Accelerating Master Data Updates with Incremental Loads & Real-Time Alerts in Medallion Architecture
Accelerating Master Data Updates with Incremental Loads & Real-Time Alerts in Medallion Architecture
Keeping your master data fresh and your teams informed is crucial for enterprise analytics. By combining Delta Lake’s CDC features with a layered Medallion Architecture, you can achieve high-performance ingestion, robust history tracking, and automated notifications—all without reprocessing terabytes of data.
1. Bronze Layer: CDC-Driven Ingestion
- Delta Change Data Feed: Leverage Delta Lake’s built-in CDC to capture inserts, updates, and deletes from your operational system.
- Watermark Control Table: Maintain a control table that stores the maximum last_updated timestamp processed. On each run, query only records newer than this watermark.
- Partitioned Landing Zone: Write raw deltas into a date-partitioned Bronze table for immutable auditability.
Key SQL Snippet
MERGE INTO bronze.master_raw t
USING (
SELECT * FROM source_system.cdc_feed
WHERE last_updated > (SELECT last_ts FROM control_master)
) s
ON t.id = s.id
WHEN NOT MATCHED THEN INSERT *
WHEN MATCHED AND t.hash <> s.hash THEN INSERT *; — history capture
2. Silver Layer: Type-2 SCD & History
- Delta MERGE for SCD-2: Use Spark’s optimized Delta merges to upsert only the changed records into a Type-2 SCD table. This preserves full change history while marking the current version as active.
- Efficient Processing: Since Bronze only contains deltas, the Silver job runs in minutes, not hours, dramatically reducing compute costs.
Example
PySpark
bronze_delta = spark.table(“bronze.master_raw”)
silver = spark.table(“silver.master_scd”)
bronze_delta.alias(“src”) \
.merge(
silver.alias(“tgt”),
“src.id = tgt.id AND tgt.active = true”
) \
.whenMatchedUpdate(
set={“active”: F.lit(False), “effective_end”: F.current_date()}
) \
.whenNotMatchedInsertAll() \
.execute()
3. Gold Layer: Event-Driven Notifications
- Change Detection Job: After the Silver merge, run a lightweight Spark or Python job that filters for records with today’s effective_start.
- Webhook Integration: Dispatch JSON-formatted payloads via Slack, Teams, or email using a Databricks notebook webhook or a serverless function (Azure Function/AWS Lambda).
- Digest Report: Include counts, sample records, and links to BI dashboards so business users can act immediately.
Pseudo-Code
updates = spark.table(“silver.master_scd”) \
.filter(“effective_start = current_date()”)
if updates.count() > 0:
payload = {
“text”: f”{updates.count()} master records updated”,
“samples”: updates.limit(5).toJSON().collect()
}
requests.post(WEBHOOK_URL, json=payload)
Benefits & Best Practices
- Lean Compute: Process only deltas—cutting costs by up to 80%.
- Auditability: Bronze raw logs + Silver SCD-2 ensure a bulletproof lineage.
- Proactive Alerts: Business teams stay informed in real time, improving data trust and adoption.
