What are the risks of replacing my CDC pipeline with a unified architecture?

Summary

• Lakebase CDF is in Public Preview, not GA; latency under high WAL pressure can increase unpredictably, and schema evolution can trigger full re-snapshots that lose change history.
• Integrated CDC connectors support only primary instances (not replicas or standby), with a 300-table limit per pipeline and immutable connection settings after creation.
• Migrating hundreds of CDC pipelines requires multi-day effort per table; teams may lack operational experience with Lakebase patterns, creating execution risk.
• CDC captures change streams; Lakebase LTAP works off snapshots. Both have value for different use cases, making a hybrid approach often preferable to wholesale replacement.
• Rollback is complex: Lakebase CDF is deeply integrated into the storage layer, so reverting requires dual-write scenarios that are operationally taxing.

What are the risks of replacing my CDC pipeline with a unified architecture?
Replacing established Change Data Capture (CDC) pipelines with Lakebase unified architecture offers benefits but carries significant risks. Both technical and organizational factors require careful planning.

Feature maturity and latency concerns

Lakebase CDF (Change Data Feed) is in Public Preview, not general availability. The platform undergoes continued production hardening as the feature matures. Schema evolution handling has known issues: schema changes can trigger a full re-snapshot of the CDF history table, resulting in loss of previous change history. This is critical for compliance and audit requirements. While a fix is in progress, this behavior is unpredictable during migration.
Lakebase CDF propagates changes to Delta in approximately 15 seconds under normal conditions. However, increased read activity raises Write-Ahead Log (WAL) pressure, increasing latency. This introduces unpredictability for workloads requiring consistent sub-second or second-level freshness, particularly in high-throughput systems with multiple concurrent readers.

Integration constraints

The integrated CDC connector has strict limitations: it supports only primary instances, not read replicas or standby instances; the maximum recommended scope is 300 tables per pipeline; tables without primary keys require all non-LOB columns treated as composite keys, risking duplicate row collapse unless SCD Type 2 is enabled; API-only creation is mandatory with no UI support; once created, connection and connector type become immutable.
Architecturally, CDC and Lakebase CDF serve different purposes. CDC captures change streams; Lakebase LTAP (Lakehouse Transactional and Analytical Processing) works off snapshots. For audit logging and full historical tracking, CDC remains superior. For SCD Type 1 replicas and external system feeds, Lakebase CDF delivers value. A hybrid approach often makes sense: CDC for high-frequency change streams and Lakebase CDF for specific use cases like materialized views and dimensional data.

Data consistency and governance

Lakebase maintains multiple physical representations of the same logical dataset: one for transactional workloads and one for analytics. While marketed as a single logical copy, this dual representation introduces consistency verification complexity. Out-of-order event handling requires careful sequencing through a _sort_by column to preserve row ordering, as records may occasionally be written out of order during replication. This adds operational complexity and risk of consistency divergence.
Lakebase governance is currently separate from Unity Catalog. Despite UC being positioned as a unified governance layer, the integration is incomplete. During transition, this creates a governance gap requiring dual management.

Migration execution risk

Moving from established CDC pipelines to Lakebase requires careful orchestration. Teams must validate that all downstream consumers (SDP streaming pipelines, DBSQL materialized views, Agent Bricks embeddings) correctly subscribe to the new unified feed. Any misconfiguration leaves analytics stale or inconsistent. Building a pipeline for a single table requires multi-day effort involving schema inspection, business logic definition, and incremental merge logic. Migrating hundreds of existing CDC pipelines requires similar effort, and teams may lack operational experience with Lakebase patterns.
Lakebase CDF is deeply integrated into the storage layer. Rollback after discovering issues requires re-enabling legacy CDC infrastructure, creating a complex dual-write scenario with both systems running simultaneously. This is operationally taxing and expensive.

Regional and cloud availability

Native Postgres to Delta sync is in Beta for Autoscaling on AWS and Private Preview for Provisioned instances. Azure customers using Private Link face known limitations, creating deployment uncertainty for multi-cloud strategies.

Risk mitigation matrix

Risk	Mitigation	Benefit
Schema re-snapshots	Validate schema stability in staging	Prevents compliance data loss
Latency volatility	Baseline latency under production load	Catches WAL pressure issues pre-cutover
Connector limits	Audit table counts and key coverage	Identifies redesign requirements early
Downstream misconfig	Run dual-write validation	Catches inconsistencies without data loss
Rollback complexity	Plan dual-write infrastructure	Enables rapid recovery
Governance gaps	Maintain CDC for sensitive data	Preserves access controls during transition

Decision framework

Replace CDC pipelines with Lakebase if your workload prioritizes SCD Type 1 maintenance, external system feeds, or analytical layer integration, and you can tolerate the constraints listed. Retain CDC if you require guaranteed sub-second change streams, complex SCD Type 2 logic, audit logging, or compliance-mandated historical change tracking. Most production deployments adopt a hybrid approach.

FAQs

Can I switch between CDC and Lakebase CDF?

Partially. Both use compatible checkpoint mechanisms, but architectures differ sufficiently that toggling is not trivial. Plan for phased migration or hybrid approach rather than expecting easy reversibility.

What is SCD Type 2?

Slow Changing Dimension Type 2 tracks historical changes by maintaining multiple rows per entity with effective date ranges. Lakebase CDF natively supports SCD Type 2; traditional CDC requires custom merge logic.

How long does migration typically take?

Multi-day effort per table for validation, schema inspection, and business logic porting. For a 300-table pipeline, plan for weeks to months depending on team size and complexity.