How to load data from Ashby to BigQuery

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

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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 Ashby 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 Ashby 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 Ashby 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 CloudManual 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.

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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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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.

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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.

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.

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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 Ashby

Begin by exporting the data you need from Ashby. Depending on the options available, you can typically export data in formats such as CSV or JSON. Navigate to the data export section of Ashby, select the required datasets, and choose an appropriate format for export. Save the exported file(s) locally on your machine.

Open the exported file(s) to review and clean the data. Ensure there are no corrupted entries or unnecessary columns that could affect the import process into BigQuery. This step is crucial for ensuring data integrity and simplifying data transformation later.

Set up your Google Cloud Platform (GCP) environment if you haven't already. Ensure you have a Google Cloud account and have created a project where you plan to import the data. Activate the BigQuery API within your project through the GCP Console.

Use Google Cloud Storage as an intermediary to move your data to BigQuery. Navigate to the GCS section in your GCP Console, create a new bucket (if necessary), and upload your cleaned data files to this bucket. Ensure that the bucket's permissions allow BigQuery to access the files.

In the BigQuery section of the GCP Console, create a new dataset where you intend to store your imported data. A dataset in BigQuery acts as a container for your tables, and you can configure it with specific permissions and settings as needed.

Utilize the BigQuery UI or the command-line tool to load your data from Google Cloud Storage into BigQuery. You will need to specify the source data format (e.g., CSV or JSON), the schema for your BigQuery table, and the GCS file path. Use the BigQuery import wizard to map the data fields appropriately.

Once the data loading process is complete, verify the imported data within BigQuery. Run queries to ensure data accuracy and completeness, checking that all fields are correctly populated and that no data is missing. Validate that the data types and structures align with your expectations and make adjustments as necessary.

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