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, ensure you have access to the Amazon Seller Partner API. You'll need to create an Amazon Developer account and then register an application in the Amazon Developer Console. During this setup, obtain your API credentials, which include the `Client ID`, `Client Secret`, and any necessary tokens like the `Refresh Token` for authentication and authorization.
Use your `Client ID`, `Client Secret`, and `Refresh Token` to programmatically authenticate and generate an access token. You can achieve this by making a POST request to the Amazon Seller Partner API token endpoint, which will return an access token. This token is required for subsequent API requests to access data.
With your access token, send requests to the Amazon Seller Partner API to extract the desired data. This could include sales reports, inventory data, or any other available information. Ensure that your requests handle pagination if there is a large dataset, and save the data in a structured format, such as JSON or CSV, temporarily on your local machine or a server.
Log into your Snowflake account and create a database and schema if they do not already exist. Set up the necessary tables with the appropriate data types and structures to match the data you extracted from Amazon. This step is crucial to ensure that the data can be loaded smoothly.
Convert the extracted data into a format compatible with Snowflake, typically CSV files. Ensure that the data is clean and that any necessary transformations are applied. For example, handle null values, ensure data types are consistent, and, if needed, adjust date formats to align with Snowflake's requirements.
Use Snowflake's `PUT` command to upload your formatted CSV files to Snowflake's internal staging area. This involves using the SnowSQL command-line tool or Snowflake's Web Interface. Ensure that your data files are correctly uploaded, as this is a crucial step before final loading into the tables.
Execute the `COPY INTO` command in Snowflake to load data from the staging area into your target tables within the database. This command will move the data from the CSV files in the staging area into the predefined tables. Make sure to define the correct file format options and error handling strategies to avoid data loss or corruption during the load process.
By following these steps, you can successfully move data from the Amazon Seller Partner API to the Snowflake Data Cloud 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.
Amazon Selling Partner’s API (SP-API) is a much-improved iteration of Amazon Marketplace Web Service (Amazon MWS) APIs. This next generation suite offers increased automation functionality, with many new features including state-of-the-art JSON-based REST API design standards and 0Auth2.0 selling partner authorization using Login with Amazon. With this generation of updates, Amazon Selling Partners continues to deliver reliable programmatic access to essential Amazon features, in the same tradition their customers have come to expect for over 10 years.
Amazon Seller Partner's API provides access to a wide range of data related to Amazon seller accounts. The API allows developers to retrieve data related to orders, products, inventory, and pricing. Here are the categories of data that the API provides access to:
1. Orders: The API provides access to order details such as order ID, order status, shipping address, payment information, and order items.
2. Products: The API provides access to product details such as product ID, product title, product description, product images, and product variations.
3. Inventory: The API provides access to inventory details such as inventory levels, inventory status, and inventory updates.
4. Pricing: The API provides access to pricing details such as product prices, discounts, and promotions.
5. Fulfillment: The API provides access to fulfillment details such as shipment tracking information, shipping labels, and fulfillment status.
6. Reports: The API provides access to various reports such as sales reports, inventory reports, and financial reports.
Overall, the Amazon Seller Partner's API provides a comprehensive set of data that can help sellers manage their Amazon business more effectively.
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:
