TL;DR

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 Postgres as a source connector (using Auth, or usually an API key)
2. set up Clickhouse as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Postgres

An object-relational database management system, PostgreSQL is able to handle a wide range of workloads, supports multiple standards, and is cross-platform, running on numerous operating systems including Microsoft Windows, Solaris, Linux, and FreeBSD. It is highly extensible, and supports more than 12 procedural languages, Spatial data support, Gin and GIST Indexes, and more. Many webs, mobile, and analytics applications use PostgreSQL as the primary data warehouse or data store.

What is Clickhouse

ClickHouse is an open-source, column-oriented OLAP database management system that allows users to generate analytical reports using SQL queries. Also offered as a secure and scalable service in the cloud, ClickHouse Cloud allows anyone to effortlessly take advantage of efficient real time analytical processing​.

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Prerequisites

1. A Postgres account to transfer your customer data automatically from.
2. A Clickhouse account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Postgres and Clickhouse, for seamless data migration.

When using Airbyte to move data from Postgres to Clickhouse, it extracts data from Postgres using the source connector, converts it into a format Clickhouse can ingest using the provided schema, and then loads it into Clickhouse via the destination connector. This allows businesses to leverage their Postgres data for advanced analytics and insights within Clickhouse, simplifying the ETL process and saving significant time and resources.

Methods to Move Data From postgres to clickhouse 

• Method 1: Connecting postgres to clickhouse using Airbyte.
• Method 2: Connecting postgres to clickhouse manually.

Method 1: Connecting postgres to clickhouse using Airbyte

Step 1: Set up Postgres as a source connector

1. Open your PostgreSQL database and create a new user with the necessary permissions to access the data you want to replicate.

2. Obtain the hostname or IP address of your PostgreSQL server and the port number it is listening on.

3. Create a new database in PostgreSQL that will be used to store the replicated data.

4. Obtain the name of the database you just created.

5. In Airbyte, navigate to the PostgreSQL source connector and click on "Create Connection".

6. Enter a name for your connection and fill in the required fields, including the hostname or IP address, port number, database name, username, and password.

7. Test the connection to ensure that Airbyte can successfully connect to your PostgreSQL database.

8. Select the tables or views you want to replicate and configure any necessary settings, such as the replication frequency and the replication method.

9. Save your configuration and start the replication process.

10. Monitor the replication process to ensure that it is running smoothly and troubleshoot any issues that arise.

Step 2: Set up Clickhouse as a destination connector

Step 3: Set up a connection to sync your Postgres data to Clickhouse

Once you've successfully connected Postgres as a data source and Clickhouse as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Postgres from the dropdown list of your configured sources.
3. Select your destination: Choose Clickhouse from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific Postgres objects you want to import data from towards Clickhouse. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Postgres to Clickhouse according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Clickhouse data warehouse is always up-to-date with your Postgres data.

Method 2: Connecting postgres to clickhouse manually

Moving data from PostgreSQL to ClickHouse without using third-party connectors or integrations involves several steps, including exporting data from PostgreSQL, transforming it into a format that ClickHouse can import, and then importing the data into ClickHouse. Below is a detailed step-by-step guide to accomplish this task:

Step 1: Export Data from PostgreSQL

1. Determine the Data to Export: Identify the tables and columns you want to transfer from PostgreSQL to ClickHouse.

2. Choose an Export Format: Decide on a file format that is compatible with both PostgreSQL and ClickHouse for the export. CSV is a common choice due to its simplicity and wide support.

3. Export the Data: Use the `COPY` command in PostgreSQL to export the data to a CSV file. For example:

   ```sql

   COPY (SELECT * FROM your_table) TO '/path/to/your_file.csv' WITH CSV HEADER;

   ```

   Replace `your_table` with the name of your table and `/path/to/your_file.csv` with the desired file path.

Step 2: Prepare the Data for ClickHouse

1. Review Data Types: Ensure that the data types in the CSV file are compatible with ClickHouse's data types. You may need to convert data types that don't have a direct equivalent in ClickHouse.

2. Modify the CSV if Necessary: If there are any discrepancies in the data, such as date formats or string encodings, adjust the CSV file accordingly. You can use scripting languages like Python or tools like `sed` and `awk` for this purpose.

Step 3: Create a Table in ClickHouse

1. Design the Schema: Define the schema of the table in ClickHouse, ensuring that it matches the structure and data types of the data you exported from PostgreSQL.

2. Create the Table: Use the ClickHouse client or UI to execute the `CREATE TABLE` statement. For example:

   ```sql

   CREATE TABLE clickhouse_db.your_table (

       column1 DataType1,

       column2 DataType2,

       ...

   ) ENGINE = MergeTree()

   ORDER BY (column1);

   ```

   Replace `clickhouse_db.your_table` with the desired database and table name, and define the columns and data types according to your data.

Step 4: Import Data into ClickHouse

1. Transfer the CSV File: Move the CSV file to a location that is accessible by the ClickHouse server. This could be done via `scp`, `rsync`, or by placing the file on a shared network drive.

2. Import the Data: Use the ClickHouse client to import the data from the CSV file into the table you created. You can use the `clickhouse-client` command-line tool with the `--query` parameter:

   ```sh

   clickhouse-client --query="INSERT INTO clickhouse_db.your_table FORMAT CSV" < /path/to/your_file.csv

   ```

   This command reads the CSV file and inserts the data into the ClickHouse table.

Step 5: Verify the Data Transfer

1. Check the Row Count: Compare the row count in the PostgreSQL table with the row count in the ClickHouse table to ensure all rows have been transferred.

   ```sql

   -- PostgreSQL

   SELECT COUNT(*) FROM your_table;

   -- ClickHouse

   SELECT COUNT(*) FROM clickhouse_db.your_table;

   ```

2. Sample Data Check: Run a few sample queries on both databases to compare the results and verify the data integrity.

Step 6: Troubleshooting

1. Data Discrepancies: If there are discrepancies, check the export and import logs for errors and warnings. You may need to adjust the CSV file or the table schema in ClickHouse.

2. Performance Tuning: If the import process is slow, consider tuning ClickHouse settings or breaking the CSV into smaller chunks to import in parallel.

Additional Notes:

- Ensure that the PostgreSQL server allows exporting data to a file, and the necessary permissions are in place.

- For large datasets, it's recommended to export and import data in chunks to avoid memory issues and to allow for parallel processing.

- Always back up your databases before performing such operations to prevent data loss.

- Make sure that the ClickHouse server has enough disk space to accommodate the imported data.

By following these steps, you should be able to move data from PostgreSQL to ClickHouse without using third-party connectors or integrations. Remember to test the process with a small subset of data before attempting to transfer large volumes of data.

Use Cases to transfer your Postgres data to Clickhouse

Integrating data from Postgres to Clickhouse provides several benefits. Here are a few use cases:

1. Advanced Analytics: Clickhouse’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Postgres data, extracting insights that wouldn't be possible within Postgres alone.
2. Data Consolidation: If you're using multiple other sources along with Postgres, syncing to Clickhouse allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: Postgres has limits on historical data. Syncing data to Clickhouse allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: Clickhouse provides robust data security features. Syncing Postgres data to Clickhouse ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: Clickhouse can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Postgres data.
6. Data Science and Machine Learning: By having Postgres data in Clickhouse, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Postgres provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Clickhouse, providing more advanced business intelligence options. If you have a Postgres table that needs to be converted to a Clickhouse table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Postgres account as an Airbyte data source connector.
2. Configure Clickhouse as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Postgres to Clickhouse after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!