Change Data Capture Isn’t Magic: The Reality of Handling CDC Yourself

TL;DR: Change Data Capture (CDC) enables real-time replication of changes from OLTP systems to OLAP systems. While open-source tools like Debezium and Kafka Connect are powerful, they come with steep operational complexity, including snapshot issues, JVM tuning, and monitoring overhead. Estuary Flow offers a fully managed CDC alternative that simplifies setup, reduces engineering burden, and supports both streaming and batch use cases, without sacrificing flexibility.

Operational Data vs. Analytical Needs

Replicating data from operational databases into analytics systems is one of the most common tasks in a data engineer’s toolkit. It sits at the intersection of system design, real-time processing, and business needs.

At the core of this challenge is the difference between two types of systems:

• OLTP systems (Online Transaction Processing) are designed for speed and concurrency. They’re optimized to handle frequent inserts and updates efficiently, Think shopping carts, payments, and inventory updates. These systems power day-to-day operations and need to respond quickly to user actions. Common examples include Postgres, MySQL, MongoDB, and Oracle Database.
• OLAP systems (Online Analytical Processing) are designed for complex queries, aggregations, and large-scale data exploration. They power dashboards, reports, and business intelligence workflows. Common examples include BigQuery, Snowflake, ClickHouse, and MotherDuck.

Because of this natural division of labor, moving data from OLTP to OLAP systems is a necessity. You want your analysts querying clean, fast, purpose-built systems, not production databases.
 

When designing that bridge, there are typically two approaches:

• Batch loading: Data is extracted from source systems on a schedule (say, every hour or night), then loaded into the warehouse.
• Streaming: Changes are picked up continuously, often within milliseconds of occurring, and propagated to the destination.

While batch loading is straightforward and well-understood, it comes with trade-offs. Between scheduled runs, a record might be updated multiple times, or deleted altogether. You lose the granularity of those changes unless you’re doing some heavy lifting with audit tables or change logs, and sometimes, that granularity really matters.

Imagine being able to track every stage an order goes through: from placement to fulfillment. You could detect delays, optimize delivery times, and define meaningful KPIs for logistics teams. In fraud detection, catching a suspicious update to a transaction in real time can be the difference between damage done and disaster averted.

This level of visibility and timeliness unlocks operational efficiency. Teams can observe what’s happening now, not just what happened last hour. They can act faster, with more confidence and that’s a big deal.

That’s the promise of Change Data Capture, the foundation for real-time pipelines that don’t just mirror your data, but reflect your business in motion.

What Is Change Data Capture (CDC)?

Change Data Capture (CDC) is a data integration pattern that enables real-time propagation of row-level changes from a source system to a downstream destination. 

In simple terms, every time a new record is inserted, an existing one is updated, or a row is deleted, CDC ensures that those changes are captured and forwarded, often, within seconds to another system that needs them. 

This isn’t just about speed. CDC plays a critical role in maintaining data fidelity across systems:

• It creates a more complete audit trail of how your data evolves over time.
• It reduces data gaps caused by snapshot-based syncing.
• It helps build systems that reflect the true state of you business, as it changes

For many organizations, CDC becomes the backbone of their streaming architecture, feeding everything from real-time dashboards and anomaly detection engines to machine learning features and operational tooling. And like most things in the modern data world, you typically face two routes when implementing CDC:

1. Open-source tooling: Flexible, community-driven, and often free to use. Think tools like Debezium or frameworks like Kafka Connect.
   
   
2. SaaS integrations: Fully managed platforms designed to abstract the complexity of CDC setup and maintenance. These often come with user-friendly interfaces, observability baked-in, and support for dozens of data sources out of the box. A great example of such a system is Estuary.

On the surface, open-source tools can seem like the obvious choice, powerful, battle-tested, and cost-effective. But as this article will explore, implementing and operating CDC pipelines yourself, especially with tools like Debezium and Kafka Connect, introduces real complexity that is often underestimated.

And as with most things in engineering, the devil is in the details.

6 Common Misconceptions About CDC

Before diving deeper into the challenges of managing a CDC pipeline, it’s important to address a few common misconceptions that often lead teams down the wrong path. These assumptions can create unrealistic expectations, increase complexity, and leave engineers unprepared for what their choices actually demand.

Every CDC pipeline needs Kafka

Kafka is often treated as a mandatory part of modern data infrastructures, especially for real-time use cases. But even when streaming is genuinely needed, kafka can introduce significant operational overhead. Managing Kafka clusters, Kafka Connect, and debezium can be complex and resource intensive. If your downstream systems don’t require streaming or event driven integration, or if you’re simply seeking a lighter operational footprint, alternative architectures like batch-based ELT, micro batch ingestion, or data lake staging may be more appropriate. These patterns often involve extracting data from a source system and landing it in object storage before landing it into analytical systems. Tools like Airbyte and Estuary leverage these patterns while a framework like dlt can fetch data in mini batches and write directly to your sink system.

CDC is just syncing my database to Kafka

While engines like Debezium can indeed stream changes into Kafka, that’s only part of the story. The core value and complexity of CDC lies in capturing all row-level changes accurately, in the correct order, and without gaps. It requires mechanisms that handle schema evolution, failure recovery, and delivery guarantees across components. Simply pushing data into Kafka does not guarantee that all mutations will be preserved or delivered reliably.

Debezium and Kafka Connect give you CDC out of the box

Debezium is a powerful and widely used open-source framework, and Kafka Connect provides a flexible plugin architecture for moving data between systems. However, neither tool is plug-and-play. Debezium is a Java application that must be carefully configured, tuned, and monitored. It relies on the right database permissions, a correctly initialized Kafka Connect cluster, tuned snapshot strategies, and specific database settings like replica identity and WAL retention. The learning curve can be steep, and edge cases can cause subtle failures if not well understood.

Once set up, CDC pipelines run themselves

This is rarely the case in practice. CDC pipelines built on open-source tools require active maintenance. Connectors can fail, initial snapshots can stall, Kafka topics can fill up, and schema changes can cause silent breakages. Without robust monitoring, alerting, and deep familiarity with the internals of each component, issues can go unnoticed until they cause downstream impact.

Open source is free, so it must be cheaper

While open-source tooling has no licensing costs, the real cost lies in engineering time. Maintaining a CDC pipeline built with Kafka Connect and Debezium often involves writing custom glue code, managing Docker images, dealing with noisy logs, and debugging configuration drift. These operational burdens can significantly increase the total cost of ownership and erode the productivity of the teams involved.

Debezium is the only way to do open-source CDC

Debezium is robust, extensible, and supports a wide range of databases, but it’s not the only option. At its core, CDC is a pattern, not a product. It involves parsing logs, formatting changes, and propagating events, which are all functionalities that can be implemented in many ways.

Lighter-weight alternatives are emerging across different ecosystems. For example the pg_replication source connector by dlthub is a python based library built for log-based CDC from Postgresql, while Olake by Datazip, is a Golang-powered framework focused on delivering CDC data directly to lakehouse architectures. These tools may not yet match Debezium’s breadth of source coverage or production maturity, they offer simplicity, easier integration, and more flexibility for teams working in Python or Go environments.

Ultimately, the best tool depends on your use case. Understanding your operational constraints, team capabilities, and architectural goals is key to choosing a CDC solution that fits, rather than defaulting to the most popular one.

The Hidden Complexity of DIY CDC: Kafka Connect and Debezium in Practice

When evaluating a CDC solution, it’s tempting to focus on surface-level features: whether it supports your database, what connectors are available, and how fast it can move data. But a thoughtful evaluation should also consider factors like ease of use, system complexity, scalability, infrastructure footprint and of course, cost.

Cost is a particularly deceptive variable. At the outset, open-source tools like Debezium and Kafka Connect may appear cost-effective. But in practice, the real price often shows up later in engineering time, operational burden, and the total cost of ownership.

A Common Architecture: Kafka + Kafka Connect + Debezium

A popular architecture for CDC pipelines pairs Debezium with Kafka and Kafka Connect. On paper, it’s an elegant system:

• Debezium parses change events from your source database (using the write-ahead log).
• Kafka connect standardizes data movement into and out of Kafka.
• Kafka acts as the durable buffer and backbone for downstream integrations.

Kafka Connect itself is designed to abstract away the complexity of writing producers and consumers. It exposes a standard API, built around three primary models: the connector, model, worker model and data model. This abstraction makes it easy to build new connectors or configure pre-built ones. For many architects, this config-driven model, with no code to manage, is one of Kafka Connect’s most attractive features.

In theory, this stack promises:

• Seamless data movement through configuration
• Scalability and fault tolerance via Kafka
• Extensibility through custom or community-built connectors

But in Practice, Things Get Complicated

While the architecture sounds clean, its effectiveness depends heavily on context, especially scale, team expertise, and performance requirements. One of the most overlooked challenges is the implicit dependency on Java. Although you don’t have to write Java code to use Kafka Connect, understanding and tuning the system often requires digging into Java stack traces, connector source code, and JVM-specific behavior. This becomes a real barrier for teams unfamiliar with the ecosystem. Moreover, performance tuning is rarely trivial. To squeeze the most out of connectors, you’ll often need to understand:

• The inner workings of the connector you’re using
• Kafka Connect internals (e.g., task rebalancing, offset management)
• Kafka’s own configuration and throughput mechanics

Each tuning attempt becomes a three-way debugging effort; connector, framework, and Kafka itself.

Even basic observability becomes a challenge. While Kafka Connect exposes metrics, interpreting them isn’t always straightforward. Logs tend to be extremely verbose, and without careful configuration, teams can accidentally blow their logging quotas on observability platforms like Datadog.

Worse still, many out-of-the-box connectors ship with conservative defaults that are not optimized for high-throughput environments. You’ll often need to adjust batch sizes, commit intervals, or memory usage just to reach acceptable performance levels.

Debezium’s Pain Points: Snapshots and Beyond

Debezium introduces its own operational challenges, especially around historic loads and snapshotting.

By default, Debezium performs an initial snapshot of your source tables before switching to real-time streaming. This step is critical for capturing the baseline state of the data but comes with multiple pitfalls:

• Snapshots are sequential. If you have 500 large tables, the snapshot can take hours (or longer).
• They’re not fault-tolerant. If your Kafka Connect cluster restarts mid-snapshot, Debezium cannot resume from where it left off. You may end up with duplicated records or worse, an overloaded Kafka topic.
• Retries are silent. Debezium will silently restart snapshots on retriable error failures without clear feedback to the operator.
• Write-ahead logs can accumulate. Since WAL segments are only processed after the snapshot completes, long snapshots can impact your database’s performance or fill up disk space.
• Configuration misalignment is common. For example, in PostgreSQL, the table.include.list parameter must be explicitly set. If omitted, Debezium attempts to snapshot all non-system tables regardless of whether they’ve been included in your publication.
• Duplicates are commonplace. By default Debezium is designed for at least once delivery which means that while trying to ensure messages are delivered there are chances of seeing the same message multiple times when there are failures, restarts or disruption in database connections. Forcing you to implement and maintain your own solution for deduplication of messages.

To address these issues, Debezium offers a more fault-tolerant approach called incremental snapshots, triggered via signaling. This mechanism allows you to send custom signals (e.g., execute-snapshot) to the connector, instructing it to re-snapshot individual tables in chunks.

While incremental snapshots are more robust, they introduce a different set of complexities:

• Event semantics are harder to reason about.
• Event ordering is not always guaranteed.
• Behavior may vary depending on connector and database versions.

Operational Overhead Adds Up

At a certain point, the combined burden of managing Debezium, Kafka Connect, and Kafka itself becomes a full-blown engineering responsibility. You’re no longer running a data pipeline, you’re operating a distributed system with many points of failure, dozens of interdependent configs, and limited room for error. To do it well, you need engineers who are comfortable with:

• Kafka Internals
• JVm tuning
• Connector lifecycle management
• Observability tooling
• Backpressure handling
• Schema evolution strategies

This is often too much to ask from a team that just wants to move data reliably from point A to point B.

Estuary Flow: CDC That Just Works

After covering the operational complexity of self-managed CDC pipelines with Debezium and Kafka Connect, it’s worth looking at what a managed alternative can offer. Estuary Flow is one such option, built to simplify change data capture without the overhead.

What sets Estuary apart is its ease of use. There’s no need to manage Kafka or Kafka Connect, no tuning of JVMs, and no connector deployment headaches. You configure your sources and destinations through a clear UI, and Flow handles the orchestration. This shortens setup time, reduces the learning curve, and frees engineers to focus on higher-value work.

While Debezium has no license fee, the real cost shows up in infrastructure and engineering time. Estuary’s usage-based pricing is often significantly lower than competing tools, and it becomes more cost-efficient at higher volumes.

Despite this simplicity, Estuary remains flexible. It supports streaming and batch ingestion, allows for backfills, enables transformations in SQL or TypeScript, and handles schema evolution from source to sink. It’s powerful enough for complex use cases but accessible enough for non-specialists to manage.

For teams that want the benefits of CDC without the burden of operating a distributed system, Estuary Flow presents a clear, modern path forward.

Wrapping Up: CDC Isn’t Magic

Change Data Capture solves a real and growing need in modern data systems, but implementing it yourself reveals the hard edges quickly. It’s not just about moving rows, it’s about orchestrating state, handling failure, evolving schemas, and doing all of it without breaking your pipelines or burning out your team.

Tools like Debezium opened the door for open-source CDC, but they also introduced operational weight that many teams underestimate. Building and maintaining a CDC pipeline with Kafka and Kafka Connect often feels like trading one complexity for another.

What Estuary Flow shows is that CDC doesn’t have to be that hard. The value CDC brings is real, but so are the costs if the wrong tool is chosen. By leaning on platforms that reduce the surface area of complexity, you get the benefits of real-time data movement without taking on a distributed systems project.

At the end of the day, CDC isn’t magic. It’s plumbing. And good plumbing should be reliable, invisible, and not require a team of specialists to keep the water flowing.