CDC, Without the Drama
Automating Change Data Capture for Batch and Streaming Workloads
November 25, 2025
Change Data Capture (CDC) should be a force multiplier for delivery speed and freshness, not a source of brittle integrations. When designed once in metadata and applied across platforms, CDC can generate consistent pipelines for both batch and streaming. This avoids hand-coded orchestration that often collapses under schema changes or scale. A metadata-first approach turns CDC from a fragile collection of jobs into a reusable, governed pattern. The result is standardized capture, transformation, and integration without the drama that so often surrounds CDC projects.
Why Change Data Capture Is Powerful, But Often Painful
CDC delivers efficiency by moving only what changed. That efficiency quickly turns fragile in the messiness of real platforms. Teams must configure sources, align inserts and deletes, and maintain consistency as schemas evolve. Batch and streaming logic often diverge when implemented separately, which multiplies risk and cost. The pattern is attractive in theory but taxing in practice.
- CDC moves only changed data, reducing latency and compute.
- Real platforms require careful handling of inserts, updates, and deletes.
- Schema evolution breaks hand-written logic.
Without guardrails, small differences across jobs lead to major outages.
CDC Use Cases: From Reporting to Real Time
CDC supports a wide range of use cases, from traditional reporting to event-driven applications. Each scenario benefits from fresher data, but also demands consistency across environments.
- Data warehouse loads for dimensions and facts, often with SCD Type 1 or 2 behavior.
- Near real-time dashboards and operational services that act on current data.
- Event-driven decisions such as fraud detection or personalization.
Because these uses share capture logic, a metadata-driven model lets you design once and apply everywhere.
Common CDC Patterns, And Their Pitfalls
Teams often turn to familiar CDC patterns. Each comes with trade-offs in fidelity, complexity, and fit. The difficulty is not the pattern itself but how it behaves at scale.
- Timestamp-based: simple and cheap, but vulnerable to late data and clock drift.
- Log-based: high fidelity, but harder to configure and monitor.
- Trigger-based: easy to start, but brittle under volume.
- Streaming-first: lowest latency, but difficult to reconcile without common rules.
CDC without consistent policy creates duplication, inconsistency, and lost updates.
Pattern Comparison at a Glance
This comparison highlights why metadata is the right place to decide capture strategy and then generate implementations consistently.
Automating CDC with Metadata
A metadata-driven approach declares CDC policy at the source and table level, then generates ingestion and merge logic for both batch and streaming. Capture method, watermarks, delete handling, and SCD mode are stored beside the model so behavior remains consistent even as schemas evolve.
A minimal metadata policy might look like:
source: sqlserver_erp
capture: change_tracking
default_watermark: ModifiedDateUtc
tables:
- name: SalesOrderHeader
deletes: soft_delete_flag
scd_mode: scd2
With this approach, every pipeline is generated from the same rules — no matter the platform.
Unified CDC Pipelines for Batch and Streaming
Batch processing is scheduled and reliable. Streaming is event-driven and low latency. When both are generated from shared metadata, logic stays aligned. Hybrid models become possible, where streaming covers real-time needs and batch backfills or repairs missed changes.
A simplified orchestration intent:
pipeline: sales_cdc
mode: hybrid
stream: {engine: databricks_structured_streaming}
batch: {engine: synapse, schedule: hourly}
By capturing orchestration intent in metadata, teams eliminate drift between batch and streaming implementations.
Merge and Upsert Transformations
CDC’s real value arrives when captured changes are merged into target tables. With metadata stored up front, the system can generate SQL or Spark code for both Type 1 and Type 2 SCDs.
Example: a simplified Type 1 merge in SQL Server.
MERGE dbo.DimCustomer AS tgt
USING dbo.stg_DimCustomer_Changes AS src
ON tgt.CustomerKey = src.CustomerKey
WHEN MATCHED THEN UPDATE SET tgt.CustomerName = src.CustomerName
WHEN NOT MATCHED THEN INSERT (CustomerKey, CustomerName)
VALUES (src.CustomerKey, src.CustomerName);
Templates extend this logic for SCD2, adding history rows and current flags. The key is that the rules live in metadata, not in one-off scripts.
BimlFlex: CDC Without the Chaos
BimlFlex applies customer-provided metadata to generate CDC assets across supported platforms. By modeling capture strategies once, you can generate ingestion pipelines in Azure Data Factory or Synapse and streaming jobs in Databricks from the same definitions. Templates include ingestion, merge logic, SCD handling, and observability so teams share rules instead of duplicating code.
- Model-driven CDC design for SQL Server Change Tracking and Oracle sources.
- Generated ingestion pipelines for Azure Data Factory and Synapse.
- SCD-compliant transformations that stay consistent across tables.
- Built-in audit logs, schema validation, error handling, and retries.
- Support for Delta tables and schema evolution in Databricks.
This standardization turns CDC into an enabler of delivery speed, not a source of outages.
Real World Results: Streaming Without Stress
One finance team implemented metadata-driven CDC across ten source systems. Before the change, they relied on ad hoc scripts and nightly refreshes. After automation, they applied the same patterns everywhere, regenerated pipelines automatically when schemas shifted, and delivered near real-time visibility. Incidents dropped, delivery accelerated, and governance improved — all without reinventing capture logic for each system.
Capture Change, Not Complexity
CDC is essential, but it does not have to be chaotic. By storing CDC rules in metadata and generating ingestion and merge logic across platforms, you can unify batch and streaming, reduce risk, and improve freshness. The result is predictable, governed change capture that scales.
See BimlFlex in action. Schedule a demo and modernize your ingestion pipelines today.
