How to load data from Looker to BigQuery

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

Try Airbyte CloudManual 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 Looker 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 Looker 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 Looker 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.

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 CloudManual 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

Learn more
Chase Zieman headshot

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.”

Learn more

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."

Learn more

How to Sync to Manually

Step 1: Prepare the Data in Looker

1. Create a Look or Explore: Identify the data you want to export from Looker. You can create a new Look or use an existing one that contains the data you need.

2. Run the Query: Execute the query in Looker to ensure it returns the desired data. Make any necessary adjustments to the query to get the correct dataset.

3. Export the Data: Once you are satisfied with the dataset, export the data from Looker. You can usually do this by clicking on the "Gear" icon or "Download" button and selecting the option to export the results, typically in a CSV or TXT format. Choose a format that is compatible with BigQuery data import requirements.

1. Clean the Data: If necessary, clean the data using a text editor or a script to ensure it meets BigQuery's data formatting requirements, such as proper delimiters, escaping, and encoding.

2. Data Schema: Define the schema that matches the data you've exported from Looker. You'll need this schema to create a table in BigQuery or to ensure the data aligns with an existing table's schema.

1. Create a GCS Bucket: If you don't already have one, create a new Google Cloud Storage bucket in your Google Cloud project where you can upload the exported data file.

2. Upload the File: Upload the data file to the newly created GCS bucket. You can use the Google Cloud Console, `gsutil`, or the Google Cloud Storage API to upload the file.

1. Create a BigQuery Dataset: If you haven't already, create a new dataset in BigQuery where your new table will reside.

2. Create a BigQuery Table: Create a table in BigQuery with the appropriate schema that matches the data you're importing. You can do this in the BigQuery UI or using the `bq` command-line tool.

3. Load Data into the Table: Import the data from the GCS bucket into your BigQuery table. You can do this through the BigQuery UI by selecting your dataset, clicking on "Create Table," and specifying the source as your GCS file. Alternatively, you can use the `bq` command-line tool to load the data:

‍
bq load --source_format=CSV --autodetect \
mydataset.mytable gs://mybucket/mydata.csv

Replace `mydataset`, `mytable`, `mybucket`, and `mydata.csv` with your dataset name, table name, GCS bucket name, and filename, respectively. The `--autodetect` flag is optional and instructs BigQuery to automatically detect the schema.

1. Check the Data: After loading the data into BigQuery, run some queries to ensure that the data has been imported correctly and completely.

2. Data Validation: Compare the results of a few queries in BigQuery with the data you see in Looker to ensure consistency.

1. Remove Temporary Files: After verifying the data integrity, you can delete the exported data file from the GCS bucket to avoid incurring storage costs.

2. Documentation: Document the process, including the schema and any transformations applied to the data, for future reference or for other team members.

By following these steps, you should be able to move data from Looker to BigQuery without using third-party connectors or integrations. Remember to handle sensitive data with care and ensure that you comply with all relevant data protection regulations.