Change Data Capture (CDC) has become essential for modern data architectures, enabling real-time analytics, audit trails, and downstream system synchronization. While traditional CDC solutions require managing complex infrastructure—database servers, streaming platforms, and processing clusters—AWS Lambda and Kinesis Firehose offer a fully serverless alternative that scales automatically, requires no infrastructure management, and costs nothing when idle. This combination provides an elegant architecture for capturing, transforming, and delivering database changes to data lakes, warehouses, and analytics systems.
The serverless CDC approach shifts operational complexity from infrastructure management to event-driven design patterns. Instead of provisioning servers to process change streams, Lambda functions automatically scale to handle incoming changes, while Firehose manages batching, compression, and delivery to destinations like S3, Redshift, or Elasticsearch. This architecture excels for use cases where simplicity, cost efficiency, and automatic scaling outweigh the need for sub-second latency that dedicated streaming infrastructure provides.
Understanding Serverless CDC Architecture
Before building a serverless CDC pipeline, understanding how Lambda and Firehose complement each other clarifies design decisions and architectural patterns.
The Serverless CDC Data Flow
A serverless CDC pipeline follows this pattern:
- Change capture trigger: Database changes trigger events through DynamoDB Streams, RDS event notifications, or application-level triggers
- Lambda processing: Event triggers Lambda function execution, which transforms raw changes into analytical formats
- Firehose buffering: Lambda writes processed records to Kinesis Firehose
- Batch delivery: Firehose buffers records, compresses them, and delivers batches to destination systems
- Target storage: Data lands in S3, Redshift, Elasticsearch, or custom HTTP endpoints
This architecture differs fundamentally from real-time streaming approaches. Where Kinesis Data Streams provides low-latency streaming with per-record processing, Firehose optimizes for batch delivery with built-in transformation, compression, and data format conversion. The trade-off is latency—Firehose delivers data in batches every 60-900 seconds rather than sub-second streaming—but gains simplicity, lower cost, and zero infrastructure management.
When Serverless CDC Makes Sense
Serverless CDC pipelines excel in specific scenarios:
Analytics and reporting: When downstream systems consume data in batches (data warehouses, business intelligence tools), Firehose’s batching aligns perfectly with consumption patterns. A 5-minute delivery delay doesn’t impact daily or hourly reports.
Audit and compliance logging: Change tracking for regulatory compliance doesn’t require real-time processing. Storing changes in S3 with automatic partitioning and compression provides cost-effective, queryable audit trails.
Data lake ingestion: Building data lakes from operational database changes benefits from Firehose’s format conversion (CSV to Parquet), partitioning, and compression—reducing storage costs and improving query performance.
Variable workloads: Applications with unpredictable change volumes avoid over-provisioning. Lambda and Firehose scale automatically from zero to thousands of events per second without manual intervention.
Development and testing: Serverless architecture enables rapid prototyping without infrastructure setup, making it ideal for proof-of-concepts and development environments.
Conversely, serverless CDC may not suit use cases requiring sub-second latency, complex stateful transformations, or guaranteed ordering across multiple tables.
Serverless CDC Pipeline Flow
Implementing DynamoDB Streams CDC
DynamoDB provides the most straightforward serverless CDC implementation through DynamoDB Streams, which capture every table modification automatically.
Enabling and Configuring DynamoDB Streams
DynamoDB Streams record item-level changes (inserts, updates, deletes) with configurable views:
- KEYS_ONLY: Only the key attributes of modified items
- NEW_IMAGE: The entire item as it appears after modification
- OLD_IMAGE: The entire item before modification
- NEW_AND_OLD_IMAGES: Both before and after images
For comprehensive CDC, use NEW_AND_OLD_IMAGES to enable downstream systems to understand what changed:
import boto3
dynamodb = boto3.client('dynamodb')
# Enable streams on an existing table
response = dynamodb.update_table(
TableName='Orders',
StreamSpecification={
'StreamEnabled': True,
'StreamViewType': 'NEW_AND_OLD_IMAGES'
}
)
Building the Lambda Stream Processor
The Lambda function processes stream records, transforms them, and writes to Firehose:
import json
import boto3
from datetime import datetime
from decimal import Decimal
firehose = boto3.client('firehose')
class DecimalEncoder(json.JSONEncoder):
"""Handle Decimal types from DynamoDB"""
def default(self, obj):
if isinstance(obj, Decimal):
return float(obj)
return super(DecimalEncoder, self).default(obj)
def lambda_handler(event, context):
"""
Process DynamoDB stream records and send to Firehose
"""
firehose_records = []
for record in event['Records']:
# Extract change information
event_name = record['eventName'] # INSERT, MODIFY, REMOVE
event_time = record['dynamodb']['ApproximateCreationDateTime']
# Build CDC record with metadata
cdc_record = {
'event_type': event_name,
'event_timestamp': datetime.fromtimestamp(event_time).isoformat(),
'table_name': record['eventSourceARN'].split('/')[1],
'keys': record['dynamodb']['Keys']
}
# Include new image for INSERT and MODIFY
if 'NewImage' in record['dynamodb']:
cdc_record['new_data'] = record['dynamodb']['NewImage']
# Include old image for MODIFY and REMOVE
if 'OldImage' in record['dynamodb']:
cdc_record['old_data'] = record['dynamodb']['OldImage']
# Add any business-specific enrichment
cdc_record['pipeline_version'] = '1.0'
cdc_record['processed_at'] = datetime.utcnow().isoformat()
# Prepare for Firehose (newline-delimited JSON)
firehose_record = {
'Data': json.dumps(cdc_record, cls=DecimalEncoder) + '\n'
}
firehose_records.append(firehose_record)
# Batch write to Firehose (up to 500 records per call)
if firehose_records:
batch_size = 500
for i in range(0, len(firehose_records), batch_size):
batch = firehose_records[i:i + batch_size]
response = firehose.put_record_batch(
DeliveryStreamName='orders-cdc-stream',
Records=batch
)
# Handle partial failures
if response['FailedPutCount'] > 0:
print(f"Failed to write {response['FailedPutCount']} records")
# Implement retry logic or dead letter queue handling
return {
'statusCode': 200,
'recordsProcessed': len(event['Records'])
}
Key implementation details:
- Decimal handling: DynamoDB uses Decimal types for numbers; convert to float for JSON serialization
- Newline delimiter: Firehose expects newline-delimited JSON for proper record separation
- Batch processing: Process up to 500 records per Firehose call to minimize API requests
- Error handling: Implement retry logic for failed records to ensure data integrity
- Enrichment: Add pipeline metadata, timestamps, or business logic during transformation
Configuring Lambda Stream Triggers
Connect Lambda to DynamoDB Streams with appropriate configuration:
import boto3
lambda_client = boto3.client('lambda')
# Create event source mapping
response = lambda_client.create_event_source_mapping(
EventSourceArn='arn:aws:dynamodb:us-east-1:123456789012:table/Orders/stream/2024-01-15T10:00:00.000',
FunctionName='process-orders-cdc',
Enabled=True,
StartingPosition='LATEST', # or 'TRIM_HORIZON' for all available records
BatchSize=100, # Number of records per Lambda invocation
MaximumBatchingWindowInSeconds=5, # Wait up to 5s to accumulate records
ParallelizationFactor=10, # Process up to 10 batches per shard concurrently
MaximumRecordAgeInSeconds=3600, # Discard records older than 1 hour
BisectBatchOnFunctionError=True, # Split failed batches for partial retry
MaximumRetryAttempts=3,
DestinationConfig={
'OnFailure': {
'Destination': 'arn:aws:sqs:us-east-1:123456789012:cdc-dlq'
}
}
)
Configuration considerations:
- BatchSize: Balance Lambda invocation costs (fewer, larger batches) with latency (smaller, more frequent batches)
- MaximumBatchingWindowInSeconds: Introduce slight delay to accumulate records and reduce Lambda invocations
- ParallelizationFactor: Increase concurrency for high-throughput streams
- DestinationConfig: Configure dead letter queue for failed records requiring manual intervention
Setting Up Kinesis Firehose Delivery
Firehose handles the heavy lifting of batching, compression, format conversion, and delivery to destination systems.
Creating the Firehose Delivery Stream
Configure Firehose with appropriate buffering, compression, and destination settings:
import boto3
firehose = boto3.client('firehose')
response = firehose.create_delivery_stream(
DeliveryStreamName='orders-cdc-stream',
DeliveryStreamType='DirectPut',
ExtendedS3DestinationConfiguration={
'RoleARN': 'arn:aws:iam::123456789012:role/firehose-delivery-role',
'BucketARN': 'arn:aws:s3:::data-lake-bucket',
'Prefix': 'cdc/orders/year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/',
'ErrorOutputPrefix': 'cdc-errors/orders/!{firehose:error-output-type}/year=!{timestamp:yyyy}/month=!{timestamp:MM}/day=!{timestamp:dd}/',
'BufferingHints': {
'SizeInMBs': 128, # Buffer size: 1-128 MB
'IntervalInSeconds': 300 # Buffer interval: 60-900 seconds
},
'CompressionFormat': 'GZIP', # GZIP, SNAPPY, ZIP, or UNCOMPRESSED
'DataFormatConversionConfiguration': {
'Enabled': True,
'SchemaConfiguration': {
'RoleARN': 'arn:aws:iam::123456789012:role/firehose-delivery-role',
'DatabaseName': 'cdc_catalog',
'TableName': 'orders',
'Region': 'us-east-1',
'VersionId': 'LATEST'
},
'InputFormatConfiguration': {
'Deserializer': {
'OpenXJSONSerDe': {}
}
},
'OutputFormatConfiguration': {
'Serializer': {
'ParquetSerDe': {
'Compression': 'SNAPPY',
'EnableDictionaryCompression': True,
'BlockSizeBytes': 268435456, # 256 MB
'PageSizeBytes': 1048576 # 1 MB
}
}
}
},
'CloudWatchLoggingOptions': {
'Enabled': True,
'LogGroupName': '/aws/kinesisfirehose/orders-cdc',
'LogStreamName': 'S3Delivery'
},
'S3BackupMode': 'Disabled' # or 'Enabled' for raw data backup
}
)
Critical configuration decisions:
Buffering strategy: Balance latency with cost efficiency. Larger buffers (128 MB / 900 seconds) reduce S3 request costs but increase delivery latency. Smaller buffers (5 MB / 60 seconds) deliver faster but cost more.
Compression: GZIP provides the best compression ratio (typically 5-10x reduction) but higher CPU usage. SNAPPY compresses less (3-5x) but faster. For analytics workloads, GZIP usually wins.
Format conversion: Converting JSON to Parquet reduces storage costs 10-20x and dramatically improves query performance in Athena or Redshift Spectrum. The trade-off is additional processing time and complexity.
Partitioning: Use dynamic partitioning with timestamp-based prefixes for efficient querying. Partition by year/month/day enables partition pruning in queries, reducing scan costs.
Implementing Format Conversion for Analytics
Firehose’s native format conversion requires a Glue Data Catalog schema:
import boto3
glue = boto3.client('glue')
# Create database if not exists
glue.create_database(
DatabaseInput={
'Name': 'cdc_catalog',
'Description': 'Schema catalog for CDC pipelines'
}
)
# Define table schema matching CDC record structure
glue.create_table(
DatabaseName='cdc_catalog',
TableInput={
'Name': 'orders',
'StorageDescriptor': {
'Columns': [
{'Name': 'event_type', 'Type': 'string'},
{'Name': 'event_timestamp', 'Type': 'timestamp'},
{'Name': 'table_name', 'Type': 'string'},
{'Name': 'order_id', 'Type': 'bigint'},
{'Name': 'customer_id', 'Type': 'bigint'},
{'Name': 'total_amount', 'Type': 'double'},
{'Name': 'status', 'Type': 'string'},
{'Name': 'created_at', 'Type': 'timestamp'},
{'Name': 'processed_at', 'Type': 'timestamp'}
],
'SerdeInfo': {
'SerializationLibrary': 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
}
}
}
)
The schema defines the target Parquet structure. Firehose maps JSON fields to Parquet columns during conversion, handling type casting automatically.
Handling RDS and Aurora CDC
While DynamoDB Streams provide native CDC, capturing changes from RDS or Aurora databases requires different approaches in a serverless architecture.
Application-Level Change Tracking
The simplest serverless CDC for RDS involves application-level triggers that emit change events:
Database triggers: PostgreSQL or MySQL triggers can invoke AWS Lambda through external procedures or by writing changes to a queue table that Lambda polls.
Application writes: Modify application code to publish changes to SNS topics or SQS queues that trigger Lambda functions. This dual-write pattern requires careful error handling to maintain consistency.
Event sourcing: Design applications using event sourcing patterns where all state changes are events. Lambda functions consume these events for CDC.
Using AWS Database Migration Service
For production RDS CDC without application changes, combine DMS with serverless processing:
- DMS captures changes from RDS using native CDC (binlog, WAL)
- DMS writes to Kinesis Data Streams
- Lambda consumes from Kinesis and writes to Firehose
This hybrid approach leverages DMS’s robust CDC while maintaining serverless processing and delivery layers.
Serverless CDC Best Practices
Advanced Patterns and Optimizations
Production serverless CDC pipelines benefit from patterns that improve reliability, performance, and cost efficiency.
Implementing Idempotency and Deduplication
Stream processing can deliver duplicate records due to retries. Implement idempotency to prevent duplicate data in destinations:
Deduplicate in Lambda: Maintain a DynamoDB table tracking recently processed record IDs with TTL. Check before processing and skip duplicates.
Use Firehose dynamic partitioning: Include unique record identifiers in partition keys, enabling downstream deduplication queries.
Leverage target system capabilities: S3 object versioning, Redshift UPSERT, or Elasticsearch document IDs can handle duplicates at the destination.
Enriching CDC Records
Transform raw change events into business-meaningful records:
Lookup enrichment: Query reference data (customer segments, product categories) to add context to change records. Use DynamoDB for low-latency lookups or cache frequently accessed data in Lambda memory.
Aggregation: Compute derived metrics (running totals, moving averages) during CDC processing rather than in queries, improving downstream performance.
Filtering: Exclude unnecessary changes (internal fields, system columns) to reduce data volume and downstream processing costs.
Masking sensitive data: Redact or hash PII fields before delivery to analytics systems, implementing privacy controls at ingestion.
Multi-Table CDC Coordination
When capturing changes from multiple tables that reference each other, maintain consistency:
Fan-out from SNS: Lambda writes to SNS topics that fan out to multiple Firehose streams, ensuring each table’s changes reach appropriate destinations.
Unified stream with routing: Write all changes to a single Firehose stream with metadata identifying the source table. Downstream consumers route based on table name.
Separate pipelines: Independent Lambda functions and Firehose streams per table provide isolation but complicate cross-table consistency.
Monitoring and Operational Excellence
Serverless architectures require different monitoring approaches than traditional infrastructure.
Key Metrics and Alarms
Lambda metrics:
- Duration: Execution time trends indicate performance degradation
- Errors: Failed invocations require investigation and potential dead letter queue processing
- Throttles: Concurrent execution limits hit during traffic spikes
- Iterator age: For stream processing, growing iterator age indicates processing lag
Firehose metrics:
- DeliveryToS3.Success: Successful delivery rate should be >99.9%
- DeliveryToS3.DataFreshness: Time from record ingestion to S3 delivery
- DataReadFromKinesisStream.Bytes: Throughput monitoring
- BackupToS3.Bytes: If S3 backup enabled, track backup volume
Set CloudWatch alarms on error rates, iterator age, and delivery freshness to detect issues before they impact business operations.
Cost Optimization Strategies
Serverless CDC can be extremely cost-effective when optimized:
Lambda memory tuning: Higher memory allocations provide proportionally more CPU. Test with different memory settings to find the optimal price-performance ratio.
Batch size optimization: Larger batches reduce Lambda invocation count but increase individual execution time. Find the balance that minimizes total costs.
Compression: GZIP compression reduces S3 storage and data transfer costs by 80-90% with minimal processing overhead.
Format conversion: Converting to Parquet reduces storage costs and query costs in Athena/Redshift Spectrum, typically providing 10x return on the conversion processing cost.
Reserved capacity: For predictable workloads, Lambda reserved concurrency and Savings Plans reduce costs 20-40%.
S3 lifecycle policies: Archive historical CDC data to S3 Glacier or Intelligent-Tiering for long-term retention at 80-90% lower cost.
Typical costs for a moderate serverless CDC pipeline (1 million changes daily, 5 GB compressed output) run $50-150 monthly—dramatically less than provisioned infrastructure alternatives.
Testing and Validation
Ensure pipeline reliability through comprehensive testing:
Load testing: Use AWS Lambda load testing tools or custom scripts to simulate high change volumes and validate scaling behavior.
Failure injection: Intentionally fail Lambda invocations or Firehose deliveries to verify dead letter queues, retry logic, and alerting work as designed.
Data validation: Implement automated validation comparing source record counts with delivered records to detect data loss.
Schema evolution: Test how the pipeline handles source schema changes—new columns, type changes, or dropped fields.
Conclusion
Building serverless CDC pipelines with Lambda and Firehose provides an elegant, cost-effective architecture for capturing and delivering database changes to analytics systems. The serverless approach eliminates infrastructure management, scales automatically from zero to high volumes, and costs only for actual usage, making it ideal for analytics, audit logging, and data lake ingestion where near-real-time delivery (1-5 minutes) suffices. Success requires thoughtful configuration of Lambda batch processing, Firehose buffering strategies, format conversion, and comprehensive monitoring.
While serverless CDC trades the sub-second latency of dedicated streaming infrastructure for simplicity and cost efficiency, this trade-off aligns perfectly with analytics and reporting use cases where delivery within minutes provides sufficient business value. By following the patterns outlined here—efficient Lambda processing, appropriate Firehose buffering, format optimization, and robust error handling—you can build production-grade CDC pipelines that deliver reliable, cost-effective data integration at scale.