Catalog
concept#Integration#Architecture#Data#Reliability

Sync Strategies

Concepts and patterns for synchronizing data state across systems, services, and stores.

Sync strategies describe principles and patterns for aligning data state across systems, services, or stores.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Streaming platforms (e.g., Kafka)Change Data Capture tools (e.g., Debezium)ETL/ELT pipelines and data warehouses

Principles & goals

Idempotence of operationsAccurate change capture (change logging / CDC)Explicit conflict resolution and versioning
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Data loss on faulty replication
  • Scaling issues with central coordinators
  • Faulty conflict rules leading to inconsistent states
  • Version events and changes unambiguously
  • Implement idempotent consumer logic
  • Set up monitoring for latency, throughput and errors

I/O & resources

  • Source data stream or change log
  • Network and authentication information
  • Conflict and mapping specifications
  • Replicated target states or events
  • Monitoring metrics and error logs
  • Audit and change logs for traceability

Description

Sync strategies describe principles and patterns for aligning data state across systems, services, or stores. They compare approaches such as push vs. pull, real-time streaming (CDC), and periodic batch replication, including conflict resolution. The goal is reliable consistency, performant transfers, and reduced latency while informing architectural decisions.

✔

  • Reduced data inconsistencies across systems
  • Improved latency via local copies or streaming
  • Better traceability through change logs

✖

  • Complexity in multi-master scenarios
  • Network and storage overhead at high change rates
  • Eventual consistency may violate strong consistency requirements

  • Replication latency

    Time between a change in the source system and visibility in the target.

  • Conflict rate

    Share of synchronizations that require conflict resolution.

  • Data volume per time unit

    Transferred data volume to measure bandwidth and storage needs.

Change Data Capture with Debezium

Using Debezium to capture data changes from relational databases and enrich downstream systems in real time.

Offline-first synchronization in mobile apps

Client-side delta sync and conflict resolution when mobile devices reconnect.

Batch replication to data warehouse

Scheduled ETL jobs transfer aggregated data from production systems into a central warehouse.

1

Analyze synchronization requirements (latency, consistency, volume)

2

Choose strategy (push, pull, CDC, batch) based on requirements

3

Prototype with relevant technology (e.g., Debezium, Kafka, ETL)

4

Define monitoring, SLAs and recovery processes

5

Incremental rollout and validation in production

⚠️ Technical debt & bottlenecks

  • Ad-hoc scripts instead of robust replication tools
  • No automated schema migration strategy
  • Monolithic sync components with high coupling
Network bandwidthDatabase write and read performanceCoordination for conflict resolution
  • Using real-time streaming for infrequent batch workloads
  • Synchronizing non-critical systems fully and incurring costs
  • Missing schema versioning during replication
  • Underestimating side effects of conflict resolution
  • Unconsidered backpressure in the streaming path
  • Lack of end-to-end observability for replication paths
Knowledge of replication and CDC principlesExperience with distributed systems and fault toleranceAbility to define conflict resolution rules
Requirements for data consistency and latencyScalability and resilience of replicationEffort for monitoring, recovery and schema management
  • • Regulatory requirements for data transfer and storage
  • • Limitations of existing DB engines (e.g., missing CDC)
  • • Network latency and intermittent connectivity