How to load data from Workable to Weaviate
Learn how to use Airbyte to synchronize your Workable data into Weaviate within minutes.
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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 secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
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"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
How to Sync to Manually
Step 1: Export Data from Workable
Begin by logging into your Workable account. Navigate to the section where your data is stored, such as candidate profiles or job postings. Use the export feature to download the data you need. Typically, this data can be exported in CSV or Excel format. Ensure that you export all relevant fields that you want to transfer to Weaviate.
Step 2: Prepare Data for Import
Open the exported data file in a spreadsheet application like Microsoft Excel or Google Sheets. Clean and structure the data to match Weaviate's schema requirements. This includes ensuring that each column corresponds to a particular property or attribute in Weaviate. Validate data types (e.g., text, numbers, dates) and remove any unnecessary columns or rows.
Step 3: Set Up a Weaviate Instance
If you haven’t already, set up a Weaviate instance. You can do this by deploying Weaviate on a server or using a cloud service. Follow the Weaviate documentation to install and configure your instance. Make sure it is accessible and ready to receive data.
Step 4: Define a Weaviate Schema
In your Weaviate instance, define a schema that matches the structure of your data. This involves creating classes and properties that correspond to the columns in your prepared data file. Use the Weaviate RESTful API or the Weaviate Console to create and configure the schema.
Step 5: Convert Data to JSON Format
Convert your cleaned and structured data into JSON format, as Weaviate accepts data in this format for import. Each row of your data should be converted into a JSON object, with keys corresponding to the property names defined in your Weaviate schema. You can write a script in Python or another language to automate this conversion.
Step 6: Import Data to Weaviate
Use the Weaviate API to upload your JSON data to your Weaviate instance. This can be done using HTTP POST requests to the Weaviate data endpoint. You may need to write a script to loop through your JSON objects and send them individually or in batches. Ensure that your API requests are correctly authenticated if required.
Step 7: Verify Data Integrity
After importing, verify that the data in Weaviate matches your original dataset. Use Weaviate's search and query capabilities to check that all records are present and correctly structured. Validate that all relationships and attributes are properly set. If discrepancies are found, review your data conversion and import steps, and make necessary corrections.