In my previous blog, I provided an overview of Debezium and its core capabilities for capturing and streaming database changes in real time. You can read it here, https://datasturdy.com/understanding-debezium-real-time-data-streaming-for-change-data-capture/ , In this blog, we will take a closer look at Debezium Server, a standalone and lightweight solution for real-time data streaming.
Debezium Server is a separate component part of the same ecosystem that provides a simpler, standalone way for us to capture and stream database changes without needing Kafka or Kafka Connect.
It is designed for use cases where we don’t want to set up the complexity of Kafka-based infrastructure but still need to stream changes from a database. Instead of streaming to Kafka, Debezium Server allows us to stream change events to cloud-native streaming platforms like Google Cloud Pub/Sub, Amazon Kinesis, or Redis. In other words, Debezium Server makes it easier for us to use Debezium in environments where Kafka may not be needed or wanted. It serves as a lightweight solution for directly pushing CDC (Change Data Capture) data to other destinations.
Debezium typically integrates with Kafka and Kafka Connect to stream the captured data. However, Debezium Server is designed to work without Kafka, providing a simpler way for us to send data to other sinks.
Another way for us to deploy Debezium is by using the Debezium Server. The Debezium Server is a configurable, ready-to-use application that streams change events from a source database to a variety of messaging infrastructures. The following image shows the architecture of a change data capture pipeline that uses the Debezium Server:
We can configure Debezium Server to use one of the Debezium source connectors to capture changes from a source database. Change events can be serialized into different formats like JSON or Apache Avro and then sent to one of a variety of messaging infrastructures such as Amazon Kinesis, Google Cloud Pub/Sub, or Apache Pulsar.
Using Debezium Server instead of the full Debezium platform (typically with Kafka and Kafka Connect) can be a great option for simpler use cases, but there are some trade-offs to consider. Here are the key disadvantages of using Debezium Server compared to using the full Debezium setup:
Debezium with Kafka provides highly scalable and distributed architecture. Kafka acts as a durable, fault-tolerant message broker that can handle massive volumes of change data from multiple databases and stream it to various downstream systems.
Debezium Server, on the other hand, is designed to be lightweight and simpler, which means it doesn’t have the same level of scalability. If we’re dealing with large volumes of changes across multiple databases and need a high degree of fault tolerance, Kafka’s ecosystem provides better reliability, scalability, and fault-tolerant data pipelines.
Debezium with Kafka provides strong durability guarantees, meaning that the captured change events are stored in Kafka and can be replayed if necessary. Kafka ensures that even if a downstream consumer fails, the events are retained and can be reprocessed.
Debezium Server does not have this built-in durability. If we’re sending data directly to a database or an external system using cloud native event streams, we lose the ability to replay events. If something goes wrong in the middle of processing, the events might be lost or require special handling to recover.
By sending CDC events directly to cloud-native services like Google Cloud Pub/Sub, we avoid the need to manage infrastructure like Kafka. This is particularly useful for cloud-based architectures that rely heavily on managed services.
Debezium Server running in a serverless model (e.g., directly pushing events to Google Cloud Pub/Sub or Amazon Kinesis) reduces operational overhead. We don’t have to manage Kafka brokers, topics, or clusters ourselves and can focus on consuming the events from these cloud-native services.
Sending events to Google Cloud Pub/Sub enables us to easily integrate Debezium with the rest of the Google Cloud ecosystem—such as processing the events with Dataflow, storing them in BigQuery, or triggering workflows with Cloud Functions. Similarly, for Amazon Kinesis and Azure Event Hubs, the integration with AWS Lambda, Kinesis Analytics, and other cloud-native processing tools is streamlined.
Cloud messaging platforms like Google Cloud Pub/Sub, Kinesis, and Azure Event Hubs are often the backbone of event-driven architectures, where CDC events can be consumed asynchronously by downstream systems like data pipelines, microservices, or real-time analytics platforms.
Managed services like Google Cloud Pub/Sub are designed for high availability, scalability, and durability. They take care of the underlying infrastructure for us, ensuring that the CDC events will reliably reach their destination, even if there are spikes in traffic.
In many cases, using cloud-native messaging platforms can be more cost-effective than maintaining our own Kafka infrastructure, especially for event streaming workloads that don’t require the complexity and overhead of managing Kafka clusters.
In this blog, we’ve explored Debezium Server as a lightweight and simplified solution for real-time data streaming. By removing the need for Kafka and Kafka Connect, Debezium Server allows us to capture and stream database changes directly to cloud-native platforms like Google Cloud Pub/Sub, Amazon Kinesis, and Redis.
While Debezium Server offers clear advantages in terms of simplicity, serverless operations, and cloud-native flexibility, it’s important to understand its limitations, especially in scalability, fault tolerance, and durability compared to the full Debezium setup with Kafka.
For use cases where managing Kafka is an overkill, Debezium Server provides an excellent alternative for more lightweight and cost-effective data streaming solutions. Whether we’re working with event-driven architectures or building scalable data pipelines in the cloud, Debezium Server makes it easier to focus on processing real-time change data while offloading much of the infrastructure management to cloud providers.
Ultimately, choosing between Debezium Server and the full Debezium platform depends on the specific needs of our system. If simplicity and integration with cloud-native services are the priority, Debezium Server is a strong contender. But for larger, more complex distributed systems requiring robust scalability and fault tolerance, the full Debezium with Kafka remains the ideal choice.
Nayan Sagar N K