Debezium CDC Platform

Debezium Change Data Capture Consulting

Executive Direct Answer · Debezium CDC Engineering Scope

Debezium is a distributed open-source Change Data Capture (CDC) platform built on Apache Kafka Connect that tails database transaction logs to stream row-level insert, update, and delete events with sub-100ms latency. JusDB delivers end-to-end Debezium SRE, schema registry integration, replication slot protection, and exactly-once pipeline resilience under a 99.99% uptime SLA.

Latency: <100ms End-to-End·Delivery: Exactly-Once Semantics·P1 Response: <15 Minutes·Engines: Postgres, MySQL, Mongo, MSSQL·Compliance: SOC 2 Aligned

Stream database changes in real-time with Debezium's log-based CDC platform. Build event-driven architectures with reliable, low-latency data streaming.

Log-based CDC: Capture changes directly from database transaction logs with minimal impact.
Kafka Integration: Stream database changes to Apache Kafka for real-time processing.
Schema Evolution: Handle schema changes gracefully with automatic schema registry integration.

CDC Capabilities

Debezium Services We Provide

From connector setup to production monitoring, we handle your entire CDC pipeline.

Log-based CDC

Capture changes directly from database transaction logs with minimal impact.

Kafka Integration

Stream database changes to Apache Kafka for real-time processing.

Schema Evolution

Handle schema changes gracefully with automatic schema registry integration.

Multi-Database Support

Connect MySQL, PostgreSQL, MongoDB, SQL Server, and more.

Fault Tolerance

Built-in resilience with automatic recovery and exactly-once delivery.

Security & Monitoring

Secure data streaming with comprehensive monitoring and alerting.

CDC Features

What's Included in Our Debezium Service

MySQL binlog capture

PostgreSQL logical replication

MongoDB change streams

SQL Server CDC integration

Kafka Connect framework

Schema registry support

Exactly-once semantics

Real-time monitoring

Information Gain · High-Consequence CDC Edge Cases

Debezium & Kafka Connect: Critical Failure Modes

Log-based streaming eliminates source query load, but misconfigured connectors can destabilize the primary database. Here are the 3 critical failure modes our Streaming SRE team permanently mitigates:

Critical · Outage Trigger

Replication Slot Unconsumed WAL Bloat & Disk Exhaustion

If a Debezium connector task pauses, stalls, or crashes, the source PostgreSQL logical replication slot or MySQL binlog stream remains open and unconsumed. The database holds all subsequent transaction logs on disk, rapidly exhausting 100% of storage and causing immediate database crash.

JusDB Engineering Mitigation:

Configuring max_slot_wal_keep_size in PostgreSQL, deploying automated replication slot heartbeat tables, and monitoring unread WAL byte thresholds with automated disconnect triggers.

High · Pipeline Stall

Incompatible DDL Schema Evolution & Kafka Poison Pills

Upstream developers alter a column type or drop a column without schema registry pre-registration. Debezium produces an event schema incompatible with downstream consumers, causing Kafka Connect sink tasks to fail repeatedly in a crash loop.

JusDB Engineering Mitigation:

Enforcing schema registry compatibility checks (BACKWARD/FULL_TRANSITIVE) via Confluent or Apicurio, deploying Single Message Transforms (SMTs) for schema masking, and configuring dead-letter queues (DLQ).

High · Data Duplication

Kafka Connect Task Rebalance Storms & Duplicate Events

Network blips or consumer group offset commit timeouts trigger frequent Kafka Connect rebalances. Debezium connectors restart from the last committed source offset, re-emitting unacknowledged change events and causing duplicate state mutations in downstream sinks.

JusDB Engineering Mitigation:

Configuring idempotent Kafka producers (enable.idempotence = true), tuning heartbeat.interval.ms and max.poll.interval.ms, and implementing deduplication keys in downstream sink consumers.

Telemetry Runbooks · Non-Blocking Debezium & Slot Forensics

Our Streaming SREs execute read-only queries to monitor replication slot lag and inspect Kafka Connect task health without halting active data streams:

PostgreSQL Logical Slot Lag & Disk Footprintpg_replication_slots

SELECT slot_name,
       plugin,
       active,
       pg_size_pretty(pg_wal_lsn_diff(pg_current_wal_lsn(), restart_lsn)) AS unconsumed_wal_lag,
       confirmed_flush_lsn
FROM pg_replication_slots
WHERE slot_type = 'logical';

Kafka Connect Debezium Connector Task HealthREST API

# Check connector task state, worker ID and error trace
curl -s http://localhost:8083/connectors/debezium-postgres-connector/status | jq '{
  name: .name,
  connector_state: .connector.state,
  tasks: [.tasks[] | {id: .id, state: .state, worker: .worker_id}]
}'

Comparative Matrix · Change Data Capture & Streaming

How JusDB Debezium CDC compares to alternative approaches.

Log-based Change Data Capture with Debezium and Kafka unlocks sub-100ms data synchronization, but demands rigorous guardrails against replication slot bloat, schema drift, and rebalance storms. Here is how JusDB compares to managed CDC offerings, batch polling, and DIY setups.

Swipe horizontally to compare CDC models→

Engineering Dimension	JusDB Debezium SRE	Managed Cloud CDC	Batch Polling ETL	DIY Kafka Connect
Transaction Log-Based Capture vs Polling	Zero source database query overhead, capturing INSERT/UPDATE/DELETE events and intermediate states directly from WAL/binlog with <50ms latency	Cloud CDC services support basic tables but introduce read latency spikes and lack transaction boundary controls	Uses batch SQL queries (WHERE updated_at > ?) causing table locking, missing deleted records, and high CPU spikes	Periodic polling jobs miss transient data mutations and overload primary database connection pools
Replication Slot & Binlog Purge Safeguards	Automated WAL/binlog retention guards, heartbeat table integration, and fail-safe alerting preventing primary disk exhaustion	Basic slot metrics provided; unconsumed replication slots lead to primary database disk full crashes without automated remediation	Unaware of logical replication slot disk consumption; risks bringing down production databases when consumers stall	Stuck Kafka consumer causes PostgreSQL pg_wal directory to fill 100% disk, forcing emergency database shutdown
Schema Evolution & Registry Compatibility	Confluent/Apicurio Schema Registry integration, Avro/Protobuf serialization, and backward-compatible DDL schema migration handling	Schema evolution often breaks downstream connectors, requiring manual restart and connector re-registration	Uses unversioned JSON payloads, causing downstream microservices to fail silently when columns are added or modified	Schema alterations in source database cause Debezium task crashes and poison pill messages in Kafka topics
Exactly-Once Delivery & Deduplication	Kafka idempotent producers, transaction coordinators, and sink deduplication strategies preventing duplicate records during rebalance	Guarantees at-least-once delivery; customer is responsible for designing application-level deduplication layers	Duplication spikes during network reconnects, corrupting downstream analytical and reporting aggregates	Consumer group rebalances cause duplicate transaction events to process twice in accounting systems
24/7 SLA & Streaming SRE Incident Response	Contractual 15-minute Sev-1 first response with direct war room access to senior streaming and CDC reliability engineers	Managed streaming support routed through general cloud queues with hours of delay for connector restart guidance	Offshore ticket dispatcher with no expertise in Kafka Connect offsets, source partitions, or Debezium metrics	Engineers struggling to inspect Debezium JMX metrics and offset topics during critical data pipeline stalls
Multi-Engine Support & Single Message Transforms (SMTs)	Production connectors for MySQL, PostgreSQL, MongoDB, SQL Server, and Oracle with custom SMT masking and event filtering	Limited connector catalog with rigid transformation capabilities and steep licensing costs for proprietary engines	Only supports simple MySQL binlog streaming without data masking or sensitive PII redaction pipelines	Unmasked customer PII streamed into Kafka topics, creating severe compliance and GDPR violations

How does Performance scale?

Our Debezium implementations deliver enterprise-grade performance.

Latency: <100ms
Throughput: 1M+ events/sec
Uptime: 99.99%
Supported DBs: 10+

Technology Stack

Debezium Ecosystem

DebeziumApache KafkaKafka ConnectConfluent PlatformApache AvroSchema RegistryApache ZookeeperPrometheus

Success Stories

Debezium CDC in Action

Real-world change data capture implementations powering event-driven architectures.

Real-Time Inventory System

E-commerce

Challenge:

Sync inventory changes across 50+ microservices in real-time

Solution:

Implemented Debezium CDC with Kafka for event-driven inventory updates

Results:

99.9% data consistency
Sub-second propagation
Zero inventory conflicts

<100ms

latency

50+

services

99.9%

consistency

Financial Transaction Streaming

FinTech

Challenge:

Stream transaction data for real-time fraud detection and analytics

Solution:

Deployed Debezium with PostgreSQL and Kafka for transaction event streaming

Results:

Real-time fraud detection
Regulatory compliance
50% faster analytics

1M TPS

throughput

50ms

latency

Real-time

detection

Customer Data Platform

SaaS

Challenge:

Unify customer data from multiple databases for 360° customer view

Solution:

Used Debezium to stream changes from MySQL, PostgreSQL, and MongoDB

Results:

Unified customer profiles
Real-time personalization
Data quality improved 90%

3 DBs

sources

10M+

profiles

+90%

quality

Client Testimonials

What Our Clients Say

Trusted by leading companies for their change data capture implementations.

"JusDB's Debezium implementation transformed our data architecture. We now have real-time data consistency across all our microservices with minimal latency."

Alex Thompson

VP of Engineering

E-commerce Platform

"The team's expertise in Kafka and Debezium helped us build a robust event-driven architecture that scales with our business growth. Exceptional technical knowledge and support."

Maria Garcia

Data Architecture Lead

FinTech Solutions

FAQ

Frequently Asked Questions

Direct technical answers from our Principal Change Data Capture and Streaming SREs.

What databases does Debezium support for change data capture?

Debezium supports MySQL, PostgreSQL, MongoDB, SQL Server, Oracle, Db2, Cassandra, and Vitess. We help you implement CDC for any of these databases with proper connector configuration and optimization.

How does Debezium ensure exactly-once delivery?

Debezium uses Kafka's exactly-once semantics combined with database transaction logs to ensure no data loss or duplication. We configure proper offset management, transaction boundaries, and idempotent producers for guaranteed delivery.

What's the typical latency for change data capture with Debezium?

Debezium typically achieves sub-100ms latency for change propagation. The actual latency depends on database configuration, network conditions, and Kafka cluster setup. We optimize all components for minimal latency.

How do you handle schema evolution in Debezium?

We implement schema registry integration with Confluent Schema Registry or Apicurio Registry to handle schema changes gracefully. This includes backward/forward compatibility rules and proper versioning strategies.

Can Debezium handle high-volume transactional databases?

Yes, Debezium is designed for high-throughput scenarios. We've implemented solutions handling millions of transactions per second with proper partitioning, parallel processing, and cluster scaling strategies.

What monitoring and alerting do you provide for Debezium?

We set up comprehensive monitoring using Prometheus, Grafana, and Kafka monitoring tools. This includes connector health, lag monitoring, error tracking, and custom alerts for proactive issue resolution.

Technical Verification & Streaming Architecture Governance

Reviewed by Ajith Daniel, Principal Database Reliability Engineer

Debezium connector configurations, Kafka Connect cluster topologies, replication slot retention safeguards, and Avro schema registries are audited and operated under ISO 27001 and SOC 2 Type II standards. Bastion access operates via ephemeral, audited sessions.

LinkedIn Profile·GitHub Repository·Verification: September 2026

SOC 2 & ISO 27001 Aligned

Ready for Real-time CDC?

Let our Debezium experts build your event-driven architecture with reliable change data capture.

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