Scalable CDC Architecture for Oracle Data Lake with AWS DMS

Implementing a scalable architecture for Change Data Capture (CDC) and building a Medallion data lake using AWS DMS to migrate Oracle data to Apache Iceberg on AWS S3 requires careful planning across multiple layers. This approach involves configuring Oracle CDC through AWS DMS, establishing a three-layer data lake architecture (bronze, silver, gold), implementing proper tracking mechanisms for incremental data, and optimizing AWS Glue jobs for efficient data transformation between layers.

---

Contents

• Understanding Oracle CDC with AWS DMS
• Medallion Data Lake Architecture Overview
• Implementing CDC Across Bronze, Silver, and Gold Layers
• Tracking Incremental Data Between Layers
• AWS Glue Processing for Data Lake Transformation
• Apache Iceberg Integration with AWS S3
• Scalable Architecture Best Practices
• Optimizing AWS Glue Jobs for Performance
• Sources
• Conclusion

---

Understanding Oracle CDC with AWS DMS

Change Data Capture from Oracle databases is the foundation of your data lake architecture, and AWS DMS provides a robust solution for this purpose. Before creating an AWS DMS task for oracle database migration, you must configure several critical components: a source endpoint targeting your Oracle database, a target endpoint pointing to your S3 destination, and a replication instance powerful enough to handle your data throughput. Oracle specifically requires supplemental logging to enable change data capture from the database logs—a configuration step that’s often overlooked but absolutely essential for CDC functionality.

The Oracle CDC implementation in AWS DMS operates through two primary mechanisms: the Oracle LogMiner API or the binary reader API. Both approaches read ongoing changes from online or archive redo logs based on the System Change Number (SCN), but they differ in performance characteristics and compatibility. The LogMiner API is generally more compatible with various Oracle versions but may have higher overhead, while the binary reader can offer better performance but requires specific Oracle configurations.

For your data lake migration, you’ll want to configure AWS DMS tasks with “Full load plus CDC” mode. This approach first migrates all existing data from Oracle to S3, then continuously applies changes as they occur. The alternative is “CDC only” mode, which only captures ongoing changes after initial data exists—useful when you already have a baseline dataset in S3. You can start replication from custom CDC start time, CDC native start point, or specific SCN values, giving you precise control over your data migration timeline.

What makes this approach particularly powerful for oracle database scenarios is the ability to capture not just simple inserts, updates, and deletes, but also complex DDL operations and LOB (Large Object) data types. This comprehensive change tracking ensures your Medallion data lake maintains full fidelity with the source system while enabling near-real-time analytics capabilities.

---

Medallion Data Lake Architecture Overview

The Medallion architecture represents a powerful approach to organizing data in your lake, providing a structured progression from raw source data to refined, business-ready analytics. This three-layer model—bronze, silver, and gold—creates a clear separation of concerns that simplifies data governance, improves performance, and enables self-service analytics while maintaining traceability back to original sources.

Your bronze layer serves as the landing zone for all raw data ingested from various sources, including the Oracle database through AWS DMS. This layer preserves the original data structure and format, making it ideal for data quality checks, debugging, and auditing. The bronze layer should be partitioned by ingestion date and source system to optimize query performance while maintaining data provenance. For oracle database migrations, this typically means storing the data in Apache Iceberg format on S3, preserving the original table structures and data types as closely as possible.

The silver layer represents your first major transformation step, where data is cleaned, standardized, and enriched. This is where you implement business rules, handle data quality issues, and integrate data from multiple sources. Silver tables typically conform to a star or snowflake schema, making them more suitable for analytics applications. The incremental data processing between bronze and silver layers is where CDC tracking becomes most valuable, as you need to efficiently identify and process only the changed records.

The gold layer contains your curated, business-ready datasets optimized for specific analytical use cases. These tables often represent dimensional models, KPIs, or aggregated metrics that directly support business intelligence and reporting. The gold layer typically has the highest query performance but may sacrifice some data granularity for speed and simplicity.

What makes this architecture particularly scalable is its additive nature—each layer builds upon the previous one without overwriting data. This design allows you to maintain multiple versions of your data at different stages of refinement, enabling historical analysis and rollback capabilities while supporting diverse analytics needs.

---

Implementing CDC Across Bronze, Silver, and Gold Layers

Implementing Change Data Capture effectively across all three layers of your Medallion architecture requires a carefully orchestrated approach that builds upon the Oracle CDC foundation established with AWS DMS. The bronze layer implementation is your starting point, where AWS DMS directly captures changes from the oracle database and writes them to S3 in Apache Iceberg format. This process leverages the SCN-based change tracking to ensure that every modification, insert, and delete is accurately recorded.

For the bronze layer, configure your AWS DMS task to write CDC records as JSON files partitioned by date and table name. Each record should include metadata about the operation type (INSERT, UPDATE, DELETE), the SCN timestamp, and the actual data changes. This metadata becomes crucial for tracking changes as data moves through your pipeline. The bronze layer should maintain a “raw” copy of data without transformation, preserving the original structure for auditing and data quality verification.

When implementing CDC for the silver layer, you need a different approach since you’re no longer directly connected to the source oracle database. Here, you’ll implement change tracking based on the bronze layer CDC records. The key is to maintain a “last processed SCN” or watermark for each table, allowing your transformation jobs to identify which records need processing. This incremental approach significantly reduces compute costs compared to full table scans, especially as your data volumes grow.

The gold layer CDC implementation builds upon silver layer changes rather than directly tracking from the source. In this layer, you’ll implement business logic-driven change detection, where changes are identified based on business key values rather than database operations. For example, if a customer’s name changes in the oracle database, this might trigger updates to multiple gold tables—customer master, sales summary, and marketing analytics—all based on business rules rather than direct CDC tracking.

What makes this layered approach particularly powerful is its scalability. As your oracle database grows and changes, the bronze layer CDC captures everything with minimal overhead. The silver layer transformations can be optimized to process only changed records, while the gold layer business logic can be tuned to update only relevant analytical datasets. This cascading change tracking ensures that your entire data lake remains current without requiring massive full-refresh operations.

---

Tracking Incremental Data Between Layers

Effective tracking of incremental data between layers is the cornerstone of a scalable data lake architecture, ensuring that your transformation pipelines efficiently process only what’s necessary rather than resorting to expensive full-table scans. The tracking mechanism you implement directly impacts both performance and cost, making it a critical architectural decision in your oracle database migration strategy.

For tracking between bronze and silver layers, you’ll want to leverage the SCN-based change tracking that AWS DMS provides to your bronze layer data. Each record in your bronze Iceberg tables should include metadata about the operation type and the source SCN timestamp. Your silver layer transformation jobs can then use this information to identify records that need processing. The key is maintaining a “high watermark” for each table—essentially the highest SCN value that has been successfully processed. When a job runs, it simply processes all records with SCN values greater than this watermark, ensuring no changes are missed.

What happens when your pipeline fails or needs to be reprocessed? This is where transactional integrity becomes crucial. For oracle database CDC scenarios, you’ll want to implement idempotent processing in your silver layer jobs, ensuring that running the same transformation multiple times produces consistent results. One effective approach is to include a “processed timestamp” field in your silver tables, allowing you to easily identify and reprocess records if needed without duplicating work.

Tracking between silver and gold layers requires a different approach since you’re no longer directly connected to the oracle database. Here, you’ll typically implement business key-based change detection. For example, if you’re processing customer data, you might track changes by customer ID rather than database SCN. When a silver layer transformation job runs, it can compare current values with previous values to determine what changed in the oracle database, then update only the relevant gold layer records.

For large-scale deployments, consider implementing a change data capture table in each layer that records which records have been modified and when. This auxiliary table can dramatically improve query performance for identifying changes, especially when dealing with fact tables that might have millions of rows. The pattern typically involves creating a CDC table that stores the business key, the timestamp of the change, and the operation type, which your transformation jobs can query efficiently.

What makes these tracking approaches particularly valuable in oracle database migrations is their ability to handle the complexity of relational data. When a single change in the oracle database affects multiple tables (through foreign key relationships), your tracking mechanism must be able to propagate these changes appropriately through your pipeline, ensuring data consistency across all layers of your Medallion architecture.

---

AWS Glue Processing for Data Lake Transformation

AWS Glue serves as the engine that powers your data transformations between the bronze, silver, and gold layers, providing the scalability and flexibility needed to handle oracle database CDC workloads efficiently. As a serverless data integration service, AWS Glue automatically provisions the compute resources needed for your jobs, scaling up to handle large volumes of incremental data and scaling down to zero when idle—making it particularly cost-effective for your data lake architecture.

For your oracle database migration, you’ll typically need multiple AWS Glue jobs organized by layer and transformation complexity. Bronze layer jobs are relatively simple, primarily focused on validating and cataloging data as it arrives from AWS DMS. These jobs run frequently—perhaps every 5-15 minutes—and perform basic quality checks on the CDC records from your oracle database. Silver layer jobs are more complex, handling data cleansing, standardization, and integration of multiple sources. These typically run on a schedule aligned with your business needs, perhaps hourly or daily depending on your latency requirements.

Gold layer jobs represent your most sophisticated transformations, implementing business logic and aggregations that directly support analytics. These jobs often run on a less frequent schedule—daily or weekly—since they typically process summarized or aggregated data rather than individual transactions from the oracle database. The key insight here is that your AWS Glue job requirements scale with transformation complexity rather than data volume, allowing you to optimize costs while maintaining performance.

One of the powerful features of AWS Glue for oracle CDC scenarios is its built-in support for change data processing. The AWS Glue ETL library includes transforms specifically designed to handle CDC records, making it easier to implement the incremental processing logic needed between your bronze, silver, and gold layers. For example, the ApplyMapping transform can help you standardize column names and data types as data moves from bronze to silver, while the Join transform can efficiently merge data from multiple oracle tables in your silver layer.

What makes AWS Glue particularly valuable for your architecture is its integration with other AWS services. The AWS Glue Data Catalog automatically discovers and catalogs your bronze layer data from S3, making it easy for data engineers and analysts to find and understand the oracle database CDC data. Additionally, AWS Glue Studio provides a visual interface for creating and monitoring your transformation jobs, allowing you to build complex data pipelines without writing extensive code.

For optimal performance with oracle database CDC workloads, consider implementing partitioning strategies in your AWS Glue jobs. By processing data in parallel across partitions based on date ranges or other logical divisions, you can dramatically improve throughput and reduce processing times. This approach is especially valuable when dealing with large oracle databases that generate significant CDC volumes.

---

Apache Iceberg Integration with AWS S3

Integrating Apache Iceberg with AWS S3 creates a powerful foundation for your Medallion data lake, providing the transactional integrity and advanced querying capabilities needed for effective oracle database CDC scenarios. Apache Iceberg is an open table format built for data lakes that brings database-like features to your S3 data, including ACID transactions, time travel, and schema evolution—making it an ideal choice for storing oracle database CDC data at scale.

The integration begins with storing your bronze layer data as Iceberg tables in S3. Unlike traditional Parquet or ORC files that are difficult to update once written, Iceberg tables support ACID transactions, allowing you to handle CDC records from your oracle database with integrity. When AWS DMS captures changes from Oracle, it writes these as new data files to your Iceberg tables, while Iceberg manages the metadata that tracks which files belong to which versions of the table. This approach enables you to maintain a complete history of changes from your oracle database, supporting both current analysis and historical trend detection.

For silver and gold layers, Iceberg provides additional capabilities that significantly enhance your data lake architecture. Schema evolution allows you to modify table structures as your oracle database evolves, without requiring complex migration processes. Time travel enables you to query previous versions of your data—valuable when auditing changes or recovering from data quality issues. And partition evolution allows you to optimize your data organization as query patterns change, all while maintaining data consistency across your oracle database CDC pipeline.

What makes Iceberg particularly powerful for oracle database migrations is its support for multi-table transactions. When changes in your oracle database affect multiple related tables, Iceberg can ensure that all these changes are written atomically across your bronze layer tables. This capability maintains referential integrity in your data lake, even though the underlying oracle database relationships aren’t directly preserved in the raw CDC data.

The metadata management capabilities of Iceberg also provide significant advantages for tracking incremental data between layers. Iceberg maintains detailed metadata about each table, including snapshots that capture the complete state at a point in time. This metadata can be queried directly to identify which records have changed since a previous snapshot, enabling efficient incremental processing in your silver and gold layer transformations.

For cost optimization, Iceberg supports data file compaction and expiration policies that help manage storage costs as your oracle database CDC data accumulates over time. By automatically merging small files and removing outdated data snapshots, Iceberg keeps your S3 storage costs under control while maintaining the performance benefits of your data lake architecture.

---

Scalable Architecture Best Practices

Building a scalable architecture for oracle database CDC and Medallion data lakes requires careful consideration of multiple factors, from data ingestion patterns to transformation strategies. The best practices outlined here help ensure your architecture can handle growing data volumes, increasing complexity, and evolving business requirements while maintaining performance and cost efficiency.

One fundamental principle is implementing proper partitioning strategies at every layer. For your oracle database CDC data, consider partitioning bronze layer Iceberg tables by date and table name, which optimizes both ingestion and querying performance. Silver layer tables might benefit from business-key partitioning, while gold layer tables often work well with date-range or hierarchical partitioning based on your analytical access patterns. This strategic partitioning reduces the amount of data scanned during incremental processing, dramatically improving performance and reducing costs.

Another critical practice is implementing idempotent processing throughout your pipeline. Oracle database CDC scenarios often involve reprocessing data when jobs fail or when business logic changes. By designing your transformations to produce consistent results regardless of how many times they’re executed, you can handle these scenarios without data duplication or corruption. This approach is particularly valuable when working with CDC records that might be processed multiple times during recovery operations.

For scalability, consider implementing a distributed processing architecture where possible. While AWS Glue provides excellent out-of-the-box scalability for many workloads, extremely large oracle database migrations might benefit from combining AWS Glue with other processing engines like Apache Spark or EMR. This hybrid approach allows you to leverage the strengths of each processing framework while maintaining a unified architecture for your data lake.

Monitoring and observability represent another cornerstone of scalable architecture. Implement comprehensive monitoring at every layer of your pipeline, tracking metrics like CDC lag, job success rates, data volumes, and processing times. For oracle database scenarios, pay special attention to SCN tracking metrics, as these indicate how current your data lake remains relative to the source system. This observability enables proactive identification of bottlenecks before they impact business users.

What makes these practices particularly effective is their ability to address the unique challenges of oracle database CDC. Unlike other database systems, Oracle’s change tracking through SCN values and redo logs requires careful handling to ensure data consistency. By implementing proper partitioning, idempotent processing, distributed architecture, and comprehensive monitoring, you create a foundation that can scale with your oracle database while maintaining the data integrity essential for reliable analytics.

Cost optimization deserves special attention in scalable architectures. Implement strategies like data lifecycle policies to automatically move older oracle database CDC data from S3 Standard to cheaper storage classes, and schedule gold layer transformations to run during cost-optimized hours. These practices help control costs as your data volumes grow, ensuring your architecture remains economically viable over time.

---

Optimizing AWS Glue Jobs for Performance

Optimizing your AWS Glue jobs is crucial for maintaining the performance and cost-effectiveness of your oracle database CDC pipeline, especially as data volumes grow over time. The right optimization strategies can dramatically reduce processing times and lower costs while ensuring your data lake remains current with minimal latency.

One of the most impactful optimizations is implementing proper job parallelism. For oracle database CDC scenarios, consider structuring your bronze layer jobs to process multiple tables in parallel, rather than sequentially. AWS Glue supports dynamic frame partitioning, allowing you to process different partitions of your data simultaneously. This approach is particularly valuable when dealing with large oracle databases that generate CDC records across multiple tables, as it can reduce overall processing times by 50-80% in many cases.

Memory optimization represents another critical performance consideration. Oracle database CDC processing often involves handling complex data types and large record volumes that can strain job memory. By implementing proper data sampling and profiling in your development phase, you can identify memory-intensive operations and optimize them accordingly. Consider using Spark configuration parameters to adjust memory allocation, and implement strategies like data type conversion to reduce memory overhead.

For silver layer transformations, which typically involve more complex logic, implement incremental processing strategies that build upon the CDC tracking mechanisms. Rather than processing entire tables, design your jobs to process only the records that have changed since the last run. This approach leverages the SCN-based tracking from your oracle database CDC, dramatically reducing the amount of data processed and improving job performance.

Code optimization practices can significantly impact job performance. For oracle database CDC workloads, focus on optimizing join operations—often the most expensive part of silver layer transformations. Implement broadcast joins where appropriate, and consider partitioning strategies that minimize data movement during join operations. Additionally, leverage AWS Glue’s built-in optimizations like the AWS Glue Elastic Views feature for maintaining materialized views across your data lake layers.

What makes these optimizations particularly valuable is their cumulative effect. While individual optimizations might provide modest improvements, combining proper parallelism, memory management, incremental processing, and code optimization can result in order-of-magnitude performance improvements for your oracle database CDC pipeline. The key is to approach optimization systematically, measuring performance before and after each change to understand the true impact.

Monitoring and tuning should be ongoing activities rather than one-time efforts. Implement comprehensive monitoring of your AWS Glue jobs, tracking metrics like execution time, data processed, shuffle spill, and memory utilization. For oracle database CDC scenarios, pay special attention to CDC lag metrics, which indicate how current your data lake remains relative to the source system. This continuous monitoring enables proactive identification of performance issues before they impact business users.

---

Sources

1. AWS DMS Tasks Documentation - Comprehensive guide on creating migration tasks for oracle database with CDC: https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Tasks.html
2. AWS DMS CDC Configuration - Detailed information on implementing change data capture for oracle databases: https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Task.CDC.html
3. AWS Glue Service Overview - Serverless data integration service documentation for data lake processing: https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html
4. Apache Iceberg Table Format - Open table format specification for scalable data lake architectures: https://iceberg.apache.org/
5. AWS Glue Performance Tuning - Best practices for optimizing AWS Glue ETL jobs: https://docs.aws.amazon.com/glue/latest/dg/monitoring-tuning-glue-jobs.html
6. AWS Data Lake Architecture - Reference architecture for building scalable data lakes on AWS: https://aws.amazon.com/big-data/datalakes-and-analytics/
7. Oracle CDC Implementation - Technical guidance on implementing change data capture from oracle databases: https://docs.oracle.com/en/database/oracle/oracle-database/
8. AWS Glue Data Catalog - Documentation on using AWS Glue for data discovery and cataloging: https://docs.aws.amazon.com/glue/latest/dg/aws-glue-data-catalog.html

---

Conclusion

Implementing a scalable architecture for Change Data Capture and building a Medallion data lake using AWS DMS to migrate oracle data to Apache Iceberg on AWS S3 requires careful consideration of multiple interconnected components. The key to success lies in understanding how CDC tracking functions across each layer—leveraging Oracle’s SCN-based change tracking in the bronze layer, implementing business-key based change detection in the silver layer, and using analytical change tracking in the gold layer. This layered approach ensures that your data lake remains current with minimal overhead while supporting diverse analytical needs.

For tracking incremental data between layers, maintain appropriate watermarks and metadata that enable efficient processing of only changed records rather than full table scans. This approach becomes increasingly valuable as your oracle database grows, preventing your transformation costs from scaling linearly with data volume. The number of AWS Glue jobs required will depend on your specific transformation complexity and latency requirements, but a typical implementation might include 1-2 bronze layer jobs for frequent CDC processing, 3-5 silver layer jobs for standardization and integration, and 2-3 gold layer jobs for business logic and aggregation.

By implementing the architecture patterns outlined in this guide—including proper partitioning, idempotent processing, comprehensive monitoring, and strategic optimization—you can create a data lake that scales efficiently with your oracle database while maintaining the performance and reliability needed for modern analytics. The combination of AWS DMS for CDC, Apache Iceberg for transactional integrity, and AWS Glue for transformation processing provides a powerful foundation for organizations looking to leverage their oracle data in scalable data lake environments.