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Begin by familiarizing yourself with the Zendesk Sunshine data model. Understand the structure, types of data, and relationships within your Zendesk data. This will help you identify which data you need to extract and how to handle it later in AWS.
Prepare your AWS environment by setting up the necessary resources. This includes creating an S3 bucket where you will store the data, setting up AWS IAM roles with appropriate permissions for accessing the S3 bucket, and setting up any required AWS services, such as AWS Lambda or AWS Glue, for processing or transforming data.
Use Zendesk Sunshine’s API to export data. Programmatically access the API to extract the required data. You can write a script in a language like Python or JavaScript to make HTTP GET requests to the API endpoints and retrieve the data in JSON format. Make sure to handle pagination and rate limits appropriately.
Once you have the data, transform it into a format suitable for loading into AWS, such as CSV or Parquet. Use a programming language or a data processing tool to parse the JSON data, clean it, and convert it into your chosen format. Ensure that data types and structures are consistent with what you plan to use in AWS.
Upload the transformed data files to your AWS S3 bucket. Use the AWS CLI or SDKs to programmatically upload your files. Ensure that the correct IAM roles and permissions are in place to allow for secure data transfer. Organize your data in S3 using a logical folder structure for easier access and management later.
Use AWS Glue to catalog the newly uploaded data in S3. Create a Glue Crawler to connect to your S3 bucket and automatically create table definitions in the Glue Data Catalog. This will allow you to query the data using AWS Athena or other AWS analytics services. Configure the crawler to run periodically if you plan on regular updates.
Finally, validate the data in AWS to ensure it has been transferred accurately and is available for querying. Use AWS Athena to run queries against the Glue Data Catalog and verify the integrity and accuracy of the data. Make sure to address any discrepancies and adjust your extraction or transformation processes if necessary.
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.
Taking the customer relationship management (CRM) platform up a notch, Zendesk Sunshine makes it possible for businesses to connect the dots to build the full picture of their customer from data anywhere. Offering much more than the old legacy CRM platform, Zendesk Sunshine takes a new and more modern approach, native to AWS, that provides the tools needed for developers and admins to create superior customer experiences.
Zendesk Sunshine's API provides access to a wide range of data categories, including:
1. Customer data: This includes information about customers such as their name, email address, phone number, and other contact details.
2. Ticket data: This includes information about customer support tickets, such as the status of the ticket, the customer's issue, and any notes or comments added by support agents.
3. Agent data: This includes information about support agents, such as their name, email address, and performance metrics.
4. Analytics data: This includes data about customer support performance, such as response times, ticket volume, and customer satisfaction ratings.
5. Integration data: This includes data about integrations with other systems, such as CRM or marketing automation platforms.
6. Custom data: This includes any custom data fields that have been added to the Zendesk platform, such as customer preferences or product information.
Overall, Zendesk Sunshine's API provides access to a wide range of data that can be used to improve customer support performance, gain insights into customer behavior, and integrate with other systems for a more seamless customer experience.
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:
