Summarize this article with:
Trusted by data-driven companies
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
Begin by familiarizing yourself with the data structure within Zendesk Sunshine. Identify the objects and attributes you need to export, such as tickets, users, or custom objects. This will help you formulate the right API queries to extract data.
Use the Zendesk Sunshine API to access the data. You will need to authenticate using OAuth tokens or API keys. Refer to the Zendesk Sunshine API documentation to understand the available endpoints and how to authenticate your requests.
Write scripts to make API calls to Zendesk Sunshine. Use programming languages like Python or JavaScript to send GET requests to the appropriate endpoints and retrieve data in JSON format. Handle pagination if the data set is large to ensure you retrieve all records.
Convert the extracted JSON data into a format compatible with MS SQL Server. This typically involves transforming JSON into CSV or directly into SQL insert statements. You can use libraries like Pandas in Python to parse JSON and convert it to CSV.
Set up your MS SQL Server environment by creating the necessary databases and tables to hold the data. Define the schema based on the data structure you analyzed. Ensure data types in SQL match the data types from Zendesk Sunshine.
Use SQL Server Management Studio (SSMS) or a script to import the transformed data into MS SQL Server. If you transformed your data into CSV, you can use the BULK INSERT command in SQL to load data efficiently. For direct inserts, execute the SQL insert statements generated from your transformation script.
After loading the data, perform checks to ensure data integrity and consistency. Compare record counts between Zendesk and MS SQL Server, and perform spot checks on the data to ensure accuracy. Create automated tests or scripts to validate that all data has been transferred correctly.
Following these steps will allow you to manually move data from Zendesk Sunshine to MS SQL Server without relying on third-party connectors.
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:
