How to load data from PartnerStack to Clickhouse
Learn how to use Airbyte to synchronize your PartnerStack data into Clickhouse 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: Understand PartnerStack API
Start by reviewing the PartnerStack API documentation to understand how to authenticate and access the data you need. Ensure you have the necessary API keys or credentials to access the PartnerStack API.
Step 2: Extract Data from PartnerStack
Use a programming language such as Python to make API requests to PartnerStack. Utilize libraries like `requests` to send HTTP GET requests to the relevant endpoints. Ensure you handle pagination if the dataset is large, and filter the data as needed.
Step 3: Transform Data to ClickHouse Format
Once you have the data extracted, transform it into a format compatible with ClickHouse. This might include converting data types or structuring JSON data into a tabular format. Use Python’s pandas library to facilitate this transformation process.
Step 4: Prepare ClickHouse Environment
Set up your ClickHouse environment by creating the necessary database and table structures. Use SQL commands in ClickHouse to define tables that match the schema of your transformed data.
Step 5: Load Data into ClickHouse
Use ClickHouse's HTTP interface to load data directly into the database. Convert your transformed data into a CSV format if necessary, and use HTTP POST requests with `curl` or Python’s `requests` library to insert data into ClickHouse.
Step 6: Validate Data Integrity
Once the data is loaded, perform validation checks to ensure data integrity. Run SQL queries in ClickHouse to verify record counts, data types, and any other integrity constraints. Compare these results against your source data from PartnerStack.
Step 7: Automate the Process
After successfully transferring and validating the data, automate the process using a script. Schedule this script to run at regular intervals using a cron job (Linux) or Task Scheduler (Windows) to ensure your ClickHouse data warehouse is regularly updated with the latest data from PartnerStack.
By following these steps, you can manually transfer data from PartnerStack to ClickHouse without relying on third-party connectors or integrations.