Hey there! Let’s break down your CDC and medallion architecture questions step by step—since I’ve built similar pipelines with Oracle on-prem, AWS DMS, Apache Iceberg, and Glue, this should align perfectly with your use case.

1. Raw → Bronze Layer: Scalable CDC Processing & New File Detection #

First, let’s tackle the mixed-table CDC files in your Raw S3 bucket and how to move data to Bronze efficiently.

Handling Multi-Table CDC Files #

If your DMS task outputs CDC records for multiple tables in the same files, the first step to scalability is isolate table-specific data early:

• Option 1 (Preferred): Adjust DMS Output Configuration
  If you can modify your DMS task, configure it to write CDC data to S3 with a table-specific prefix like raw/cdc/{table_name}/. This lets you process each table’s CDC data independently, which is far more scalable than handling mixed files.
• Option 2: Pre-Split Mixed CDC Files
  If you can’t change DMS settings, run a lightweight Glue pre-processing job to split mixed CDC files into table-specific directories (e.g., raw/staging_cdc/{table_name}/). This job reads all new mixed CDC files, filters records by the table_name field (DMS includes this by default), and writes them to per-table paths. You can use Glue Bookmarks to track which mixed files you’ve already split.

New File Detection: Bookmarks vs. Alternatives #

• Glue Bookmarks (Best for Most Cases)
  Glue Bookmarks are natively integrated with Glue Jobs and S3—they automatically track which S3 objects your job has processed, even across job runs. For per-table Raw paths, you can use a single parameterized Glue Job (passing table_name as a parameter) with bookmarks enabled. This way, each table’s processing is tracked independently, and you don’t have to manage custom tracking logic.
• Alternative: Custom Metadata Tracking Table
  If you need more control (e.g., cross-job tracking), create a small metadata table in the Glue Data Catalog (e.g., etl_metadata.raw_processed_files) that records processed S3 file paths, timestamps, and table names. Your Glue Job will query this table to find unprocessed files, process them, and update the metadata table. This works well with Airflow orchestration, but requires extra logic for idempotency.
• Alternative: S3 Event Notifications + Airflow
  Use S3 event notifications to trigger an Airflow DAG whenever new CDC files land in Raw. Airflow can then trigger the appropriate Glue Job to process the new files. This is great for near-real-time use cases, but you’ll need to handle duplicate events (e.g., using a deduplication key in the metadata table).

2. Bronze → Silver Layer: CDC for Apache Iceberg Tables (Delta-Only Sync) #

Moving only delta changes to Silver (and maintaining Iceberg table integrity) relies on Iceberg’s native merge capabilities and careful state tracking. Here’s how to implement it:

Core CDC Logic for Iceberg #

First, ensure your Bronze Iceberg tables retain DMS’s CDC metadata:

• Keep the Op field (DMS outputs I for insert, U for update, D for delete)
• Retain a commit timestamp (e.g., dms_commit_timestamp) from DMS to track when the change occurred
• Preserve primary key fields for each table (critical for matching records during merges)

Incremental Sync Implementation #

You have two robust options for detecting delta changes from Bronze to Silver:

• Option 1: Iceberg Snapshot Tracking
  Apache Iceberg automatically maintains snapshots of your table state. You can track the last processed snapshot ID for each Silver table in a metadata table (e.g., etl_metadata.silver_sync_state). Your Glue Job will:

  1. Fetch the last snapshot ID for the target Silver table from the metadata table
  2. Read only the records in the Bronze table that were added after this snapshot:

     from pyspark.sql import SparkSession
     spark = SparkSession.builder.getOrCreate()
     
     # Get last synced snapshot ID from metadata
     last_snapshot_id = spark.sql("SELECT last_snapshot_id FROM etl_metadata.silver_sync_state WHERE table_name = 'your_table'").collect()[0][0]
     
     # Read delta from Bronze Iceberg table
     bronze_delta = spark.read.table("bronze.your_table").filter(f"_snapshot_id > {last_snapshot_id}")
     

  3. Merge the delta into the Silver Iceberg table using Spark SQL:

     MERGE INTO silver.your_table s
     USING bronze_delta c
     ON s.primary_key = c.primary_key
     WHEN MATCHED AND c.Op = 'U' THEN UPDATE SET *
     WHEN MATCHED AND c.Op = 'D' THEN DELETE
     WHEN NOT MATCHED AND c.Op = 'I' THEN INSERT *
     

  4. Update the metadata table with the latest Bronze snapshot ID.

• Option 2: Time-Based Partition Filtering
  If your Bronze table is partitioned by dms_commit_timestamp, you can sync only data from the last sync time onwards. This is simpler to implement but relies on accurate timestamp partitioning:

  1. Fetch the last sync timestamp from etl_metadata.silver_sync_state
  2. Filter Bronze data to records where dms_commit_timestamp > last_sync_time
  3. Run the same merge logic as above, then update the sync timestamp in the metadata table.

Orchestration with Airflow #

To scale this across all your 80 tables:

• Use Airflow Dynamic Task Mapping (available in Airflow 2.2+) to generate parallel tasks for each table. This lets you define a single DAG template that dynamically creates a task per table, rather than writing 80 separate DAGs.
• Your DAG workflow would look like:
  1. Check for new delta data in Bronze (via metadata table or Glue Job status)
  2. Trigger a parameterized Glue Job (passing table_name, last_sync_value as parameters) for each table
  3. Run data quality checks (e.g., validate no duplicate primary keys) after merge
  4. Update the sync state metadata table for successful runs
  5. Add retries and alerts for failed tasks (e.g., notify via Slack/email if a table’s merge fails)

3. Glue Job Count & Scalability #

You can minimize job maintenance while keeping the pipeline scalable with parameterized jobs:

• Raw → Bronze Layer
  • 1 pre-split job (only if you have mixed-table CDC files in Raw)
  • 1 parameterized Glue Job (reused for all tables) that takes table_name as input. Airflow triggers this job in parallel for each table, or sequentially based on your resource constraints.
• Bronze → Silver Layer
  • 1 parameterized Glue Job (reused for all tables) that handles the Iceberg merge logic. Again, Airflow’s dynamic tasks trigger this job for each table.
• Exception: Specialized Tables
  If some tables require unique business logic (e.g., complex data transformations during sync), create separate dedicated jobs for those—but keep this to a minimum to avoid maintenance overhead.

4. End-to-End CDC Flow Walkthrough #

Let’s tie this all together with a concrete example for one table (scalable to all 80):

1. Oracle → Raw S3: DMS runs full load to raw/full_load/customer/, then CDC changes to raw/cdc/customer/ (per-table prefix).
2. Raw → Bronze:
   • Airflow triggers the parameterized Glue Job with table_name=customer
   • Job uses Glue Bookmarks to read only new files from raw/full_load/customer/ and raw/cdc/customer/
   • Writes all records (full load + CDC) to a Bronze Iceberg table bronze.customer, retaining Op and dms_commit_timestamp
3. Bronze → Silver:
   • Airflow fetches the last sync timestamp for customer from etl_metadata.silver_sync_state
   • Triggers the parameterized merge job, which reads Bronze data after the last sync timestamp
   • Runs the Iceberg merge to apply inserts/updates/deletes to silver.customer
   • Updates the sync state table with the current timestamp
4. Orchestration: Airflow’s DAG runs this flow in parallel for all 80 tables, with retries for failed jobs and alerts for anomalies.

5. Key Scalability & Reliability Tips #

• Idempotency First: All jobs must be idempotent—reprocessing the same CDC file shouldn’t break data integrity. Iceberg’s merge operation is naturally idempotent (primary key matches prevent duplicate inserts/updates).
• Partition Strategically: Partition Bronze and Silver Iceberg tables by dms_commit_timestamp to reduce data scanning during syncs, which speeds up job runs and cuts costs.
• Monitor Everything: Use CloudWatch to track Glue Job runtime, Iceberg snapshot size, and data latency. Set up alerts for job failures, long-running tasks, or unexpected data volume spikes.
• Data Quality Checks: Add lightweight checks in the Silver layer (e.g., validate primary key uniqueness, ensure Op values are only I/U/D) using tools like Great Expectations or Glue DataBrew to catch bad CDC data early.

Let me know if you need deeper dives into specific parts—like configuring DMS for per-table CDC, setting up Airflow dynamic tasks, or tuning Iceberg merge performance!