How to load data from Harvest to Databricks Lakehouse

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Harvest connector in Airbyte

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

Set up Databricks Lakehouse for your extracted Harvest data

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

Configure the Harvest 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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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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How to Sync to Manually

Step 1: Export Data from Harvest

Begin by exporting your data from Harvest. Log into your Harvest account, navigate to the "Reports" section, and select the data you wish to export (e.g., time entries, projects, invoices). Use the available export options to download the data in CSV format, as this is a widely compatible format for data transfer.

Set up a local environment where you will temporarily store and process the CSV files. Ensure you have adequate storage space on your local machine or server, and organize the files in a systematic folder structure to facilitate easy access and processing later.

Log into your Databricks account and create a new workspace if you don't have one already. Ensure that you have the necessary permissions to create clusters and upload data. Familiarize yourself with the Databricks interface, especially the data upload and cluster management features.

Navigate to the "Data" tab in your Databricks workspace. Use the "Upload Data" option to transfer your CSV files from your local environment to Databricks. Choose an appropriate directory in the Databricks File System (DBFS) to store these files, ensuring they are organized for easy access during processing.

Set up a new Databricks cluster. Choose an appropriate cluster configuration based on your processing needs and budget, taking into account factors like the size of the data and the complexity of any transformations you might perform. Ensure that the cluster has access to the uploaded CSV files in DBFS.

Use Databricks notebooks to read the CSV files into Spark DataFrames. Perform any necessary data transformations or cleaning operations using PySpark, Scala, or SQL as required. Once the data is prepared, write the transformed data into the Databricks Lakehouse using the Delta Lake format, which supports ACID transactions and efficient data queries.

After loading the data into the Databricks Lakehouse, perform checks to ensure data integrity and accuracy. Run queries to verify that the data is complete and correctly transformed. Additionally, create documentation or data catalogs as necessary to facilitate future data access and utilization by other team members or systems.

By following these steps, you can efficiently move data from Harvest to the Databricks Lakehouse without relying on third-party connectors or integrations.