Modern Data Integration and Change Data Capture Training Course

Course Introduction

The Modern Data Integration and Change Data Capture Training Course provides an advanced exploration of contemporary data integration architectures, engineering practices, and change data capture techniques used to connect enterprise applications, databases, cloud platforms, analytical systems, and real-time data environments. The course focuses on building reliable, scalable, secure, and observable data flows that continuously synchronize information across complex technology ecosystems while supporting modern analytics, operational intelligence, and AI-driven applications.

This training examines the evolution from traditional batch-oriented integration toward modern integration patterns based on incremental processing, log-based change data capture, event streaming, APIs, replication, microservices, and cloud-native data platforms. Participants will learn how to select and combine integration approaches according to data volume, velocity, latency, consistency, source-system characteristics, business criticality, and downstream consumption requirements. The course addresses practical architectural decisions required to modernize fragmented integration landscapes.

A major emphasis is placed on Change Data Capture as a foundation for near-real-time data movement and synchronization. Participants will explore database logs, transaction streams, timestamps, version columns, triggers, event records, and other mechanisms for identifying and propagating data changes. The training covers initial snapshots, incremental capture, ordering, offsets, checkpoints, duplicate handling, transaction boundaries, schema evolution, deletes, updates, late-arriving changes, and recovery strategies required to maintain trustworthy downstream data.

The course also explores enterprise integration across data warehouses, data lakes, lakehouses, operational databases, SaaS applications, APIs, messaging platforms, and distributed cloud environments. Participants will learn how to engineer batch and streaming integration pipelines, manage heterogeneous source systems, establish reusable integration frameworks, and implement orchestration and automation. Special attention is given to data quality, reconciliation, metadata, lineage, observability, security, privacy, governance, and operational controls that ensure integrated data remains trustworthy throughout its lifecycle.

Advanced topics address event-driven integration, streaming architectures, cloud-native CDC, hybrid and multi-cloud synchronization, data mesh, data fabric, data products, and AI-ready data integration. Participants will examine how CDC and real-time integration can provide continuously updated datasets for machine learning, generative AI, operational analytics, fraud detection, customer intelligence, and other latency-sensitive workloads. The course also considers emerging intelligent automation capabilities that can improve schema mapping, anomaly detection, pipeline monitoring, incident analysis, and integration operations.

By the end of the course, participants will be equipped to design, implement, optimize, monitor, and govern modern data integration and CDC solutions across enterprise environments. The training provides practical frameworks for selecting CDC technologies, architecting reliable synchronization patterns, managing integration complexity, improving data freshness, and reducing operational risk. Participants will also gain the strategic knowledge required to modernize legacy integration platforms and establish scalable, automated, event-driven data foundations for analytics, data products, and AI.

Duration

10 days

Who Should Attend

• Data architects designing enterprise data integration, synchronization, replication, and CDC architectures.

• Data engineers responsible for building batch, incremental, streaming, and real-time data integration pipelines.

• ETL and ELT developers modernizing legacy integration workflows and implementing incremental processing patterns.

• Cloud data engineers integrating databases, applications, analytical platforms, and cloud-native data services.

• Data platform engineers responsible for scalable integration infrastructure, orchestration, reliability, and automation.

• Database administrators implementing transaction-log-based replication and change data capture mechanisms.

• Integration architects designing API, event-driven, messaging, replication, and distributed integration solutions.

• Streaming engineers developing real-time event pipelines and continuously synchronized analytical data environments.

• Data quality specialists responsible for reconciliation, validation, consistency, completeness, and freshness of integrated data.

• Data governance professionals managing metadata, lineage, ownership, privacy, security, and compliance across integration platforms.

• DataOps and DevOps professionals automating integration development, testing, deployment, monitoring, and operational management.

• Analytics engineers consuming continuously updated datasets for reporting, business intelligence, and analytical applications.

• Machine learning and AI engineers requiring current, governed, and reliable data feeds for AI and machine learning workloads.

• Enterprise technology managers overseeing integration modernization, cloud migration, and data platform transformation initiatives.

• Solution architects and technical leads establishing enterprise integration standards, reusable patterns, and modernization roadmaps.

Course Objectives

• Explain modern data integration architectures, Change Data Capture concepts, synchronization patterns, replication approaches, and enterprise integration strategies.

• Design scalable integration architectures that combine batch, incremental, streaming, API-based, event-driven, and CDC processing according to business requirements.

• Implement database Change Data Capture mechanisms using transaction logs, timestamps, triggers, versioning, events, and other source-system change detection techniques.

• Engineer reliable initial-load and incremental synchronization workflows that preserve data consistency, ordering, completeness, and downstream usability.

• Manage inserts, updates, deletes, transaction boundaries, duplicate events, late-arriving changes, out-of-order records, and schema evolution within CDC pipelines.

• Integrate operational databases, cloud services, SaaS platforms, APIs, messaging systems, data warehouses, data lakes, and lakehouses through reusable integration frameworks.

• Apply data quality, reconciliation, validation, observability, lineage, metadata, and monitoring practices to continuously assess integration reliability and data freshness.

• Design resilient CDC pipelines using checkpoints, offsets, replay mechanisms, idempotency, retries, recovery workflows, and failure-isolation techniques.

• Optimize integration performance through partitioning, parallel processing, workload management, batching, compression, resource allocation, and efficient data movement strategies.

• Implement security, privacy, access control, encryption, secrets management, and compliance controls across data integration and CDC environments.

• Apply DataOps, DevOps, CI/CD, infrastructure-as-code, automated testing, and deployment practices to modern data integration engineering.

• Develop enterprise modernization strategies that use CDC and event-driven integration to support real-time analytics, data products, cloud platforms, AI, and continuously synchronized data ecosystems.

Comprehensive Course Outline

Module 1: Foundations of Modern Data Integration

• Evolution from traditional ETL and batch integration toward cloud-native, incremental, streaming, and event-driven data integration architectures.

• Core data integration concepts including ingestion, replication, transformation, synchronization, federation, orchestration, and data movement.

• Comparative analysis of batch, micro-batch, CDC, API, messaging, event-driven, and streaming integration patterns.

• Enterprise data integration use cases across operational systems, analytics, data warehousing, lakehouses, applications, and AI platforms.

Module 2: Enterprise Data Integration Architecture

• Reference architectures for integrating heterogeneous databases, applications, cloud services, analytical platforms, and enterprise data stores.

• Integration layers covering ingestion, transport, transformation, orchestration, metadata, governance, quality, and consumption.

• Designing loosely coupled and reusable integration services that support scalability, interoperability, resilience, and maintainability.

• Architectural patterns for hybrid, multi-cloud, distributed, event-driven, and enterprise-scale integration environments.

Module 3: Change Data Capture Fundamentals

• Core CDC concepts for identifying, extracting, transporting, and applying database changes to downstream systems.

• Comparison of log-based, trigger-based, timestamp-based, version-based, query-based, and event-driven change capture techniques.

• Understanding inserts, updates, deletes, transactions, commit positions, offsets, sequence numbers, and change-event structures.

• Selecting CDC mechanisms according to source database capabilities, workload characteristics, latency requirements, and consistency expectations.

Module 4: Transaction Logs and Log-Based CDC

• Architecture and operational principles of transaction-log-based Change Data Capture for high-volume database environments.

• Reading database transaction logs, managing log positions, interpreting change events, and maintaining capture continuity.

• Handling transaction ordering, commit boundaries, rollback behavior, delete events, updates, and multi-operation transactions.

• Designing log-based CDC pipelines for scalable, low-latency, minimally invasive data synchronization.

Module 5: Initial Loads and Incremental Synchronization

• Designing consistent initial snapshots that establish downstream datasets before continuous change processing begins.

• Coordinating snapshot and CDC streams while preventing data gaps, duplication, inconsistency, or synchronization conflicts.

• Watermarking, checkpointing, offset management, restartability, and incremental state tracking for continuous processing.

• Strategies for backfills, historical reloads, partial synchronization, reprocessing, replay, and controlled data recovery.

Module 6: Advanced CDC Event Processing

• Processing inserts, updates, deletes, tombstones, before-and-after values, transaction metadata, and source-system context.

• Managing duplicate events, out-of-order changes, late-arriving records, retries, replay scenarios, and idempotent processing.

• Event filtering, routing, enrichment, transformation, aggregation, and selective replication for downstream consumers.

• Designing CDC event schemas and standardized message structures for interoperable enterprise integration.

Module 7: Schema Evolution and Metadata Management

• Managing source schema changes including new columns, removed attributes, renamed fields, data type changes, and structural modifications.

• Designing schema compatibility, versioning, validation, and automated change-detection mechanisms for CDC pipelines.

• Using metadata to manage source definitions, mappings, transformations, dependencies, ownership, and operational context.

• Integrating CDC pipelines with data catalogues, lineage platforms, business glossaries, and active metadata systems.

Module 8: Data Quality, Reconciliation and Consistency

• Establishing completeness, accuracy, consistency, timeliness, uniqueness, and validity controls for continuously synchronized data.

• Source-to-target reconciliation using record counts, checksums, aggregates, balancing rules, and transaction-level validation.

• Detecting missing events, duplicated changes, synchronization gaps, transformation defects, and downstream inconsistencies.

• Designing automated quality gates, exception workflows, remediation processes, and continuous data reliability monitoring.

Module 9: Streaming and Event-Driven Data Integration

• Designing event-driven architectures that use message brokers, streaming platforms, producers, consumers, and distributed event processing.

• Integrating CDC events with real-time streaming pipelines for operational intelligence and continuously updated analytical environments.

• Managing partitions, offsets, consumer groups, delivery guarantees, ordering, retention, replay, and fault-tolerant processing.

• Building event-driven integration patterns for applications, microservices, analytics, monitoring, IoT, and customer intelligence.

Module 10: Cloud-Native and Hybrid CDC Architectures

• Designing CDC solutions across managed cloud databases, object storage, cloud warehouses, lakehouses, and distributed analytical services.

• Implementing scalable cloud-native ingestion and synchronization architectures using managed services and elastic processing.

• Integrating on-premises databases with cloud platforms while addressing connectivity, security, latency, synchronization, and operational requirements.

• Designing hybrid and multi-cloud CDC architectures that provide interoperability, portability, resilience, and controlled data movement.

Module 11: CDC for Data Warehouses, Lakes and Lakehouses

• Applying CDC to continuously update enterprise data warehouse tables, dimensional models, analytical datasets, and reporting structures.

• Integrating change streams with data lake and lakehouse architectures to support incremental data processing and efficient storage.

• Managing slowly changing dimensions, historical tracking, upserts, merges, snapshots, and temporal analytical requirements.

• Designing CDC-driven analytical architectures that improve data freshness while minimizing unnecessary full-table processing.

Module 12: CDC Security, Governance and Compliance

• Implementing authentication, authorization, encryption, secure connectivity, secrets management, and least-privilege access for CDC platforms.

• Protecting sensitive and regulated data during extraction, transport, transformation, replication, and downstream consumption.

• Applying data classification, masking, tokenization, retention, privacy, auditability, and policy controls across integration workflows.

• Establishing governance frameworks covering ownership, stewardship, lineage, operational accountability, compliance monitoring, and controlled data usage.

Module 13: CDC Observability and Reliability Engineering

• Monitoring CDC latency, throughput, event counts, processing lag, replication status, failures, offsets, and synchronization health.

• Detecting stalled pipelines, source-log interruptions, schema changes, processing bottlenecks, data gaps, and abnormal event patterns.

• Designing alerts, service-level objectives, incident workflows, automated recovery, replay mechanisms, and operational escalation processes.

• Applying reliability engineering principles to achieve resilient, continuously operating CDC and data integration services.

Module 14: DataOps, DevOps and Integration Automation

• Applying version control, automated testing, code review, CI/CD, release management, and environment promotion to integration solutions.

• Automating CDC configuration, pipeline deployment, connector management, schema validation, and infrastructure provisioning.

• Using infrastructure-as-code and standardized deployment patterns for repeatable development, testing, staging, and production environments.

• Establishing DataOps operating practices that improve collaboration, deployment velocity, reliability, governance, and operational transparency.

Module 15: Advanced CDC for Data Products, AI and Intelligent Analytics

• Using CDC to create continuously refreshed data products that provide current information to analytical and operational consumers.

• Integrating change streams with machine learning pipelines, feature engineering, real-time inference, generative AI, and intelligent applications.

• Applying CDC and event-driven architectures to semantic layers, knowledge graphs, data mesh, data fabric, and real-time analytical ecosystems.

• Exploring AI-assisted integration capabilities for schema mapping, anomaly detection, pipeline optimization, documentation, and operational support.

Module 16: Enterprise CDC Strategy and Continuous Optimization

• Developing enterprise CDC standards, reusable integration patterns, technology selection frameworks, governance models, and operating procedures.

• Modernizing legacy integration environments through phased CDC adoption, platform consolidation, automation, and event-driven architecture.

• Establishing performance, latency, quality, reliability, security, cost, and adoption metrics for continuous integration optimization.

• Creating long-term integration roadmaps that support cloud transformation, real-time analytics, data products, AI adoption, and enterprise modernization.

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.