DataConsultant designs, implements and improves Change Data Capture solutions for organisations that need dependable incremental data movement from operational systems into warehouses, lakehouses, cloud platforms, event streams and downstream applications. The service covers source readiness, capture patterns, initial-load coordination, schema evolution, replay and recovery, reconciliation, observability, security and production handover.
CDC latency, throughput and recovery behaviour depend on the source, capture method, network, target, transaction profile and operational configuration. Scope and service expectations are confirmed after discovery.
Move only relevant changes at a cadence suited to analytical or operational needs.
Avoid unnecessary full-source scans where incremental capture is technically appropriate.
Design checkpoints, replay, retries and reconciliation so failures can be handled deliberately.
Support warehouses, lakehouses, event-driven services and migrations from one governed change source where suitable.
DataConsultant does not publish a fixed CDC price. The appropriate commercial model depends on the source and target estate, capture technology, change rate, environments, availability needs, transformation logic, security controls, testing, cutover and operating support.
For teams deciding whether CDC is appropriate, which sources are suitable and what target pattern and controls are required.
For validating one representative source-to-target flow before committing to a larger replication programme.
For production-grade replication across agreed sources, targets and environments with tested operational controls.
For estates that need continuing monitoring, incident handling, upgrade support and reliability improvement.
Typical quote factors: number of source and target systems; source database editions and versions; transaction volume and change rate; required latency; retention and replay needs; target apply complexity; transformations; network and security setup; HA/DR requirements; non-production environments; testing and reconciliation depth; cutover constraints; documentation; knowledge transfer; and ongoing support coverage.
CDC should be introduced for a clear operating reason. The service helps teams separate genuine low-latency or incremental-change needs from workloads that remain better served by simpler batch extraction.
Reporting, operational decisions or downstream services need updates sooner than the existing batch cycle can provide.
Repeated table scans or bulk exports consume source resources even when only a small proportion of records changed.
A source and target must remain synchronized during staged migration, validation, parallel operation or controlled cutover.
Teams lack clear checkpoints, lag metrics, replay procedures, error ownership or evidence that the target remains complete.
Source DDL and model evolution reach downstream systems without compatibility rules, versioning or release coordination.
Independent ingestion jobs duplicate source access and transformation logic instead of using governed reusable change streams where suitable.
Start with a source, consumer and freshness requirement. We can assess feasibility, operational risk and whether CDC, micro-batch, API integration or another pattern is the better engineering choice.
A dependable CDC design is more than a connector. It links source behaviour, capture state, transport, target application, schema governance, reconciliation and operations into one recoverable flow.
The target architecture should reflect the business use case, not the other way around. Different consumers have different latency, ordering, history, replay, modelling and control needs.
Incrementally land operational changes for analytics, BI, data products, machine learning and governed downstream transformation.
Convert committed database changes into controlled events for downstream services where database-originated events are an appropriate pattern.
Keep target platforms synchronized after an initial load while workloads are validated, switched in waves or run in parallel.
Maintain secondary stores, search platforms, caches or reporting databases without coupling every consumer directly to the transactional source.
Implementation can cover the complete source-to-target path or a focused remediation area. Scope is adjusted to the selected technology, transaction profile, operational criticality and client responsibilities.
We can help turn a working connector into a supportable production service with environments, tests, schema controls, monitoring, reconciliation, recovery procedures and handover.
Outputs are agreed during scoping and should be usable by engineering, architecture, security, operations and business owners rather than remaining as isolated implementation notes.
Source suitability, constraints, target requirements, pattern comparison, risks, assumptions and agreed architecture decisions.
Capture components, offsets, transport, target apply, environments, connectivity, security zones and operational dependencies.
Agreed connectors, replication tasks, mappings, transformations, target handling, infrastructure and deployment configuration.
Functional scenarios, failure tests, restart and replay checks, row-level or aggregate reconciliation and acceptance evidence.
Lag, throughput, error, backlog, checkpoint and target-apply measures with dashboards, thresholds and escalation ownership.
Startup, shutdown, recovery, re-sync, schema change, incident triage, support boundaries and practical handover to client teams.
The delivery sequence is adapted to the environment, but the core control points remain consistent: evidence, architecture, safe capture, verification, production readiness and accountable ownership.
Clarify business purpose, freshness, sources, targets, consumers, constraints and ownership.
Review logs, keys, versions, transactions, network, security, data volume and platform support.
Define capture, snapshot, checkpoints, transport, target apply, schema and recovery patterns.
Configure environments, connectors, mappings, filters, transformations, monitoring and deployment.
Test changes, failures, restarts, throughput, schema events, reconciliation and cutover behaviour.
Handover runbooks, service ownership, alerting, lifecycle controls and improvement backlog.
We can review source pressure, connector behaviour, backlogs, checkpointing, target apply rates, schema handling, alerting and reconciliation to identify the highest-priority reliability improvements.
No single technique is right for every database or workload. The decision should consider accuracy, source impact, delete capture, transaction context, schema behaviour, operational complexity and supported platform capabilities.
| Pattern | Typical strengths | Key design questions | Common fit |
|---|---|---|---|
| Transaction-log / native log CDC | Incremental capture with low source-query overhead and strong change fidelity where supported. | Log retention, privileges, replication slots or identifiers, DDL behaviour, recovery position and source impact. | Production replication, low-latency analytics, migrations and event feeds. |
| Database-native CDC tables / change features | Uses source-engine supported change records and can simplify integration with compatible consumers. | Edition/version support, retention, cleanup, capture jobs, change-table semantics and operational ownership. | SQL Server and other database-native change mechanisms where supported. |
| Timestamp / high-watermark extraction | Simple and portable for append/update workloads with reliable modification columns. | Deletes, clock consistency, late updates, same-timestamp rows, reprocessing windows and key stability. | Micro-batch pipelines where full CDC semantics are not required. |
| Trigger-based change tables | Can capture application changes when no suitable log-based option exists. | Source transaction overhead, trigger maintenance, recursion, bulk operations, deployment and failure behaviour. | Selective legacy scenarios after source-impact assessment. |
| Application / domain events | Can carry business intent and domain semantics rather than database-row semantics. | Event completeness, transactional outbox or dual-write risk, versioning, ownership and replay. | Domain-driven and event-oriented architectures where application change is available. |
A connector marked “running” is not proof that downstream data is complete, timely or recoverable. Production CDC needs telemetry and operating procedures that expose both technical health and data movement outcomes.
Measure how far capture and target apply are behind the source, and distinguish source-reading lag from downstream application lag.
Define what happens after connector, broker, target, network or platform interruption.
Use evidence beyond connector status to detect missing, duplicated, misapplied or delayed changes.
Detect source evolution before it silently corrupts or stops downstream processing.
Make responsibilities explicit across source DBA, platform, data engineering, application and business teams.
Connect transaction rate, retention, storage, broker volume, compute, egress and target apply behaviour to operating cost.
CDC can replicate sensitive data quickly and widely. Controls should therefore be designed around source access, data minimisation, transit, target access, retention, lineage and evidence rather than added only after the pipeline is live.
Use the minimum database and platform permissions needed for the selected capture technology, with credentials protected through approved secrets management.
Define which schemas, tables and columns are permitted to move, where they may be delivered and whether masking, tokenisation or filtering is required.
Record source-to-target mappings, schema versions, deployment decisions, operational logs and reconciliation evidence where traceability matters.
Coordinate source-log retention, broker or trail retention and target replay needs with privacy, storage, recovery and operational requirements.
Clarify who owns source configuration, CDC infrastructure, schemas, target correctness, incident handling, change approval and business acceptance.
CDC controls can support privacy, security and audit requirements, but legal or regulatory compliance depends on the wider processing context and applicable obligations.
Technology selection is requirements-led. Supported features vary by source version, managed service, connector release and target, so compatibility must be verified during design rather than assumed from a product name.
Useful where database change events should be captured from supported sources and delivered through Kafka-compatible streaming infrastructure with explicit topic, offset and consumer design.
Can support full-load plus ongoing replication or CDC-only tasks for supported sources and targets. Production design should account for source and target latency, logs, instance capacity and target apply behaviour.
Can provide platform-managed changed-data patterns for supported sources and destinations. Architecture should verify continuous versus batch behaviour, checkpoints, supported connectors and target operations.
Can capture changes from supported relational and application sources into Google Cloud destinations. Source-specific log configuration, retention, connectivity and downstream processing remain part of the design.
Supports continuous extraction and replication using capture, trail and apply components across supported technologies. Checkpoints, trails, topology, recovery and operational procedures should be engineered explicitly.
PostgreSQL logical decoding, MySQL binary logs, SQL Server CDC and other engine features can provide source-native change information when versions, permissions and operating constraints are suitable.
Useful evidence reduces assumptions and prevents a technically possible CDC design from failing later because of source restrictions, missing keys, security approvals, target limits or unsupported operational expectations.
Share the database versions, target platform, approximate change rate, required freshness, current issues and security constraints. We can use that information to frame an appropriate CDC scope and quote.
The service combines source-database realities, integration architecture, data engineering, governance and production operations so CDC is evaluated as a dependable service rather than a one-time connector configuration.
Connect source assessment, capture design, target handling, monitoring, recovery and support ownership in one delivery approach.
Use failure tests, reconciliation, restart scenarios and measurable lag instead of treating a healthy connector status as sufficient proof.
Design change events, keys, schema evolution and target semantics around what downstream consumers can safely process.
Consider access, data minimisation, lineage, retention and evidence requirements as part of the replication architecture.
Work with client-selected cloud, database and integration technologies without forcing CDC where a simpler pattern is more appropriate.
Provide runbooks, ownership guidance, monitoring expectations and knowledge transfer for the teams that will operate the service.
Answers to common enterprise questions about CDC patterns, platforms, source and target behaviour, latency, schema evolution, reliability, controls, deliverables, pricing and ongoing support.
Share your contact details and requirement. DataConsultant can review the likely engineering scope, source dependencies, target pattern, assurance needs and appropriate next step.
