Change Data Capture (CDC)

What is Change Data Capture?

Change Data Capture (CDC) is a data integration pattern that identifies and tracks changes made to database records—inserts, updates, deletes—and propagates those changes to downstream systems in near real-time without requiring full table scans or batch exports. CDC technologies monitor database transaction logs or table triggers to detect modifications as they occur, capturing only changed records rather than entire datasets, enabling efficient data synchronization between operational databases, data warehouses, analytics platforms, and operational tools.

Unlike traditional ETL (Extract, Transform, Load) processes that periodically copy entire tables whether data changed or not, CDC operates continuously and incrementally. When a customer updates their email address in a CRM database, CDC detects this specific modification within seconds and transmits only that changed record to destination systems—the data warehouse powering analytics dashboards, the Customer Data Platform unifying customer profiles, and the email marketing platform ensuring accurate contact information. This incremental approach dramatically reduces data transfer volumes, computational overhead, and latency compared to batch processing.

Modern data architectures increasingly rely on CDC as foundational infrastructure enabling real-time analytics, event-driven automation, and cross-system data consistency. According to Gartner research, organizations implementing CDC for data warehouse synchronization reduce data latency from hours to minutes while decreasing infrastructure costs by 40-60% through elimination of full table scans and off-peak batch processing windows. For B2B SaaS companies requiring real-time customer profiles, operational analytics, and system synchronization, CDC has evolved from specialized technique to standard practice.

Key Takeaways

• Incremental Change Tracking: CDC captures only modified, inserted, or deleted records rather than extracting entire tables, dramatically reducing data transfer volumes and processing overhead for large databases

• Near Real-Time Latency: Unlike batch ETL running hourly or nightly, CDC propagates changes within seconds to minutes of source modification, enabling real-time analytics and operational automation

• Log-Based Implementation: Modern CDC reads database transaction logs (MySQL binlog, PostgreSQL WAL, SQL Server transaction log) without impacting application performance through queries or table locks

• Schema Evolution Support: Advanced CDC systems track not just data changes but schema modifications (new columns, altered types, renamed fields), automatically adapting downstream pipelines to structural changes

• Exactly-Once Semantics: Enterprise CDC platforms ensure each change propagates to destinations exactly once despite network failures or system restarts, preventing duplicate records or missed updates

How Change Data Capture Works

CDC implementations vary in technical approach, but all follow similar conceptual flows for detecting, capturing, and propagating database changes:

Change Detection Methods

CDC systems employ different techniques for identifying database modifications:

Log-Based CDC (preferred modern approach): Reads database transaction logs—the sequential record of all committed changes databases maintain for disaster recovery and replication. MySQL records changes in the binary log (binlog), PostgreSQL in Write-Ahead Log (WAL), SQL Server in transaction logs. CDC connectors parse these logs extracting INSERT, UPDATE, DELETE operations with before/after values for modified fields. Log-based CDC operates without touching source tables or impacting application performance since logs already exist for database functionality.

Trigger-Based CDC: Database triggers (stored procedures automatically executing on data modifications) capture changes by writing to separate audit tables when applications modify records. When users update customer records, an UPDATE trigger fires, inserting old and new values into a changes table that CDC processes. While effective, triggers add latency to application transactions and increase database load proportional to change volume.

Query-Based CDC: Periodic polling compares current table states against previously captured snapshots, identifying differences as changes. Typically relies on timestamp columns (last_modified_date) to identify records changed since last scan. Simple to implement but resource-intensive for large tables, introduces latency equal to polling intervals, and fails to capture deleted records unless using soft-delete patterns.

Timestamp-Based CDC: Applications maintain updated_at timestamps on records; CDC queries for records with timestamps greater than last synchronization time. Efficient for tables with reliable timestamp maintenance but misses changes when applications bypass timestamp updates or don't track deletions.

Modern data integration platforms like Fivetran, Airbyte, and Debezium primarily use log-based CDC for production databases due to performance characteristics and data completeness guarantees.

Change Capture Process

After detecting modifications, CDC systems extract relevant change information:

Record-Level Changes: For each database modification, CDC captures the operation type (INSERT/UPDATE/DELETE), affected record identifier (primary key), changed column values, and operation metadata (timestamp, transaction ID, database user). For updates, CDC may capture both "before" images (original values) and "after" images (new values) enabling downstream systems to see exactly what changed.

Transaction Ordering: Databases process changes within transactions maintaining atomicity and consistency. CDC preserves transaction boundaries and ordering, ensuring downstream systems apply changes in the same sequence source systems committed them. This ordering prevents scenarios where downstream analytics see updates before the original inserts, creating invalid states.

Metadata Enrichment: CDC augments change records with context: source database identifier, table name, capture timestamp, schema version, and CDC system metadata enabling debugging and audit trails. This metadata helps downstream systems route changes appropriately, handle schema evolution, and troubleshoot synchronization issues.

Change Propagation

Captured changes flow to destination systems through various mechanisms:

Message Queue Delivery: CDC publishes change events to message brokers (Apache Kafka, AWS Kinesis, Google Pub/Sub) that buffer changes and enable multiple consumers. When a Salesforce opportunity updates, CDC writes the change event to Kafka; downstream consumers (data warehouse, analytics platform, customer success tool) each read and process the event independently at their own pace.

Direct Database Writes: Some CDC implementations write directly to destination databases using JDBC connections or database-native protocols. Captured changes from production MySQL replicate to analytical PostgreSQL instance through CDC connector performing schema translation and data transformation during transfer.

API Integration: CDC systems invoke REST or GraphQL APIs on destination systems, translating database changes into API calls. When customer records update in operational database, CDC calls Customer Data Platform APIs with changed attributes, triggering profile unification workflows.

File-Based Sync: For systems requiring batch-like interfaces, CDC accumulates changes into files (CSV, JSON, Parquet) periodically landing them in cloud storage (S3, GCS) where downstream ETL processes ingest them. Provides batch compatibility while maintaining incremental efficiency.

Destination Application

Receiving systems apply captured changes to maintain synchronized state:

Append-Only Event Logs: Data warehouses often store CDC events in append-only tables preserving complete change history. Rather than updating records in-place, each change appends as new row enabling temporal queries like "show customer profile as of last quarter" or "identify accounts that churned then returned."

Merge/Upsert Operations: Destination databases apply changes using MERGE or UPSERT operations (update if exists, insert if new) maintaining current state views. This approach keeps destination tables structurally similar to sources while staying synchronized through incremental updates rather than full refreshes.

Change Event Processing: Streaming applications consume CDC events triggering business logic: when opportunity stage advances to "Closed Won," CDC event triggers automated workflows creating customer success onboarding tickets, provisioning accounts, and sending welcome emails—all initiated by database changes without application-level webhooks.

Schema Evolution Handling

Production databases evolve—adding columns, renaming fields, changing data types. CDC systems must adapt pipelines to schema changes:

Automatic Schema Detection: Advanced CDC monitors transaction logs for DDL (Data Definition Language) operations like ALTER TABLE, automatically updating internal schemas and propagating changes to destinations. When developers add lead_source column to contacts table, CDC detects the schema change and begins capturing the new field for subsequent records.

Backward Compatibility: Some CDC implementations add new columns to destination tables automatically while preserving existing fields, ensuring downstream reports and queries don't break when sources add attributes. Requires destination systems supporting dynamic schema evolution (common in data lakes, less common in rigid data warehouses).

Version Management: Enterprise CDC maintains schema versions, labeling change events with schema identifiers enabling downstream consumers to handle multiple concurrent schemas during transition periods. This versioning supports zero-downtime migrations where old and new application versions coexist temporarily.

Key Features

Production-grade CDC platforms share common capabilities ensuring reliable data synchronization:

• Low Latency Propagation: Sub-second to few-second latency between source changes and destination availability, enabling near real-time analytics and operational automation without batch processing delays

• Scalable Change Volume: Efficiently handles high-throughput databases with thousands of changes per second, automatically scaling capture and propagation infrastructure based on load

• Fault Tolerance: Maintains change capture and delivery despite network interruptions, database restarts, or system failures through checkpointing, retry logic, and distributed processing

• Exactly-Once Guarantees: Ensures each change propagates to destinations exactly once through transaction management, deduplication, and idempotent delivery preventing duplicate records

• Multi-Source Federation: Captures changes from multiple source databases (production, staging, regional replicas) routing to appropriate destinations based on data governance rules and access policies

Use Cases

Real-Time Data Warehouse Synchronization

B2B SaaS companies maintain data warehouses aggregating customer data from operational systems—CRM, product databases, billing systems, support tickets—for analytics and reporting. Traditional nightly batch ETL creates 12-24 hour data staleness: dashboards showing yesterday's customer status, reports missing today's closed deals, forecasts based on outdated pipeline.

Implementing CDC for all operational sources reduces warehouse latency to minutes: when sales reps close opportunities in Salesforce, CDC propagates changes to Snowflake within seconds; when customers upgrade subscriptions in billing systems, revenue analytics update immediately; when support tickets resolve, customer health scores refresh in real-time. This freshness enables intraday performance monitoring, accurate pipeline dashboards for sales leadership, and timely intervention on at-risk accounts customer success teams identify through current usage patterns.

Organizations migrating from batch to CDC-based warehouses report transformational impacts: executive dashboards reflecting current business state rather than yesterday's snapshot, marketing campaigns targeting based on today's behaviors not last week's exports, and customer-facing teams accessing accurate product usage and engagement data during conversations. One implementation reduced data warehouse latency from 24 hours to under 5 minutes while decreasing ETL infrastructure costs 50% by eliminating full table scans processing mostly unchanged records.

Customer Data Platform Synchronization

Customer Data Platforms unify customer profiles from dozens of sources: website tracking, CRM contacts, email engagement, product usage, support interactions. Maintaining profile accuracy requires continuous synchronization as customers interact across touchpoints.

CDC enables this synchronization without performance-impacting polling: when customers update email addresses in preference centers, CDC propagates changes to CDP and downstream marketing automation within seconds ensuring consistency; when CRM fields update with new company information, firmographic data refreshes across all systems; when product databases record feature adoption events, customer profiles update triggering personalization rules.

Without CDC, CDPs either poll source systems (creating load and latency) or require applications to push changes via APIs (development overhead and reliability concerns). CDC provides the middle path: application-transparent change capture with near real-time latency. Marketing teams see immediate profile updates enabling timely personalization—welcome emails triggered minutes after trial signups rather than hours later when batch ETL runs, abandoned cart campaigns launching while prospects remain engaged, and churn prevention outreach based on current usage declines not last week's metrics.

Operational Database Replication and Disaster Recovery

Companies replicate production databases to geographically distributed data centers for disaster recovery, read scaling, and regional performance. Traditional replication uses database-native tools (MySQL replication, PostgreSQL streaming replication) but these require homogeneous systems and don't support filtering or transformation.

CDC-based replication enables heterogeneous scenarios: replicating MySQL production to PostgreSQL analytics replica with schema optimization, filtering sensitive PII columns during replication for privacy compliance, replicating subset of tables to regional replicas supporting local read performance, and maintaining multiple replica types (operational read replicas, analytical data warehouses, cache invalidation feeds) from single CDC capture.

One e-commerce platform uses CDC to maintain synchronized product catalogs across 12 regional data centers: headquarters MySQL database holds authoritative catalog, CDC propagates changes to regional PostgreSQL replicas within seconds, enabling low-latency product searches for customers worldwide. When merchandising teams update product descriptions or pricing, changes ripple globally within 10 seconds. This approach reduced cross-region query latency 80% while providing consistency guarantees traditional caching couldn't achieve.

Implementation Example

A B2B SaaS company implements CDC pipeline synchronizing production databases to Snowflake data warehouse:

CDC Architecture Overview

Change Data Capture Pipeline Architecture
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Source Databases          CDC Layer              Message Queue         Destinations
────────────────         ─────────              ─────────────        ────────────

PostgreSQL (App DB)  →                      →  Kafka Topic:      →  Snowflake DW
├─ users                Debezium                  app_changes         ├─ users_sync
├─ accounts             Connectors             ↓                     ├─ accounts_sync
├─ opportunities        ↓                      Buffer & Order        ├─ opps_sync
└─ activities           Read WAL logs          Guarantees            └─ activities_sync
                        Extract changes        ↓                     ↓
MySQL (Billing)      →  Transform to          Kafka Topic:      →  Snowflake DW
├─ subscriptions        JSON events              billing_changes     ├─ subscriptions_sync
├─ invoices             ↓                      ↓                     ├─ invoices_sync
└─ payments             Maintain offsets       Kafka Connect      →  BigQuery
                                                  Consumer             └─ Revenue Analytics

MongoDB (Support)    →  CDC Capture        →  Kafka Topic:      →  Elasticsearch
└─ tickets              Change streams           support_changes     └─ Support Analytics

Source Configuration Example

PostgreSQL CDC Setup (Debezium connector configuration):

{
  "name": "postgres-cdc-connector",
  "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
  "database.hostname": "prod-db.internal",
  "database.port": "5432",
  "database.user": "cdc_user",
  "database.dbname": "production",
  "database.server.name": "prod_server",
  "plugin.name": "pgoutput",
  "publication.name": "cdc_publication",
  "table.include.list": "public.users,public.accounts,public.opportunities",
  "snapshot.mode": "initial",
  "transforms": "unwrap",
  "transforms.unwrap.type": "io.debezium.transforms.ExtractNewRecordState"
}

This configuration enables log-based CDC reading PostgreSQL WAL, filtering specific tables, and transforming change events into simplified format for downstream consumption.

Change Event Format Example

When user updates email address in database, CDC produces change event:

{
  "schema": {
    "type": "struct",
    "name": "prod_server.public.users.Envelope",
    "version": 1
  },
  "payload": {
    "before": {
      "id": 12847,
      "email": "old.email@company.com",
      "first_name": "Jane",
      "last_name": "Smith",
      "updated_at": 1705392847000
    },
    "after": {
      "id": 12847,
      "email": "jane.smith@newcompany.com",
      "first_name": "Jane",
      "last_name": "Smith",
      "updated_at": 1705566123000
    },
    "source": {
      "version": "2.1.0",
      "connector": "postgresql",
      "name": "prod_server",
      "ts_ms": 1705566123456,
      "db": "production",
      "schema": "public",
      "table": "users",
      "txId": 78653421,
      "lsn": 987654321,
      "snapshot": false
    },
    "op": "u",
    "ts_ms": 1705566123500
  }
}

This event shows exact changes (before/after values), operation type ("u" for update), timing metadata, and source context enabling downstream systems to apply changes accurately.

Performance Metrics Comparison

Destination Processing Logic

Snowflake warehouse applies CDC events using MERGE operations:

-- Simplified MERGE logic for applying CDC changes
MERGE INTO users_production AS target
USING users_cdc_staging AS source
ON target.id = source.id
WHEN MATCHED AND source.op = 'u' THEN
  UPDATE SET
    email = source.after_email,
    first_name = source.after_first_name,
    updated_at = source.after_updated_at
WHEN MATCHED AND source.op = 'd' THEN
  DELETE
WHEN NOT MATCHED AND source.op IN ('c', 'r') THEN
  INSERT (id, email, first_name, last_name, created_at)
  VALUES (source.id, source.after_email, source.after_first_name,
          source.after_last_name, source.after_created_at);

This MERGE statement processes CDC events maintaining synchronized current-state view in data warehouse.

Related Terms

• ETL (Extract Transform Load): Traditional batch data integration approach CDC modernizes through incremental, real-time change propagation

• Data Warehouse: Analytical databases that CDC keeps synchronized with operational systems in near real-time rather than nightly batch loads

• Reverse ETL: Complementary pattern activating warehouse data back into operational tools, often using CDC-like incremental sync mechanisms

• Customer Data Platform: Systems relying on CDC for continuous profile synchronization from CRMs, product databases, and engagement platforms

• Identity Resolution: Process benefiting from CDC's real-time change propagation ensuring unified customer profiles update immediately as new data arrives

• Event-Driven Architecture: Design pattern leveraging CDC change events to trigger automated workflows and business logic

• Data Integration: Broader discipline encompassing CDC alongside batch ETL, API integration, and file-based synchronization methods

• Stream Processing: Real-time data processing frameworks consuming CDC event streams for analytics, transformation, and enrichment

Frequently Asked Questions

What is Change Data Capture (CDC)?

Quick Answer: Change Data Capture (CDC) is a data integration technique that detects and tracks database modifications (inserts, updates, deletes) in real-time, propagating only changed records to downstream systems rather than copying entire tables.

CDC monitors database transaction logs or uses triggers to identify specific records that applications modify, capturing details about what changed (before/after values), when changes occurred, and what operation happened (create/update/delete). These change events propagate to destination systems—data warehouses, analytics platforms, operational tools—within seconds, enabling near real-time synchronization without the performance impact of full table scans. Organizations implement CDC to reduce data warehouse latency from hours to minutes, maintain synchronized Customer Data Platforms, and enable event-driven automation triggered by database changes. CDC has become foundational infrastructure for modern data architectures requiring fresh analytics and cross-system consistency.

How does CDC work technically?

Quick Answer: Modern CDC reads database transaction logs (MySQL binlog, PostgreSQL WAL, SQL Server transaction log) capturing committed changes without querying tables or impacting application performance, then streams change events to message queues or destinations for processing.

Database systems maintain transaction logs recording every committed change for disaster recovery and replication. CDC connectors parse these logs extracting INSERT, UPDATE, and DELETE operations with before/after values, transforming them into structured change events. For example, when applications update customer records in PostgreSQL, the database writes changes to Write-Ahead Log (WAL); Debezium CDC connector reads WAL entries, identifies customer table modifications, extracts changed fields, and publishes events to Apache Kafka. Downstream consumers (data warehouse sync jobs, CDP update processes, operational automation) subscribe to these Kafka topics, processing changes and applying updates to destination systems. According to Confluent documentation, log-based CDC is preferred over alternatives (triggers, timestamp polling) because it captures complete change history including deletes, maintains transaction ordering, and operates without adding latency to application transactions.

What's the difference between CDC and ETL?

CDC tracks and propagates incremental changes continuously in near real-time by monitoring transaction logs, while traditional ETL periodically extracts entire tables in batch jobs running hourly or nightly. ETL copies all records whether changed or not, processes transformations in staging areas, and loads results into destinations during scheduled windows—creating data latency measured in hours and requiring significant compute resources to process mostly unchanged data. CDC identifies only modified records through log monitoring, streams changes immediately, and applies updates incrementally—achieving minute-scale latency while reducing data transfer volumes 90-95% for mature databases where small percentages of records change daily. ETL remains appropriate for initial historical loads, complex multi-source transformations, and systems lacking real-time requirements, while CDC serves scenarios demanding current data: real-time dashboards, operational automation, and synchronized customer profiles. Many modern data architectures use both: CDC for continuous incremental sync after initial ETL-based historical load.

What are the main types of CDC implementation?

The three primary CDC approaches are log-based (reading database transaction logs), trigger-based (using database triggers to capture changes), and query-based (polling tables for modifications). Log-based CDC is the modern standard—reading MySQL binlog, PostgreSQL WAL, or similar transaction logs to capture all committed changes without touching source tables or impacting application performance. Tools like Debezium, AWS DMS, and Oracle GoldenGate use log-based CDC. Trigger-based CDC creates database triggers firing on INSERT, UPDATE, DELETE operations, writing change details to audit tables that CDC processes—effective but adds latency to transactions and database load proportional to change frequency. Query-based CDC periodically queries tables comparing current state to previous snapshots, typically relying on updated_at timestamps—simple to implement but resource-intensive for large tables, introduces latency equal to polling intervals, and struggles capturing deletions. Log-based CDC is preferred for production systems due to minimal source impact, complete change capture including deletes, and real-time latency, though trigger-based approaches remain useful for databases lacking log access or requiring pre-CDC transformation.

When should companies implement CDC?

Organizations should consider CDC when experiencing: data warehouse staleness preventing timely decision-making (24-hour-old dashboards showing outdated business state), batch ETL failures from full table scans timing out or locking databases, need for real-time operational automation triggered by database changes, or requirements to maintain synchronized customer profiles across multiple systems. Specific triggers include data warehouse latency exceeding business tolerance (hourly refreshes needed but nightly ETL runs currently), growing database sizes making full table extraction prohibitively expensive, operational workflows requiring immediate action on data changes (customer onboarding, churn intervention), or analytics use cases depending on current state (intraday sales performance, real-time customer health scoring). Companies also implement CDC during cloud data warehouse migrations (Snowflake, BigQuery, Redshift) as opportunity to modernize from batch to streaming architectures. The calculation is straightforward: if data freshness creates measurable business value (faster sales response, improved customer experience, reduced churn) or current ETL consumes excessive resources, CDC typically delivers ROI within quarters through latency reduction and efficiency gains.

Conclusion

Change Data Capture has evolved from specialized database replication technique to foundational data infrastructure for modern B2B SaaS companies. By efficiently tracking and propagating only modified records rather than processing entire tables, CDC enables real-time analytics, operational automation, and cross-system synchronization previously impossible with batch ETL approaches. The shift from nightly data warehouse refreshes to continuous synchronization transforms how organizations operate—executives viewing current business performance rather than yesterday's snapshot, customer-facing teams accessing accurate real-time profiles during conversations, and automated workflows responding immediately to customer behaviors and state changes.

For data engineering teams, CDC simplifies data pipeline architecture by eliminating complex batch scheduling, reducing compute costs through incremental processing, and providing consistent change event streams that multiple consumers leverage independently. Analytics teams benefit from warehouse data reflecting current operational state, enabling intraday performance monitoring and timely intervention on emerging opportunities or risks. Revenue operations groups use CDC-powered real-time customer profiles to trigger automated workflows, prioritize accounts based on current signals, and maintain synchronized data across CRM, customer success platforms, and analytics systems.

As B2B SaaS companies scale operational database sizes and expand system ecosystems, CDC becomes increasingly essential for maintaining data consistency and freshness without unsustainable infrastructure costs. Organizations evaluating CDC should assess current data latency tolerance, ETL resource consumption, and real-time use case opportunities alongside technical considerations like log access availability, message queue infrastructure, and schema evolution management capabilities. For companies experiencing batch ETL limitations or pursuing real-time operational excellence, CDC represents not optional modernization but necessary foundation for competitive data-driven operations.

Last Updated: January 18, 2026