-day free trial to experience a better way to manage data pipelines.

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What Can You Achieve by Replicating Your Data from Freshdesk to Databricks?

Here’s a little something for the data analyst on your team. We’ve mentioned a few core insights you could get by replicating data from Freshdesk to Databricks. Does your use case make the list?

  • What percentage of customers’ queries from a region are through email?
  • The customers acquired from which channel have the maximum number of tickets raised?
  • What percentage of agents respond to customers’ tickets acquired through the organic channel?
  • Customers acquired from which channel have the maximum satisfaction ratings?

Summing It Up 

If the data replication requests come once in a while, it won’t take much time to download and upload CSV files from Freshdesk to Databricks. But, if you require this data at regular intervals, it would take a toll on you. To channel your time into productive tasks, you can opt-in for an automated solution that will help accommodate regular data replication needs. This would be genuinely helpful to support & product teams as they would need regular updates about customer queries, experiences and satisfaction levels with the product. Even better, now your support teams would get immediate access to data from multiple channels and thus deliver contextual, timely, and personalized customer experiences.

So, what are you waiting for? Grab this opportunity and we’re ready to help you with this journey of building an automated no-code data pipeline with Hevo. It’s 150+ plug-and-play native integrations will help you replicate data smoothly from multiple tools to a destination of your choice. It’s intuitive UI will help you smoothly navigate through its interface. And with its pre-load transformation capabilities, you don’t even need to worry about manually finding errors and cleaning & standardizing it.

Try Hevo’s no-code data pipeline solution. It offers a 14-day free trial of its product. You can build a data pipeline from Freshdesk to Databricks and try out the experience.

Here’s a short video that will guide you through the process of building a data pipeline with Hevo.

Hevo, being fully automated along with 150+ plug-and-play sources, will accommodate a variety of your use cases. Worried about the onboarding? Its incredible support team will be available around the clock to help you at every step of your journey with Hevo.

Feel free to catch up and let us know about your experience of employing a data pipeline from Freshdesk to Databricks using Hevo.

Manisha Jena
Research Analyst, Hevo Data

Manisha Jena is a data analyst with over three years of experience in the data industry and is well-versed with advanced data tools such as Snowflake, Looker Studio, and Google BigQuery. She is an alumna of NIT Rourkela and excels in extracting critical insights from complex databases and enhancing data visualization through comprehensive dashboards. Manisha has authored over a hundred articles on diverse topics related to data engineering, and loves breaking down complex topics to help data practitioners solve their doubts related to data engineering.

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