How to load data from PartnerStack to Clickhouse

Learn how to use Airbyte to synchronize your PartnerStack data into Clickhouse within minutes.

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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 PartnerStack connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Clickhouse for your extracted PartnerStack 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 PartnerStack to Clickhouse 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.

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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.

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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.

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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

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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.”

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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: 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.

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.

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.

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.

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.

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.

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.