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
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
“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.”
"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."
Before starting, thoroughly review the API documentation. Note the endpoints, authentication methods, data formats (like JSON or XML), rate limits, and any other specific requirements. This step ensures that you can effectively communicate with the API.
Use a programming language such as Python to make HTTP requests to the API. The `requests` library in Python is a simple and effective tool for this task. Write a script to send GET requests to the API endpoints and receive the data in the desired format. Make sure to handle authentication if required, and implement error handling for failed requests.
Once the data is retrieved, it may require transformation to align with your Teradata schema. Use Python libraries like `pandas` to manipulate and clean the data. This includes converting data types, renaming columns, and handling missing or erroneous values.
Use Teradata's native tools to connect to your Teradata database. Teradata provides Python modules such as `teradatasql` for this purpose. Install and configure the module, then use it to establish a connection to your Teradata environment using proper credentials.
Before inserting data, ensure that the target tables in Teradata are ready. This may involve creating new tables or modifying existing ones to accommodate the new data structure. Use SQL commands to define table schemas that match the transformed data.
With the connection established and tables prepared, use the `teradatasql` module to execute SQL commands that insert the data into Teradata. This can be done in batch processes to efficiently handle large datasets. Use prepared statements or parameterized queries to streamline the insertion process and to safeguard against SQL injection.
After loading the data, run queries to verify that it has been correctly inserted into Teradata. Check for data completeness and integrity. Once verified, automate the entire process using a scheduling tool like `cron` on Unix-based systems or Task Scheduler on Windows to run your script at regular intervals, ensuring continuous data synchronization between the API and your Teradata database.
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.
Public API connector permits users the flexibility to connect to any existing REST API and quickly abstract the necessary data. The API Connector also permits you to connect to almost any external API from Bubble. It provides Azure Active Directory with the information needed to call the API endpoint by defining the HTTP endpoint URL and authentication for the API call. API Connector is a dynamic, comfortable-to-use extension that pulls data from any API into Google Sheets.
Public APIs provide access to a wide range of data, including:
1. Weather data: Public APIs provide access to real-time weather data, including temperature, humidity, wind speed, and precipitation.
2. Financial data: Public APIs provide access to financial data, including stock prices, exchange rates, and economic indicators.
3. Social media data: Public APIs provide access to social media data, including user profiles, posts, and comments.
4. Geographic data: Public APIs provide access to geographic data, including maps, geocoding, and routing.
5. Government data: Public APIs provide access to government data, including census data, crime statistics, and public health data.
6. News data: Public APIs provide access to news data, including headlines, articles, and trending topics.
7. Sports data: Public APIs provide access to sports data, including scores, schedules, and player statistics.
8. Entertainment data: Public APIs provide access to entertainment data, including movie and TV show information, music data, and gaming data.
Overall, Public APIs provide access to a vast array of data, making it easier for developers to build applications and services that leverage this data to create innovative solutions.
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:
