Change Data Capture Engineering with Debezium Training Course  

Course Introduction

Change Data Capture Engineering with Debezium Training Course provides a comprehensive framework for designing, implementing, and operating reliable change data capture architectures using Debezium. The course focuses on capturing database changes as events and delivering them into modern streaming and data integration environments, enabling organizations to synchronize operational and analytical systems, build event-driven pipelines, reduce batch-processing dependencies, and maintain fresher enterprise data across distributed platforms.

The course examines the architecture and operating principles of Debezium, including connectors, source databases, transaction logs, change events, offsets, snapshots, schemas, serialization, and downstream event consumers. Participants develop a strong understanding of how database changes are detected and represented as events, how ordering and transaction boundaries affect downstream processing, and how CDC architectures can be designed for reliability, scalability, recoverability, and operational transparency.

A major focus is placed on database-specific CDC engineering and source-system integration. Participants explore CDC patterns for relational database platforms, initial snapshots, incremental snapshots, transaction log processing, schema changes, deletes, updates, tombstones, primary keys, and event metadata. The course emphasizes the engineering decisions required to prevent duplicate processing, data loss, ordering problems, schema incompatibilities, and uncontrolled downstream dependencies in enterprise CDC environments.

The program develops practical expertise in integrating Debezium with event streaming platforms and downstream data services. Participants learn how Debezium connects with Apache Kafka and Kafka Connect architectures, how CDC events can be transformed and routed, and how captured changes can feed data warehouses, lakehouses, search platforms, caches, microservices, analytical systems, and machine learning pipelines. Particular attention is given to event contracts, serialization formats, partitioning, delivery semantics, and downstream consumer design.

Reliability, observability, governance, security, and production operations are integrated throughout the course. Participants examine connector monitoring, lag management, failure recovery, schema evolution, access controls, sensitive-data handling, data quality, replay strategies, operational alerting, and capacity planning. Emerging patterns involving event-driven architectures, real-time analytics, cloud databases, distributed transactions, data products, streaming lakehouses, and AI-ready data pipelines are also addressed.

By the end of Change Data Capture Engineering with Debezium Training Course, participants will be able to design and operate production-grade CDC solutions that capture database changes accurately and deliver them reliably across enterprise data ecosystems. The course provides practical engineering knowledge for improving data freshness, reducing batch integration complexity, enabling event-driven architectures, supporting operational synchronization, and establishing scalable foundations for real-time data engineering.

Duration

10 days

Who Should Attend

• Data engineers responsible for implementing enterprise change data capture pipelines.

• Senior data engineers designing real-time data integration and streaming architectures.

• Data architects responsible for CDC strategy, event-driven architecture, and integration standards.

• Streaming engineers developing event-based data pipelines and distributed messaging solutions.

• Kafka engineers managing Kafka Connect and Debezium-based data integration environments.

• Database engineers responsible for source-system transaction logs and replication technologies.

• Cloud data engineers integrating operational databases with cloud analytical platforms.

• Data integration specialists implementing synchronization between operational and analytical systems.

• Backend engineers building event-driven applications that consume database change events.

• Application architects designing microservices and event-driven integration patterns.

• Analytics engineers requiring fresh operational data for downstream transformation workflows.

• Data platform engineers operating shared streaming, integration, and data infrastructure.

• Data warehouse and lakehouse engineers building continuously updated analytical datasets.

• Machine learning engineers requiring low-latency operational data for features and model pipelines.

• Data governance professionals responsible for CDC data lineage, security, classification, and controls.

• DevOps and SRE professionals supporting production CDC infrastructure and connector operations.

• Solution architects evaluating real-time integration and database synchronization technologies.

• Technology managers overseeing data modernization, streaming adoption, and enterprise integration initiatives.

Course Objectives

• Develop a comprehensive understanding of change data capture principles, Debezium architecture, connectors, transaction logs, snapshots, and event-driven integration.

• Design Debezium CDC architectures that reliably capture inserts, updates, deletes, schema changes, and transaction-related information from operational databases.

• Configure and operate Debezium connectors while applying appropriate source-database, connector, serialization, offset, and event-management settings.

• Implement initial and incremental snapshot strategies that establish complete downstream datasets while minimizing source-system and CDC infrastructure impact.

• Design reliable Kafka Connect and streaming architectures that distribute Debezium events to analytical, operational, integration, and application consumers.

• Apply event ordering, partitioning, transaction metadata, delivery semantics, and idempotency techniques to improve downstream CDC reliability.

• Manage schema evolution, serialization formats, event envelopes, tombstone records, deletes, and compatibility requirements across changing source systems.

• Design CDC pipelines that address duplicate events, connector restarts, source outages, offset recovery, replay scenarios, and downstream processing failures.

• Integrate Debezium CDC events with warehouses, lakehouses, search platforms, caches, microservices, data products, and real-time analytical applications.

• Establish security, governance, privacy, lineage, access control, and sensitive-data handling practices appropriate for enterprise CDC environments.

• Implement observability, monitoring, alerting, capacity management, troubleshooting, and operational procedures for production-grade Debezium deployments.

• Evaluate emerging CDC, event-driven, real-time analytics, streaming lakehouse, cloud database, and AI data-engineering patterns for future-ready architectures.

Comprehensive Course Outline

Module 1: Foundations of Change Data Capture Engineering

• Change data capture concepts, evolution, architectural patterns, and the role of CDC in modern enterprise data integration.

• Differences between log-based CDC, trigger-based capture, timestamp-based extraction, polling, replication, and conventional batch integration approaches.

• Business and technical use cases for CDC including analytics, synchronization, event-driven applications, migration, data products, and operational intelligence.

• Key CDC engineering challenges involving consistency, ordering, duplicates, latency, schema changes, source impact, recovery, and downstream dependencies.

Module 2: Debezium Architecture and Core Components

• Debezium architecture, connectors, source databases, Kafka Connect, transaction logs, offsets, snapshots, schemas, and downstream event consumers.

• Understanding how Debezium identifies database changes and converts transactional modifications into structured change events.

• Connector lifecycle, task management, configuration, offset storage, restart behavior, and operational dependencies in distributed deployments.

• Evaluating Debezium architectural components according to source workload, change volume, latency requirements, reliability objectives, and downstream needs.

Module 3: Database Transaction Logs and CDC Fundamentals

• Understanding transaction logs, write-ahead logs, binary logs, redo logs, logical decoding, and other database mechanisms used for change capture.

• Relationship between database transactions and CDC events, including ordering, transaction boundaries, commit behavior, and event visibility.

• Evaluating source-database impact, log retention, replication configuration, permissions, and operational prerequisites for reliable CDC.

• Designing source-system CDC practices that preserve data completeness while controlling infrastructure overhead and production risk.

Module 4: Debezium Connectors and Source Database Integration

• Configuring Debezium connectors for relational database environments while addressing source-specific prerequisites and operational requirements.

• Managing connector properties covering connection settings, included databases, tables, columns, snapshots, offsets, polling, and event behavior.

• Applying source filtering and table-selection strategies to control captured data volumes and reduce unnecessary downstream processing.

• Establishing standardized connector deployment patterns for development, testing, staging, and production environments.

Module 5: Initial Snapshots and Incremental Snapshot Engineering

• Understanding initial snapshot processes and their role in establishing complete downstream representations before continuous change streaming begins.

• Designing snapshot strategies that balance source-system impact, data completeness, execution time, connector availability, and downstream readiness.

• Applying incremental snapshot approaches for large or continuously changing datasets where full reloads are operationally expensive.

• Managing snapshot interruptions, retries, consistency considerations, chunking, recovery, and downstream reconciliation requirements.

Module 6: Debezium Change Events and Event Data Modeling

• Understanding Debezium event envelopes, keys, payloads, source metadata, operation types, timestamps, transaction information, and schema representations.

• Designing downstream event contracts that preserve essential source context while avoiding unnecessary coupling between producers and consumers.

• Handling create, update, delete, snapshot, and tombstone events according to downstream processing and storage requirements.

• Establishing event naming, topic structures, serialization, schema standards, and compatibility practices for enterprise CDC ecosystems.

Module 7: Kafka Connect and Streaming Integration

• Designing Kafka Connect architectures that provide scalable transport, connector management, configuration control, and distributed CDC processing.

• Configuring source and sink connectors to move Debezium events between operational systems, Kafka topics, analytical platforms, and downstream services.

• Managing topics, partitions, replication, consumer groups, offset storage, worker configuration, and connector scaling for production environments.

• Applying event routing and transformation patterns to produce useful downstream streams without compromising source-change fidelity or traceability.

Module 8: Event Ordering, Transactions and Delivery Semantics

• Understanding ordering guarantees within partitions and the implications for multi-table and multi-transaction CDC processing.

• Applying transaction metadata and event context to preserve relationships between changes generated by the same database transaction.

• Designing idempotent consumers and downstream processing patterns that tolerate retries, duplicate delivery, and connector recovery scenarios.

• Evaluating at-least-once processing, replay, deduplication, and exactly-once-related architectural considerations for CDC pipelines.

Module 9: Schema Evolution and Data Contract Management

• Managing source database schema changes including new columns, altered structures, renamed fields, dropped attributes, and changed data types.

• Understanding schema registry concepts, serialization formats, compatibility strategies, and downstream consumer impacts across evolving CDC event contracts.

• Designing controlled schema migration processes that coordinate database changes, connector behavior, event contracts, and downstream application readiness.

• Establishing data-contract governance that prevents uncontrolled schema changes from disrupting analytical and operational CDC consumers.

Module 10: CDC Transformations and Downstream Data Engineering

• Applying transformation strategies to Debezium events while preserving source lineage, operational metadata, and essential change semantics.

• Building downstream pipelines that materialize CDC events into warehouses, lakehouses, search indexes, caches, operational stores, and analytical databases.

• Designing upsert, merge, delete, and history-maintenance patterns for downstream systems consuming continuously changing source data.

• Managing data normalization, enrichment, filtering, routing, and aggregation while maintaining reliable synchronization with operational sources.

Module 11: Reliability, Recovery and Data Quality

• Designing CDC pipelines to withstand connector failures, source outages, network interruptions, Kafka issues, consumer failures, and downstream processing errors.

• Managing offsets, restart behavior, replay, backfill, reconciliation, and recovery procedures after operational incidents or infrastructure changes.

• Establishing completeness, freshness, consistency, duplicate detection, reconciliation, and source-to-target validation controls for CDC datasets.

• Implementing quarantine and remediation processes for malformed events, unexpected schemas, invalid records, and data-quality exceptions.

Module 12: Security, Governance and Privacy

• Establishing secure database connectivity, connector authentication, authorization, network controls, credential management, and secret-handling practices.

• Applying access controls to CDC topics, schemas, connectors, downstream platforms, and operational management interfaces.

• Managing sensitive and regulated information captured through CDC while applying masking, filtering, minimization, retention, and controlled-access practices.

• Establishing lineage, ownership, classification, auditing, policy enforcement, and governance controls across enterprise CDC data flows.

Module 13: Observability, Monitoring and Troubleshooting

• Designing CDC monitoring frameworks covering connector health, event throughput, source lag, processing latency, task status, errors, and consumer performance.

• Diagnosing connector failures, replication interruptions, offset problems, schema incompatibilities, event-processing errors, and downstream synchronization gaps.

• Using logs, metrics, traces, connector status, Kafka monitoring, database telemetry, and reconciliation results for systematic root-cause analysis.

• Establishing proactive alerting and operational thresholds that identify CDC degradation before data freshness or synchronization requirements are materially affected.

Module 14: Scaling, High Availability and Production Operations

• Designing highly available Debezium and Kafka Connect deployments capable of supporting increasing source counts, transaction volumes, and downstream consumers.

• Applying capacity-planning practices for connectors, workers, Kafka topics, partitions, source databases, network throughput, storage, and processing resources.

• Managing connector upgrades, configuration changes, maintenance windows, rolling deployments, disaster recovery, and operational continuity.

• Establishing production runbooks, incident procedures, recovery objectives, service-level expectations, and continuous improvement processes for CDC platforms.

Module 15: Cloud, Real-Time Analytics and Emerging CDC Architectures

• Designing cloud-native CDC architectures connecting operational databases with cloud warehouses, lakehouses, streaming platforms, and analytical services.

• Integrating Debezium with real-time analytics, event-driven microservices, operational synchronization, data products, and streaming data architectures.

• Exploring CDC applications for machine learning, feature pipelines, generative AI data foundations, personalization, recommendation systems, and intelligent applications.

• Evaluating emerging patterns involving serverless platforms, distributed databases, managed streaming services, metadata-driven pipelines, and automated CDC operations.

Module 16: Enterprise Debezium Strategy and Future Readiness

• Developing enterprise CDC reference architectures aligned with data integration strategy, application modernization, analytical requirements, governance, and operating models.

• Creating implementation roadmaps covering source onboarding, connector standards, event contracts, platform infrastructure, security, monitoring, and operational readiness.

• Defining CDC performance and reliability KPIs covering latency, completeness, connector availability, processing throughput, data freshness, and reconciliation accuracy.

• Establishing a continuous improvement strategy that enables CDC platforms to scale across new databases, workloads, cloud environments, and evolving real-time data requirements.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.