How to load data from ClickHouse to Convex
Learn how to use Airbyte to synchronize your ClickHouse data into Convex 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: Set Up Environment
Begin by setting up your environment with access to both ClickHouse and Convex. Ensure you have administrative access to both systems, as you'll need to execute queries on ClickHouse and insert data into Convex.
Step 2: Export Data from ClickHouse
Use the ClickHouse command-line interface or a similar tool to export your data. You can execute a SQL query to select the data you need and export it to a CSV or JSON file. For example:
```sql
SELECT FROM your_table INTO OUTFILE 'data.csv' FORMAT CSV;
```
This command saves the data as a CSV file, which is easy to process.
Step 3: Prepare Data for Import
Inspect the exported CSV or JSON file to ensure data integrity and compatibility with Convex's data model. Make any necessary transformations or cleanups. You might need to handle data types or format issues to match Convex's schema requirements.
Step 4: Install Convex CLI
Install the Convex CLI tool if it's not already installed. This CLI will be used to manage your Convex database and import data. Follow the official Convex documentation to install and configure the CLI on your system.
Step 5: Create Convex Table
Log in to your Convex account and create a table that matches the structure of your ClickHouse data. Use the Convex CLI or dashboard to define the schema, ensuring all fields are correctly specified. This might involve defining data types, primary keys, and other constraints.
Step 6: Write a Script for Data Transfer
Develop a script, using a language like Python, Node.js, or similar, to read the exported data file and insert records into Convex. Use Convex's REST API or client libraries to programmatically insert each record. Here's an example using Python:
```python
import csv
import requests
url = 'https://your-convex-instance.convex.dev/tables/your_table'
headers = {'Authorization': 'Bearer YOUR_API_KEY'}
with open('data.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
response = requests.post(url, json=row, headers=headers)
if response.status_code != 200:
print(f"Failed to insert row: {row}")
```
Make sure to replace `'YOUR_API_KEY'` and `'your_table'` with actual values.
Step 7: Verify Data Integrity
After the data transfer, verify that all records were successfully moved to Convex. Use the Convex dashboard or execute queries to ensure the data matches what was in ClickHouse. Check for missing or malformed data and address any discrepancies by re-running the script or manually correcting errors.
By following these steps, you will successfully move data from ClickHouse to Convex without relying on third-party connectors or integrations.