Databases
Finance & Ops Analytics

How to load data from Plaid to Kafka

Learn how to use Airbyte to synchronize your Plaid data into Kafka within minutes.

TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

  1. set up Plaid as a source connector (using Auth, or usually an API key)
  2. set up Kafka as a destination connector
  3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Plaid

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.

What is Kafka

A communication solutions agency, Kafka is a cloud-based / on-prem distributed system offering social media services, public relations, and events. For event streaming, three main functionalities are available: the ability to (1) subscribe to (read) and publish (write) streams of events, (2) store streams of events indefinitely, durably, and reliably, and (3) process streams of events in either real-time or retrospectively. Kafka offers these capabilities in a secure, highly scalable, and elastic manner.

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Prerequisites

  1. A Plaid account to transfer your customer data automatically from.
  2. A Kafka account.
  3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Plaid and Kafka, for seamless data migration.

When using Airbyte to move data from Plaid to Kafka, it extracts data from Plaid using the source connector, converts it into a format Kafka can ingest using the provided schema, and then loads it into Kafka via the destination connector. This allows businesses to leverage their Plaid data for advanced analytics and insights within Kafka, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Plaid as a source connector

1. First, navigate to the Plaid website and create an account if you haven't already. Once you have an account, log in and navigate to the "API" section of the dashboard.

2. In the API section, click on "Credentials" and then "Create a new set of credentials." Follow the prompts to select the appropriate product and environment for your use case.

3. Once you have created your credentials, you will be provided with a "client ID," "secret," and "public key." Copy these values and keep them handy.

4. Next, navigate to your Airbyte dashboard and click on "Sources" in the left-hand menu. Then, click on "Create a new source" and select "Plaid" from the list of available connectors.

5. In the Plaid source configuration screen, enter a name for your source and paste in the client ID, secret, and public key that you copied from the Plaid dashboard.

6. Select the appropriate environment (sandbox or production) and enter the credentials for the Plaid account you want to connect to Airbyte.

7. Finally, click "Test connection" to ensure that Airbyte can successfully connect to your Plaid account. If the test is successful, click "Create source" to save your configuration and start syncing data.

Step 2: Set up Kafka as a destination connector

1. First, you need to have an Apache Kafka destination connector installed on your system. If you don't have it, you can download it from the Apache Kafka website.  
2. Once you have the Apache Kafka destination connector installed, you need to create a new connection in Airbyte. To do this, go to the Connections tab and click on the "New Connection" button.  3. In the "New Connection" window, select "Apache Kafka" as the destination connector and enter the required connection details, such as the Kafka broker URL, topic name, and authentication credentials.  
4. After entering the connection details, click on the "Test Connection" button to ensure that the connection is working properly.  
5. If the connection test is successful, click on the "Save" button to save the connection.  
6. Once the connection is saved, you can create a new pipeline in Airbyte and select the Apache Kafka destination connector as the destination for your data.  
7. In the pipeline configuration, select the connection you created in step 3 as the destination connection.  
8. Configure the pipeline to map the source data to the appropriate Kafka topic and fields.  
9. Once the pipeline is configured, you can run it to start sending data to your Apache Kafka destination.

Step 3: Set up a connection to sync your Plaid data to Kafka

Once you've successfully connected Plaid as a data source and Kafka as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Plaid from the dropdown list of your configured sources.
  3. Select your destination: Choose Kafka from the dropdown list of your configured destinations.
  4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
  5. Select the data to sync: Choose the specific Plaid objects you want to import data from towards Kafka. You can sync all data or select specific tables and fields.
  6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Plaid to Kafka according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Kafka data warehouse is always up-to-date with your Plaid data.

Use Cases to transfer your Plaid data to Kafka

Integrating data from Plaid to Kafka provides several benefits. Here are a few use cases:

  1. Advanced Analytics: Kafka’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Plaid data, extracting insights that wouldn't be possible within Plaid alone.
  2. Data Consolidation: If you're using multiple other sources along with Plaid, syncing to Kafka allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
  3. Historical Data Analysis: Plaid has limits on historical data. Syncing data to Kafka allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: Kafka provides robust data security features. Syncing Plaid data to Kafka ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: Kafka can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Plaid data.
  6. Data Science and Machine Learning: By having Plaid data in Kafka, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While Plaid provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Kafka, providing more advanced business intelligence options. If you have a Plaid table that needs to be converted to a Kafka table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Plaid account as an Airbyte data source connector.
  2. Configure Kafka as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Plaid to Kafka after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

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Connectors Used

What should you do next?

Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

flag icon
Easily address your data movement needs with Airbyte Cloud
Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
Get started with Airbyte for free
high five icon
Talk to a data infrastructure expert
Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
Talk to sales
stars sparkling
Improve your data infrastructure knowledge
Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
Subscribe to newsletter

Connectors Used

Frequently Asked Questions

What data can you extract from Plaid?

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 data can you transfer to Kafka?

You can transfer a wide variety of data to Kafka. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from Plaid to Kafka?

The most prominent ETL tools to transfer data from Plaid to Kafka include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from Plaid and various sources (APIs, databases, and more), transforming it efficiently, and loading it into Kafka and other databases, data warehouses and data lakes, enhancing data management capabilities.