Course Introduction
Successful data migration requires more than moving records from one system to another. Organizations must understand the quality, structure, meaning, dependencies, and business context of their data before migration begins. This course provides a practical framework for preparing, cleansing, transforming, validating, and securely migrating data across modern technology environments.
The Data Migration, Cleansing and Validation Training Course explores the complete migration lifecycle, from discovery and source assessment through profiling, cleansing, transformation, migration execution, reconciliation, validation, and post-migration quality assurance. Participants learn how to reduce data risks and establish repeatable processes for delivering accurate and trusted information to target platforms.
The course addresses migration scenarios involving databases, enterprise applications, cloud platforms, data warehouses, data lakes, legacy systems, SaaS applications, and modern analytical environments. Participants examine approaches for handling inconsistent formats, duplicate records, missing values, invalid data, obsolete information, incompatible schemas, and changing business rules during migration projects.
Particular attention is given to data profiling, cleansing, standardization, transformation, matching, deduplication, validation, and reconciliation. Participants learn how to establish measurable quality rules and automated controls that verify migrated information against source systems and target requirements while preserving critical relationships, history, metadata, and business meaning.
The course also covers migration planning, dependency analysis, mapping specifications, migration waves, testing, rollback planning, cutover management, and post-migration monitoring. Participants explore how automation, orchestration, data quality frameworks, metadata, lineage, and governance can improve migration consistency while reducing manual intervention and operational disruption.
By completing this course, participants will be prepared to plan and execute structured data migration initiatives with stronger cleansing, validation, and quality controls. The course emphasizes practical methods for minimizing migration defects, improving confidence in target data, maintaining traceability, and establishing sustainable processes for future migration and modernization projects.
Duration
5 days
Who Should Attend
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Data engineers responsible for designing and executing data migration, transformation, cleansing, and validation workflows.
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Database administrators involved in migrating databases, schemas, tables, records, and associated metadata between platforms.
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Data architects designing migration strategies for legacy modernization, cloud adoption, application replacement, or platform consolidation.
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Data quality professionals responsible for profiling, cleansing, standardization, validation, and remediation of enterprise datasets.
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Data analysts who need to assess source and target data quality before and after major migration initiatives.
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ETL and integration developers building extraction, transformation, loading, reconciliation, and migration automation workflows.
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Application modernization professionals involved in moving data from legacy systems into modern platforms and applications.
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Cloud engineers supporting database, warehouse, lake, lakehouse, SaaS, and hybrid data migration projects.
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Data governance and master data professionals responsible for maintaining trusted, standardized, and controlled information during migrations.
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Project managers and technical leads coordinating migration planning, testing, cutover, validation, and post-migration activities.
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Business analysts responsible for defining data requirements, mapping business rules, validating migrated records, and supporting user acceptance testing.
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IT professionals involved in enterprise system replacement, consolidation, modernization, data integration, and technology transformation programs.
Course Objectives
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Develop a comprehensive understanding of data migration principles, lifecycle stages, risks, dependencies, and quality requirements across modern enterprise technology environments.
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Assess source datasets through systematic profiling to identify structural problems, missing values, duplicates, inconsistencies, obsolete records, and migration readiness concerns.
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Apply practical data cleansing techniques to improve completeness, consistency, accuracy, validity, standardization, uniqueness, and overall usability before migration.
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Design source-to-target mapping specifications that document field relationships, transformations, business rules, default values, exclusions, and handling of exceptional records.
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Implement transformation strategies that reconcile incompatible schemas, formats, data types, identifiers, codes, units, relationships, and historical representations between source and target systems.
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Establish automated validation and reconciliation processes that compare migrated data with source records and target requirements to identify defects and discrepancies.
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Develop migration testing strategies covering unit testing, integration testing, data quality testing, performance testing, user acceptance testing, and post-migration verification.
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Apply migration planning techniques involving dependencies, migration waves, data volumes, cutover windows, rollback procedures, parallel processing, and business continuity requirements.
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Integrate metadata, lineage, audit trails, security, access controls, governance, and documentation into migration workflows to maintain traceability and accountability.
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Develop sustainable post-migration practices for monitoring data quality, resolving defects, validating ongoing synchronization, and maintaining trusted information in modern target platforms.
Comprehensive Course Outline
Module 1: Foundations of Data Migration and Quality Management
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Understanding the purpose, scope, lifecycle, and business drivers of modern data migration and platform modernization initiatives.
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Examining common migration scenarios involving legacy replacement, cloud adoption, application consolidation, warehouse modernization, and platform transformation.
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Identifying migration risks associated with poor data quality, incomplete mappings, incompatible structures, dependencies, and inadequate validation.
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Establishing migration objectives, quality expectations, responsibilities, success criteria, governance requirements, and measurable acceptance conditions.
Module 2: Data Discovery, Profiling, and Migration Readiness
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Conducting structured data discovery to understand source systems, datasets, relationships, dependencies, ownership, formats, and business context.
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Applying data profiling techniques to identify null values, duplicates, invalid values, outliers, inconsistent formats, and unexpected data distributions.
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Assessing source data readiness by documenting quality issues, technical limitations, dependencies, historical requirements, and transformation complexity.
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Creating data inventories and profiling reports that support migration planning, prioritization, remediation, and source-to-target design activities.
Module 3: Data Cleansing and Standardization
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Designing cleansing rules that address missing, inaccurate, inconsistent, duplicated, obsolete, and invalid information before migration.
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Applying standardization techniques for names, addresses, dates, currencies, units, identifiers, codes, categories, and other critical data elements.
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Developing deduplication and survivorship strategies that preserve trusted information while minimizing unnecessary record duplication in target environments.
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Managing exceptions and unresolved records through controlled remediation processes that document decisions and maintain data quality accountability.
Module 4: Source-to-Target Mapping and Transformation
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Developing detailed mapping specifications that connect source fields, records, relationships, and business definitions to target structures.
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Applying transformation rules for data types, formats, codes, identifiers, calculated values, derived attributes, and historical representations.
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Managing complex transformations where source and target systems use different schemas, business rules, terminology, and relationship structures.
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Establishing reusable mapping documentation that supports development, testing, validation, troubleshooting, maintenance, and future migration activities.
Module 5: Migration Architecture and ETL Pipeline Development
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Designing extraction, transformation, and loading pipelines that move cleansed and validated data efficiently into target environments.
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Comparing batch, incremental, parallel, streaming, and staged migration approaches according to data volume, system constraints, and business requirements.
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Applying orchestration, scheduling, dependency management, checkpointing, retries, and recovery techniques to improve migration reliability.
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Optimizing migration pipelines through partitioning, parallel processing, resource management, staging strategies, and efficient transformation techniques.
Module 6: Data Validation, Reconciliation, and Testing
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Designing validation frameworks that verify completeness, accuracy, consistency, uniqueness, referential integrity, and business-rule compliance after migration.
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Applying record counts, control totals, checksums, sampling, aggregate comparisons, and field-level reconciliation to compare source and target datasets.
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Developing automated data quality tests that detect missing records, unexpected values, transformation errors, broken relationships, and migration anomalies.
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Establishing comprehensive testing strategies covering development, integration, performance, user acceptance, cutover, and post-migration validation requirements.
Module 7: Migration Planning, Cutover, and Operational Execution
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Developing detailed migration plans covering scope, dependencies, migration waves, resource requirements, timelines, responsibilities, and acceptance criteria.
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Designing rehearsal and dry-run processes that identify migration defects and operational issues before production cutover activities begin.
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Managing cutover procedures including final extraction, synchronization, validation, system transition, business verification, and controlled rollback processes.
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Establishing communication, incident management, escalation, and decision-making processes for handling unexpected migration issues during execution.
Module 8: Metadata, Lineage, Governance, and Security
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Capturing metadata describing source datasets, target structures, transformations, mappings, quality rules, ownership, and migration decisions.
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Establishing lineage that enables teams to trace migrated information from target records back to original source systems and transformation processes.
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Applying governance practices for data ownership, stewardship, retention, classification, access, regulatory requirements, and migration accountability.
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Protecting sensitive information through authentication, authorization, encryption, masking, pseudonymization, secure transfer, and controlled migration environments.
Module 9: Cloud, Legacy, and Modern Platform Migration
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Examining migration approaches for legacy databases, enterprise applications, cloud warehouses, data lakes, lakehouses, SaaS platforms, and hybrid environments.
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Comparing rehost, replatform, refactor, rebuild, and consolidation strategies according to technical requirements and target platform capabilities.
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Managing migration challenges involving large data volumes, limited downtime, legacy formats, application dependencies, changing schemas, and operational constraints.
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Optimizing cloud migration processes by considering storage, compute, network transfer, performance, scalability, security, availability, and cost requirements.
Module 10: Emerging Migration Practices and Continuous Data Quality
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Exploring automated migration assessment, intelligent mapping, AI-assisted cleansing, anomaly detection, and automated validation capabilities in modern data programs.
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Applying DataOps, observability, automation, continuous testing, and pipeline monitoring practices to improve migration reliability and operational visibility.
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Developing post-migration quality monitoring processes that detect data degradation, synchronization issues, unexpected changes, and emerging quality problems.
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Creating a sustainable migration and data quality roadmap that supports future modernization projects, platform changes, integration initiatives, and evolving business 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.
