How to Get CDC Without Managing Kafka: A Complete Guide

You set out to build a real-time data pipeline. You wanted to stream database changes into your warehouse, power some dashboards, maybe feed a machine learning model. The use case was clear, the business value was obvious, and the technical requirement was straightforward: Change Data Capture.

But somewhere between that first architecture diagram and your third week of debugging connector configurations, the project changed. You weren’t building a data pipeline anymore. You were managing a Kafka cluster. You were tuning JVM heap sizes, wrestling with consumer group rebalances, and reading Stack Overflow threads about Schema Registry compatibility at 11 PM on a Tuesday.

If this sounds familiar, you’re not alone. For years, the standard answer to “How do I do CDC?” has been “Set up Kafka and Debezium.” And while that architecture is technically sound, it comes with an operational tax that catches most teams off guard. The good news is that it doesn’t have to be this way anymore. Modern managed CDC platforms give you everything Kafka provides for CDC—durability, ordering, exactly-once delivery—without asking you to become a Kafka administrator in the process.

This guide walks through exactly why Kafka became the default for CDC, what it really costs to manage that stack yourself, and how to get all the benefits of Kafka-based CDC without any of the infrastructure overhead.

Why Kafka Became the Default for CDC

To understand why Kafka dominates the CDC landscape, you need to go back to the problem that Debezium was designed to solve. Debezium, the most widely adopted open-source CDC tool, was built from the ground up as a set of Kafka Connect connectors. It reads database transaction logs—MySQL’s binlog, PostgreSQL’s write-ahead log, MongoDB’s oplog—and produces change events as messages on Kafka topics.

This architecture made a lot of sense. Kafka provides a durable, ordered, distributed log that can handle massive throughput. It decouples producers from consumers, so your CDC pipeline doesn’t care whether the downstream system is Snowflake, a microservice, or another database. Kafka Connect provides a standardized framework for building connectors, and the community rallied around it.

The result is an architecture that looks elegant on a whiteboard:

1. Debezium reads the database transaction log and converts changes into events.
2. Kafka Connect manages the Debezium connector lifecycle (start, stop, pause, resume).
3. Apache Kafka stores and distributes those events with strong ordering guarantees.
4. Schema Registry tracks the evolving shape of your data as schemas change.
5. Sink connectors deliver events from Kafka to your target systems.

Each component is well-designed and battle-tested. The challenge isn’t any single piece—it’s that you now have five distinct distributed systems to deploy, configure, monitor, and keep running 24/7. For a team that just wanted to get database changes into Snowflake, this is a lot of infrastructure to take on.

If you want to understand more about the role Kafka plays in Change Data Capture architectures, our CDC capabilities page breaks down how modern platforms handle this internally.

The Hidden Cost of Self-Managed Kafka CDC

When teams estimate the cost of building a Kafka-based CDC pipeline, they usually think about compute. A few EC2 instances for Kafka brokers, maybe a couple more for Connect workers, and you’re done. The reality is dramatically different.

Infrastructure Costs Add Up Fast

A production Kafka cluster for CDC isn’t three VMs. At minimum, you need:

• 3 Kafka brokers (for replication and fault tolerance)
• 3 ZooKeeper nodes (or KRaft controllers in newer versions)
• 2-3 Kafka Connect workers (for running Debezium and sink connectors)
• 1 Schema Registry instance (plus a standby for HA)
• Monitoring stack (Prometheus, Grafana, or a commercial APM tool)
• Persistent storage (EBS volumes, often io1/io2 for consistent IOPS)
• Networking (cross-AZ data transfer fees, VPC peering, load balancers)

On AWS, a modest setup—three m5.xlarge brokers, three t3.medium ZooKeeper nodes, two m5.large Connect workers, plus storage and networking—runs $2,000 to $5,000 per month before you’ve processed a single message. Scale up to handle more tables, higher throughput, or multi-region replication, and you’re easily looking at $10,000+ per month in infrastructure alone.

Engineering Time Is the Real Expense

But infrastructure costs are only part of the story. The far bigger expense is the engineering time required to keep everything running. Based on what teams consistently report, a small Kafka CDC cluster requires 20 to 40 hours per month of ongoing maintenance:

• Broker maintenance: Rolling upgrades, certificate rotations, configuration tuning, disk space management.
• Connect worker management: Connector task rebalancing, failed task restarts, offset management, plugin version compatibility.
• Schema Registry operations: Schema compatibility checks, subject management, cleaning up unused schemas.
• Monitoring and alerting: Setting up dashboards, tuning alert thresholds, investigating anomalies, on-call rotations.
• Incident response: Diagnosing and resolving production issues—often at inconvenient hours.

At a fully loaded cost of $150-200/hour for a senior data engineer, that’s $3,000 to $8,000 per month in engineering time alone. Add it to infrastructure costs, and self-managed Kafka CDC typically runs $5,000 to $18,000+ per month all-in for a modest deployment.

That’s a significant budget for a system whose entire purpose is to move data from point A to point B.

The DIY Kafka CDC Stack: A Component Breakdown

If you’re still evaluating whether to build or buy, it helps to understand exactly what each component in the DIY stack does and why it’s there. This isn’t to scare you away from Kafka—it’s a brilliant piece of engineering. But you should go in with your eyes open about what you’re signing up for.

Apache Kafka Brokers

The brokers are the core of the system. They receive messages from producers (your Debezium connectors), store them durably on disk, and serve them to consumers (your sink connectors or applications). Each broker manages a set of topic partitions, and data is replicated across multiple brokers for fault tolerance.

What you manage: Broker configuration (100+ settings), JVM tuning, disk capacity planning, inter-broker replication, rack awareness, security (TLS, SASL), rolling upgrades.

ZooKeeper / KRaft

ZooKeeper is Kafka’s coordination service. It handles leader election, broker registration, topic configuration, and access control lists. The newer KRaft mode replaces ZooKeeper with a built-in consensus protocol, simplifying the architecture but introducing its own migration challenges.

What you manage: ZooKeeper ensemble health, session timeouts, leader elections, migration from ZooKeeper to KRaft (if upgrading), snapshot and log management.

Kafka Connect

Kafka Connect is the framework that runs your connectors—both source connectors like Debezium and sink connectors for delivering data to destinations. It handles task distribution across workers, automatic rebalancing when workers join or leave, and offset tracking to ensure exactly-once delivery.

What you manage: Worker configuration, connector deployment, task monitoring and restarts, plugin management, offset management, REST API security, memory tuning.

Schema Registry

Schema Registry stores and enforces schemas for your Kafka messages. When Debezium captures a change from your database, it serializes the event using Avro, Protobuf, or JSON Schema. The registry ensures that producers and consumers agree on the data format, and it enforces compatibility rules so schema changes don’t break downstream consumers.

What you manage: Schema compatibility settings (backward, forward, full), subject naming strategies, schema evolution policies, storage backend (Kafka topic), high availability.

Monitoring and Observability

Kafka exposes hundreds of JMX metrics, and you need a monitoring stack to collect, store, visualize, and alert on them. Without proper monitoring, you’re flying blind—you won’t know a broker is running out of disk space until it’s too late, or that consumer lag is increasing until your dashboards show stale data.

What you manage: Metric collection agents, dashboard creation, alert rules, log aggregation, distributed tracing for end-to-end latency visibility.

When you add all of this up, the DIY Kafka CDC stack is really five or six distinct systems that all need to work together perfectly, around the clock. Each one has its own configuration language, its own failure modes, and its own operational runbooks. For teams that want CDC as a capability rather than as a project, this is a lot to take on.

What Goes Wrong: Common Kafka CDC Failure Modes

Theory is one thing. Practice is another. Here are the failure modes that Kafka CDC operators encounter most frequently—the ones that generate the late-night pages and the multi-day debugging sessions.

Rebalancing Storms

When a Kafka Connect worker joins, leaves, or becomes unresponsive, the Connect framework triggers a rebalance—redistributing connector tasks across the remaining workers. In theory, this is a self-healing mechanism. In practice, rebalances can cascade. A slow worker triggers a rebalance, which increases load on remaining workers, which makes another worker slow, which triggers another rebalance.

During a rebalancing storm, your CDC pipeline effectively stops. No data flows until the cluster stabilizes. Teams have reported rebalancing storms lasting anywhere from minutes to hours, especially under high load or with a large number of connectors.

Connector Failures and Offset Issues

Debezium connectors track their position in the database transaction log using Kafka Connect offsets. When a connector task fails and restarts, it resumes from its last committed offset. If offsets are corrupted, lost, or inconsistent—due to a storage failure, a misconfigured connector, or a bug—the connector might skip changes (data loss) or replay changes (duplicates).

Debugging offset issues is particularly painful because the symptoms appear downstream. Your data warehouse has missing rows or duplicate records, and you have to trace the problem back through Kafka consumer offsets, Connect offsets, and Debezium’s internal state.

Schema Registry Conflicts

As your database schema evolves—new columns, type changes, dropped fields—Debezium produces events with updated schemas. If the Schema Registry’s compatibility mode is too strict, legitimate schema changes get rejected and your pipeline stops cold. If it’s too loose, incompatible changes slip through and break downstream consumers.

Getting the compatibility mode right for CDC is tricky because database schema changes don’t always map cleanly to Avro or Protobuf compatibility rules. A perfectly reasonable ALTER TABLE ADD COLUMN in PostgreSQL can produce a schema change that violates backward compatibility in the registry.

Disk Full and Partition Skew

Kafka stores messages on disk, and topic retention policies determine how long they stay. If a consumer falls behind (maybe your Snowflake sink connector was paused for maintenance), messages accumulate. If you haven’t provisioned enough disk—or if your retention policy is too generous—brokers run out of space and start dropping data.

Partition skew is a subtler problem. If your topic partitions aren’t evenly distributed across brokers, some brokers handle disproportionate load. Over time, these “hot” brokers fill up faster, respond slower, and become the bottleneck for the entire cluster.

The Operational Reality

None of these problems are unsolvable. Kafka is a mature, well-documented system, and experienced operators know how to prevent and mitigate each of these failure modes. The question is whether your team wants to become experienced Kafka operators, or whether they’d rather spend that time on the data problems that actually drive business value.

If you’re weighing the operational burden of managing Debezium yourself, our comparison of Streamkap vs Debezium breaks down exactly what you avoid with a managed approach: minutes vs days-to-weeks for setup, and zero ongoing maintenance instead of constant operational attention.

The Alternative: Managed CDC with Kafka Built In

Here’s the key insight that changes the equation: you don’t have to give up Kafka to stop managing Kafka.

Modern managed CDC platforms like Streamkap are built on Kafka internally. The same durable, ordered, exactly-once event streaming that makes Kafka great for CDC is still there under the hood. The difference is that you never see it, configure it, or worry about it.

When you set up a CDC pipeline on a managed platform, the system handles everything we just discussed—brokers, coordination, connectors, schema management, monitoring—as internal implementation details. From your perspective, you configure a source (your database), configure a destination (your warehouse), and data starts flowing. The Kafka cluster that makes it all work is fully managed and included at no extra cost.

This isn’t a compromise. You’re not trading reliability for convenience. The managed Kafka cluster running your pipelines is purpose-built for CDC workloads, tuned for low-latency change streaming, and monitored 24/7 by a team whose entire job is keeping it running.

What Managed CDC Actually Looks Like

Instead of deploying six components and writing runbooks for each, a managed CDC setup involves:

1. Connect your source database by providing credentials and selecting tables.
2. Configure your destination (Snowflake, BigQuery, Databricks, ClickHouse, or any of 50+ supported targets).
3. Optionally add transformations using SQL, Python, or TypeScript—powered by managed Apache Flink under the hood.
4. Turn it on. Data starts flowing in under a minute, with sub-250ms end-to-end latency.

That’s it. No broker tuning, no Connect worker management, no Schema Registry conflicts. Schema evolution is handled automatically—when your database schema changes, the pipeline adapts without breaking. Self-healing pipelines detect and recover from transient failures without human intervention.

The platform takes care of the infrastructure so you can focus on the data. That’s the fundamental shift.

DIY vs Managed CDC: Side-by-Side Comparison

To make the trade-offs concrete, here’s a detailed comparison of building your own Kafka CDC stack versus using a managed CDC platform.

The pattern is consistent: DIY gives you maximum control at maximum cost, while managed CDC gives you the outcome you actually want—reliable, low-latency data streaming—at a fraction of the effort and expense.

For teams evaluating the full spectrum of options, our comparisons against Confluent and Redpanda highlight the specific trade-offs. The Confluent comparison is especially relevant here: Confluent’s pricing model layers CKU costs, connector fees, storage charges, and network egress into a complex bill that’s hard to predict. Streamkap’s all-inclusive pricing means no surprises.

When You Still Need Your Own Kafka

Managed CDC is the right answer for the majority of teams whose primary goal is getting database changes into analytics systems. But there are legitimate scenarios where you need your own Kafka cluster, and it’s important to be honest about that.

Event-Driven Architectures

If your organization is building a full event-driven architecture—where microservices communicate through events, event sourcing is your persistence model, and you need complex stream processing with custom topologies—you likely need your own Kafka cluster (or a managed Kafka service). CDC is just one of many event sources in this architecture, and the Kafka cluster serves a much broader purpose than just moving database changes.

Multi-Consumer Patterns

When the same CDC events need to be consumed by many different systems—a warehouse, a search index, a cache, a notification service, an ML feature store—Kafka’s multi-consumer model is powerful. Each consumer reads independently at its own pace, without affecting others. If you have five or more distinct consumers for the same event stream, a shared Kafka cluster becomes more efficient than delivering to each destination individually.

Existing Kafka Investment

If your organization already operates a Kafka cluster with a trained operations team, adding CDC to that cluster is incremental effort rather than greenfield. The operational overhead is already paid for. In this case, running Debezium on your existing Kafka Connect cluster can be a pragmatic choice.

Regulatory and Compliance Requirements

Some industries require that data never leaves a specific network boundary, that you control the encryption keys, or that you have full auditability of every component in the data path. While managed platforms increasingly support these requirements, some compliance frameworks mandate self-hosted infrastructure.

The key question to ask yourself: Is Kafka the goal, or is getting data to my destination the goal? If it’s the former, manage Kafka. If it’s the latter, let someone else manage it for you.

For teams who need a Kafka-compatible message broker but want something simpler to operate, our Streamkap vs Redpanda comparison clarifies the distinction between a message broker and a complete CDC platform.

Bring Your Own Kafka: Best of Both Worlds

Here’s where things get interesting. What if you could have managed CDC and your own Kafka cluster? Not as an either/or, but as a both/and?

Streamkap’s Bring Your Own Kafka (BYOK) feature lets you connect your existing Kafka infrastructure to the managed CDC platform. This means you can:

• Produce CDC events to your Kafka cluster alongside delivering them to warehouses and other destinations. Your microservices can consume the same change events directly from your Kafka topics.
• Consume from your Kafka topics using Streamkap as a managed sink, delivering Kafka messages to destinations like Snowflake or BigQuery without writing custom consumers.
• Connect to any Kafka-compatible system, including Confluent Cloud, Amazon MSK, self-managed Apache Kafka, and Kafka-compatible alternatives.
• Route to multiple Kafka clusters with multi-cluster support, sending different event streams to different clusters based on your architecture needs.

This bidirectional Kafka integration means you don’t have to choose. Start with fully managed Kafka (included at no extra cost in every Streamkap plan). When your architecture evolves and you need your own Kafka consumers, add BYOK without changing your CDC pipeline. Or start with BYOK from day one if you already have Kafka.

The beauty of this approach is that CDC—the thing that requires the most operational attention in a Kafka deployment—is fully managed, while your own Kafka cluster handles the use cases where you genuinely need direct access to the message broker.

To understand how Streamkap’s platform handles this internally, our architecture page walks through the Kafka and Flink layers that power the managed pipelines.

Getting Started Without Kafka Complexity

If you’ve made it this far, you’re probably in one of two situations. Either you’re currently managing a Kafka CDC stack and you’re tired of the operational burden, or you’re about to build one and you’re wondering if there’s a better way.

In both cases, the path forward is the same: start with managed CDC and add your own Kafka later if and when you need it.

What a Migration Looks Like

For teams already running DIY Kafka CDC, migrating to a managed platform typically follows this pattern:

1. Set up the managed CDC pipeline pointing at the same source database. This takes minutes, not weeks.
2. Run both pipelines in parallel while you verify that the managed platform captures all events correctly.
3. Cut over your destination to consume from the managed pipeline instead of your Kafka sink connector.
4. Decommission the DIY stack once you’ve confirmed everything works. Or keep your Kafka cluster for other use cases and use BYOK to feed it from the managed CDC platform.

Teams regularly report that the entire migration—from first login to production cutover—takes days rather than the weeks or months they expected. SpotOn, for example, achieved 4x faster performance and 3x lower total cost of ownership after switching to Streamkap from their previous self-managed setup.

What You Get Out of the Box

With Streamkap, every plan—starting at $600/month—includes:

• Managed Kafka at no extra cost. No brokers to configure, no ZooKeeper to babysit, no Connect workers to monitor.
• 50+ CDC-optimized connectors for sources like PostgreSQL, MySQL, MongoDB, SQL Server, Oracle, and DynamoDB, plus destinations like Snowflake, BigQuery, Databricks, ClickHouse, and Apache Iceberg.
• Sub-250ms end-to-end latency from database change to destination delivery.
• Automatic schema evolution that handles database schema changes without breaking pipelines.
• Self-healing pipelines that detect and recover from transient failures automatically.
• Managed Apache Flink for in-stream transformations using SQL, Python, or TypeScript.
• BYOK support to connect to your own Kafka clusters whenever you’re ready.

You can explore the full pricing details to see how plans compare and what’s included at each tier.

Try It Today

The fastest way to understand the difference between managing Kafka for CDC and having CDC just work is to experience it yourself. Streamkap offers a free trial—no credit card required, no infrastructure to provision, no weeks of setup.

Connect your database, pick a destination, and watch data flow in under five minutes. If you’ve spent the last few weeks (or months) fighting with Kafka Connect rebalances and Schema Registry conflicts, you’ll feel the difference immediately.

Start your free trial and get CDC without the Kafka management job.