Debezium vs AWS DMS: Choosing the Right Change Data Capture Solution

Selecting a Change Data Capture solution represents a critical architectural decision that impacts data freshness, operational complexity, and integration patterns for years. Debezium and AWS Database Migration Service (DMS) stand as two prominent CDC options, each with distinct philosophies, capabilities, and operational models. Debezium offers open-source flexibility and deep integration with streaming platforms, while DMS provides managed convenience within the AWS ecosystem. Understanding the nuanced differences between these tools—beyond marketing claims—enables informed decisions aligned with your technical requirements and organizational constraints.

This article dissects the practical distinctions between Debezium and DMS across architecture, operational characteristics, use case fit, and total cost of ownership. Rather than simply listing features, we examine how these differences manifest in real-world implementations and which scenarios favor each solution.

Architectural Philosophy and Integration Patterns

Debezium and DMS approach Change Data Capture from fundamentally different architectural perspectives, shaping how they integrate into data platforms and the flexibility they offer.

Debezium’s streaming-first architecture centers on Apache Kafka. Debezium functions as a Kafka Connect source connector, capturing database changes and publishing them as Kafka topics. Each database table maps to a topic, with change events flowing through Kafka’s distributed streaming platform. This design makes Debezium ideal for event-driven architectures where multiple consumers process the same change streams for different purposes—one consumer updates a search index, another maintains a cache, a third feeds analytics pipelines.

The Kafka-centric model provides powerful capabilities. Consumers can process events at their own pace without impacting database performance. Events persist in Kafka for configurable retention periods, allowing new consumers to process historical changes. Multiple consumer groups independently track their progress through event streams. Kafka’s exactly-once semantics, when properly configured, guarantee that events process exactly once despite failures.

However, this architecture requires operating Kafka infrastructure—managing clusters, partitions, replication, and monitoring. Even using managed Kafka services like Confluent Cloud or AWS MSK, you need expertise in streaming patterns, offset management, and consumer group coordination.

DMS takes a managed service approach where AWS handles infrastructure complexity. You configure source and target endpoints, define replication tasks, and DMS manages the actual data movement. DMS supports various targets beyond streaming platforms—relational databases (RDS, Aurora), data warehouses (Redshift), S3, DynamoDB, Kinesis, and Elasticsearch. This flexibility allows direct replication to destination systems without intermediate streaming layers.

DMS offers two replication modes: full load with ongoing replication (initial snapshot plus continuous CDC) or CDC only. The full load capability proves valuable when establishing new replicas or rebuilding downstream systems—DMS snapshots the entire source database then switches to CDC automatically. Debezium focuses purely on CDC, requiring separate tooling for initial snapshots.

The architectural difference manifests in deployment patterns. Debezium deployments typically include: source database, Kafka Connect cluster running Debezium connectors, Kafka cluster storing change events, and consumer applications processing events. DMS deployments are simpler: source database, DMS replication instance, and target system. DMS abstracts away the intermediate layers, reducing operational surface area at the cost of flexibility.

Database Support and Source Compatibility

The databases you need to replicate from significantly influence tool selection, as Debezium and DMS differ substantially in their source database support and implementation quality.

Debezium provides deep integration with specific databases through dedicated connectors. The project maintains first-class support for PostgreSQL, MySQL, MongoDB, SQL Server, Oracle, Db2, and Cassandra. These connectors parse database transaction logs directly—PostgreSQL’s write-ahead log (WAL), MySQL’s binlog, SQL Server’s transaction log—capturing changes with minimal database impact. Debezium understands database-specific features like PostgreSQL’s logical decoding, MySQL’s GTID-based replication, and SQL Server’s change tracking.

This depth enables sophisticated capabilities. Debezium’s PostgreSQL connector supports logical replication slots, preventing log cleanup until consumers catch up, and avoiding data loss during consumer downtime. The MySQL connector handles complex replication topologies and different binlog formats. The MongoDB connector understands replica set oplog semantics. This database-specific expertise delivers reliable CDC for supported databases.

However, Debezium’s coverage is selective. If you need CDC from databases outside its supported list—SAP HANA, Teradata, Informix, or proprietary databases—Debezium isn’t an option. The project focuses on depth over breadth, delivering excellent support for common databases rather than mediocre support for everything.

DMS offers broader database coverage supporting 20+ source databases including commercial options like Oracle, SQL Server, IBM Db2, SAP ASE, and open-source alternatives like PostgreSQL, MySQL, MariaDB, MongoDB, and Amazon-specific databases like Aurora and DocumentDB. This breadth makes DMS suitable for heterogeneous environments with diverse database technologies.

The tradeoff is that DMS’s implementation quality varies across sources. Popular databases like PostgreSQL, MySQL, Oracle, and SQL Server receive excellent support with mature CDC implementations. Less common sources may have limitations—occasional gaps in datatype support, quirks with schema changes, or edge cases requiring workarounds. AWS continuously improves DMS, but newer source database support takes time to mature.

Specific database considerations:

• PostgreSQL: Both tools offer excellent support. Debezium provides slightly more configuration flexibility and sophisticated error handling. DMS integrates more seamlessly with RDS and Aurora PostgreSQL, handling version upgrades and maintenance windows automatically.
• MySQL: Debezium excels with MySQL, offering robust handling of DDL changes and complex binlog parsing. DMS works well but occasionally struggles with schema changes requiring replication task restarts.
• Oracle: Both support Oracle, but licensing matters. Debezium requires Oracle LogMiner or GoldenGate, which may have licensing implications. DMS includes Oracle CDC capability without additional Oracle licensing for supported versions.
• SQL Server: Debezium and DMS both leverage SQL Server’s CDC or change tracking features. DMS’s managed approach simplifies configuration, while Debezium offers more control over snapshot and incremental modes.
• MongoDB: Debezium provides excellent MongoDB support through oplog parsing. DMS supports MongoDB but with some limitations on certain document structures and change operations.

For organizations with standardized database platforms (all PostgreSQL, or MySQL/Aurora exclusively), either tool works. Multi-database environments favor DMS’s breadth, while database-specific optimization needs favor Debezium’s depth.

Operational Characteristics and Management Overhead

Operating CDC in production involves monitoring, troubleshooting, scaling, and maintaining infrastructure—operational realities that differ dramatically between Debezium and DMS.

Debezium’s operational model requires managing multiple infrastructure components. Kafka Connect workers run Debezium connectors, requiring capacity planning, instance sizing, and scaling decisions. Kafka clusters need monitoring for broker health, partition balance, disk usage, and consumer lag. Connector health monitoring tracks offset progression, error rates, and snapshot completion. Schema registry (if used) requires its own operational attention.

This distributed nature provides flexibility—you can scale Kafka Connect workers independently from Kafka brokers, add consumers without impacting sources, and tune each component for your workload. However, it also multiplies potential failure points. Connector crashes, Kafka broker failures, network partitions, and consumer lag all require different troubleshooting approaches and operational responses.

Monitoring Debezium deployments requires tracking metrics across layers: connector-specific metrics (snapshot progress, log position), Kafka Connect metrics (task status, offset storage), Kafka metrics (throughput, lag, partition distribution), and consumer metrics (processing rate, error frequency). Tools like Prometheus, Grafana, and Confluent Control Center help, but you’re responsible for configuring, interpreting, and responding to these metrics.

DMS simplifies operations through its managed service model. You provision replication instances (specifying compute capacity and storage), configure source/target endpoints, and create replication tasks. AWS handles instance patching, monitoring, and basic failure recovery. When replication instances fail, DMS automatically provisions replacements and resumes tasks. Storage automatically expands as change volume grows.

CloudWatch provides built-in monitoring for DMS tasks—tracking CDC latency, full load progress, error counts, and validation metrics. CloudWatch alarms alert you when tasks fall behind or encounter errors. The AWS console offers visual task monitoring showing current state, applied changes, and performance metrics without requiring custom dashboards.

However, DMS’s managed nature limits optimization flexibility. Instance types follow AWS’s predefined sizes—if your workload needs more CPU but less memory than available instance types provide, you pay for unneeded capacity. You cannot tune underlying CDC mechanisms, adjust buffer sizes, or optimize change batch processing like you can with Debezium.

Troubleshooting and debugging differ significantly. Debezium provides detailed logging and configuration options for debugging—you can adjust log levels, examine connector state, inspect Kafka topics directly, and modify connector configurations on the fly. When issues occur, you have full visibility into every layer, though this visibility requires expertise to interpret.

DMS troubleshooting happens through CloudWatch logs and task error tables. While AWS provides reasonable visibility, you’re working within DMS’s abstractions—when tasks fail mysteriously, you’re dependent on AWS support to investigate lower-level issues. The tradeoff is that many common problems (connection failures, transient errors, minor schema issues) DMS handles automatically through retries and self-healing mechanisms.

Performance, Scalability, and Throughput Characteristics

Understanding how Debezium and DMS handle varying data volumes, change rates, and scaling requirements helps predict production behavior and identify potential bottlenecks.

Debezium’s performance characteristics closely tie to Kafka’s distributed architecture. Because change events flow through Kafka topics with configurable partitioning, Debezium scales horizontally naturally. High-volume tables can use multiple Kafka partitions, allowing parallel processing by consumer groups. A table receiving 100,000 updates per minute can be partitioned across 10 Kafka partitions, enabling 10 consumers to process changes concurrently.

Typical Debezium deployments handle 10,000-50,000 change events per second on properly sized infrastructure. Peak throughput depends primarily on Kafka cluster capacity rather than Debezium itself. Kafka Connect workers running Debezium connectors are generally not the bottleneck—database log parsing and Kafka produce operations consume most resources.

Latency in Debezium deployments typically ranges from 100ms to a few seconds under normal conditions. Low latency requires tuning across the stack—connector fetch sizes, Kafka producer batch settings, consumer fetch configurations, and network optimization. Properly tuned Debezium deployments achieve sub-second end-to-end latency (database commit to consumer processing) for most operations.

DMS performance depends heavily on replication instance sizing and workload characteristics. DMS processes changes on replication instances—compute resources you provision and pay for. Instance types range from small (t3.micro with 1 vCPU) to large (r5.24xlarge with 96 vCPUs), supporting workloads from a few hundred changes per second to tens of thousands.

AWS publishes few specific throughput numbers, but practical experience suggests DMS efficiently handles moderate workloads—5,000-10,000 changes per second on appropriately sized instances. Larger instances support higher throughput, but scaling has limits. Unlike Debezium’s distributed architecture enabling near-infinite horizontal scaling through Kafka partitioning, DMS primarily scales vertically within replication instance constraints.

DMS introduces higher baseline latency than Debezium—typically 2-10 seconds from source commit to target application under normal conditions. This latency stems from DMS’s batch-oriented processing model. DMS accumulates changes into batches before applying them to targets, optimizing throughput over latency. For use cases where 5-10 second latency suffices (data warehousing, analytics, reporting), this tradeoff works well. For near-real-time applications requiring sub-second data propagation, Debezium’s streaming model delivers better latency.

Scaling patterns differ fundamentally: Debezium scales by adding Kafka partitions and consumer instances—linear scaling limited primarily by database log parsing capacity and Kafka cluster size. DMS scales by upgrading replication instances to larger types—vertical scaling with inherent upper bounds. Organizations processing millions of daily changes across hundreds of tables may hit DMS scaling limits where Debezium’s distributed model continues scaling effectively.

Initial load performance (full database snapshots) also differs. DMS excels at initial loads, offering optimized bulk data transfer with parallel table loading, automatic resume on failure, and progress tracking. Debezium’s snapshot mechanism works reliably but provides fewer optimization options for massive initial loads. For migration scenarios requiring 100GB+ initial loads before starting CDC, DMS’s optimized snapshot handling provides advantage.

Cost Considerations and Total Ownership Analysis

The financial implications of Debezium versus DMS extend beyond obvious infrastructure costs to include engineering time, operational overhead, and scaling economics.

Debezium’s cost structure centers on infrastructure you provision and manage. For self-managed deployments, costs include EC2 instances running Kafka Connect workers and Kafka brokers, EBS storage for Kafka data retention, and network transfer fees. A typical small-scale Debezium deployment might cost $300-600 monthly: 3 Kafka brokers (m5.large), 2 Kafka Connect workers (m5.large), and associated storage.

Managed Kafka services shift this cost model. AWS MSK (Managed Streaming for Kafka) costs approximately $200-400 monthly for small clusters (3 brokers, kafka.m5.large) plus storage and data transfer. Confluent Cloud’s pay-per-use pricing starts around $1-2 per million messages, scaling with actual usage. These managed offerings reduce operational burden while increasing per-unit costs compared to self-managed infrastructure.

However, infrastructure represents only part of total cost. Debezium requires engineering expertise—initial setup, ongoing maintenance, troubleshooting, and scaling. Organizations lacking Kafka expertise often underestimate this learning curve. A senior engineer spending 20% of their time managing Debezium infrastructure represents $30,000-50,000 annual cost (assuming $150,000-250,000 fully loaded salary). For small deployments, this expertise cost exceeds infrastructure costs.

DMS pricing follows AWS’s managed service model. You pay for replication instances by the hour plus data transfer. A dms.c5.large instance costs approximately $0.24/hour or $175/month continuously running. Larger workloads requiring dms.c5.4xlarge instances cost around $1.53/hour or $1,100/month. Additional costs include storage (change processing buffers) and data transfer out from AWS.

DMS’s operational simplicity reduces engineering costs. Engineers configure replication tasks through AWS console or APIs without managing infrastructure, monitoring through CloudWatch without custom dashboards, and troubleshooting through AWS support when needed. A single engineer can manage dozens of DMS tasks part-time, whereas Debezium deployments typically require dedicated attention.

Break-even analysis depends on scale and expertise: For small deployments (single database, low change volume, limited CDC targets), DMS often proves cheaper when factoring in engineering costs. A $200/month DMS instance requiring minimal management beats a $400 infrastructure + $2,000/month engineering time Debezium deployment.

For large-scale deployments (multiple databases, high throughput, many consumers), economics shift. DMS costs scale linearly with databases requiring replication—each needs its own replication task and contributes to instance sizing requirements. Debezium’s shared infrastructure scales more economically—one Kafka cluster serves multiple connectors and unlimited consumers.

Consider a scenario replicating 10 databases to multiple targets (data warehouse, search index, cache, analytics platform). DMS requires 10 replication tasks potentially across multiple replication instances—perhaps $1,500-2,000 monthly. Each target requires separate DMS tasks or custom applications consuming from Kinesis (if using DMS to Kinesis). Debezium needs one connector per database publishing to Kafka, then multiple consumers reading shared streams—perhaps $800 infrastructure plus engineering overhead, with better scaling characteristics as consumer count grows.

Hidden costs to consider:

• DMS change processing errors: When DMS tasks fail, they often require manual investigation and intervention. Frequent errors consume engineering time despite managed infrastructure.
• Debezium consumer development: Building consumer applications adds development cost beyond infrastructure. Each consumer needs error handling, state management, and monitoring.
• Network transfer costs: Both solutions incur AWS data transfer charges. Cross-region or internet egress can exceed compute costs for high-volume replication.
• Database impact: CDC implementation quality affects source database performance. Poorly configured CDC can impact production databases, potentially requiring database upgrades to maintain performance—costs attributable to CDC choice.

Use Case Fit and Decision Framework

Different scenarios favor Debezium or DMS based on specific requirements, constraints, and priorities. Understanding these patterns helps match tools to needs effectively.

DMS fits best when:

• You need database migration with ongoing replication: Migrating from on-premises Oracle to AWS Aurora with cutover planning benefits from DMS’s full load plus CDC mode. The managed service handles massive initial loads then seamlessly switches to CDC.
• Replicating to a single target system: Point-to-point replication from production databases to data warehouses, analytics databases, or disaster recovery replicas works excellently with DMS. You avoid streaming infrastructure overhead when you don’t need fan-out to multiple consumers.
• Operating primarily within AWS ecosystem: Organizations heavily invested in AWS with RDS sources and AWS targets (Redshift, S3, Aurora) leverage DMS’s native integration. Configuration, monitoring, and operations feel native to AWS workflows.
• Limited streaming infrastructure expertise: Small teams without Kafka experience or organizations prioritizing operational simplicity over flexibility benefit from DMS’s managed approach. AWS Support handles infrastructure issues, freeing teams to focus on business logic.
• Heterogeneous database environment: Replicating from diverse sources (Oracle, SQL Server, PostgreSQL, MongoDB) to consolidated targets benefits from DMS’s broad database support. Debezium might require multiple open-source projects or custom connectors.

Debezium excels when:

• Building event-driven architectures: Multiple applications reacting to database changes—updating caches, refreshing search indexes, triggering workflows, feeding analytics—benefit from Debezium’s event streaming model. Each consumer independently processes shared event streams.
• You already operate Kafka infrastructure: Organizations with existing Kafka deployments for messaging, event streaming, or data integration add CDC capability to existing infrastructure rather than introducing separate DMS replication instances.
• Fine-grained control and optimization: Advanced use cases requiring custom processing, sophisticated error handling, or specific performance optimization benefit from Debezium’s configurability and transparency into streaming operations.
• Need event replay and time travel: Kafka’s retention enables reprocessing historical changes. New analytics might process three months of historical changes from Kafka. This capability proves valuable for debugging, auditing, or adding new consumers requiring historical context.
• Standardized on open-source technologies: Organizations preferring open-source solutions for avoiding vendor lock-in, on-premises deployment, or contributing to community development choose Debezium over proprietary AWS services.

Conclusion

Debezium and AWS DMS represent contrasting approaches to Change Data Capture—open-source streaming flexibility versus managed service simplicity. Debezium delivers superior scalability, latency, and architectural flexibility for event-driven systems, particularly valuable when multiple consumers need access to change streams. DMS provides managed convenience, broader database support, and operational simplicity, excelling at point-to-point replication and database migrations within AWS.

The choice hinges on your organization’s specific context: technical requirements, team expertise, existing infrastructure, and operational preferences. Teams with Kafka experience building event-driven architectures naturally gravitate toward Debezium. Organizations preferring managed services and operating primarily within AWS find DMS’s simplicity compelling. Both tools successfully enable CDC—success depends on matching tool characteristics to your actual needs rather than selecting based on popularity or perceived technical sophistication.