A Practical Guide to Change Data Capture (CDC) on Snowflake

What Is CDC and Why It Matters

Change Data Capture (CDC) refers to a family of techniques that capture and propagate only the data changes, rather than reloading entire tables. This approach is especially valuable for modern analytics and real-time applications.

From a data engineering perspective, CDC provides three main benefits:

• Lower cost: It avoids full-table scans and bulk reloads by processing only changed rows.
• Lower latency: Changes can be propagated to downstream systems shortly after they occur, enabling near real-time analytics.
• Better consistency: By tracking inserts, updates, and deletes explicitly, multiple systems can remain synchronized more reliably.

On Snowflake, CDC is implemented primarily through a logical mechanism based on Streams and Snowflake’s transactional metadata, rather than direct access to database logs. Streams track changes on top of tables or views and expose them as queryable change sets.

Snowflake’s Core CDC Primitive: Streams

A Snowflake Stream is an object that tracks row-level changes (DML) on a source object such as a table, view, or external table. Conceptually, you can think of a Stream as a change log view attached to a table.

When you create a Stream on a table:

• Snowflake establishes a logical snapshot of the table at creation time.
• Any subsequent DML operations (INSERT, UPDATE, DELETE) on the table are recorded as change records associated with the Stream.
• When you query the Stream, you see the set of changes that occurred since the last time the Stream was consumed.

The Stream surface typically includes metadata columns describing each change, such as:

• The type of operation (insert, update, delete).
• Whether a row is part of an update operation (before/after images).
• Unique row identifiers to help track changes across operations.

Downstream jobs usually read from the Stream and then apply the changes to a target table, often using a MERGE statement. A read operation on the Stream advances its offset, similar in spirit to a consumer reading from a message queue.

End-to-End CDC Workflow on Snowflake

A typical CDC pipeline in Snowflake (assuming the source data is already in Snowflake) follows these steps:

1. Source Table
   The source table holds operational or raw data. It may be populated by batch loads, external CDC tools, or direct application writes.

2. Stream on the Source Table
   A Stream is created on the source table to start tracking DML changes from a given snapshot onward. This Stream becomes the primary source of incremental changes.

3. Target Table
   A target table holds the modeled or curated data (for example, a data vault satellite, a dimensional table, or an analytics-ready wide table).

4. Incremental Apply with MERGE
   A downstream process periodically queries the Stream and uses a MERGE operation to apply inserts, updates, and deletes to the target table.

   • Inserts in the Stream lead to new rows in the target table.
   • Updates translate into updates on matching keys.
   • Deletes can be applied as soft deletes (flagging rows) or hard deletes, depending on requirements.

5. Retention and Monitoring
   Streams have a retention window for their change data. You must ensure that the consuming jobs run frequently enough so that changes are not lost due to retention expiry. Monitoring row counts, offsets, and job failures is essential to avoid data gaps.

In this model, CDC is achieved without re-reading the entire source table, and downstream consumers process only the incremental change set exposed by the Stream.

Using Streams, Tasks, and Dynamic Tables Together

Snowflake CDC pipelines typically combine three native capabilities:

• Streams: Track row-level changes on source objects.
• Tasks: Schedule and orchestrate SQL workloads such as MERGE operations or transformations.
• Dynamic Tables (optional): Maintain derived or aggregated tables automatically based on upstream changes.

A common design pattern is:

• A raw or staging table ingests data from upstream systems.
• A Stream on the staging table exposes the incremental changes.
• A Task runs every few minutes to read from the Stream and apply changes to a core model table via MERGE.
• Dynamic Tables are defined on top of the core model for aggregations, denormalization, or feature preparation.

This approach keeps the entire CDC pipeline largely within Snowflake, implemented in SQL and controlled through native scheduling and dependency management.

Integrating External CDC Tools with Snowflake

If your operational databases are outside Snowflake (e.g., MySQL, PostgreSQL, Oracle, SQL Server), you typically introduce an external CDC tool. These tools perform log-based CDC on the source system and land the resulting changes in Snowflake.

The division of responsibilities is usually:

• External CDC tool:

  • Connects to the source database.
  • Reads transaction logs (e.g., binlog, WAL, redo log).
  • Produces a stream of changes and loads them into a Snowflake staging table.

• Snowflake:

  • Maintains Streams on the staging table to expose incremental changes.
  • Uses Tasks and optional Dynamic Tables to merge, cleanse, and model the data.
  • Serves analytics, BI, and downstream applications.

This pattern minimizes impact on the source systems while leveraging Snowflake’s CDC primitives to standardize the downstream processing architecture.

Typical Use Cases for Snowflake CDC

Snowflake-based CDC architectures support a variety of production use cases:

• Near Real-Time Dashboards
  Operational metrics such as orders, payments, or risk signals can be updated in near real-time, enabling more responsive decision-making.

• Event-Driven ETL/ELT
  Instead of running full batch ETL, downstream transformations operate only on changed data, reducing processing time and cost while improving freshness.

• Cross-Region and Cross-Account Synchronization
  CDC-like patterns keep multiple Snowflake accounts or regions in sync for disaster recovery, regional deployments, or data sovereignty requirements.

• Feature Stores and ML Pipelines
  Behavioral events and transactional updates can be fed into feature tables continuously, improving the recency and relevance of machine learning features.

Practical Design Considerations

When implementing CDC on Snowflake, several design considerations are crucial:

1. Stream Retention and Consumption Frequency

• Ensure the schedule of your Tasks is aligned with the Stream retention window so that no changes expire before they are consumed.
• For high-volume tables, consider smaller, more frequent micro-batches to keep latency and batch sizes under control.

2. MERGE Semantics and Idempotency

• Use stable business keys or natural keys for matching rows during MERGE.
• Clearly define how to treat UPDATE and DELETE operations; decide whether deletes are soft or hard in the target model.
• Aim for idempotent logic so that re-running a batch (for recovery) does not produce inconsistent results.

3. Monitoring and Data Reconciliation

• Monitor key metrics such as unconsumed change counts, Task runs, error rates, and pipeline latency.
• Periodically reconcile row counts or checksums between source and target tables to detect silent data loss or duplication.

4. Handling Schema Evolution (Schema Drift)

• Plan for additive schema changes, such as new columns, and ensure staging tables and downstream transformations can tolerate these changes.
• Prefer backward-compatible changes where possible, and document how schema changes propagate through Streams and target tables.

5. Cost and Resource Isolation

• Isolate CDC workloads on dedicated warehouses to avoid contention with ad-hoc analytics and heavy reporting workloads.
• Use auto-scaling and sizing strategies appropriate for your expected CDC volume and SLA.

Conclusion

Snowflake does not expose CDC as a single “magic switch,” but rather as a toolkit centered on Streams, Tasks, and Dynamic Tables, combined with sound data engineering practices. By processing only incremental changes within Snowflake, you can build pipelines that are both highly responsive and operationally manageable.

For organizations moving toward real-time or event-driven architectures, Snowflake CDC provides a pragmatic and scalable path to keep analytical data in sync with operational systems without sacrificing governance, performance, or cost control.