How to load data from Orb to BigQuery

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

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Orb 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 Orb 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 Orb 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.
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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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Tech Lead at Symend

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Chase Zieman

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“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 ORB

Begin by exporting the data from your ORB system. Access the ORB interface and locate the export feature, which typically allows you to download your data in a common format such as CSV, JSON, or XML. Choose the format that best suits your data structure and download the file to your local machine.

Before importing the data into BigQuery, ensure it is clean and well-structured. Open the exported files and perform any necessary data cleaning, such as removing duplicates, handling null values, and ensuring consistent data types. This step is crucial to prevent errors during the import process.

If you haven't already, create a Google Cloud Project. Go to the Google Cloud Console, click on the project dropdown menu, and select "New Project." Fill in the required details, such as project name and billing account. Once created, ensure that BigQuery API is enabled in your project settings.

Use Google Cloud Storage (GCS) as a staging area for your data before loading it into BigQuery. Navigate to the GCS console, create a new bucket, and upload your cleaned data files. Ensure the bucket is located in the same region as your BigQuery dataset to optimize performance and reduce costs.

In the BigQuery console, create a new dataset where your data will reside. Click on the "Create Dataset" button, provide a dataset ID, choose a data location (preferably the same as your GCS bucket), and configure any dataset-level settings such as expiration date and access controls.

With your data in GCS, proceed to load it into BigQuery. In the BigQuery console, select your dataset and click on "Create Table." Choose "Google Cloud Storage" as the source, select your data file from the bucket, and configure the table schema. Choose appropriate data types for each column, and specify options like write preference (append, overwrite, etc.).

Once the data load is complete, validate the data in BigQuery to ensure it matches your expectations. Run queries to check data integrity, count records, and verify data types. This step confirms that the data transfer was successful and that the data is ready for analysis or further processing in BigQuery.