Course Introduction
The Enterprise Data Platform Strategy and Modernization Training Course provides a comprehensive framework for assessing, designing, and transforming enterprise data platforms to support modern analytics, artificial intelligence, digital services, and data-driven operations. Participants explore how organizations can evolve fragmented and aging data environments into scalable, governed, and adaptable platforms.
The course examines enterprise data platform strategy from both business and technical perspectives, covering current-state assessment, target architecture, platform selection, modernization roadmaps, migration planning, operating models, governance, and investment priorities. Participants learn how to align data platform capabilities with organizational objectives, analytical workloads, AI initiatives, regulatory requirements, and long-term technology strategies.
Participants will explore modern data warehouse, data lake, lakehouse, cloud, hybrid, and multi-platform architectures and understand the circumstances in which different approaches may be appropriate. The training addresses platform consolidation, workload modernization, data integration, metadata management, data quality, interoperability, self-service analytics, and the transition from legacy environments to contemporary data ecosystems.
The course also focuses on practical modernization challenges, including technical debt, legacy dependencies, data silos, migration risks, application integration, security, governance, operational continuity, cost management, and organizational change. Participants learn how to develop phased modernization programs that minimize disruption while progressively improving platform capabilities and business value.
Emerging approaches such as Data Mesh, Data Fabric, data products, cloud-native platforms, serverless analytics, real-time data services, vector databases, semantic layers, and AI-ready data platforms are incorporated throughout the program. Participants examine how these technologies influence enterprise platform strategy and how organizations can introduce them without creating unnecessary architectural fragmentation.
By completing this course, participants will be better prepared to assess existing data environments, define target-state architectures, prioritize modernization initiatives, evaluate technology options, and establish implementation roadmaps. The training combines strategic planning, architecture, governance, migration, financial considerations, and operational practices required for sustainable enterprise data platform transformation.
Duration
5 days
Who Should Attend
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Chief data officers and senior data leaders responsible for enterprise data strategy and modernization.
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Enterprise architects designing target-state data platforms and technology transformation programs.
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Data architects responsible for enterprise warehouse, lake, lakehouse, and hybrid platform architecture.
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Data engineering leaders overseeing modernization of pipelines, integration platforms, and analytical environments.
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Cloud architects planning migration and modernization of enterprise data workloads.
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Technology managers responsible for data platform investment, transformation, and operational performance.
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Database administrators supporting legacy modernization and migration to contemporary data platforms.
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Business intelligence leaders transitioning traditional reporting environments toward modern analytics platforms.
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Data governance professionals establishing governance frameworks for modernized enterprise data ecosystems.
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Solution architects evaluating data platform technologies, integration patterns, and modernization strategies.
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Analytics engineers developing modern transformation and analytical data environments.
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Program and project managers responsible for enterprise data modernization initiatives and implementation roadmaps.
Course Objectives
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Develop a structured enterprise data platform strategy that aligns business priorities, analytical requirements, AI initiatives, governance needs, and technology capabilities.
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Assess legacy data environments systematically to identify technical debt, data silos, architectural constraints, integration challenges, operational risks, and modernization opportunities.
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Design target-state architectures using modern data warehouses, data lakes, lakehouses, cloud platforms, hybrid environments, and distributed analytical technologies.
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Evaluate platform modernization options according to scalability, performance, security, interoperability, reliability, operational complexity, and total cost considerations.
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Develop phased migration strategies that address workload dependencies, data movement, application integration, testing, business continuity, cutover planning, and legacy decommissioning.
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Establish governance, security, metadata, data quality, lineage, access control, and compliance capabilities as integral components of enterprise platform modernization.
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Apply workload assessment and prioritization techniques to determine which data assets, applications, pipelines, and analytical services should be modernized first.
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Design operating models that define platform ownership, engineering responsibilities, governance roles, service management, architecture standards, and organizational accountability.
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Develop financial and operational frameworks for evaluating modernization investments, cloud consumption, platform costs, resource requirements, and long-term sustainability.
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Create practical modernization roadmaps that incorporate emerging technologies such as Data Mesh, Data Fabric, data products, real-time analytics, semantic platforms, and AI-ready architectures.
Comprehensive Course Outline
Module 1: Enterprise Data Platform Strategy Foundations
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Understand the strategic role of enterprise data platforms in supporting analytics, AI, digital transformation, operational intelligence, and organizational decision-making.
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Examine the relationship between business strategy, data strategy, enterprise architecture, platform capabilities, governance, and technology investment priorities.
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Assess common challenges associated with fragmented data environments, legacy technologies, duplicated platforms, inconsistent definitions, and technical debt.
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Establish strategic principles for developing data platforms that are scalable, interoperable, secure, governed, adaptable, and aligned with business objectives.
Module 2: Current-State Assessment and Data Platform Maturity
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Conduct structured assessments of existing data warehouses, lakes, integration platforms, databases, pipelines, analytical tools, and supporting infrastructure.
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Develop data platform maturity assessments covering architecture, engineering, governance, security, quality, operations, skills, processes, and technology capabilities.
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Identify technical debt, duplicated capabilities, unsupported technologies, performance limitations, operational bottlenecks, and critical modernization dependencies.
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Create current-state inventories and architectural baselines that support evidence-based modernization decisions and investment prioritization.
Module 3: Target-State Enterprise Data Architecture
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Design target-state architectures incorporating cloud data warehouses, data lakes, lakehouses, streaming platforms, semantic layers, and modern analytical services.
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Compare centralized, distributed, federated, hybrid, and multi-platform architectures according to organizational requirements and workload characteristics.
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Establish architectural principles for scalability, interoperability, resilience, data accessibility, security, governance, observability, and platform standardization.
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Develop reference architectures that connect operational systems, integration services, analytical platforms, AI environments, business intelligence, and data products.
Module 4: Cloud and Hybrid Data Platform Modernization
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Examine cloud-native approaches for modernizing enterprise data platforms while addressing workload portability, security, networking, performance, and operational requirements.
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Compare migration strategies including rehosting, replatforming, refactoring, rebuilding, retiring, and retaining workloads according to business and technical conditions.
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Design hybrid architectures that connect on-premises systems with cloud warehouses, data lakes, lakehouses, integration services, and analytical applications.
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Address cloud consumption management, workload isolation, elasticity, service selection, platform dependencies, and architectural interoperability during modernization.
Module 5: Data Integration and Pipeline Modernization
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Modernize legacy ETL environments using contemporary ELT, orchestration, API integration, change data capture, event streaming, and automated data engineering patterns.
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Design reliable ingestion architectures for batch, incremental, real-time, streaming, semi-structured, and unstructured enterprise data sources.
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Address data movement, transformation dependencies, schema evolution, data contracts, pipeline testing, observability, failure recovery, and operational continuity.
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Establish reusable integration patterns that reduce duplication and enable consistent delivery of trusted data across analytical and operational environments.
Module 6: Migration Planning, Execution, and Risk Management
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Develop structured migration strategies covering workload discovery, dependency mapping, sequencing, data movement, testing, validation, cutover, rollback, and decommissioning.
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Prioritize modernization initiatives according to business value, technical complexity, risk, regulatory requirements, platform dependencies, and organizational readiness.
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Design migration approaches that protect data integrity, minimize service disruption, preserve historical information, and maintain critical reporting and analytical capabilities.
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Establish migration governance, progress monitoring, issue management, acceptance criteria, and transition procedures for large-scale enterprise modernization programs.
Module 7: Governance, Security, and Platform Operations
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Integrate enterprise data governance into modern platform architectures through ownership, stewardship, metadata, lineage, classification, policies, and lifecycle management.
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Design security controls covering identity, authentication, authorization, encryption, network protection, secrets management, and fine-grained data access.
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Establish data quality, observability, monitoring, incident management, backup, recovery, availability, and disaster recovery capabilities for modern platforms.
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Develop operating standards that balance centralized governance requirements with flexible delivery models for analytics, data engineering, and business teams.
Module 8: Platform Economics, Performance, and Optimization
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Develop approaches for evaluating total cost of ownership across infrastructure, storage, compute, data movement, licensing, engineering resources, and operational services.
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Optimize modern data platforms through workload management, partitioning, clustering, caching, materialization, resource allocation, and appropriate architecture choices.
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Establish FinOps and cost governance practices that improve visibility into platform consumption while supporting scalability and business requirements.
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Develop performance and cost monitoring frameworks that enable continuous optimization as data volumes, workloads, users, and analytical requirements evolve.
Module 9: Emerging Enterprise Data Platform Technologies
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Explore Data Mesh, Data Fabric, data products, domain-oriented ownership, federated governance, and other approaches influencing enterprise platform modernization.
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Examine semantic layers, knowledge graphs, vector databases, retrieval architectures, and AI-ready data platforms as emerging components of modern data ecosystems.
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Investigate real-time analytics, event-driven architectures, intelligent data services, and other technologies supporting continuously available enterprise intelligence.
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Evaluate emerging technologies based on architectural fit, organizational maturity, integration requirements, governance implications, and sustainable operational value.
Module 10: Modernization Roadmaps and Transformation Governance
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Develop practical multi-phase roadmaps that connect current-state challenges with target-state capabilities, modernization initiatives, dependencies, and measurable outcomes.
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Establish transformation governance structures covering executive sponsorship, architecture decisions, program management, platform ownership, risk management, and stakeholder engagement.
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Define success measures for modernization programs including platform reliability, migration progress, adoption, data quality, performance, cost efficiency, and business enablement.
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Create sustainable modernization strategies that continuously evolve enterprise data platforms while controlling technical debt, architectural complexity, and operational risk.
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.
