How to load data from ClickHouse to Postgres destination
Learn how to use Airbyte to synchronize your ClickHouse data into Postgres destination 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 ClickHouse
Begin by exporting the data you want to transfer from ClickHouse. You can use the `clickhouse-client` command-line tool to export data into a CSV format. For example:
```bash
clickhouse-client --query="SELECT * FROM your_table" --format CSV > data.csv
```
This command exports the entire table `your_table` into a CSV file named `data.csv`. Adjust the query as needed to select specific columns or rows.
Step 2: Prepare the Postgres Database and Table
Before importing data into Postgres, ensure that the destination database and table are properly set up to receive the data. Use the Postgres `psql` command-line tool to create the necessary table, ensuring that the schema matches the data structure of the ClickHouse export.
```sql
CREATE TABLE your_table (
column1 datatype,
column2 datatype,
...
);
```
Adjust the column names and data types to match those in your CSV file.
Step 3: Transfer the CSV File to the Postgres Server
If your Postgres server is on a different machine, securely transfer the CSV file to the server. You can use tools like `scp` (Secure Copy Protocol) for this purpose:
```bash
scp data.csv user@postgres-server:/path/to/destination/
```
Replace `user`, `postgres-server`, and `/path/to/destination/` with the appropriate values for your setup.
Step 4: Set Up Necessary Permissions in Postgres
Ensure the Postgres user has the necessary permissions to perform the data import. You may need to grant additional privileges if your user does not already have them:
```sql
GRANT INSERT ON your_table TO your_user;
```
Step 5: Import Data into Postgres
Use the `COPY` command in Postgres to import the data from the CSV file into the target table. This can be done within the `psql` command-line tool:
```sql
COPY your_table FROM '/path/to/destination/data.csv' WITH (FORMAT CSV);
```
This command reads the CSV file and inserts its contents into `your_table`.
Step 6: Verify Data Integrity in Postgres
After the import, verify that the data has been correctly transferred by running some basic queries to check the count of rows and sample data. For example:
```sql
SELECT COUNT(*) FROM your_table;
SELECT * FROM your_table LIMIT 10;
```
Compare these results with your original data in ClickHouse to ensure accuracy.
Step 7: Clean Up Temporary Files
Once you have verified the data transfer, clean up any temporary files, such as the CSV file, to free up space and maintain security:
```bash
rm /path/to/destination/data.csv
```
Ensure you do this on both the source and destination servers if applicable.
By following these steps, you will be able to successfully move data from ClickHouse to Postgres without the use of third-party connectors or integrations.