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 exporting your data from Typeform. Log in to your Typeform account, navigate to the form whose data you want to export, and use the built-in export feature to download the data in a CSV format. This file will serve as the intermediate data source for transferring information to MSSQL.
Once you have the CSV file, open it using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data for any inconsistencies or errors. Ensure that the column headers match the structure of the destination MSSQL table. If necessary, clean or modify the data to fit the schema of your MSSQL database.
Ensure you have access to the MSSQL server where you intend to import the data. Use a tool like SQL Server Management Studio (SSMS) to manage your database. Create a new database and table if they do not exist, ensuring the table's schema matches the structure of the CSV file.
Create a Transact-SQL (T-SQL) script to import data from the CSV file into your MSSQL database. Use the `BULK INSERT` statement for this purpose. Here's an example of how your script might look:
```sql
BULK INSERT YourDatabase.YourTable
FROM 'C:\path\to\your\data.csv'
WITH
(
FIRSTROW = 2,
FIELDTERMINATOR = ',',
ROWTERMINATOR = '\n',
TABLOCK
);
```
Adjust the file path and table names according to your setup.
Ensure the SQL Server instance has permissions to read the file location. This might involve setting the correct file path permissions on the server and ensuring the SQL Server service account has access to the directory where the CSV file resides.
Run your T-SQL script in SSMS to import the data. Open a new query window, paste the script, and execute it. Monitor the process for any errors or warnings. If issues arise, address them by reviewing error messages and adjusting the script or data accordingly.
After executing the import script, verify that the data has been correctly transferred by running SELECT queries on your MSSQL table. Compare a few records from the table with the original CSV file to ensure accuracy. Check for missing or mismatched data and rectify any discrepancies as needed.
By following these steps, you can effectively transfer data from Typeform to an MSSQL database 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.
Typeform makes collecting and sharing information comfortable and conversational. It's a web-based platform you can use to create anything from surveys to apps, without needing to write a single line of code.
Typeform's API provides access to a wide range of data related to surveys and forms. The following are the categories of data that can be accessed through Typeform's API:
1. Form data: This includes all the questions and responses from a form or survey.
2. Response data: This includes all the responses submitted by users for a particular form or survey.
3. User data: This includes information about the users who have responded to a form or survey, such as their name, email address, and other contact details.
4. Analytics data: This includes data related to the performance of a form or survey, such as the number of responses, completion rates, and other metrics.
5. Theme data: This includes information about the visual appearance of a form or survey, such as the colors, fonts, and other design elements.
6. Webhook data: This includes data related to the integration of a form or survey with other applications, such as the data that is sent to a third-party application when a form is submitted.
Overall, Typeform's API provides access to a comprehensive set of data that can be used to analyze and optimize the performance of forms and surveys.
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:
