Architecture Guide
ETL vs ELT vs Data Sync: Architecture Guide
Understanding when to use each data movement pattern, the trade-offs involved, and implementation considerations for enterprise teams.
1. Defining ETL, ELT, and data sync
ETL (Extract, Transform, Load)
Data is extracted from sources, transformed in a staging environment, then loaded into the destination. Transformation happens before data reaches the target system.
Classic use case: On-premise data warehouse loading with complex business logic
ELT (Extract, Load, Transform)
Data is extracted from sources, loaded raw into the destination (typically a cloud warehouse), then transformed using the destination's compute. Transformation happens after loading.
Classic use case: Cloud data warehouse with dbt or SQL-based transformations
Data Sync (Operational Synchronization)
Real-time or near-real-time data movement between operational systems. Often bidirectional. Focuses on keeping systems in sync rather than analytical aggregation.
Classic use case: CRM-to-ERP sync, Customer 360, operational workflows
2. Comparison: when each pattern wins
Choose ETL when:
- Complex transformations must happen before data lands
- Destination has limited compute (legacy warehouse)
- Data privacy requires filtering before loading
- Transformation logic is stable and doesn't change often
Choose ELT when:
- Destination is a modern cloud warehouse (Snowflake, BigQuery, Redshift)
- Transformation logic evolves frequently
- You want raw data preserved for future analysis
- SQL-based transformations (dbt) are preferred
Choose Data Sync when:
- Systems need to stay in sync in real-time
- Bi-directional data flow is required
- Operational workflows depend on data freshness
- Integration is between SaaS applications
3. Trade-offs and considerations
Latency vs. complexity
Real-time sync adds operational complexity. You need idempotency, conflict resolution, and robust error handling. Batch patterns are simpler but introduce latency.
Compute costs
ELT shifts compute costs to the destination. This works well with cloud warehouse pricing but can become expensive for large-scale transformations.
Data governance
ETL allows sensitive data to be filtered before loading. ELT loads raw data first, requiring governance at the destination. Sync patterns require governance at both ends.
Schema drift handling
Source schemas change. ETL catches drift at transform time. ELT surfaces drift at query time. Sync platforms need explicit schema evolution handling.
4. Implementation guidance
CDC vs. webhooks vs. polling
CDC: Lowest latency, captures all changes, requires database access
Webhooks: Event-driven, depends on source support, needs retry handling
Polling: Simplest to implement, higher latency, more source API calls
Idempotency requirements
For any pattern involving retries or at-least-once delivery, idempotency is critical. Design operations so that processing the same event twice produces the same result.
Error handling patterns
- Dead-letter queues: Capture failed events for manual review
- Automatic retries: Exponential backoff with jitter
- Circuit breakers: Stop calling failing destinations
- Alerting: Notify teams before failures cascade
5. Decision framework
Use this flowchart to determine which pattern fits your use case:
Q1: Is this for analytics or operations?
→ Analytics: Continue to Q2
→ Operations: Data Sync
Q2: Is the destination a modern cloud warehouse?
→ Yes: ELT
→ No: Continue to Q3
Q3: Must transformations happen before loading?
→ Yes (privacy, compliance): ETL
→ No: ELT with filtering
Many organizations use multiple patterns. Analytics pipelines often use ELT while operational systems use sync. See ThreadSync Platform for how we support all patterns.
Need help choosing the right pattern?
Our architects can help you design an integration architecture that fits your requirements.