Summarize this article with:


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 setting up access to the Plaid API. You will need to create a Plaid developer account and register your application to receive your client ID and secret. These credentials will allow you to authenticate and interact with the Plaid API. Make sure to save these credentials securely.
Use the client ID and secret to authenticate with the Plaid API. Start by obtaining a public token through the Plaid Link flow, which can be exchanged for an access token. The access token will authorize you to access user financial data. Follow the Plaid documentation to accomplish this using secure API calls.
With the access token, use Plaid's API endpoints to fetch the necessary financial data. This data could include transactions, account balances, or other financial information available through Plaid. Utilize HTTP GET requests to retrieve JSON-formatted data from endpoints like `/transactions/get` or `/accounts/balance/get`.
Once the data is fetched from Plaid, transform it into a format suitable for ClickHouse ingestion. This may involve converting JSON data into CSV or another structured format, and performing any necessary data cleaning or transformation tasks, such as renaming fields or changing data types to align with your ClickHouse schema.
Install and configure the ClickHouse client on your local machine or server. This involves downloading the ClickHouse package and setting up the client to connect to your ClickHouse server. Ensure that you have network access and appropriate permissions to write data to the ClickHouse instance.
Define and create a table in ClickHouse to store the fetched data. Use SQL commands through the ClickHouse client to create a table that matches the data structure you prepared in the previous step. Ensure that the data types and column names align with the transformed data to facilitate smooth data insertion.
Finally, load the prepared data into the ClickHouse table. This can be achieved by using the ClickHouse client to execute `INSERT` commands or by using the `clickhouse-client` tool with a command like `cat your_data_file.csv | clickhouse-client --query="INSERT INTO your_table FORMAT CSV"`. Monitor the insertion process for any errors and verify data integrity once the process is complete.
By following these steps, you can manually move data from Plaid to a ClickHouse warehouse 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.
Plaid is a technology platform that makes it possible for companies to develop digitally-enabled financial systems. It enables developers to build financial services and applications safely and easily for financial institutions of any size. Plaid powers many financial apps including Venmo, Betterment, Chime, and Dave, encrypting your data before sharing it with your chosen app to keep your connection secure.
Plaid's API provides access to a wide range of financial data, including:
1. Account Information: Plaid's API allows access to account information such as account balances, transaction history, and account holder details.
2. Transactions: Plaid's API provides access to transaction data, including transaction amounts, dates, and descriptions.
3. Investments: Plaid's API allows access to investment account data, including holdings, transactions, and performance metrics.
4. Loans: Plaid's API provides access to loan account data, including loan balances, payment history, and interest rates.
5. Identity Verification: Plaid's API allows for identity verification through bank account information, including name, address, and account ownership.
6. Authentication: Plaid's API provides authentication services to verify account ownership and prevent fraud.
7. Payment Initiation: Plaid's API allows for payment initiation through bank accounts, enabling users to make payments directly from their accounts.
Overall, Plaid's API provides a comprehensive suite of financial data services that can be used by developers to build innovative financial 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:





