Should I build my own CDC pipeline or use a managed platform?

Summary

• Change Data Capture (CDC) pipelines track every insert, update, and delete from source databases and replicate them to your lakehouse, enabling real-time analytics and synchronization.
• Building your own CDC requires running Debezium (the open-source CDC engine) alongside Kafka infrastructure, then writing custom logic to apply changes to target tables, creating operational overhead.
• Managed CDC platforms eliminate the infrastructure burden by handling Debezium, applying changes to Lakeflow Pipelines automatically, and managing schema evolution and error recovery without customer code.
• Lakebase offers multiple CDC options: fully managed Lakeflow Connect with Direct CDC connectors (scheduled, cost-efficient), customer-managed Debezium for teams with existing Kafka, and Lakebase Change Data Feed (CDF) for native Postgres to Delta replication.
• Managed CDC is recommended for most organizations because it reduces complexity, provides built-in resilience, and eliminates the need to run and maintain always-on CDC infrastructure.

Should I build my own CDC pipeline or use a managed platform?
Change Data Capture (CDC) pipelines replicate every insert, update, and delete from source databases into your lakehouse, enabling real-time analytics, cross-system synchronization, and event-driven workflows. You can build CDC yourself using open-source tools like Debezium and Kafka, or use a managed platform like Lakeflow Connect that handles the infrastructure and complexity. The build path gives you control but requires operational expertise and always-on compute. The managed path eliminates infrastructure overhead and provides built-in resilience. For most organizations, managed CDC accelerates time-to-value and reduces operational risk.

Build your own: The operational burden

Building a custom CDC pipeline means running Debezium independently to extract changes from your source database and sink them into Kafka:

-- Typical architecture: Debezium -> Kafka -> Spark Streaming
spark.readStream().format("kafka")
  .option("kafka.bootstrap.servers", "...")
  .option("subscribe", "my_database.source_table")
  .load()
  .transform(from_avro(...)  # deserialize binary payloads
  .create_auto_cdc_flow()  # apply changes to target streaming table

What you inherit:

• Debezium configuration and tuning – Choose connector type, set up snapshot modes, configure binlog offsets or WAL (write-ahead log) reading
• Kafka operations – Provision brokers, manage topics, handle rebalancing, monitor lag
• Schema management – Integrate with a schema registry to deserialize Avro payloads
• Error recovery – Implement retry logic, dead-letter queues, and monitoring for stalled pipelines
• Always-on compute – CDC infrastructure runs continuously, consuming resources even when no changes occur

This approach is viable if you have a Kafka platform already deployed and data engineers comfortable managing it. But for teams starting fresh, the operational overhead is substantial.

Managed CDC: Platform approach

Fully Managed Lakeflow Connect

Databricks runs the Debezium engine as part of an Ingestion Gateway pipeline, connecting directly to your source database. No Kafka or third-party systems required. New events are written into a staging Delta table, where the Applier processes them and applies changes to target streaming tables.
Key benefits:

• No-code setup – Provide only database connection details and configuration
• Broad database support – Debezium supports all major relational databases (MySQL, PostgreSQL, Oracle, etc.)
• Built-in resilience – Error recovery, schema evolution, and out-of-order events handled automatically through AUTO CDC APIs
• Simplified onboarding – Databricks sets up both the Ingestion Gateway and Applier pipelines

Direct CDC Connectors (Cost-Optimized)

Newer Direct CDC connectors eliminate the always-on compute requirement. Extraction and replication run in a single scheduled pipeline that starts, catches up on changes, and shuts down automatically. This reduces costs significantly for workloads that don't require sub-minute latency.

Lakebase-specific alternative: Change Data Feed (CDF)

If your source is Lakebase Postgres, Lakebase Change Data Feed (CDF) provides a native alternative. It captures every insert, update, and delete from Postgres tables directly into Unity Catalog managed Delta tables, batched every ~15 seconds. This eliminates the need for external CDC infrastructure entirely and supports ETL pipelines, audit logs, and external system consumption.

Build vs. Managed comparison

Aspect	Build (Debezium + Kafka)	Managed CDC (Lakeflow Connect)
Setup complexity	High; requires Debezium tuning, Kafka provisioning, schema registry	Low; no-code configuration
Infrastructure cost	Always-on compute; Kafka platform	Managed; pay-per-use
Latency	Sub-second (always-on)	Near-real-time (fully managed); batch intervals for Direct CDC
Error handling	Manual retry logic and dead-letter queues	Built-in; auto-recovery
Schema evolution	Manual schema registry management	Automatic
Operational burden	High; monitor pipelines, handle failures	Low; platform managed

When to build

Build if you have an existing Kafka platform deployed and data engineers trained on Debezium, want to preserve custom logic for record transformation, or have specialized requirements not met by managed connectors.

When to use managed CDC

Use a managed platform if you want rapid time-to-value, have limited CDC operational expertise, don't want to run always-on compute, or require built-in resilience and automatic schema evolution.

Use cases

E-commerce platform – Replicate product inventory from the transactional database to the lakehouse in near-real time. Use managed Lakeflow Connect CDC to keep inventory synchronized for real-time analytics and personalization.
Financial services – Capture every transaction from core banking systems into the lakehouse for compliance reporting. Managed CDC with automatic error recovery ensures no changes are missed.

FAQs

What latency should I expect from managed CDC?

Fully managed Lakeflow Connect provides near-real-time replication (seconds to minutes). Direct CDC connectors batch changes on a schedule (e.g., hourly) for cost efficiency. Lakebase CDF batches every ~15 seconds.

Can I change CDC configuration after it's running?

Yes. Both Debezium and managed CDC platforms allow you to adjust snapshot modes, table filters, and data transformations. Managed platforms make this simpler through UI configuration.

What if I want to transform data during CDC?

Both approaches support transformation. Custom CDC can transform in Spark code; managed platforms support declarative transformations through the Applier stage.