Course Introduction
The Change Data Capture and Real-Time Data Integration Training Course provides a comprehensive foundation for capturing, processing, transporting, and integrating data changes as they occur across modern enterprise systems. Participants explore how CDC techniques enable organizations to identify inserts, updates, and deletes from source systems and deliver those changes efficiently to analytical, operational, and downstream data platforms.
This course examines the principles behind change data capture and explains how log-based, trigger-based, query-based, timestamp-based, and other CDC approaches differ in architecture, reliability, performance, and implementation requirements. Participants learn how to select suitable CDC patterns according to source-system capabilities, data volumes, latency expectations, consistency requirements, and target-platform characteristics.
The Change Data Capture and Real-Time Data Integration Training Course covers the architecture of real-time data integration pipelines from source databases through capture mechanisms, event or messaging layers, transformation services, processing engines, and target platforms. Participants learn how CDC can support data warehouses, data lakes, lakehouses, operational applications, event-driven architectures, analytics platforms, and machine learning environments.
Participants also explore practical challenges such as transaction ordering, duplicate events, schema evolution, late-arriving changes, deletes, checkpoints, offsets, replay, fault recovery, idempotency, and exactly-once or at-least-once processing considerations. The course emphasizes engineering practices that help maintain data consistency and reliability while delivering information with low latency across distributed systems.
Emerging topics are incorporated throughout the course, including streaming data integration, event-driven architectures, cloud-native CDC, real-time analytics, open table formats, data products, AI-ready data pipelines, and intelligent monitoring. Participants examine how modern CDC architectures can support increasingly dynamic analytical and operational environments while addressing governance, security, observability, scalability, and cost management requirements.
By completing the Change Data Capture and Real-Time Data Integration Training Course, participants will be able to design, evaluate, implement, and operate CDC-driven integration solutions. The course equips professionals with practical knowledge for building reliable real-time pipelines, modernizing batch-oriented data movement, reducing data latency, and creating responsive data platforms capable of supporting analytics, automation, operational applications, and AI workloads.
Duration
5 days
Who Should Attend
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Data engineers responsible for building and maintaining real-time and near-real-time data integration pipelines.
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Data architects designing CDC, streaming, event-driven, and enterprise data integration architectures.
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Database administrators responsible for source database performance, transaction logs, replication, and change capture technologies.
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Cloud engineers implementing real-time data pipelines across cloud databases, storage services, messaging platforms, and analytical systems.
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Analytics engineers requiring fresh operational data for cloud warehouses, lakehouses, dashboards, and advanced analytical workloads.
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Integration developers building event-driven data movement between applications, databases, APIs, and enterprise platforms.
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Data platform engineers responsible for streaming infrastructure, pipeline reliability, observability, and operational automation.
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Business intelligence professionals seeking to understand how real-time data integration improves analytical data freshness.
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Machine learning and AI professionals requiring continuously updated datasets and low-latency data pipelines for intelligent applications.
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Data governance professionals responsible for lineage, data quality, security, privacy, and lifecycle controls across real-time data environments.
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Technology managers overseeing data modernization, real-time analytics, and enterprise integration transformation initiatives.
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Consultants and solution architects designing scalable CDC and real-time data integration strategies for organizations.
Course Objectives
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Understand the fundamental concepts, architectures, benefits, limitations, and practical applications of change data capture in modern data environments.
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Compare log-based, trigger-based, query-based, timestamp-based, and other CDC techniques according to source capabilities and integration requirements.
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Design real-time and near-real-time data pipelines that capture changes from operational systems and deliver them reliably to downstream platforms.
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Apply CDC patterns for databases, cloud applications, data warehouses, data lakes, lakehouses, operational systems, and event-driven architectures.
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Manage transaction ordering, offsets, checkpoints, duplicate events, deletes, schema changes, late-arriving changes, replay, and failure recovery.
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Understand streaming concepts including events, topics, partitions, consumers, delivery guarantees, buffering, backpressure, and continuous data processing.
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Implement data transformation, validation, enrichment, filtering, routing, and loading practices within CDC-driven integration workflows.
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Establish security, governance, metadata, lineage, monitoring, observability, and data quality controls for real-time data integration environments.
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Optimize CDC pipelines for latency, throughput, scalability, source-system impact, infrastructure consumption, storage, and operational cost.
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Apply emerging real-time integration practices involving cloud-native CDC, event-driven architectures, open table formats, AI-ready pipelines, and real-time analytics.
Comprehensive Course Outline
Module 1: Change Data Capture Fundamentals
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Understanding change data capture concepts and how CDC identifies inserts, updates, deletes, and other source-system changes.
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Comparing CDC with traditional full-load, batch extraction, replication, polling, and scheduled data integration approaches.
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Evaluating CDC use cases for analytics, data warehousing, operational integration, event processing, synchronization, and AI workloads.
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Understanding key CDC design considerations involving latency, source impact, consistency, scalability, reliability, and implementation complexity.
Module 2: CDC Methods and Source-System Architecture
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Exploring log-based CDC using database transaction logs, redo logs, write-ahead logs, binary logs, and similar mechanisms.
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Understanding trigger-based, timestamp-based, version-based, query-based, and snapshot-based approaches to capturing data changes.
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Evaluating CDC methods according to database technology, workload characteristics, source constraints, data volume, and freshness requirements.
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Assessing source-system impact and designing CDC solutions that minimize operational disruption and unnecessary database workload.
Module 3: Real-Time Data Integration Architecture
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Designing end-to-end CDC architectures covering source systems, capture layers, event brokers, processing engines, and target platforms.
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Understanding the role of messaging systems, event streams, queues, connectors, processors, and integration services in real-time data movement.
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Designing pipelines for analytical warehouses, data lakes, lakehouses, operational databases, search platforms, and downstream applications.
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Comparing batch, micro-batch, near-real-time, and continuous streaming architectures according to business and technical requirements.
Module 4: Streaming Data and Event Processing
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Understanding event streams, topics, partitions, producers, consumers, offsets, consumer groups, and message delivery mechanisms.
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Designing streaming workflows that process CDC events continuously while maintaining appropriate ordering and delivery characteristics.
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Managing buffering, backpressure, throughput, latency, partitioning, event ordering, and consumer scalability in streaming environments.
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Applying filtering, routing, enrichment, transformation, aggregation, and event processing techniques to real-time data streams.
Module 5: Data Consistency and CDC Reliability
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Managing duplicate events, idempotency, transaction boundaries, ordering, checkpoints, offsets, replay, and recovery in CDC pipelines.
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Understanding at-most-once, at-least-once, and exactly-once processing concepts and their practical implementation considerations.
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Designing reconciliation processes that compare source and target states to identify missing, duplicated, delayed, or inconsistent changes.
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Implementing failure handling, retry, dead-letter, recovery, and reprocessing mechanisms for reliable real-time data integration.
Module 6: Schema Evolution and Data Transformation
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Managing source schema changes including new columns, renamed fields, altered data types, dropped fields, and structural changes.
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Designing transformation workflows that convert CDC events into target-specific formats, schemas, models, and analytical structures.
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Applying validation, cleansing, enrichment, filtering, masking, and business-rule processing to continuously changing data.
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Establishing schema governance and compatibility practices that reduce disruption when source systems evolve over time.
Module 7: CDC with Cloud Warehouses, Lakes, and Lakehouses
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Integrating CDC pipelines with cloud data warehouses to maintain fresh analytical tables and reduce dependence on recurring full loads.
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Designing CDC architectures for object storage, data lakes, lakehouses, and open table formats supporting incremental data processing.
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Applying merge, upsert, delete, and incremental loading strategies for continuously changing analytical datasets.
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Optimizing storage, partitioning, compaction, processing frequency, and data layout for high-volume CDC workloads.
Module 8: Security, Governance, and Observability
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Securing CDC pipelines through authentication, authorization, encryption, secrets management, network controls, and sensitive data protection.
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Establishing data lineage and metadata practices that trace changes from operational sources through integration pipelines to downstream consumers.
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Implementing monitoring for latency, throughput, lag, errors, failed events, consumer health, source impact, and pipeline availability.
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Developing alerting, incident management, audit, compliance, and operational response processes for real-time integration environments.
Module 9: Performance, Scalability, and Operational Optimization
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Measuring and optimizing CDC latency, throughput, event processing capacity, database impact, storage consumption, and infrastructure utilization.
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Designing horizontally scalable CDC architectures using parallel processing, partitioning, distributed consumers, and elastic cloud resources.
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Managing source database workload and minimizing performance impact through efficient capture configurations and controlled extraction strategies.
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Applying cost optimization techniques for compute, storage, network transfer, streaming infrastructure, retention, and operational monitoring.
Module 10: Emerging Real-Time Data Integration Practices
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Exploring cloud-native CDC, serverless integration, event-driven architectures, streaming analytics, and increasingly automated data pipelines.
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Understanding how CDC supports AI-ready data foundations, real-time machine learning, operational intelligence, data products, and intelligent applications.
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Evaluating open table formats, real-time lakehouse architectures, event-driven data platforms, and unified batch-streaming integration patterns.
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Developing modernization roadmaps that transition organizations from batch-oriented data movement toward governed, observable, scalable real-time integration.
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.
