Seamlessly integrating diverse data systems is crucial for building robust, real-time analytics and event-driven architectures. This article explores two powerful approaches (using Airbyte's PosgreSQL Kafka connector & a manual method of integration) to connecting and synchronizing data between PostgreSQL, a popular relational database, and Apache Kafka, a distributed event streaming platform.

What is PostgreSQL?

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 Kafka?

A communication solutions agency, Kafka is a cloud-based / on-prem distributed system offering social media services, public relations, and events. For event streaming, three main functionalities are available: the ability to (1) subscribe to (read) and publish (write) streams of events, (2) store streams of events indefinitely, durably, and reliably, and (3) process streams of events in either real-time or retrospectively. Kafka offers these capabilities in a secure, highly scalable, and elastic manner.

‍

{{COMPONENT_CTA}}

Methods to Move Data From PostgreSQL to Kafka

• Method 1: Connecting PostgreSQL to Kafka using Airbyte.
• Method 2: Connecting PostgreSQL to Kafka manually.
• Method 3: PostgreSQL Kafka connector using Confluent CLI

Method 1: Airbyte's PosgreSQL Kafka connector

Prerequisites

1. A Postgres account to transfer your customer data automatically from.
2. A Kafka 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 Kafka, for seamless data migration.

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

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 Kafka as a destination connector

1. First, you need to have an Apache Kafka destination connector installed on your system. If you don't have it, you can download it from the Apache Kafka website.  
2. Once you have the Apache Kafka destination connector installed, you need to create a new connection in Airbyte. To do this, go to the Connections tab and click on the "New Connection" button.  3. In the "New Connection" window, select "Apache Kafka" as the destination connector and enter the required connection details, such as the Kafka broker URL, topic name, and authentication credentials.  
4. After entering the connection details, click on the "Test Connection" button to ensure that the connection is working properly.  
5. If the connection test is successful, click on the "Save" button to save the connection.  
6. Once the connection is saved, you can create a new pipeline in Airbyte and select the Apache Kafka destination connector as the destination for your data.  
7. In the pipeline configuration, select the connection you created in step 3 as the destination connection.  
8. Configure the pipeline to map the source data to the appropriate Kafka topic and fields.  
9. Once the pipeline is configured, you can run it to start sending data to your Apache Kafka destination.

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

Once you've successfully connected Postgres as a data source and Kafka 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 Kafka 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 Kafka. 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 Kafka according to your settings.

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

Method 2: Connecting PostgreSQL to Kafka manually

Moving data from PostgreSQL to Kafka without using third-party connectors or integrations involves several steps, including setting up Kafka, writing a custom application to read data from PostgreSQL and publish it to Kafka, and ensuring data consistency and fault tolerance. Below is a step-by-step guide to help you through the process:

Step 1: Set Up Kafka

1. Download Kafka: Go to the Apache Kafka website and download the latest version of Kafka.

2. Extract Kafka: Unzip the downloaded file to your desired location.

3. Start ZooKeeper: Kafka uses ZooKeeper, so you need to start it before starting Kafka.

```shell

bin/zookeeper-server-start.sh config/zookeeper.properties

```

4. Start Kafka Server: Open another terminal and run the Kafka server.

```shell

bin/kafka-server-start.sh config/server.properties

```

Step 2: Create a Kafka Topic

1. Create Topic: Create a Kafka topic where the data from PostgreSQL will be published.

```shell

bin/kafka-topics.sh --create --topic postgres-data --bootstrap-server localhost:9092 --replication-factor 1 --partitions 1

```

Step 3: Set Up PostgreSQL

1. Install PostgreSQL: If you haven't already, install PostgreSQL on your system.

2. Create a Database: Create a database and tables that you want to move data from.

3. Enable Logical Replication: Modify `postgresql.conf` to enable logical replication by setting `wal_level` to `logical`.

4. Create Publication: For PostgreSQL 10 and above, you can create a publication for the tables you want to watch.

```sql

CREATE PUBLICATION my_publication FOR TABLE my_table;

```

Step 4: Write a Custom Application

1. Set Up Your Development Environment: Make sure you have a suitable programming environment with necessary dependencies installed, such as a PostgreSQL driver and Kafka client library.

2. Database Connection: Write code to connect to your PostgreSQL database.

3. Polling or Trigger-Based Data Retrieval: Decide on a method for retrieving data from PostgreSQL. You can poll the database at intervals or use triggers to act on data changes.

4. Read Data: Write a function to read data from PostgreSQL. This could be new rows or updated rows depending on your application.

5. Format Data: Format the data into a structure suitable for sending to Kafka.

6. Kafka Producer: Write a Kafka producer in your application that connects to the Kafka cluster.

7. Send Data to Kafka: Send the formatted data to the Kafka topic you created earlier.

8. Error Handling: Implement error handling to deal with any issues during data retrieval or publishing.

9. Logging: Add logging to your application for monitoring and debugging purposes.

Step 5: Run Your Custom Application

1. Compile and Run: Compile your application and run it.

2. Monitor: Monitor the application for any errors and ensure data is being published to Kafka successfully.

Step 6: Test and Validate

1. Consume Messages: Use a Kafka consumer to consume messages from the Kafka topic to validate that data is being moved correctly.

```shell

bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic postgres-data --from-beginning

```

2. Check Data Integrity: Verify that the data in Kafka matches the source data in PostgreSQL.

Step 7: Deployment and Scaling

1. Deploy: Deploy your custom application to a suitable production environment.

2. Scale: If necessary, scale your Kafka cluster and application to handle the load.

Step 8: Maintenance and Monitoring

1. Set Up Monitoring: Set up monitoring for both your Kafka cluster and custom application to ensure they are running smoothly.

2. Regular Maintenance: Perform regular maintenance checks on both PostgreSQL and Kafka.

Additional Considerations:

Security: Ensure that your Kafka cluster and PostgreSQL database are secured, using SSL/TLS and SASL if necessary.

Transaction Support: If your application requires transaction support, make sure to handle transactions properly in your custom application.

Data Serialization: Choose an appropriate data serialization format for Kafka messages (e.g., JSON, Avro, Protobuf).

Backpressure Handling: Implement backpressure handling in your application in case Kafka cannot handle the rate of incoming messages.

‍

Remember, this approach requires a significant amount of custom development and testing. It's important to consider the trade-offs between building a custom solution and using existing third-party connectors that may offer additional features and robustness.

Method 3: PostgreSQL Kafka connector using Confluent CLI

Before diving in, ensure you have:

1. A Confluent Cloud account with a running cluster
2. Confluent CLI installed and configured
3. Access to a PostgreSQL database
4. Basic familiarity with JSON and command-line interfaces

Step 1: Exploring Available Connectors

First, let’s see what connectors are at our disposal:

```

confluent connect plugin list

```

Look for “PostgresSource” in the output. If you don’t see it, double-check your Confluent CLI setup.

Step 2: Understanding Connector Configuration

Before we configure our connector, let’s examine its properties:

```

confluent connect plugin describe PostgresSource

```

This command provides a wealth of information. Take some time to read through it – understanding these properties will help you troubleshoot issues later.

Step 3: Crafting Your Configuration

Now comes the fun part – creating your configuration file. I recommend using a text editor you’re comfortable with. Let’s call our file postgres-to-kafka.json:

```json

{

  "name": "pg-to-kafka-stream",

  "config": {

    "connector.class": "PostgresSource",

    "kafka.auth.mode": "SERVICE_ACCOUNT",

    "kafka.service.account.id": "sa-abc123",

    "topic.prefix": "pg_",

    "connection.host": "your-db-host.com",

    "connection.port": "5432",

    "connection.user": "your_username",

    "connection.password": "your_password",

    "db.name": "your_database",

    "table.whitelist": "users,orders",

    "timestamp.column.name": "updated_at",

    "incrementing.column.name": "id",

    "output.data.format": "JSON",

    "db.timezone": "UTC",

    "tasks.max": "1",

    "mode": "timestamp+incrementing"

  }

}

```

Some notes on this configuration:

• The name field is how you’ll refer to this connector later. Choose something memorable!
• kafka.auth.mode: Using a service account is generally more secure than API keys.
• topic.prefix: This helps organize your Kafka topics. I like using short prefixes like “pg_”.
• mode: “timestamp+incrementing” is often a good choice for capturing both new and updated rows.

Remember, this is just a starting point. You may need to adjust based on your specific PostgreSQL setup.

Step 4: Launching Your Connector

With your configuration file ready, it’s time to create the connector:

```

confluent connect cluster create --config-file postgres-to-kafka.json

```

If successful, you’ll get a response with your connector’s ID. Save this ID – you’ll need it for monitoring.

Step 5: Monitoring Your Connector

Connecting systems can be tricky. Let’s check on our connector’s status:

```

confluent connect cluster status <your-connector-id>

```

Look for “RUNNING” in the output. If you see “FAILED”, check the error messages and revisit your configuration.

Step 6: Verifying Data Flow

Now for the moment of truth – is data flowing into Kafka? Let’s check:

```

confluent kafka topic consume pg_users --from-beginning

```

Replace “pg_users” with whatever your topic name is based on your prefix and table name.

If you see JSON data streaming by, congratulations! You’ve successfully connected PostgreSQL to Kafka.

Use Cases to transfer your Postgres data to Kafka

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

1. Advanced Analytics: Kafka’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 Kafka 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 Kafka allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: Kafka provides robust data security features. Syncing Postgres data to Kafka ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: Kafka 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 Kafka, 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 Kafka, providing more advanced business intelligence options. If you have a Postgres table that needs to be converted to a Kafka table, Airbyte can do that automatically.

Wrapping Up

Syncing PostgreSQL with Kafka opens up a world of possibilities for real-time data processing and event-driven architectures. While manual methods offer fine-grained control, Airbyte provides a user-friendly, efficient solution for quickly setting up and managing your data pipelines. Ready to streamline your data synchronization process? Try Airbyte free for 14 days and experience the ease of connecting PostgreSQL to Kafka with just a few clicks.

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!