How to load data from Plaid to Databricks Lakehouse

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

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Set up a Plaid connector in Airbyte

Connect to Plaid or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Databricks Lakehouse for your extracted Plaid data

Select Databricks Lakehouse where you want to import data from your Plaid source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Plaid to Databricks Lakehouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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TL;DR

set up Plaid as a source connector (using Auth, or usually an API key)
set up Databricks Lakehouse as a destination connector
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 Databricks Lakehouse

Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.

Integrate Plaid with Databricks Lakehouse in minutes

Try for free now

Prerequisites

A Plaid account to transfer your customer data automatically from.
A Databricks Lakehouse account.
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 Databricks Lakehouse, for seamless data migration.

When using Airbyte to move data from Plaid to Databricks Lakehouse, it extracts data from Plaid using the source connector, converts it into a format Databricks Lakehouse can ingest using the provided schema, and then loads it into Databricks Lakehouse via the destination connector. This allows businesses to leverage their Plaid data for advanced analytics and insights within Databricks Lakehouse, 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 Databricks Lakehouse as a destination connector

1. First, navigate to the Airbyte website and log in to your account.
2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.
3. Scroll down until you find the "Databricks Lakehouse" connector and click on it.
4. You will be prompted to enter your Databricks Lakehouse credentials, including your account name, personal access token, and workspace ID.
5. Once you have entered your credentials, click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Save" button to save your Databricks Lakehouse destination connector settings.
7. You can now use the Databricks Lakehouse connector to transfer data from your source connectors to your Databricks Lakehouse destination.
8. To set up a data transfer, navigate to the "Sources" tab and select the source connector that you want to use.
9. Follow the prompts to enter your source connector credentials and configure your data transfer settings.
10. Once you have configured your source connector, select the Databricks Lakehouse connector as your destination and follow the prompts to configure your data transfer settings.
11. Click on the "Run" button to initiate the data transfer.

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

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

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Plaid from the dropdown list of your configured sources.
Select your destination: Choose Databricks Lakehouse from the dropdown list of your configured destinations.
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.
Select the data to sync: Choose the specific Plaid objects you want to import data from towards Databricks Lakehouse. You can sync all data or select specific tables and fields.
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.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Plaid to Databricks Lakehouse according to your settings.

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

Use Cases to transfer your Plaid data to Databricks Lakehouse

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

Advanced Analytics: Databricks Lakehouse’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.
Data Consolidation: If you're using multiple other sources along with Plaid, syncing to Databricks Lakehouse 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.
Historical Data Analysis: Plaid has limits on historical data. Syncing data to Databricks Lakehouse allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Databricks Lakehouse provides robust data security features. Syncing Plaid data to Databricks Lakehouse ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Databricks Lakehouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Plaid data.
Data Science and Machine Learning: By having Plaid data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Plaid provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Databricks Lakehouse, providing more advanced business intelligence options. If you have a Plaid table that needs to be converted to a Databricks Lakehouse table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Plaid account as an Airbyte data source connector.
Configure Databricks Lakehouse as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Plaid to Databricks Lakehouse 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!

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How to Sync Plaid to Databricks Lakehouse Manually

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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.

What is Plaid?

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.

How do I transfer data from Plaid?

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 to Databricks Lakehouse as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Plaid to Databricks Lakehouse and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

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.

Back to all tutorials

Warehouses and Lakes

Finance & Ops Analytics

How to load data from Plaid to Databricks Lakehouse

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

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is Plaid

What is Databricks Lakehouse

Prerequisites

Step 1: Setup Plaid

Step 2: Setup Databricks Lakehouse

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Plaid and Databricks Lakehouse

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Plaid as a source connector (using Auth, or usually an API key)
set up Databricks Lakehouse as a destination connector
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

What is Databricks Lakehouse

Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.

Integrate Plaid with Databricks Lakehouse in minutes

Try for free now

Prerequisites

A Plaid account to transfer your customer data automatically from.
A Databricks Lakehouse account.
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.

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.

Step 2: Set up Databricks Lakehouse as a destination connector

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

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

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Plaid from the dropdown list of your configured sources.
Select your destination: Choose Databricks Lakehouse from the dropdown list of your configured destinations.
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.
Select the data to sync: Choose the specific Plaid objects you want to import data from towards Databricks Lakehouse. You can sync all data or select specific tables and fields.
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.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Plaid to Databricks Lakehouse according to your settings.

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

Use Cases to transfer your Plaid data to Databricks Lakehouse

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

Advanced Analytics: Databricks Lakehouse’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.
Data Consolidation: If you're using multiple other sources along with Plaid, syncing to Databricks Lakehouse 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.
Historical Data Analysis: Plaid has limits on historical data. Syncing data to Databricks Lakehouse allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Databricks Lakehouse provides robust data security features. Syncing Plaid data to Databricks Lakehouse ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Databricks Lakehouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Plaid data.
Data Science and Machine Learning: By having Plaid data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Plaid provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Databricks Lakehouse, providing more advanced business intelligence options. If you have a Plaid table that needs to be converted to a Databricks Lakehouse table, Airbyte can do that automatically.