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 logging into your GoCardless dashboard. Use the export feature to download the necessary data as a CSV file. GoCardless typically allows you to export various data, such as payments, customers, and mandates. Ensure that you export the data in a format that contains all the fields you need for analysis in BigQuery.
Once downloaded, open the CSV files in a spreadsheet application like Microsoft Excel or Google Sheets. Clean and format the data if necessary. This may include removing unnecessary columns, standardizing date formats, or ensuring there are no empty rows. Save the cleaned data as a CSV file, which will be ready for upload to BigQuery.
Navigate to the Google Cloud Console and set up a new project if you don't already have one. Ensure that the BigQuery API is enabled for your project. This is a necessary step for managing and querying your datasets within BigQuery.
In the Google Cloud Console, go to the BigQuery section. Create a new dataset within your project. A dataset is a container that holds your tables, and you can create it by clicking on "Create Dataset" and filling in the required details such as dataset ID, data location, and any expiration settings.
Within the BigQuery interface, select your newly created dataset. Click on "Create Table" and choose the "Upload" option. Select your prepared CSV file from local storage. Specify the file format as CSV. Configure the schema by either allowing BigQuery to auto-detect or by manually entering the field names and types if your data requires specific configurations.
During the upload process, ensure you review the schema settings. Double-check that data types are correctly interpreted (e.g., INTEGER, STRING, DATE). Set any additional table options such as partitioning or clustering, which can help optimize query performance.
Once the data is uploaded, run a few sample queries to verify that the data was imported correctly. You can use the BigQuery query editor to write SQL queries that check data integrity and perform basic analysis. Ensure everything is working as expected and that the data matches what you exported from GoCardless.
By following these steps, you can manually transfer data from GoCardless to BigQuery without using 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.
Gocardless is an online tool that collects direct bank payments on behalf of other businesses and It was founded in January 2011. GoCardless is an online Direct Debit supplier with a secure set-up process that permits the customer to pay both easily and safely. We ask all our customers to sign up to gain a streamlined payment procedure whereby the amount is automatically debited from the account provided every month. GoCardless is aims at becoming the world's bank payment network.
GoCardless's API provides access to a wide range of data related to payments and customers. The following are the categories of data that can be accessed through the API:
1. Payment data: This includes information about payments made by customers, such as the amount, currency, status, and date of payment.
2. Customer data: This includes information about customers, such as their name, email address, phone number, and billing address.
3. Subscription data: This includes information about subscriptions, such as the amount, frequency, and start and end dates.
4. Mandate data: This includes information about mandates, which are the authorizations given by customers to allow GoCardless to collect payments from their bank accounts.
5. Bank account data: This includes information about the bank accounts used by customers to make payments, such as the account number, sort code, and bank name.
6. Refund data: This includes information about refunds issued to customers, such as the amount, currency, and date of refund.
7. Dispute data: This includes information about disputes raised by customers, such as the reason for the dispute and the status of the dispute resolution process.
Overall, GoCardless's API provides comprehensive access to data related to payments and customers, enabling businesses to manage their payment processes more efficiently and 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:
