Change Data Capture for Real-Time Analytics: Streaming Database Changes

Change data capture (CDC) is a pattern for tracking changes in database systems and streaming those changes for downstream consumption. For analytics, CDC enables real-time data pipelines that reflect source system changes within seconds rather than waiting for batch extraction schedules.

CDC transforms database changes into event streams that power real-time dashboards, operational analytics, and low-latency data warehousing.

Why CDC for Analytics

The Batch Limitation

Traditional batch extraction has inherent delays:

• Daily extracts mean data is always 24 hours stale
• Hourly extracts still lag operational reality
• Full table scans impact source system performance
• Large tables take significant time to extract

When business needs fresher data, batch extraction can't deliver.

The Query Impact Problem

Querying production databases for analytics hurts performance:

• Analytical queries compete for resources
• Complex joins slow transactional workloads
• Large result sets consume memory and I/O
• Peak analytics times may coincide with peak operations

CDC avoids this by reading from logs, not tables.

The Change Tracking Challenge

Identifying what changed is hard with traditional methods:

• Timestamp columns may be unreliable
• Not all tables have audit columns
• Deletes leave no trace in source tables
• Schema changes complicate incremental logic

CDC captures changes reliably regardless of table design.

How CDC Works

Log-Based CDC

The preferred approach reads database transaction logs:

Database writes changes: Every insert, update, delete goes to transaction log.

CDC tool reads log: Connector reads log entries as they're written.

Changes become events: Log entries transform into change events.

Events stream to consumers: Kafka or similar transports events.

Source DB → Transaction Log → CDC Connector → Event Stream → Analytics

Log-based CDC is reliable, low-impact, and captures all changes.

Query-Based CDC

An alternative that queries source tables:

Periodic queries: Run queries to find changed records.

Compare to baseline: Identify inserts, updates, deletes.

Generate change events: Create events for detected changes.

Query-based CDC is simpler but impacts sources and may miss changes.

Trigger-Based CDC

Database triggers capture changes:

Triggers on tables: Fire on insert, update, delete.

Write to change tables: Triggers record changes.

Extract from change tables: Analytics reads change records.

Trigger-based CDC is precise but adds overhead and complexity.

CDC Event Anatomy

Change Event Structure

CDC events typically contain:

{
  "operation": "UPDATE",
  "timestamp": "2024-05-15T10:30:00Z",
  "source": {
    "database": "orders_db",
    "table": "orders",
    "transaction_id": "tx-12345"
  },
  "before": {
    "order_id": "ord-67890",
    "status": "pending",
    "total": 99.99
  },
  "after": {
    "order_id": "ord-67890",
    "status": "shipped",
    "total": 99.99
  }
}

Both before and after states enable rich analytics.

Operation Types

Insert: New record created - only "after" state.

Update: Existing record modified - both "before" and "after" states.

Delete: Record removed - only "before" state.

Truncate/DDL: Schema or bulk changes - handled specially.

Different operations require different processing logic.

CDC Architecture for Analytics

Source Connectors

Connect to source databases:

Debezium: Open-source CDC platform supporting many databases.

Database-native: PostgreSQL logical replication, MySQL binlog connectors.

Vendor solutions: AWS DMS, Fivetran, Airbyte include CDC capabilities.

Choose connectors based on database and requirements.

Stream Processing

Transform and route change events:

Filtering: Select relevant tables and operations.

Transformation: Reshape events for target schemas.

Enrichment: Add context from reference data.

Routing: Direct events to appropriate destinations.

Processing prepares changes for analytical consumption.

Target Systems

Land changes in analytical systems:

Data warehouses: Snowflake, BigQuery, Redshift support streaming ingestion.

Data lakes: Land raw change events in object storage.

Lakehouses: Delta Lake, Iceberg support merge operations.

Real-time systems: Elasticsearch, Redis for operational analytics.

Codd Integrations connect CDC pipelines to semantic layers, ensuring that real-time data flows through consistent business definitions.

CDC Analytics Patterns

Real-Time Dashboards

Stream changes to low-latency visualization:

• Order status updates appear instantly
• Inventory levels reflect current state
• Sales metrics update continuously
• Operational KPIs stay current

Real-time dashboards require real-time data.

Change History Analytics

Analyze how data evolved:

• Track status progression over time
• Measure time between state changes
• Identify patterns in change sequences
• Audit who changed what when

CDC preserves history that snapshots lose.

Incremental Warehouse Loading

Keep warehouses current efficiently:

• Apply changes rather than full reloads
• Reduce processing time and cost
• Enable near-real-time freshness
• Handle large tables efficiently

CDC transforms warehouse loading economics.

Event Sourcing Analytics

Derive analytics from change streams:

• Reconstruct state at any point in time
• Replay changes for new analyses
• Build aggregate views from changes
• Enable temporal queries

Changes become the analytical foundation.

Implementing CDC for Analytics

Assess Source Systems

Evaluate CDC readiness:

• What database versions support log-based CDC?
• Are required permissions available?
• What log retention exists?
• Are schemas stable or frequently changing?

Source capabilities determine CDC options.

Choose CDC Approach

Select the right method:

Log-based: Preferred for production systems, minimal impact.

Query-based: Acceptable for lower volume or less critical sources.

Hybrid: Combine approaches for different sources.

Match approach to source characteristics.

Design Change Schema

Structure change events for analytics:

• Decide what fields to capture
• Handle schema evolution gracefully
• Include operational metadata
• Plan for null handling

Schema design affects analytical capabilities.

Build Processing Pipeline

Implement change stream processing:

• Connect to source CDC
• Transform for target requirements
• Handle late and out-of-order events
• Implement error handling

Robust pipelines ensure reliable delivery.

Configure Target Loading

Apply changes to analytical systems:

Upsert logic: Insert or update based on key.

Delete handling: Soft delete or hard delete.

Schema evolution: Handle new columns gracefully.

Ordering: Ensure changes apply in correct order.

Target loading determines final data state.

CDC Challenges

Initial Load

CDC captures changes, not existing data:

• Need initial full load before CDC
• Coordinate cutover from batch to CDC
• Handle data during transition
• Verify completeness after cutover

Initial load requires careful planning.

Schema Evolution

Source schemas change:

• New columns need propagation
• Column type changes cause issues
• Dropped columns require handling
• Table renames need coordination

Plan for schema change management.

Ordering Guarantees

Ensuring correct order is complex:

• Network delays cause reordering
• Partitioned topics may deliver out of order
• Multiple tables may have dependencies
• Transaction boundaries matter

Design for ordering requirements.

Exactly-Once Semantics

Avoiding duplicates or losses:

• Network failures cause retries
• Consumer failures require replay
• Idempotent processing needed
• Deduplication may be necessary

Understand delivery guarantees.

CDC and AI Analytics

CDC enables real-time AI capabilities:

Fresh training data: Models train on current data, not stale snapshots.

Real-time features: Feature stores update from CDC streams.

Instant scoring: Apply models to changes as they occur.

Drift detection: Monitor for data pattern changes.

Context-aware analytics platforms can leverage CDC to ensure AI systems always operate on current, consistent data with proper business context.

Getting Started

Organizations implementing CDC for analytics should:

1. Identify priority sources: Which databases need real-time analytics?
2. Assess CDC readiness: What CDC methods do sources support?
3. Select CDC tooling: Choose connectors and processing infrastructure
4. Design target schema: How will changes be stored and queried?
5. Implement pipeline: Build end-to-end change capture flow
6. Monitor and optimize: Track latency, errors, and throughput

CDC transforms analytics from periodic snapshots to continuous streams, enabling organizations to analyze what's happening now rather than what happened hours or days ago.