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To extract data from Freshdesk, you need to use their RESTful API. First, log into your Freshdesk account and navigate to the API settings. Ensure API access is enabled and note down your API key, which will be used for authentication in the subsequent steps.
Go to the AWS Management Console and create an S3 bucket where you will store the Freshdesk data. Note the bucket name and region. Ensure that the bucket policy allows the necessary permissions for data upload.
Create an IAM role in AWS with permissions to access both S3 and AWS Glue. Attach the policies `AmazonS3FullAccess` and `AWSGlueServiceRole`. This role will be used by AWS Glue to interact with your S3 bucket.
Develop a Python script that uses the Freshdesk API to fetch the required data. Use the `requests` library to make API calls. Parse the JSON responses and write the data to a CSV or JSON format suitable for uploading to S3. Ensure the script handles pagination if the data set is large.
Incorporate boto3, the AWS SDK for Python, in your script to upload the formatted data to your S3 bucket. Use the `put_object` method to place the data file into your designated bucket. Ensure the file is named appropriately with a timestamp if necessary.
In the AWS Glue Console, create a new Glue Crawler. Configure it to crawl the data in your S3 bucket and specify the appropriate IAM role with the permissions created earlier. This crawler will catalog your data and create tables in the Glue Data Catalog for future querying.
Execute the Glue Crawler to populate the Glue Data Catalog with metadata about your Freshdesk data. Once the crawler completes, navigate to the Glue Data Catalog to verify that the tables have been created correctly. Use AWS Athena to query the data to ensure it has been transferred and structured as expected.
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
