CDC Pipeline Architecture on AWS Using Firehose and Glue

Change Data Capture (CDC) has become essential for modern data architectures, enabling real-time data synchronization, analytics, and event-driven workflows. When building CDC pipelines on AWS, combining Kinesis Firehose with AWS Glue creates a powerful, serverless architecture that scales automatically and requires minimal operational overhead. This approach leverages AWS-managed services to capture database changes, stream them reliably, transform data in-flight, and land it in your data lake—all without managing servers or complex infrastructure.

For data engineers working in AWS environments, understanding how to architect CDC pipelines using these native services is crucial. Let’s explore how to design and implement production-grade CDC architectures that are reliable, cost-effective, and maintainable.

The AWS CDC Stack: Components and Their Roles

Building a CDC pipeline on AWS involves several services working together, each playing a specific role in capturing, streaming, transforming, and storing change data.

AWS DMS (Database Migration Service) as the CDC source:

AWS DMS is the primary service for capturing changes from databases. While originally designed for database migration, DMS includes robust CDC capabilities that continuously read transaction logs from source databases and convert changes into a stream of events. DMS supports major databases including Oracle, MySQL, PostgreSQL, SQL Server, MongoDB, and more.

DMS operates as a managed replication instance—essentially a server that AWS maintains on your behalf. You configure source and target endpoints, define table mappings to specify which tables to replicate, and set transformation rules. For CDC, you configure “ongoing replication” mode, which keeps the replication running continuously after the initial full load completes.

The power of DMS lies in its managed nature. AWS handles database-specific CDC logic, transaction log reading, and failure recovery. You don’t need to worry about binlog formats, WAL configurations, or connector implementations—DMS abstracts these complexities behind a unified API.

Kinesis Firehose as the streaming buffer:

Kinesis Firehose acts as the delivery stream between DMS and your data lake. DMS writes change events to Firehose, which batches, compresses, and delivers them to destinations like S3. Firehose is fully managed and automatically scales to handle varying throughput without provisioning capacity.

Firehose provides several critical capabilities. It buffers data for configurable time periods (60 to 900 seconds) or size thresholds (1 to 128 MB), batching records efficiently before writing to S3. It compresses data using GZIP, Snappy, or other formats, reducing storage costs. It handles retries and error handling when delivery fails, ensuring data reliability.

Crucially, Firehose supports data transformation using AWS Lambda or integration with AWS Glue. This allows you to enrich, filter, or restructure change events before they land in S3, enabling clean, analysis-ready data in your lake.

AWS Glue for schema management and transformation:

AWS Glue serves dual purposes in CDC architectures. First, Glue’s Data Catalog acts as a centralized metadata repository, storing schemas for your CDC data. As change data lands in S3, Glue crawlers discover the schema and register table definitions, making data queryable via Athena, EMR, or other analytics tools.

Second, Glue ETL jobs (or Glue DataBrew for visual transformations) can process the raw change data, applying complex transformations that exceed Lambda’s capabilities. You might merge changes to create current-state snapshots, apply business logic, join with reference data, or restructure nested JSON into analytics-friendly columnar formats.

S3 as the data lake foundation:

Amazon S3 serves as the durable storage layer for your CDC data. Raw change events land in a “raw” or “bronze” zone, preserving complete change history. Transformed data lives in “processed” or “silver/gold” zones, optimized for analytics queries.

S3’s lifecycle policies automatically transition older data to cheaper storage classes (S3 IA, Glacier) as it ages, balancing cost with accessibility. Versioning and replication features provide data protection and compliance capabilities essential for production systems.

Designing the DMS CDC Configuration

Setting up DMS for CDC requires careful configuration to ensure reliable change capture and optimal performance. The decisions you make here impact data quality, latency, and operational stability.

Replication instance sizing and configuration:

DMS replication instances come in various sizes, from small development instances to large production instances with multiple vCPUs and substantial memory. Sizing depends on several factors: the volume of changes per second, the number of tables being replicated, the complexity of transformation rules, and target write throughput.

For production CDC workloads, start with at least a dms.c5.xlarge instance (4 vCPUs, 8 GB memory). Monitor CPU and memory utilization—sustained high utilization indicates you need larger instances. Multi-AZ deployment provides high availability but doubles costs, so evaluate your uptime requirements carefully.

Network configuration matters significantly. Place the replication instance in a VPC with connectivity to both source databases and Firehose endpoints. Security groups must allow traffic on appropriate ports. For source databases in on-premises data centers, use AWS Direct Connect or VPN connections rather than traversing the public internet.

Table selection and filtering:

DMS uses table mapping rules to define which tables to replicate and how to filter or transform data. Selection rules specify which schemas and tables to include or exclude. You can replicate entire schemas, specific tables, or use wildcards for pattern matching.

Filter rules allow you to capture only relevant changes. For example, you might filter to replicate only active customer records (WHERE status = ‘ACTIVE’) or recent transactions (WHERE created_at > ‘2023-01-01’). These filters execute at the source, reducing unnecessary data transfer and storage.

Transformation rules enable basic changes during replication—renaming tables, changing column names, or removing columns containing sensitive data. While powerful, use these sparingly. Complex transformations are better handled downstream in Lambda or Glue where you have more flexibility and can iterate without restarting DMS tasks.

Handling full load and CDC transition:

When you start a DMS task, it typically performs a full load—copying all existing data—before switching to ongoing CDC. Managing this transition smoothly requires attention to several details.

During full load, DMS caches changes that occur to avoid losing them. Once the full load completes, it applies these cached changes before switching to real-time CDC. This ensures consistency, but the cache consumes memory on the replication instance. For large, active tables, ensure your instance has sufficient memory to cache changes during the full load period.

You can configure whether DMS truncates target tables before full load or appends to existing data. For S3 targets via Firehose, the concept of truncation doesn’t directly apply, but understanding the behavior matters for recovery scenarios.

CDC-specific settings and optimizations:

DMS offers numerous CDC-specific settings that affect behavior and performance. LOB (Large Object) handling determines how BLOB and CLOB columns are replicated—full LOB mode is safest but slower, while limited LOB mode with appropriate size limits often provides better performance.

Batch apply settings control how DMS applies changes to targets. Larger batches improve throughput but increase latency. For Firehose targets, tuning batch sizes helps optimize the interaction between DMS batching and Firehose’s own batching behavior.

Task settings for error handling define retry behavior and how to handle problematic records. You might configure the task to skip records that cause errors and log them for later investigation, or to stop on any error for critical pipelines where data completeness is paramount.

Kinesis Firehose Configuration and Optimization

Firehose sits at the heart of your CDC pipeline, managing the flow from DMS to S3. Proper configuration ensures reliable delivery, optimal performance, and cost efficiency.

Buffer and delivery settings:

Firehose’s buffering configuration significantly impacts latency and cost. The buffer interval (how long Firehose waits before delivering) and buffer size (how much data to accumulate) work together. Firehose delivers when either threshold is met—if 60 seconds pass OR 5 MB accumulates, it writes to S3.

For CDC workloads with consistent change rates, smaller buffer intervals (60-120 seconds) reduce latency, ensuring change data arrives in your lake quickly. For bursty workloads or when cost optimization is critical, larger intervals (300-900 seconds) reduce the number of S3 objects created, lowering request costs.

Buffer size affects object sizes in S3. Smaller buffers create many small files, which are inefficient for analytics queries. Larger buffers create fewer, larger files that compress better and query faster. Balance this against your latency requirements—larger buffers mean longer delivery delays.

Compression and format considerations:

Firehose compresses data before writing to S3, substantially reducing storage costs. GZIP compression is universal—all analytics tools can read it—but offers modest compression ratios. Snappy compresses faster but with lower ratios and less tool support. For new architectures, consider using Parquet format (via Glue transformation) which includes columnar compression and dramatically improves query performance.

The record format matters too. DMS outputs JSON by default, which is human-readable but verbose. For analytics workloads, converting to Parquet using Glue transformation reduces storage by 80%+ and accelerates queries by an order of magnitude. The trade-off is added transformation complexity and cost.

Data partitioning in S3:

How Firehose partitions data in S3 significantly impacts query performance and costs. Firehose supports dynamic partitioning based on record attributes, allowing you to organize data by date, table name, or other dimensions.

A typical CDC partition scheme: s3://my-bucket/cdc-data/table=customers/year=2024/month=01/day=15/hour=10/. This structure enables efficient querying—when analyzing customer changes for January 15th, queries scan only that partition rather than the entire dataset.

Configure partitioning based on your query patterns. Date/time partitioning suits time-series analysis. Table-based partitioning helps when you query individual tables frequently. Multi-level partitioning (table + date) provides flexibility but creates deeper directory structures.

Dynamic partitioning uses JQ expressions to extract partition values from records. For CDC data from DMS, you might extract the table name from metadata and parse timestamps to create date partitions. This requires understanding DMS’s output format and writing appropriate JQ expressions.

Lambda Transformation Layer

Firehose’s integration with Lambda enables real-time data transformation before delivery to S3. This transformation layer is powerful for cleaning, enriching, and restructuring CDC data.

Common transformation patterns:

Schema flattening: DMS outputs nested JSON structures. Lambda can flatten these into simpler schemas better suited for analytics. Extract the “data” payload, remove metadata wrapper fields, and create a clean record structure.

Data enrichment: Join change events with reference data stored in DynamoDB or S3. For example, enrich order changes with customer tier information, adding context that makes the data more valuable for analysis.

Filtering and routing: Discard irrelevant changes (like updates to internal metadata columns) or route different change types to different S3 prefixes. Delete operations might go to one location, inserts and updates to another.

Format conversion: While Firehose can convert to Parquet directly, Lambda provides more control. Custom conversion logic can handle complex nested structures, apply type coercion, or handle edge cases that automated conversion misses.

Lambda implementation considerations:

Lambda functions processing Firehose data must follow specific patterns. Firehose invokes Lambda with batches of records (up to 500 per invocation, 6 MB total). Your function processes each record, returning a response indicating success, failure, or dropped status for each.

import base64
import json

def lambda_handler(event, context):
    output_records = []
    
    for record in event['records']:
        # Decode the base64-encoded data
        payload = json.loads(base64.b64decode(record['data']))
        
        # Extract the actual change data from DMS format
        if 'data' in payload:
            change_data = payload['data']
            
            # Add metadata from DMS
            change_data['operation'] = payload.get('metadata', {}).get('operation')
            change_data['timestamp'] = payload.get('metadata', {}).get('timestamp')
            
            # Transform and clean the data
            transformed = transform_record(change_data)
            
            # Re-encode for Firehose
            output_data = base64.b64encode(
                json.dumps(transformed).encode('utf-8')
            ).decode('utf-8')
            
            output_records.append({
                'recordId': record['recordId'],
                'result': 'Ok',
                'data': output_data
            })
        else:
            # Drop malformed records
            output_records.append({
                'recordId': record['recordId'],
                'result': 'Dropped',
                'data': record['data']
            })
    
    return {'records': output_records}

def transform_record(record):
    # Apply business logic, enrichment, filtering
    # Return transformed record
    return record

import base64
import json

def lambda_handler(event, context):
    output_records = []
    
    for record in event['records']:
        # Decode the base64-encoded data
        payload = json.loads(base64.b64decode(record['data']))
        
        # Extract the actual change data from DMS format
        if 'data' in payload:
            change_data = payload['data']
            
            # Add metadata from DMS
            change_data['operation'] = payload.get('metadata', {}).get('operation')
            change_data['timestamp'] = payload.get('metadata', {}).get('timestamp')
            
            # Transform and clean the data
            transformed = transform_record(change_data)
            
            # Re-encode for Firehose
            output_data = base64.b64encode(
                json.dumps(transformed).encode('utf-8')
            ).decode('utf-8')
            
            output_records.append({
                'recordId': record['recordId'],
                'result': 'Ok',
                'data': output_data
            })
        else:
            # Drop malformed records
            output_records.append({
                'recordId': record['recordId'],
                'result': 'Dropped',
                'data': record['data']
            })
    
    return {'records': output_records}

def transform_record(record):
    # Apply business logic, enrichment, filtering
    # Return transformed record
    return record

Performance matters for Lambda transformations. Your function must process records and return within the Lambda timeout (maximum 5 minutes for Firehose). Slow processing increases latency and costs. Monitor execution duration and optimize accordingly—use connection pooling for external lookups, cache reference data in global variables, and avoid expensive operations in hot paths.

Error handling requires thought. If Lambda returns an error, Firehose retries with exponential backoff, then delivers failed records to a specified S3 bucket. Design your error handling to gracefully handle bad records without failing entire batches—mark problematic records as ‘ProcessingFailed’ and continue processing remaining records.

Glue Integration and Schema Management

AWS Glue provides the metadata layer and transformation capabilities that make your CDC data usable for analytics. Proper Glue integration transforms raw change events into queryable datasets.

Glue Crawler configuration:

Glue Crawlers automatically discover schemas in your S3 data and register them in the Glue Data Catalog. Schedule crawlers to run periodically (hourly, daily) to detect new partitions and schema changes. For CDC data landing in S3, crawlers make the data immediately queryable via Athena without manual schema definition.

Configure crawlers to recognize your partitioning scheme. Specify partition keys that match your S3 structure—if data is partitioned by table and date, tell the crawler to extract these as partition columns. This enables partition pruning in queries, dramatically improving performance.

Schema evolution handling is critical. Configure whether crawlers should update existing table schemas when they detect changes, create new table versions, or ignore changes. For CDC data where source schemas evolve, allowing updates enables the Data Catalog to track schema history while maintaining backward compatibility.

Building Glue ETL jobs for CDC processing:

While Lambda handles lightweight transformations, Glue ETL jobs process CDC data at scale. Glue jobs can read the raw change events from S3, apply complex transformations, and write processed data to analytics-optimized formats.

A common pattern is building change data tables (CDT) that maintain current state. CDC captures all changes—inserts, updates, deletes—but analytics often need the current state of each record. Glue jobs process change events, group by primary key, apply changes in order, and write the resulting current state.

Another pattern merges changes into existing datasets. An initial full load creates a base dataset. Subsequent CDC events represent changes. A Glue job reads both the base dataset and new changes, merges them (applying updates, adding inserts, removing deletes), and writes a new current snapshot. This incremental approach avoids reprocessing the entire dataset.

Data Catalog as the source of truth:

The Glue Data Catalog becomes your central metadata repository. Define not just table schemas but also partition specifications, format information, compression details, and SerDe (Serialization/Deserialization) properties. This metadata enables any analytics tool to read your data correctly.

Use table properties to store CDC-specific metadata—source database name, table name, last processed timestamp, or schema version. This metadata helps downstream consumers understand the data’s provenance and characteristics.

Database-level organization in the Catalog helps manage complexity. Create separate databases for raw CDC data, processed data, and analytics-ready datasets. This separation clarifies data lineage and allows different access controls for raw versus processed data.

Monitoring and Operational Excellence

Production CDC pipelines require comprehensive monitoring to ensure reliability and quickly identify issues. AWS provides rich monitoring capabilities across all services involved.

DMS monitoring metrics:

CloudWatch metrics for DMS replication tasks reveal performance and health. Key metrics include:

• CDCLatencySource: Time between source database commit and DMS reading the change—high values indicate log reading problems
• CDCLatencyTarget: Time between DMS reading and writing to target—high values suggest target bottlenecks
• FullLoadThroughputRowsSource/Target: During full load, tracks row processing rate
• CPUUtilization and MemoryUtilization: Resource consumption on replication instance

Set CloudWatch alarms on these metrics. Alert when CDC latency exceeds acceptable thresholds (perhaps 60 seconds), when CPU sustains above 80%, or when tasks fail. Proactive monitoring catches issues before they become outages.

DMS task logs provide detailed information about what the replication instance is doing. Enable CloudWatch Logs integration to centralize these logs for analysis. Log errors, statistics, and task state changes help troubleshoot problems.

Firehose monitoring:

Firehose metrics track delivery health and performance. Monitor:

• DeliveryToS3.Success/Records: Count of successful deliveries—drops indicate problems
• DeliveryToS3.DataFreshness: Age of oldest record in Firehose—increases suggest delivery problems
• IncomingRecords/Bytes: Volume of data entering Firehose—helps capacity planning
• DataTransformation.Duration: Lambda transformation time—high durations increase latency

Firehose also maintains error logs in CloudWatch. Failed deliveries and transformation errors appear here. Configure error delivery to a separate S3 bucket to preserve problematic records for investigation.

Glue job monitoring:

Glue jobs emit metrics including job duration, records processed, and failures. Monitor job run history to identify failures or performance degradation. Long-running jobs might indicate data volume growth requiring optimization or resource increases.

Glue also provides detailed job logs—driver and executor logs for Spark-based ETL jobs—enabling debugging when jobs fail or produce unexpected results. Configure job bookmarks to track what data has been processed, enabling incremental processing that avoids reprocessing entire datasets.

Cost monitoring and optimization:

CDC pipelines have ongoing costs across multiple services. CloudWatch combined with Cost Explorer helps track spending. Monitor:

• DMS replication instance hours (largest fixed cost)
• Firehose ingestion volume (charged per GB)
• Lambda invocations and duration (transformation costs)
• S3 storage and request costs (particularly important with many small files)
• Glue job runs and DPU (Data Processing Unit) hours

Optimization opportunities include right-sizing DMS instances, tuning Firehose buffer settings to reduce S3 requests, optimizing Lambda functions for faster execution, and consolidating small S3 objects through compaction jobs.

Handling Schema Evolution and Breaking Changes

Source database schemas evolve—columns are added, removed, or changed. Your CDC pipeline must handle these changes gracefully to avoid breaks.

DMS schema change handling:

DMS can automatically handle certain schema changes during CDC. Adding columns to source tables appears in the change stream automatically (if using full LOB mode). However, DMS has limitations—renaming columns, changing types, or restructuring tables can cause issues.

Configure DMS task settings for schema change handling. You might choose to stop the task on schema changes, log warnings but continue, or automatically apply compatible changes. For production pipelines, conservative approaches (stop or log) are safer, allowing human review before proceeding.

Glue schema evolution strategies:

The Glue Data Catalog tracks schema versions. When crawlers detect schema changes, they create new table versions while preserving historical schemas. This versioning enables queries against specific schema versions or allows Athena to merge schemas when querying.

For breaking schema changes (removed columns, type changes), consider creating new tables rather than modifying existing ones. Maintain both old and new table definitions during a transition period, allowing downstream consumers to migrate at their own pace. Once migration completes, deprecate and remove the old table.

Testing schema changes:

Never apply schema changes directly to production CDC pipelines. Implement a testing workflow:

1. Apply schema change in a development database
2. Observe how DMS captures and represents the change
3. Verify Firehose and Lambda handle the new format
4. Confirm Glue crawlers recognize the schema correctly
5. Test that downstream queries and jobs work with the new schema
6. Document the change and communicate to stakeholders
7. Apply to production with monitoring

This disciplined approach prevents schema changes from causing production outages or data quality issues.

Conclusion

Building CDC pipelines on AWS using Kinesis Firehose and Glue creates a robust, scalable architecture for real-time data integration. By combining DMS for change capture, Firehose for reliable streaming, Lambda for lightweight transformation, Glue for schema management and complex ETL, and S3 as the durable data lake foundation, you create an end-to-end solution that captures every database change and makes it available for analytics. The serverless nature of most components means the architecture scales automatically and requires minimal operational overhead compared to self-managed alternatives.

Success with this architecture requires attention to configuration details, comprehensive monitoring, and operational discipline. Proper sizing of DMS instances, thoughtful Firehose buffering, efficient Lambda transformations, and well-designed Glue jobs determine whether your pipeline operates smoothly or becomes a maintenance burden. With the architectural patterns and best practices outlined here, data engineers can build production-grade CDC pipelines that reliably deliver database changes to data lakes, enabling real-time analytics and event-driven architectures.