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, access your Unleash application and navigate to the section from where you want to export data. Most applications allow you to export data as a CSV, JSON, or similar file format. If Unleash provides an API, you can use it to extract the required data by sending an HTTP request and saving the response.
Once you have exported the data from Unleash, save the file locally on your computer. Ensure that the data format is well-structured and consistent, as it will facilitate a smooth import process into DuckDB. Common formats to save this data are CSV or JSON.
If you haven't already, install DuckDB on your local machine. DuckDB can be installed via various methods including using package managers like pip for Python (`pip install duckdb`) or by downloading the appropriate binaries from the DuckDB website. Follow the installation instructions specific to your operating system.
Open your terminal or command prompt and launch DuckDB. Create a new database where you will store the Unleash data. You can do this by running the command:
```shell
duckdb my_database.db
```
This command creates a new DuckDB database file named `my_database.db`.
Depending on the format of your exported Unleash data, you may need to perform some preprocessing. For instance, if the data is in JSON format, you may need to convert it to CSV for easier import into DuckDB. Tools like Python or command-line utilities like `jq` can help with this conversion.
With your data file ready, use DuckDB's SQL commands to load the data into your database. For CSV files, you can use the following SQL command after entering the DuckDB CLI:
```sql
COPY my_table FROM 'path/to/your/file.csv' (DELIMITER ',', HEADER);
```
Replace `my_table` with your desired table name and adjust the file path and options as needed.
Once the import is complete, verify that the data has been successfully imported into DuckDB. You can do this by running simple SQL queries within DuckDB, like:
```sql
SELECT * FROM my_table LIMIT 10;
```
This will display the first 10 rows of your data, allowing you to check that everything has been transferred correctly.
By following these steps, you can successfully move data from Unleash to DuckDB 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.
Unleash is a global innovation lab that brings together entrepreneurs, investors, and corporations to collaborate on solutions to some of the world's most pressing challenges. The program focuses on themes such as sustainable energy, food security, and healthcare, and provides participants with access to mentorship, funding, and resources to develop their ideas into viable businesses. Unleash also emphasizes diversity and inclusion, with a goal of bringing together individuals from diverse backgrounds and perspectives to drive innovation and create positive social impact. The program culminates in a week-long innovation lab where participants pitch their ideas and collaborate on solutions to global challenges.
Unleash's API provides access to various types of data related to feature flags and experimentation. The following are the categories of data that can be accessed through the API:
1. Feature flags: The API provides access to all the feature flags created in the Unleash dashboard, including their names, descriptions, and configurations.
2. Metrics: The API provides access to various metrics related to feature flags, such as the number of times a feature flag was evaluated, the number of times it was enabled, and the percentage of users who saw the feature flag.
3. Events: The API provides access to events related to feature flags, such as when a feature flag was toggled on or off, when it was evaluated, and when it was enabled or disabled.
4. User targeting: The API provides access to user targeting information, such as the rules used to target specific users for a feature flag and the percentage of users who were targeted.
5. Experiments: The API provides access to information related to experiments, such as the name of the experiment, the variations being tested, and the metrics being tracked.
Overall, Unleash's API provides a comprehensive set of data related to feature flags and experimentation, allowing developers to gain insights into how their features are performing and make data-driven decisions.
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:
