Course Introduction
Data engineering provides the foundation for reliable analytics, business intelligence, and machine learning by transforming raw, diverse, and rapidly changing data into trusted and usable data products. This course provides a practical understanding of modern data engineering practices for building scalable pipelines and platforms that support analytical and machine learning workloads.
The Data Engineering for Analytics and Machine Learning Training Course explores the complete data lifecycle, from ingestion and storage to transformation, orchestration, quality management, serving, and operational monitoring. Participants learn how engineering decisions affect analytical accuracy, machine learning performance, scalability, cost, and the reliability of downstream data consumers.
The course covers modern architectures including cloud data warehouses, data lakes, lakehouses, batch processing, streaming pipelines, and distributed data processing environments. It also addresses how structured and unstructured data can be integrated and prepared for analytics and machine learning while maintaining consistency, traceability, security, and operational resilience.
Participants will examine practical approaches for developing robust ETL and ELT pipelines, implementing reusable transformations, managing dependencies, and automating data workflows. The course also introduces engineering practices that improve data quality, observability, testing, documentation, lineage, and governance across increasingly complex analytical and machine learning ecosystems.
Special attention is given to machine learning data requirements, including feature engineering, training datasets, feature pipelines, data versioning, model-ready data products, and the prevention of training-serving inconsistencies. Participants will understand how data engineering teams can collaborate effectively with data scientists, analysts, ML engineers, and platform teams.
By completing this course, participants will be better prepared to design, implement, evaluate, and improve modern data engineering solutions that serve both analytics and machine learning use cases. The course emphasizes practical architecture decisions, automation, scalability, reliability, and emerging engineering practices relevant to modern data-driven organizations.
Duration
5 days
Who Should Attend
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Data engineers responsible for designing and maintaining analytical and machine learning data pipelines.
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Analytics engineers developing trusted datasets and transformation workflows for business intelligence.
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Data analysts seeking a stronger understanding of data pipeline architecture and engineering practices.
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Data scientists who need reliable, scalable, and well-structured data for machine learning projects.
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Machine learning engineers working with feature pipelines, training datasets, and production data workflows.
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Database professionals transitioning toward modern cloud-based data engineering and analytical platforms.
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BI developers responsible for preparing and serving data for reporting and analytical applications.
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Cloud engineers supporting data platforms, distributed processing, and data infrastructure environments.
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Software engineers involved in building data-intensive applications, ingestion services, or processing pipelines.
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Data architects designing modern data platforms for analytics, artificial intelligence, and machine learning.
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Technical leads and engineering managers overseeing data platform development and operational practices.
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Professionals responsible for data quality, governance, lineage, observability, and reliable data delivery.
Course Objectives
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Develop a comprehensive understanding of modern data engineering principles and how they support scalable analytics and machine learning workloads across enterprise environments.
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Design robust end-to-end data pipelines that integrate diverse sources, process large datasets, and deliver trusted information to analytical and machine learning consumers.
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Apply appropriate ETL and ELT strategies for transforming raw data into reusable, governed, and high-quality datasets suitable for reporting and advanced analytics.
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Evaluate data storage architectures including warehouses, data lakes, lakehouses, and distributed storage systems according to workload, scalability, performance, and cost requirements.
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Implement batch and streaming data processing approaches that support both traditional analytical workloads and near-real-time machine learning and decision-making use cases.
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Apply orchestration, dependency management, scheduling, and automation techniques to create reliable, maintainable, and observable production-grade data workflows.
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Establish effective data quality, testing, validation, lineage, and observability practices that identify data problems early and improve confidence in analytical and machine learning outputs.
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Prepare high-quality machine learning datasets through feature engineering, data versioning, feature pipelines, and reproducible data preparation processes that support model development and deployment.
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Integrate security, governance, metadata management, access controls, and responsible data practices into engineering workflows without unnecessarily limiting data accessibility or productivity.
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Assess emerging data engineering technologies and architectural patterns to design scalable, resilient, cost-aware, and future-ready data platforms for analytics and artificial intelligence.
Comprehensive Course Outline
Module 1: Foundations of Data Engineering for Analytics and Machine Learning
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Understanding the role of data engineering in modern analytics, artificial intelligence, and machine learning ecosystems.
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Examining end-to-end data lifecycles from source systems and ingestion through transformation, storage, serving, and consumption.
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Comparing analytical, operational, and machine learning data requirements across different organizational and technical use cases.
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Identifying key architectural principles for building scalable, reliable, maintainable, and reusable data engineering platforms.
Module 2: Data Ingestion and Integration Engineering
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Designing batch and real-time ingestion pipelines for databases, applications, APIs, files, event streams, and external data sources.
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Applying incremental loading, change data capture, replication, and synchronization techniques for continuously changing enterprise datasets.
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Managing schema evolution, data contracts, source variability, and compatibility challenges across complex ingestion environments.
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Evaluating ingestion architectures based on latency, throughput, reliability, scalability, security, and downstream analytical requirements.
Module 3: Data Storage and Modern Data Platform Architecture
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Comparing cloud data warehouses, data lakes, lakehouses, object storage, and distributed data platforms for analytical workloads.
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Designing storage structures that support scalable querying, efficient processing, data sharing, lifecycle management, and machine learning consumption.
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Applying partitioning, clustering, compression, file formats, indexing, and storage optimization techniques for large-scale analytical datasets.
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Exploring modern platform patterns that combine structured, semi-structured, unstructured, streaming, and machine learning data assets.
Module 4: Data Transformation and Distributed Processing
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Designing reliable ETL and ELT transformations that convert raw source data into trusted analytical and machine learning datasets.
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Applying distributed processing concepts to efficiently transform large datasets while managing computation, memory, parallelism, and resource utilization.
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Developing reusable transformation logic using modular data engineering practices that improve maintainability and consistency across pipelines.
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Addressing complex transformation requirements involving joins, aggregations, nested structures, slowly changing data, and historical datasets.
Module 5: Workflow Orchestration and Data Pipeline Automation
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Designing automated workflows that coordinate ingestion, transformation, validation, publishing, and downstream data processing activities.
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Managing task dependencies, scheduling, retries, failure handling, backfills, and parameterization in production data pipelines.
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Applying workflow orchestration principles to improve pipeline reliability, operational visibility, maintainability, and deployment consistency.
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Integrating automated data workflows with development, testing, deployment, monitoring, and incident management processes.
Module 6: Data Quality, Testing, and Observability
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Establishing automated data quality checks covering completeness, accuracy, consistency, uniqueness, validity, timeliness, and business-rule compliance.
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Designing data tests that detect schema changes, unexpected values, missing records, pipeline failures, and downstream data integrity issues.
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Implementing data observability practices using freshness, volume, distribution, lineage, and operational health indicators across critical datasets.
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Developing practical approaches for incident detection, root-cause analysis, alerting, remediation, and continuous improvement of data reliability.
Module 7: Data Engineering for Machine Learning and Feature Pipelines
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Understanding the data requirements of machine learning workflows from experimentation and training through validation, deployment, and monitoring.
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Designing feature engineering pipelines that produce consistent, reusable, traceable, and production-ready machine learning features.
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Addressing training-serving skew, feature freshness, data leakage, reproducibility, dataset versioning, and feature lifecycle management.
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Integrating data engineering workflows with machine learning platforms to support repeatable and scalable model development processes.
Module 8: Streaming Data and Real-Time Analytics Engineering
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Understanding event-driven architectures and streaming pipelines for applications requiring real-time analytics, monitoring, personalization, and machine learning.
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Designing streaming ingestion and processing workflows that address ordering, duplication, latency, late-arriving events, and fault tolerance.
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Comparing batch, micro-batch, and continuous processing approaches according to business requirements and operational constraints.
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Exploring real-time feature generation and event processing patterns for machine learning and intelligent data-driven applications.
Module 9: Data Governance, Security, Cost, and Platform Operations
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Applying metadata management, data lineage, data catalogs, access controls, and governance practices across modern engineering environments.
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Designing secure data pipelines that protect sensitive information through authentication, authorization, encryption, masking, and controlled data access.
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Optimizing cloud data engineering environments by managing compute consumption, storage costs, workload scheduling, and resource utilization.
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Establishing operational practices for platform reliability, disaster recovery, scalability, capacity management, and responsible data engineering.
Module 10: Emerging Data Engineering Practices and Implementation Strategy
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Exploring DataOps, data products, data contracts, platform engineering, and other approaches for improving modern data engineering collaboration.
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Examining emerging architectures involving AI-ready data platforms, vector data, unstructured data processing, and intelligent data pipelines.
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Evaluating automation and AI-assisted engineering capabilities for pipeline development, documentation, testing, optimization, and operational support.
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Developing a practical roadmap for implementing scalable data engineering capabilities that support evolving analytics and machine learning 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.
