How to load data from Drift to BigQuery
Learn how to use Airbyte to synchronize your Drift data into BigQuery within minutes.
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
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
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.
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
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
Chase Zieman
“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.”
Rupak Patel
"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 Drift
Begin by logging into your Drift account and navigating to the data or reports section. Use Drift's export functionality to download the data you need. Typically, this will be in a CSV or Excel format. Ensure that you export all necessary fields and that the data is structured correctly for your needs.
Step 2: Prepare the Data for Upload
After exporting the data from Drift, review the file to ensure it's clean and formatted correctly. Open the CSV in a spreadsheet application like Excel or Google Sheets to check for any inconsistencies, such as missing data, incorrect data types, or formatting errors. Clean the data as necessary to ensure it adheres to the schema you plan to use in BigQuery.
Step 3: Create a BigQuery Dataset
Log in to your Google Cloud Platform account and navigate to the BigQuery console. Create a new dataset in your project to store the Drift data. This dataset acts as a container for your tables and must be named according to your organization's naming conventions.
Step 4: Define Table Schema in BigQuery
In the BigQuery console, within the newly created dataset, create a table to hold the Drift data. During table creation, define the schema by specifying the column names and data types that match your cleaned CSV file. Ensure data types in BigQuery (e.g., STRING, INTEGER, TIMESTAMP) align with the CSV data to prevent errors during data import.
Step 5: Upload Data to Google Cloud Storage
Before loading data into BigQuery, upload your cleaned CSV file to Google Cloud Storage (GCS). Navigate to the GCS console and create a bucket if you don't have one. Upload the CSV file into this bucket. Remember the file path as it will be required for the next step.
Step 6: Load Data into BigQuery from GCS
Return to the BigQuery console and use the load data feature to import the CSV file from Google Cloud Storage into your BigQuery table. Specify the GCS file path, select the correct file format (CSV), and ensure the schema matches the table definition. Execute the load job and monitor for any errors.
Step 7: Verify Data Integrity in BigQuery
Once the data is loaded, run queries in the BigQuery console to verify that the data has been imported correctly. Check for completeness, data type accuracy, and overall integrity by comparing the results with the original data file from Drift. Make any necessary adjustments by re-uploading the data if errors are found.
By following these steps, you can manually transfer data from Drift to BigQuery without relying on third-party connectors or integrations.