Change Data Capture (CDC) has become essential for modern data architectures that demand real-time synchronization between operational databases and analytical systems. Traditional batch ETL processes introduce latency that can render data obsolete by the time it reaches downstream consumers. By combining Debezium’s robust CDC capabilities with AWS Kinesis Firehose’s managed streaming service, you can build reliable, low-latency pipelines that capture every database change and deliver it to your data lake or warehouse within seconds. This architectural pattern has proven itself across industries, from financial services tracking transactions to e-commerce platforms maintaining inventory consistency.
Understanding the CDC Architecture Foundation
Before diving into implementation details, it’s crucial to understand how Debezium and Kinesis Firehose complement each other in a CDC pipeline. Debezium operates at the database level, reading transaction logs to capture every INSERT, UPDATE, and DELETE operation. It doesn’t query tables or add load to your production database—instead, it monitors the same write-ahead logs that databases use for replication and recovery. This approach ensures you capture changes with minimal performance impact.
Kinesis Firehose serves as the delivery mechanism that bridges the gap between real-time change streams and batch-oriented storage systems. While Debezium produces a continuous stream of change events, your data lake built on S3 works best with larger files written periodically. Firehose handles this impedance mismatch by buffering events, applying transformations if needed, and writing optimally-sized files to your destination. This managed service eliminates the operational burden of running your own buffering and delivery infrastructure.
The complete pipeline flow follows this pattern: database operations trigger transaction log entries, Debezium detects these entries and converts them to structured change events, Kafka temporarily stores these events for durability, a Kafka Connect sink forwards events to Kinesis Firehose, Firehose buffers and batches the data, and finally writes consolidated files to S3, Redshift, or other supported destinations. Each component plays a specific role, and understanding these responsibilities helps you configure the pipeline appropriately.
CDC Pipeline Architecture Flow
Configuring Debezium for Optimal Change Capture
Debezium supports multiple databases including PostgreSQL, MySQL, MongoDB, SQL Server, and Oracle. Each database requires specific configuration to enable CDC and grant Debezium appropriate permissions. The configuration choices you make here significantly impact pipeline reliability and performance.
Database-Specific Setup Requirements
For PostgreSQL, you must enable logical replication by setting wal_level = logical in postgresql.conf and creating a replication slot that Debezium uses to track its position in the write-ahead log. This replication slot is crucial—if Debezium goes offline, the slot prevents PostgreSQL from discarding log entries, ensuring no changes are lost. However, if Debezium remains offline for extended periods, these retained logs can consume significant disk space.
MySQL configuration requires enabling the binary log with row-level event format. Set binlog_format = ROW and binlog_row_image = FULL to ensure Debezium captures complete before and after images of changed rows. The FULL row image setting is particularly important because it allows downstream consumers to see both the old and new values, enabling sophisticated change processing logic.
Debezium Connector Configuration
Deploy Debezium connectors through Kafka Connect, which provides a distributed, fault-tolerant runtime for running connectors. The connector configuration defines what to capture and how to format the change events. Here’s a comprehensive configuration for a PostgreSQL source:
{
"name": "inventory-connector",
"config": {
"connector.class": "io.debezium.connector.postgresql.PostgresConnector",
"database.hostname": "postgres-prod.example.com",
"database.port": "5432",
"database.user": "debezium_user",
"database.password": "${file:/secrets/db-password.txt:password}",
"database.dbname": "inventory",
"database.server.name": "inventory_db",
"table.include.list": "public.orders,public.order_items,public.inventory",
"plugin.name": "pgoutput",
"publication.autocreate.mode": "filtered",
"slot.name": "debezium_inventory_slot",
"heartbeat.interval.ms": "10000",
"snapshot.mode": "initial",
"transforms": "unwrap",
"transforms.unwrap.type": "io.debezium.transforms.ExtractNewRecordState",
"transforms.unwrap.drop.tombstones": "false",
"transforms.unwrap.delete.handling.mode": "rewrite",
"transforms.unwrap.add.fields": "op,ts_ms,source.table",
"key.converter": "org.apache.kafka.connect.json.JsonConverter",
"value.converter": "org.apache.kafka.connect.json.JsonConverter",
"value.converter.schemas.enable": "false"
}
}
Several configuration parameters deserve detailed explanation. The table.include.list restricts CDC to specific tables, preventing unnecessary change capture for tables you don’t need to replicate. The heartbeat.interval.ms setting ensures Debezium periodically updates its position even when no changes occur, preventing the replication slot from growing unbounded during quiet periods.
The snapshot.mode parameter controls initial behavior when the connector first starts. The initial setting captures the current state of all included tables before switching to ongoing change capture. This ensures your downstream systems have a complete picture of the data. Alternative modes like schema_only skip the initial data snapshot if you’re only interested in changes going forward.
The transforms configuration, particularly ExtractNewRecordState, simplifies the change event structure by extracting just the after-state for inserts and updates, making downstream processing easier. The add.fields parameter enriches events with metadata like operation type and source table, which proves invaluable for routing and filtering downstream.
Handling Schema Evolution
Database schemas change over time—columns get added, types get modified, and tables get restructured. Debezium automatically captures these schema changes and includes them in the change stream. However, you must consider how Kinesis Firehose and your final destination handle schema evolution.
Firehose doesn’t perform schema validation or evolution—it simply delivers the data you provide. If you’re writing to S3 in JSON format, schema changes flow through naturally. However, if you’re writing to Redshift or converting to Parquet with AWS Glue, you need strategies for handling schema changes:
- Schema versioning: Include schema version in each record, allowing downstream processing to handle different versions appropriately
- Schema registry integration: Use Confluent Schema Registry or AWS Glue Schema Registry to manage schemas centrally and validate compatibility
- Additive-only changes: Establish policies that only allow adding new columns, never removing or changing existing ones, simplifying downstream handling
- Schema evolution alerts: Monitor for schema changes and alert data engineering teams to update downstream processing logic
Integrating Kafka Connect with Kinesis Firehose
Moving change events from Kafka to Kinesis Firehose requires the Kinesis Firehose connector for Kafka Connect. This connector reads messages from Kafka topics and writes them to specified Firehose delivery streams, handling authentication, retries, and error handling.
Firehose Connector Configuration
Install the Amazon Kinesis Firehose Kafka connector plugin in your Kafka Connect cluster. This connector acts as a sink, consuming messages from Kafka topics that Debezium populates. The configuration defines the mapping between Kafka topics and Firehose delivery streams:
{
"name": "firehose-sink-connector",
"config": {
"connector.class": "com.amazon.kinesis.kafka.FirehoseSinkConnector",
"tasks.max": "3",
"topics": "inventory_db.public.orders,inventory_db.public.order_items",
"region": "us-east-1",
"deliveryStream": "cdc-data-stream",
"batch.size": "500",
"batch.size.bytes": "3670016",
"linger.ms": "1000",
"rate.limit": "500",
"max.connections": "50",
"connector.error.retry.ms": "5000",
"connector.error.retry.max.count": "10"
}
}
The batch.size and batch.size.bytes parameters control how many records or how much data the connector accumulates before writing to Firehose. Larger batches improve throughput but increase latency. The linger.ms setting provides a time-based trigger—even if the batch size isn’t reached, the connector flushes after this duration. Tuning these parameters requires understanding your throughput requirements and acceptable latency.
The rate.limit parameter protects Firehose from being overwhelmed during traffic spikes. Firehose has service limits on requests per second, and exceeding these limits results in throttling. The connector’s rate limiting ensures you stay within these bounds. The max.connections setting controls connection pooling to Firehose, and tuning it based on your connector task count improves efficiency.
Topic Routing and Multiple Firehose Streams
In many scenarios, you want different tables routed to different Firehose delivery streams based on destination requirements, data sensitivity, or processing needs. While the basic connector configuration sends all topics to a single stream, you can deploy multiple connector instances, each configured for specific topics and destinations.
Alternatively, use Single Message Transforms (SMTs) to modify routing keys or record headers that determine the destination stream. You can also implement custom SMTs that route based on record content, allowing sophisticated routing logic like sending high-priority orders to a different stream than regular orders.
Optimizing Kinesis Firehose for CDC Workloads
Kinesis Firehose provides several configuration options that significantly impact your pipeline’s cost, latency, and reliability. Understanding these options helps you tune Firehose for your specific CDC requirements.
Buffer Size and Interval Configuration
Firehose buffers incoming records before writing them to the destination. You control buffering through two parameters: buffer size (in MB) and buffer interval (in seconds). Firehose flushes data whenever either threshold is reached. For CDC workloads with steady throughput, configure these values based on your latency tolerance and desired file sizes.
Smaller buffer intervals (60-120 seconds) minimize latency but create many small files, increasing storage costs and complicating downstream processing. Larger intervals (300-900 seconds) create fewer, larger files but introduce more latency. For data lakes, 128-256 MB buffer sizes typically provide a good balance, producing files large enough for efficient Spark processing without excessive delay.
CDC workloads often have variable throughput—busy during business hours and quiet overnight. During quiet periods, the buffer interval becomes the primary trigger, meaning you might write very small files. Consider implementing time-based partitioning in your S3 prefix pattern and using AWS Glue or other tools to compact small files during off-peak hours.
Data Transformation and Format Conversion
Firehose supports inline data transformation through AWS Lambda functions and format conversion using AWS Glue. For CDC pipelines, these capabilities prove valuable for several use cases.
Lambda transformations allow you to enrich, filter, or restructure change events before delivery. You might add geolocation data based on IP addresses, filter out personally identifiable information for compliance, or aggregate multiple related changes into a single record. Keep Lambda processing lightweight—complex transformations introduce latency and can cause Lambda timeouts under high load.
Format conversion is particularly useful when writing to data lakes. Debezium produces JSON change events, but analytical queries perform better against columnar formats like Parquet. Configure Firehose to convert JSON to Parquet using an AWS Glue table schema:
{
"DataFormatConversionConfiguration": {
"SchemaConfiguration": {
"DatabaseName": "cdc_catalog",
"TableName": "orders",
"Region": "us-east-1",
"RoleARN": "arn:aws:iam::123456789012:role/FirehoseGlueRole"
},
"InputFormatConfiguration": {
"Deserializer": {
"OpenXJsonSerDe": {}
}
},
"OutputFormatConfiguration": {
"Serializer": {
"ParquetSerDe": {
"Compression": "SNAPPY"
}
}
},
"Enabled": true
}
}
This configuration requires maintaining Glue table schemas that match your change event structure. When Debezium captures schema changes, you must update the corresponding Glue tables to prevent conversion failures. Implement monitoring that alerts when conversion errors occur, indicating schema drift.
Error Handling and Recovery Strategies
Firehose can fail to deliver records for various reasons—S3 access denied, invalid data format, Lambda transformation failures, or service issues. Understanding Firehose’s error handling behavior helps you build resilient pipelines.
When delivery fails, Firehose retries according to configured retry parameters. If retries are exhausted, Firehose writes failed records to an S3 error bucket you specify. Monitor this error bucket vigilantly—accumulating errors indicate pipeline problems requiring immediate attention. Common error patterns include:
- Format conversion failures: Schema mismatches between change events and Glue table definitions
- Lambda timeout errors: Transformation logic taking too long for the configured Lambda timeout
- Destination access errors: IAM permission issues preventing Firehose from writing to S3 or Redshift
- Service quota errors: Exceeding Firehose or destination service limits during traffic spikes
Implement automated alerting on error bucket writes and establish runbooks for investigating and resolving each error type. For critical pipelines, consider implementing a secondary processing path that consumes from the error bucket, applies fixes, and redelivers data.
⚙️ Pipeline Configuration Checklist
Debezium Configuration
☑ Table filters defined
☑ Heartbeat interval set
☑ Snapshot mode selected
☑ Transform chain configured
Kafka Connect Setup
☑ Error handling configured
☑ Offset management verified
☑ Monitoring enabled
☑ Dead letter queue defined
Firehose Settings
☑ Buffer interval tuned
☑ Error bucket configured
☑ IAM roles properly scoped
☑ CloudWatch alarms created
Monitoring and Operational Considerations
A production CDC pipeline requires comprehensive monitoring across all components. Failures at any stage can cause data loss or delays that impact downstream systems.
Critical Metrics to Monitor
Implement monitoring for these key indicators across your pipeline:
Debezium Metrics:
- Replication slot lag measuring how far behind Debezium is from the current database state
- Snapshot completion status and duration for initial loads
- Error rate for individual connectors
- Events processed per second to detect throughput degradation
- Connector uptime and restart frequency
Kafka Connect Metrics:
- Task status ensuring all tasks are running
- Offset lag for each connector and task
- Record processing rate and batch sizes
- Error and retry counts
- Dead letter queue depth if configured
Kinesis Firehose Metrics:
- Incoming records and bytes per second
- Delivery success and failure rates
- Buffer flush frequency and size
- Throttling errors indicating rate limit issues
- Data freshness measuring end-to-end latency from source to destination
Configure CloudWatch alarms for anomalies in these metrics. Alert thresholds should reflect your SLAs—if you promise five-minute data freshness, alert when data freshness exceeds four minutes, giving you time to investigate before violating your SLA.
Capacity Planning and Scaling
CDC pipelines must handle variable loads gracefully. Database activity spikes during business hours, promotional events, or batch processing windows. Your pipeline components should scale to accommodate these peaks without data loss or excessive latency.
Kafka provides natural buffering that absorbs temporary spikes—even if Firehose falls behind, Kafka retains the events until they’re consumed. Configure Kafka retention based on the longest acceptable recovery time. If you need to handle a six-hour Firehose outage without data loss, set topic retention to at least twelve hours to provide buffer.
Scale Kafka Connect by increasing the number of tasks for your connectors. The Firehose sink connector supports parallel tasks, each handling a subset of Kafka topic partitions. Match task count to Kafka partition count for optimal parallelism. If you have ten partitions, configure ten tasks to maximize throughput.
Kinesis Firehose scales automatically, but it has service limits on throughput per delivery stream. If a single stream can’t handle your throughput, shard your data across multiple streams based on table, tenant, or other logical boundaries. This approach also improves downstream processing by separating data into more manageable units.
Conclusion
Building an end-to-end CDC pipeline with Debezium and Kinesis Firehose provides a powerful foundation for real-time data integration. By capturing changes directly from database transaction logs, transforming them through Kafka Connect, and delivering them via Firehose’s managed infrastructure, you create a reliable pipeline that keeps downstream systems synchronized with minimal latency. The architecture’s modularity allows you to tune each component independently, optimizing for your specific requirements around latency, throughput, and cost.
Success with this architecture requires attention to configuration details and operational excellence. Properly tuned Debezium connectors minimize database impact while capturing every change reliably. Correctly configured Firehose delivery streams balance latency against file size optimization. Comprehensive monitoring across all pipeline stages enables proactive issue detection before users experience problems. By following the patterns and practices outlined here, you can build CDC pipelines that serve as the nervous system of your data infrastructure, ensuring that every change flows reliably from operational systems to analytical platforms.