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."
First, you need an API key from News API. Register on the News API website and obtain your API key. This key will be required to authenticate your requests and fetch data from the News API.
Use a scripting language like Python to make HTTP GET requests to the News API endpoints. Use the `requests` library in Python to retrieve the data. Ensure to include your API key in the request headers for authentication. Store the fetched data in a suitable data structure such as a list of dictionaries or a JSON object.
Once the data is fetched, transform it into a structured format suitable for loading into Starburst Galaxy. This may involve selecting specific fields, renaming them, or converting data types. Use Python’s built-in capabilities or libraries like `pandas` to clean and prepare your data.
Convert the structured data into a CSV format, which is compatible with Starburst Galaxy's data loading capabilities. Use the `pandas` library to create a DataFrame from your structured data and then use the `to_csv()` method to write it to a local file or a temporary storage location.
Starburst Galaxy can access data stored in cloud storage like AWS S3, Google Cloud Storage, or Azure Blob Storage. Choose a cloud storage service and upload your CSV file to a bucket or container there. Ensure your cloud storage permissions allow Starburst Galaxy to access the data.
In Starburst Galaxy, set up a catalog to access the cloud storage where your CSV file is located. Configure the necessary connection properties like the access key, secret key, and bucket/container name. Ensure Starburst Galaxy has the required permissions to read from your cloud storage.
Once the catalog is configured, use SQL queries within Starburst Galaxy to load the data from the cloud storage into a table in Starburst Galaxy. Execute a `CREATE TABLE AS SELECT` statement or a similar command to import the data, ensuring the table schema matches the CSV structure.
By following these steps, you can successfully move data from News API to Starburst Galaxy 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.
The News API gives a lot of flexibility in how you create and manage your news content. This connector is a simple and easy-to-use REST API that offers JSON search results for recent and historical news articles published by over 80,000 sources worldwide. As a result, you can quickly show trending news headlines in your web application. Also, combining the Google News API is very easy. API is short for application programming interface, which is a software intermediary that permits two applications to talk to each other.
News API provides access to a wide range of data related to news articles and sources. The following are the categories of data that can be accessed through News API's API:
1. News articles: News API provides access to articles from various news sources around the world. These articles can be filtered by language, country, and category.
2. News sources: News API provides a list of news sources that can be used to filter articles. These sources can be filtered by language, country, and category.
3. Top headlines: News API provides access to the top headlines from various news sources around the world. These headlines can be filtered by language, country, and category.
4. Search results: News API provides access to search results based on a keyword or phrase. These search results can be filtered by language, country, and category.
5. Article metadata: News API provides metadata for each article, including the title, author, description, URL, and published date.
6. Image URLs: News API provides access to the URLs of images associated with each article.
7. Article content: News API provides access to the full content of each article, including the text and any embedded media.
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:
