Warehouses and Lakes

How to load data from CSV File to Databricks Lakehouse

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


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 CSV File as a source connector (using Auth, or usually an API key)
  2. set up Databricks Lakehouse 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 CSV File

A CSV (Comma Separated Values) file is a type of plain text file that stores tabular data in a structured format. Each line in the file represents a row of data, and each value within a row is separated by a comma. CSV files are commonly used for exchanging data between different software applications, such as spreadsheets and databases. They are also used for importing and exporting data from web applications and for data analysis. CSV files can be easily opened and edited in any text editor or spreadsheet software, making them a popular choice for data storage and transfer.

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.


  1. A CSV File account to transfer your customer data automatically from.
  2. A Databricks Lakehouse 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 CSV File and Databricks Lakehouse, for seamless data migration.

When using Airbyte to move data from CSV File to Databricks Lakehouse, it extracts data from CSV File 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 CSV File data for advanced analytics and insights within Databricks Lakehouse, simplifying the ETL process and saving significant time and resources.

Step 1: Set up CSV File as a source connector

1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "CSV File" source connector and select "Create new connection."
3. Enter a name for your connection and click "Next."
4. In the "Configuration" tab, select the CSV file you want to connect to by clicking on the "Choose File" button and selecting the file from your local machine.
5. In the "Schema" tab, you can customize the schema of your data by selecting the appropriate data types for each column.
6. In the "Credentials" tab, enter the necessary credentials to access your CSV file. This may include a username and password or other authentication details.
7. Once you have entered your credentials, click "Test Connection" to ensure that Airbyte can successfully connect to your CSV file.
8. If the connection is successful, click "Create Connection" to save your settings and start syncing your data.
9. You can monitor the progress of your sync in the "Connections" tab and view your data in the "Destinations" tab.

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 CSV File data to Databricks Lakehouse

Once you've successfully connected CSV File 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:

  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 CSV File from the dropdown list of your configured sources.
  3. Select your destination: Choose Databricks Lakehouse 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 CSV File objects you want to import data from towards Databricks Lakehouse. 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 CSV File 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 CSV File data.

Use Cases to transfer your CSV File data to Databricks Lakehouse

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

  1. Advanced Analytics: Databricks Lakehouse’s powerful data processing capabilities enable you to perform complex queries and data analysis on your CSV File data, extracting insights that wouldn't be possible within CSV File alone.
  2. Data Consolidation: If you're using multiple other sources along with CSV File, syncing to Databricks Lakehouse allows you to centralize your data for a holistic view of your operations
  3. Historical Data Analysis: CSV File has limits on historical data. Syncing data to Databricks Lakehouse allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: Databricks Lakehouse provides robust data security features. Syncing CSV File data to Databricks Lakehouse ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: Databricks Lakehouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding CSV File data.
  6. Data Science and Machine Learning: By having CSV File data in Databricks Lakehouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While CSV File provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Databricks Lakehouse, providing more advanced business intelligence options.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a CSV File account as an Airbyte data source connector.
  2. Configure Databricks Lakehouse as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from CSV File 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!

Frequently Asked Questions

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 data can you extract from CSV File?

CSV File gives access to various types of data in a structured format that can be easily integrated into various applications and systems.

What data can you transfer to Databricks Lakehouse?

You can transfer a wide variety of data to Databricks Lakehouse. 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 CSV File to Databricks Lakehouse?

The most prominent ETL tools to transfer data from CSV File to Databricks Lakehouse include:

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

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

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.