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Simplifying Real-Time Data Streaming with Debezium and PostgreSQL Logical Replication

In the realm of data integration and analysis, extracting, transforming, and loading (ETL) pipelines play a crucial role. They facilitate the flow of data from various sources to data warehouses, allowing organizations to make informed decisions based on up-to-date information. PostgreSQL, a powerful and feature-rich open-source database, provides logical replication functionality that enables real-time data streaming. In this blog post, we will explore how to leverage Debezium, a popular change data capture (CDC) tool, to harness PostgreSQL's logical replication and simplify the process of building robust ETL pipelines.

Simplifying Real-Time Data Streaming with Debezium and PostgreSQL Logical Replication

Understanding Change Data Capture (CDC)

Change Data Capture is a method for capturing and delivering individual data changes in real-time. It enables continuous monitoring of database activity and efficiently propagates those changes to downstream systems. By employing CDC, organizations can build event-driven architectures, power microservices, and streamline data integration processes.

Introducing Debezium

Debezium, an open-source distributed platform, specializes in CDC. It captures row-level changes from database transaction logs and converts them into a stream of events. Debezium supports multiple databases, including PostgreSQL, MySQL, MongoDB, and SQL Server. In this article, we focus on Debezium’s integration with PostgreSQL and its logical replication capabilities.

Debezium captures changes in the database at the granular level, allowing you to track individual data modifications such as inserts, updates, and deletes. These changes are transformed into a standardized event format, such as Apache Kafka messages or JSON documents, which can be consumed by downstream systems or services.

Leveraging PostgreSQL Logical Replication

PostgreSQL’s logical replication feature enables the selective replication of database changes to remote replicas or consumer applications. It provides a fine-grained approach to capture and transmit only the required data modifications, thereby minimizing network bandwidth and resource consumption. By combining Debezium with PostgreSQL’s logical replication, you can unlock real-time data streaming and integrate it seamlessly into your ETL pipelines.

Setting up Debezium for PostgreSQL

To get started, you’ll need to configure Debezium to connect with your PostgreSQL database. Debezium relies on PostgreSQL’s logical decoding feature, which allows it to access the database transaction logs. Let’s go through the steps involved:

Install and Configure Debezium:

Download the Debezium connector for PostgreSQL and extract it into your preferred location. Next, configure Debezium by modifying the connector’s properties file. Specify the necessary connection details such as the PostgreSQL host, port, database name, username, and password.

Define the PostgreSQL Connector:

In the connector properties file, you’ll need to specify the connector class name as io.debezium.connector.postgresql.PostgresConnector. Additionally, provide a unique identifier for the connector instance.

Set Up the Publication:

PostgreSQL’s logical replication requires the creation of a publication to define the tables and data modifications to replicate. In the connector properties file, you can specify the publication name using the publication.name property. Select the desired tables to replicate by including them in the table.include.list property.

Configure the Slot Name:

Debezium relies on a replication slot in PostgreSQL to ensure consistent data capture. Assign a unique slot name to the connector by setting the slot.name property.

Performing Initial Snapshot and Ongoing Streaming

After configuring Debezium with the PostgreSQL connection details, you can proceed with performing an initial snapshot and enabling ongoing streaming:

Initial Snapshot:

Before starting the streaming process, Debezium captures an initial snapshot of the selected tables. This snapshot ensures that the downstream systems begin with a complete and consistent set of data. The snapshot is stored in a separate topic in the messaging system. You can control the snapshot behavior by configuring properties such as snapshot.mode, snapshot.delay.ms, and snapshot.include.collection.list in the connector properties file.

Ongoing Streaming:

Once the snapshot is complete, Debezium starts streaming the real-time changes from the transaction logs. It captures inserts, updates, and deletes as events, which are published to the specified messaging system topic. The downstream applications or ETL processes can consume these events and process them accordingly.

Downstream Consumption of ETL Events:

The events generated by Debezium offer significant flexibility in terms of downstream processing and integration possibilities. These events can be consumed by various systems and services, enabling real-time data integration, microservice orchestration, data synchronization, stream processing, and more. Here are a few examples:

Apache Kafka:

Events can be published to Apache Kafka, a distributed streaming platform. Kafka acts as a central hub for the events, and multiple consumer applications or stream processing frameworks can subscribe to the event stream. This enables real-time data processing, analytics, and event-driven workflows.

Messaging Systems:

Other messaging systems like Apache Pulsar, RabbitMQ, or AWS Kinesis can also serve as downstream consumers of Debezium events. These systems enable reliable and scalable event-driven architectures, allowing multiple consumers to subscribe to the event stream and process the data accordingly.

Data Warehouses and Data Lakes:

Events captured by Debezium can be consumed and loaded into data warehouses or data lakes for further analysis, reporting, and business intelligence purposes. Platforms such as Amazon Redshift, Google BigQuery, or Snowflake can be used to store and query the event data, enabling advanced analytics and data exploration.

Stream Processing Frameworks:

In addition to Apache Kafka, stream processing frameworks like Apache Flink, Apache Samza, or Spark Streaming can consume the events and perform real-time processing, aggregations, machine learning, or complex event processing. These frameworks provide powerful capabilities for transforming, enriching, and deriving insights from the event data stream.

Microservices and Service-Oriented Architectures:

Debezium events can be consumed by microservices or services within a service-oriented architecture (SOA). Each microservice can subscribe to the event stream relevant to its domain or data requirements. This enables building loosely coupled and scalable systems that react to changes in the underlying data.

Data Replication and Synchronization:

The captured events can be used for data replication and synchronization purposes. Downstream systems or databases can consume the events to ensure consistent data across distributed environments or replicas. By consuming the events, the systems can update their own data stores to reflect the changes made in the source database, facilitating near-real-time data synchronization.

Business Process Automation:

Events can trigger workflows or business processes within workflow automation tools like Apache Airflow, Camunda, or AWS Step Functions. These tools can consume the event and initiate the appropriate workflow or task based on the captured data changes, enabling streamlined and automated business processes.

Debezium Signal Table for Event Coordination:

In addition to capturing and transforming data changes into events, Debezium introduces the concept of a signal table. The signal table is a special table within the database schema that serves as a coordination mechanism for event-driven architectures and enables inter-service communication.

The Debezium signal table acts as a coordination point between different services or microservices within an event-driven architecture. Services can update the signal table to indicate state changes or specific events that need to be propagated to other parts of the system. By subscribing to the changes in the signal table as events, other services can react to the signals and trigger corresponding actions or workflows. The signal table plays a crucial role in event-driven architectures by providing a coordination mechanism and inter-service communication.


By combining Debezium with PostgreSQL’s logical replication, organizations can achieve real-time data streaming and simplify their ETL pipelines. Debezium captures row-level changes from the PostgreSQL database and converts them into a stream of events that can be consumed by various downstream systems and services.

The events generated by Debezium can be consumed by messaging systems like Apache Kafka or other systems like data warehouses, stream processing frameworks, microservices, and workflow orchestration tools. These downstream consumers enable real-time data integration, analytics, event-driven architectures, and more.

By leveraging Debezium’s CDC capabilities, PostgreSQL’s logical replication, and downstream event consumption, organizations can build scalable, responsive, and data-driven systems that thrive in today’s fast-paced business landscape. That’s why at Lassoo.io we use Postgres not just as a Database but as a data communication protocol.

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Max Kremer
Max Kremer  Co-founder & CTO @ Lassoo. Startup guy with multiple exits. Lover of technology and data.