How to load data from PostHog to BigQuery
Learn how to use Airbyte to synchronize your PostHog 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 PostHog
Begin by exporting the data you need from PostHog. Navigate to your PostHog dashboard, and use the available export options to download the data as a CSV file. This is typically done through the "Export" feature within the UI, where you can specify the type of events and the date range you wish to export.
Step 2: Prepare Your Google Cloud Project
Ensure you have a Google Cloud project set up. If not, create a new project in the Google Cloud Console. Make sure billing is enabled, as BigQuery services require billing to be active. Also, ensure you have the necessary permissions to create datasets and tables within BigQuery.
Step 3: Create a BigQuery Dataset
In the Google Cloud Console, navigate to the BigQuery section. Create a new dataset that will contain the tables for your PostHog data. A dataset serves as a container for your tables and helps organize your data.
Step 4: Design Table Schema in BigQuery
Before loading data, define the schema for the table that will store your PostHog data. The schema should correspond to the structure of your exported CSV file, including field names and data types. You can do this in the BigQuery web UI by selecting the dataset you created, then clicking "Create Table," and specifying the schema details manually.
Step 5: Upload CSV to Google Cloud Storage
Upload the exported CSV file to a Google Cloud Storage (GCS) bucket. This can be done through the Google Cloud Console's Storage section. Create a new bucket if necessary, and upload the CSV file using the "Upload Files" option. The CSV file in GCS will be used as the source file for loading data into BigQuery.
Step 6: Load Data into BigQuery
With the CSV file in GCS, navigate back to the BigQuery section of the Google Cloud Console. Select your dataset and click "Create Table." Choose "Google Cloud Storage" as the source, and specify the path to your CSV file in the GCS bucket. Ensure you select the correct schema you defined earlier, configure any additional options like data format, and load the data into the table.
Step 7: Verify Data Integrity
After loading the data, verify the integrity and correctness of the data in BigQuery. Run basic SQL queries to check row counts, data types, and spot-check some of the entries to ensure they match those originally exported from PostHog. This step ensures that the data transfer was successful and that no discrepancies exist between the source and destination.
By following these steps, you can successfully move data from PostHog to BigQuery without utilizing third-party connectors or integrations.