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."
Begin by accessing your shopping cart's database directly. Use SQL queries to extract the required data. Ensure you have the necessary permissions to access the database and export data. Export the data in a structured format such as CSV or JSON, which can be easily handled for the next steps.
Once the data is extracted, inspect it for any necessary transformations. This could involve cleaning up data, normalizing values, or restructuring it to match the schema you plan to use in BigQuery. Use Python, Pandas, or any preferred data processing tool to perform these transformations, and save the transformed data in a CSV or JSON file.
Log in to your Google Cloud Console and create a new Cloud Storage bucket. This bucket will serve as a temporary staging area for your data. Ensure the bucket is in the same region as your BigQuery dataset to minimize latency and costs. Also, configure appropriate access permissions.
Use the `gsutil` command-line tool or the Google Cloud Console to upload your transformed data files to the GCS bucket you created. For example, use the command `gsutil cp [local-file-path] gs://[bucket-name]/` to upload files from your local machine to GCS.
In the Google Cloud Console, navigate to BigQuery and create a new dataset if you don’t have one already. This dataset will hold your tables. Configure the dataset with the necessary permissions to allow access for data import.
Use the BigQuery web UI, `bq` command-line tool, or BigQuery API to load data from GCS into BigQuery. Specify the GCS file path and provide the schema for the table in BigQuery. For instance, a command using `bq` might look like:
`bq load --source_format=CSV [project_id]:[dataset].[table] gs://[bucket-name]/[file-name].csv [schema]`
After loading the data, perform validation checks to ensure data integrity. Run queries in BigQuery to verify that the data matches expectations, checking for correct formatting, completeness, and data types. Address any discrepancies by re-transforming and loading the data as needed.
By following these steps, you can manually transfer data from your cart system to BigQuery without relying on third-party connectors, ensuring complete control over the data movement process.
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.
Cart.com offers an integrated, holistic approach to ecommerce, which they call ecommerce 2.0. Cart serves as Nigeria’s leading shopping community, attempting to democratize ecommerce by providing all sizes of brands ecommerce capabilities equivalent to those of the world’s largest online retailers. To fulfill their mission of putting businesses in charge of their own ecommerce journey and customer relationships, they provide software, services, and the necessary intrastructure to give even small brands the online capabilities they need to survive and grow.
Cart's API provides access to a wide range of data related to e-commerce and online shopping. The following are the categories of data that can be accessed through Cart's API:
1. Products: Information about the products available on the e-commerce platform, including their names, descriptions, prices, images, and other relevant details.
2. Orders: Details about the orders placed by customers, including the products purchased, the payment method used, and the shipping address.
3. Customers: Information about the customers who have registered on the e-commerce platform, including their names, email addresses, and shipping addresses.
4. Inventory: Data related to the availability of products in the inventory, including the stock levels and the locations where the products are stored.
5. Shipping: Information about the shipping options available to customers, including the shipping rates, delivery times, and tracking information.
6. Payments: Details about the payment methods accepted by the e-commerce platform, including credit cards, PayPal, and other payment gateways.
7. Discounts and promotions: Data related to the discounts and promotions offered by the e-commerce platform, including coupon codes, gift cards, and other special offers.
Overall, Cart's API provides a comprehensive set of data that can be used to build powerful e-commerce applications and services.
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:
