How to load data from Harvest to Weaviate

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

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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.
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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 Weaviate 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 Weaviate 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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Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

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Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

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How to Sync to Manually

Step 1: Export Data from Harvest

Begin by logging into your Harvest account. Navigate to the section where your data is stored (e.g., timesheets, projects, expenses). Use the export feature to download the data as a CSV file. This is typically found under a "Reports" or "Export" tab. Save the CSV file to your local machine for processing.

Open the exported CSV file using a spreadsheet application or a text editor. Examine the structure of the data to understand the fields and entries. Clean and format the data as necessary, ensuring it is ready for import into Weaviate. This may involve removing unnecessary columns, renaming headers to match your schema in Weaviate, and ensuring consistent data types.

On your local machine, set up a programming environment capable of interacting with both CSV files and the Weaviate API. Ensure you have Python installed along with libraries such as `pandas` for data handling and `requests` for API interactions. This setup will allow you to manipulate data and communicate with Weaviate.

Before importing data, define or review the schema in your Weaviate instance. This step involves setting up classes and properties that correspond to the data you're importing. Use Weaviate's schema API to create or update the schema as necessary. Ensure that it accommodates the structure of your Harvest data.

Write a Python script to read the CSV file using `pandas`. Transform the data into JSON format suitable for Weaviate's data import requirements. This involves converting each row into a JSON object that matches the schema defined in Weaviate. Pay attention to data types and relationships between different data points.

Using your Python script, establish a connection to your Weaviate instance. Authenticate using an API key or other credentials as required by your Weaviate setup. Ensure your connection is secure and that you have the necessary permissions to import data.

With your connection established and data transformed, use the Weaviate RESTful API to upload the data. Iterate over the JSON objects created in the previous step, sending them to the appropriate endpoint in Weaviate. Handle any errors that arise during this process, and verify the data import by checking the Weaviate instance for accuracy and completeness.

By following these steps, you can move data from Harvest to Weaviate without the need for third-party connectors or integrations.