How to load data from Workable to BigQuery

Learn how to use Airbyte to synchronize your Workable data into BigQuery within minutes.

Try Airbyte Cloud Manual method (the hard way)

Building your pipeline or Using Airbyte

Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.

Building in-house pipelines

Bespoke pipelines are:

Inconsistent and inaccurate data
Laborious and expensive
Brittle and inflexible

Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

After Airbyte

Airbyte connections are:

Reliable and accurate
Extensible and scalable for all your needs
Deployed and governed your way

All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Workable connector in Airbyte

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

Set up BigQuery for your extracted Workable 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 Workable to BigQuery 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.

Try Airbyte Cloud Manual method (the hard way)

Take a virtual tour

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

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Talk to our team

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

Simple & Easy to use Interface

Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.

Guided Tour: Assisting you in building connections

Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.

Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes

Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.

Try Airbyte Cloud Manual method (the hard way)

What sets Airbyte Apart

Modern GenAI Workflows

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.

Move Large Volumes, Fast

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.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

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.

Fully Featured & Integrated

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.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

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Chase Zieman

Chief Data Officer

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

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Rupak Patel

Operational Intelligence Manager

"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."

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How to Sync to Manually

Step 1: Export Data from Workable

Begin by logging into your Workable account. Navigate to the reports or data export section, and select the data set you wish to export. Most platforms, including Workable, offer an option to export data in CSV format. Export the data and download the CSV file to your local machine.

Step 2: Prepare Your Data for BigQuery

Open the CSV file in a spreadsheet editor like Microsoft Excel or Google Sheets. Review the data for any inconsistencies, such as missing values or incorrect data types. Ensure the column names are clear and suitable for use as field names in BigQuery (avoid spaces and special characters).

Step 3: Set Up Google Cloud Platform (GCP) Account

If you haven’t already, set up a Google Cloud Platform account. Navigate to the Google Cloud Console, create a new project, and ensure that BigQuery is enabled for your project. If BigQuery is not enabled, you can do so by navigating to the 'API & Services' section and enabling BigQuery API.

Step 4: Create a BigQuery Dataset

In the Google Cloud Console, go to the BigQuery section. Click on your project name in the Explorer panel and select "Create Dataset." Provide a name for your dataset and configure any location or expiration settings as needed. This dataset will serve as a container for your tables.

Step 5: Convert CSV to Compatible Format

Ensure your CSV file is in a format compatible with BigQuery. BigQuery can directly import CSV files, but they must adhere to specific formatting, such as using UTF-8 encoding. Check your CSV file’s encoding and update it if necessary using a text editor or tool that supports encoding changes.

Step 6: Upload and Import Data into BigQuery

In the BigQuery console, click on your dataset and select "Create Table." Choose "Upload" as the source and select your CSV file. Configure the file format as "CSV" and customize any schema options, like field names and data types. Click "Create Table" to start the import process. Monitor the job status to ensure the data is imported successfully.

Step 7: Verify and Query Data in BigQuery

Once the import process is complete, navigate to your dataset and select the new table. Use the BigQuery SQL workspace to run some basic queries to verify that the data has been imported correctly. Check for correct data types, field names, and data integrity. This validation ensures that your data is ready for analysis or further processing within BigQuery.

By following these steps, you will successfully move data from Workable to BigQuery without relying on third-party connectors or integrations.