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."
First, you need to manually export your data from Typeform. Log in to your Typeform account, navigate to the 'Results' section of the specific form. Click on 'Export' and choose 'CSV' as the format. Download the CSV file to your local machine.
Ensure that you have AWS CLI and Python installed on your local machine. AWS CLI will be used to upload data to S3, and Python can be used for any data transformation if needed.
Configure your AWS CLI with the necessary credentials. Open your terminal or command prompt and run `aws configure`. Enter your AWS Access Key ID, Secret Access Key, default region, and preferred output format. This step is crucial for performing authenticated operations with AWS services.
If you don't already have an S3 bucket, you need to create one to store your Typeform data. Go to the AWS Management Console, open the S3 service, and click on 'Create bucket'. Enter a unique bucket name and select the appropriate region, then configure any additional settings as needed before creating the bucket.
Use the AWS CLI to upload the exported CSV file to your S3 bucket. Navigate to the directory containing your CSV file in the terminal and execute the command:
```
aws s3 cp yourfile.csv s3://your-bucket-name/
```
Replace `yourfile.csv` with the actual filename and `your-bucket-name` with your S3 bucket's name.
In the AWS Management Console, navigate to AWS Glue. Create a new Glue database if you don't have one. Then, set up a new Glue Crawler to catalog the data in your S3 bucket. Configure the crawler to point to your S3 bucket and specify the IAM role that has permissions to access the S3 bucket.
Run the Glue Crawler you set up in the previous step. Once completed, the crawler will create metadata tables in the Glue Data Catalog. You can now use AWS Athena to query this data directly from S3, allowing you to perform further analysis or ETL operations using AWS Glue.
By following these steps, you will successfully move data from Typeform to S3 and set it up for processing with AWS Glue, all without relying on third-party connectors or integrations.
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:
