How to load data from ClickHouse to Postgres destination

Learn how to use Airbyte to synchronize your ClickHouse data into Postgres destination within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a ClickHouse connector in Airbyte

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

Set up Postgres destination for your extracted ClickHouse data

Select Postgres destination where you want to import data from your ClickHouse source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the ClickHouse to Postgres destination 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.
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How to Sync ClickHouse to Postgres destination Manually

How to Sync ClickHouse to Postgres destination Manually - Method 2:

FAQs

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

An open-source database management system for online analytical processing (OLAP), ClickHouse takes the innovative approach of using a column-based database. It is easy to use right out of the box and is touted as being hardware efficient, extremely reliable, linearly scalable, and “blazing fast”—between 100-1,000x faster than traditional databases that write rows of data to the disk—allowing analytical data reports to be generated in real-time.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up Clickhouse to PostgreSQL as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Clickhouse to PostgreSQL and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

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Connectors Used

ClickHouse
Postgres destination

Related Syncs with ClickHouse

Clickhouse to Apache Iceberg
Clickhouse to ClickHouse
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Clickhouse to DynamoDB
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Clickhouse to Google Firestore
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Clickhouse to Kafka
Clickhouse to MSSQL - SQL Server
Clickhouse to MongoDB
Clickhouse to MySQL
Clickhouse to Oracle DB
Clickhouse to PostgreSQL
Clickhouse to Redis
Clickhouse to Starburst Galaxy
Clickhouse to TiDB

Tags

SQL based Databases