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Sync with Airbyte
1. Open the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.
2. Click on the "Add Source" button and select "Freshdesk" from the list of available connectors.
3. Enter a name for the connector and click on "Next".
4. Enter your Freshdesk credentials, including your Freshdesk domain, API key, and password.
5. Click on "Test Connection" to ensure that the credentials are correct and the connection is successful.
6. Once the connection is successful, select the data you want to replicate from Freshdesk, including tickets, contacts, and companies.
7. Choose the replication frequency and the destination where you want to store the data.
8. Click on "Create Source" to save the configuration and start the replication process.
9. Monitor the replication process on the Airbyte dashboard and troubleshoot any errors that may occur.
10. Once the replication is complete, you can use the data in your destination of choice for analysis and reporting.
1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "BigQuery" destination connector and click on it.
3. Click the "Create Destination" button to begin setting up your BigQuery destination.
4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.
5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.
6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.
7. Finally, review your settings and click the "Create Destination" button to complete the setup process.
8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.
9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.
10. Follow the prompts to enter your source credentials and configure your sync settings.
11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.
12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Freshdesk is a service provided by Freshworks for handling the entire spectrum of customer engagement. A customer support software based in the Cloud, Freshdesk provides a scalable solution for managing customer support simply and efficiently. Freshdesk enables teams to track incoming tickets from a variety of channels; provide support across multiple platforms including phone, chat, and other messaging apps; categorize, prioritize, and assign tickets; prepare preformatted answer to common customer support questions; and much more.
Freshdesk's API provides access to a wide range of data related to customer support and service management. The following are the categories of data that can be accessed through Freshdesk's API:
1. Tickets: Information related to customer support tickets, including ticket ID, status, priority, and requester details.
2. Contacts: Data related to customer contacts, including contact ID, name, email address, and phone number.
3. Agents: Information about support agents, including agent ID, name, email address, and role.
4. Companies: Data related to companies that use Freshdesk for customer support, including company ID, name, and domain.
5. Conversations: Information related to customer conversations, including conversation ID, status, and participants.
6. Knowledge base: Data related to the knowledge base, including articles, categories, and folders.
7. Surveys: Information related to customer satisfaction surveys, including survey ID, status, and responses.
8. Time entries: Data related to time entries for support agents, including time spent on tickets and activities.
9. Custom fields: Information related to custom fields created in Freshdesk, including field ID, name, and value.
Overall, Freshdesk's API provides access to a comprehensive set of data that can be used to improve customer support and service management.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
Freshdesk is a service provided by Freshworks for handling the entire spectrum of customer engagement. A customer support software based in the Cloud, Freshdesk provides a scalable solution for managing customer support simply and efficiently. Freshdesk enables teams to track incoming tickets from a variety of channels; provide support across multiple platforms including phone, chat, and other messaging apps; categorize, prioritize, and assign tickets; prepare preformatted answer to common customer support questions; and much more.
BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.
1. Open the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.
2. Click on the "Add Source" button and select "Freshdesk" from the list of available connectors.
3. Enter a name for the connector and click on "Next".
4. Enter your Freshdesk credentials, including your Freshdesk domain, API key, and password.
5. Click on "Test Connection" to ensure that the credentials are correct and the connection is successful.
6. Once the connection is successful, select the data you want to replicate from Freshdesk, including tickets, contacts, and companies.
7. Choose the replication frequency and the destination where you want to store the data.
8. Click on "Create Source" to save the configuration and start the replication process.
9. Monitor the replication process on the Airbyte dashboard and troubleshoot any errors that may occur.
10. Once the replication is complete, you can use the data in your destination of choice for analysis and reporting.
1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "BigQuery" destination connector and click on it.
3. Click the "Create Destination" button to begin setting up your BigQuery destination.
4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.
5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.
6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.
7. Finally, review your settings and click the "Create Destination" button to complete the setup process.
8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.
9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.
10. Follow the prompts to enter your source credentials and configure your sync settings.
11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.
12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Customer service and management are the two vital aspects of improving a business. For efficient client handling, you can rely on Freshdesk, a cloud-based customer support software. The data collected by Freshdesk is of immense value for your brand visibility and making further decisions. Connecting Freshdesk to a robust data warehouse like BigQuery empowers you to make data-driven decisions, improve service performance, and achieve operational metrics. This integration enables you to analyze and derive valuable insights from the support interactions and consumer data using Bigquery’s robust analytics capabilities. These insights allow for optimized service delivery and enhance overall client satisfaction.
Let’s dive in and understand each tool and the different methods to move data from Freshdesk to BigQuery.
Freshdesk Overview
Freshdesk is an online client engagement solution developed by Freshworks. It helps you streamline client support, managing them efficiently as you scale. You can leverage the AI and ML capabilities to offer a customized experience to all your customers and the bots for efficient ticket management.
The chatbot follows a conversational flow designed for step-by-step interaction with your users, guiding them through tasks or queries smoothly. While creating a bot, you can configure it to execute specific actions. The bot flow builder enables the creation of fully functional chatbots without the need to write complex code, thereby helping in streamlined ticket handling. This allows you to improve productivity and deliver quicker resolutions without compromising quality. Some popular organizations that rely on Freshdesk are Hamleys, Pearson, Bridgestone, Lesley, Pharmeasy, and more.
Some of the key features of Freshdesk are:
- It enables your support teams to deliver timely assistance for a consistently enhanced customer experience.
- Freshdesk allows you to handle tickets efficiently via a uniform workplace, aiding to focus on personalized and prioritized issue resolution. Additionally, you can manage incoming tickets from multiple channels in a unified view.
- It provides a scalable knowledge base and custom widgets to automate self-service, helping clients find answers faster.
- You have the option to utilize collaboration features such as threads, shared ownership, and more to engage with experts within and across teams. This ensures faster, consistent service for complex inquiries.
- You can effortlessly utilize its AI-driven insights, like multi-support channels, to identify potential issues and optimize support operations. In addition, reporting and analysis allow you to consistently analyze interactions, monitor quality, and reduce TCO (Total Cost of Ownership).
BigQuery Overview
BigQuery is a fully managed data warehouse with built-in features like machine learning, geospatial analysis, and business intelligence. It helps you collect and analyze data efficiently. For analysis, it uses a columnar storage format to store data. This enables efficient scanning of individual columns across the extensive dataset, enhancing query performance.
BigQuery serverless architecture enables the utilization of SQL queries to answer your questions without managing infrastructure. The scalable, distributed analysis allows you to query terabytes in seconds and petabytes in minutes.
With BigQuery, you can also separate data analysis computing from storage, allowing you to asses data within the platform or where it is stored.
Some of the key features of BigQuery are:
- With options like nested fields, partitioned tables, and clustering, you can enhance query speed by optimizing tables.
- The BI engine allows you to accelerate queries, delivering rapid responses and cost-effective computing within the BigQuery platform.
- You can use BigQuery’s geospatial features to perform advanced analyses and visualization of geographical data.
Methods to Move Data from Freshdesk to BigQuery
- Method 1: Using Airbyte to connect Freshdesk to BigQuery
- Method 2: Manually migrating data from Freshdesk to BigQuery
Method 1: Using Airbyte to Connect Freshdesk to BigQuery
Airbyte, a cloud ETL service, provides 350+ connectors and an intuitive user interface for diverse data integration needs. It allows you to effortlessly extract and load data from multiple sources to destinations. Additionally, the process of integrating Freshdesk into Bigquery can be completed in a breeze.
Before setting up the steps, let’s take a quick look at the prerequisites to connect Freshdesk to Bigquery.
Prerequisites
Step 1: Configure Freshdesk as Source in Airbyte
- Login to your Airbyte account or Register for a new one.
- Navigate to the dashboard and select Sources.
- Search for Freshdesk and click on the connector.
- On the Freshdesk source connector page, enter the Source name, Freshdesk Domain, API Key, and specify the Start Date for data replication.
- After filling in the mandatory details, click on Set up source.
Step 2: Configure BigQuery as Destination in Airbyte
- After setting Freshdesk as the source, return to the dashboard and click Destinations.
- Type BigQuery in the Search box of the destination page and then click on the specific connector.
- On the BigQuery destination page, fill in the required details such as Project ID, Dataset Location, and Default Dataset ID. Choose the Loading Method between GCS Staging and Standard Inserts. Then click on Set up Destination.
- For further information on each field, refer to Airbyte’s BigQuery Documentation.
Step 3: Create a Connection Between Freshdesk and BigQuery
- Go to the left navigation menu and select Connections to establish a link between Freshdesk and BigQuery. Then click on Create a new connection.
- Select Freshdesk as the source and BigQuery as the destination, as created in the above steps.
- Enter a unique Connection name on the connections page and select your sync mode.
- Click on Start the sync to initiate the transfer.
These three quick steps complete Freshdesk to BigQuery data migration using Airbyte.
Method 2: Manually Migrating Data from Freshdesk to BigQuery
In this method, you will learn to migrate data from Freshdesk to BigQuery manually. For this, you need to extract Freshdesk data in the CSV format and then upload it into the BigQuery table.
Step 1: Export Data from Freshdesk in a CSV file
- Log in to your Freshdesk account and enter your credentials in the Helpdesk box.
- On the Freshdesk Dashboard, navigate to the Contacts tab in the left window pane and click on it.
- Within the contact section, click on Export.
- Choose the fields you want to export.
- Click on the Details button to access all the fields you have selected.
- Choose the Export option at the bottom of the page; your data will be downloaded as a CSV file.
Step 2: Import the CSV File into BigQuery
You can use the BigQuery web user interface to load CSV data files into a BigQuery table.
- Open the BigQuery web UI.
- Expand your Project in the Explorer pane and choose a dataset you want to load.
- Click on Create Table in the dataset info section.
- Specify the necessary details in the required fields within the Create Table page.
- In the Source section, choose among creating a table from Google Cloud Storage, Upload, Drive, Google Bigtable, or Amazon S3. As you have already downloaded the CSV file in Step 1, select the Upload option.
- In the Destination section, specify the Project, Dataset, and Table details.
- You can manually enter the required information in the Schema section or choose the Auto-detect option.
- If you scroll down further, Advanced Options like Encryption, Default collation, and Default Rounding mode exist. Enter the necessary details and click the Create Table button at the bottom.
These steps will fetch data from the CSV files, determine schema, and replicate data into the BigQuery table.
Limitations of Using Manual Method
- Time-Consuming: Manual transfers are slow and complicated as they involve human efforts to initiate and complete the process. Especially with large datasets, you would need to repeat the entire process due to file size limitations or frequent updates.
- Expertise Requirement: Freshdesk and BigQuery operate on different platforms with varying data formats and methodologies to transform. It demands expertise and a deep understanding of both systems to migrate data effectively.
- Maintenance: Continuous human handling requires ongoing maintenance and monitoring, increasing operational burdens.
- Prone to Errors: As human interference is involved, errors might occur while entering the data, such as schema mismatch, conversion errors, and incorrect credentials, requiring more time to fix.
Why Choose Airbyte for Freshdesk to BigQuery Integration?
- Ease of Use: Utilizing the features of Airbyte, like an optimized setup process, workflow orchestration, and user-friendly interface, streamlines the connection process for users with different technical expertise levels. This helps simplify the migration process and allows you to efficiently monitor the connection between Freshdeak and BigQuery.
- Scalability: With Airbyte, you can efficiently manage various data integration needs, whether tackling a small-scale task or handling a large-scale enterprise project.
- Connectors: Airbyte provides over 350+ built-in source and destination connectors, encompassing popular databases, data warehouses, APIs, and SaaS applications. Leveraging these pre-built connectors simplifies the data integration without needing extensive custom development.
Conclusion
You can replicate data from Freshdesk to BigQuery in more than one way based on your operational needs. Airbyte facilitates a straightforward setup, connecting Freshdesk and BigQuery with minimal effort through a few clicks. In contrast, transferring data via CSV files can be done for small data transfers. It demands more time and effort for large data sets, leading to potential delays due to manual involvement.
Airbyte excels in simplifying the extraction of complex data from Freshdesk and loading it to BigQuery, making it a solution to overcome integration challenges. Its pre-built connectors and user-friendly interface not only streamline the connection process between the two platforms but also between other sources and destinations of your preference. Try Airbyte today.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
Freshdesk's API provides access to a wide range of data related to customer support and service management. The following are the categories of data that can be accessed through Freshdesk's API:
1. Tickets: Information related to customer support tickets, including ticket ID, status, priority, and requester details.
2. Contacts: Data related to customer contacts, including contact ID, name, email address, and phone number.
3. Agents: Information about support agents, including agent ID, name, email address, and role.
4. Companies: Data related to companies that use Freshdesk for customer support, including company ID, name, and domain.
5. Conversations: Information related to customer conversations, including conversation ID, status, and participants.
6. Knowledge base: Data related to the knowledge base, including articles, categories, and folders.
7. Surveys: Information related to customer satisfaction surveys, including survey ID, status, and responses.
8. Time entries: Data related to time entries for support agents, including time spent on tickets and activities.
9. Custom fields: Information related to custom fields created in Freshdesk, including field ID, name, and value.
Overall, Freshdesk's API provides access to a comprehensive set of data that can be used to improve customer support and service management.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: