Cloud-Native Data Engineering and Analytics Platforms Training Course

Course Introduction

Cloud-native data engineering is transforming how organizations build, operate, and scale modern analytics platforms. Cloud-Native Data Engineering and Analytics Platforms Training Course provides an advanced exploration of architectures, engineering practices, platform technologies, and operational models required to deliver scalable, resilient, automated, and cost-efficient data capabilities across public cloud, hybrid, and multi-cloud environments.

This course examines the complete lifecycle of cloud-native data engineering, from ingestion and integration through transformation, storage, orchestration, analytics serving, governance, observability, and optimization. Participants explore modern architectures involving cloud data warehouses, data lakes, lakehouses, distributed processing engines, streaming platforms, analytical databases, object storage, metadata services, and cloud-native data services designed for enterprise-scale workloads.

A major focus is the engineering of highly automated and reliable data pipelines. Participants learn how to design batch, streaming, event-driven, and incremental processing architectures while applying infrastructure-as-code, CI/CD, DataOps, automated testing, data quality controls, workflow orchestration, and observability. The course emphasizes reusable engineering patterns that enable organizations to deliver data products and analytics services rapidly without compromising reliability or governance.

The programme also addresses the architecture of modern analytics platforms and their integration with business intelligence, advanced analytics, machine learning, artificial intelligence, and real-time decision systems. Participants explore semantic layers, analytical data models, feature engineering, streaming analytics, data APIs, self-service analytics, and governed data products while examining how cloud-native platforms can support diverse analytical workloads from a common architectural foundation.

Security, governance, privacy, and financial management are treated as core components of cloud-native data engineering rather than secondary controls. Participants examine identity and access management, encryption, data classification, policy automation, lineage, compliance, workload isolation, data protection, cost allocation, FinOps, and resource optimization. These practices help organizations establish secure and accountable analytical environments while maintaining performance and operational agility.

By the end of the course, participants will be able to design and implement enterprise cloud-native data engineering and analytics platforms aligned with business requirements, technical constraints, and future technology evolution. They will develop practical approaches for platform modernization, workload migration, automation, reliability engineering, performance optimization, and continuous improvement across cloud-based data ecosystems.

Duration

10 days

Who Should Attend

• Cloud data architects responsible for designing enterprise-scale cloud-native data platforms.

• Data engineers building ingestion, transformation, processing, and analytical data pipelines.

• Analytics engineers developing scalable analytical models and governed data transformation workflows.

• Cloud architects designing modern data and analytics environments across public, hybrid, and multi-cloud platforms.

• Data platform engineers responsible for shared data infrastructure, automation, reliability, and developer enablement.

• Business intelligence architects designing enterprise reporting, analytics, and self-service data platforms.

• DataOps and DevOps professionals supporting automated development, testing, deployment, and operational management.

• Data governance and security professionals responsible for protecting and controlling enterprise cloud data assets.

• Data quality and observability specialists responsible for improving trust, reliability, freshness, and operational performance.

• Machine learning and AI engineers integrating analytical data platforms with intelligent applications and model development.

• Technology managers leading cloud migration, data modernization, and analytics transformation initiatives.

• Enterprise architects defining target-state cloud data architectures and technology standards.

• Database and data warehouse professionals modernizing legacy analytical platforms for cloud environments.

• Data product owners and domain teams developing reusable, governed, and discoverable data products.

• Consultants and technical leaders responsible for implementing cloud-native data engineering and analytics strategies.

Course Objectives

• Explain the foundational principles, architectural patterns, and engineering practices required to build enterprise-grade cloud-native data platforms.

• Design scalable cloud-native architectures supporting data ingestion, transformation, storage, analytics, streaming, governance, and AI-enabled workloads.

• Engineer reliable batch, incremental, streaming, and event-driven data pipelines using modern cloud-native technologies and automation practices.

• Apply cloud-native storage and processing patterns across object stores, analytical databases, warehouses, lakehouses, and distributed processing platforms.

• Implement DataOps, DevOps, CI/CD, infrastructure-as-code, automated testing, and deployment practices for reliable data engineering operations.

• Design modern analytical architectures that support business intelligence, advanced analytics, real-time intelligence, machine learning, and AI applications.

• Establish data quality, observability, lineage, metadata, and reliability engineering capabilities across cloud-native data environments.

• Apply cloud security, privacy, identity, access management, encryption, governance, and compliance practices to enterprise data platforms.

• Optimize data platform performance through workload management, partitioning, caching, distributed processing, storage optimization, and query engineering.

• Apply FinOps principles to control cloud data platform expenditure through resource optimization, workload governance, capacity management, and cost visibility.

• Design hybrid and multi-cloud data architectures that address interoperability, portability, resilience, data sovereignty, and operational complexity.

• Develop enterprise implementation roadmaps for modernizing legacy data environments into automated, scalable, secure, and continuously optimized cloud-native platforms.

Comprehensive Course Outline

Module 1: Foundations of Cloud-Native Data Engineering

• Core principles of cloud-native architecture and their application to modern enterprise data engineering environments.

• Evolution from traditional data platforms toward elastic, automated, distributed, service-oriented data engineering ecosystems.

• Cloud-native characteristics including scalability, resilience, automation, observability, portability, and infrastructure abstraction.

• Enterprise considerations for selecting cloud-native data engineering patterns based on workload, business, security, and operational requirements.

Module 2: Cloud-Native Data Platform Architecture

• Designing reference architectures for enterprise data platforms spanning ingestion, processing, storage, analytics, governance, and consumption.

• Architectural patterns for integrating managed cloud services with open-source technologies and enterprise data platforms.

• Designing modular data platforms that support independent scaling, technology evolution, workload isolation, and reusable engineering capabilities.

• Evaluating architectural trade-offs involving performance, reliability, portability, complexity, operational responsibility, and platform cost.

Module 3: Cloud Data Storage and Lakehouse Architecture

• Designing cloud object storage architectures for structured, semi-structured, unstructured, and analytical data at enterprise scale.

• Applying data lake and lakehouse patterns for integrating flexible storage with transactional, analytical, and governed data processing.

• Working with open data formats, table architectures, partitioning, metadata, schema evolution, and storage lifecycle management.

• Designing storage architectures for performance, durability, accessibility, governance, interoperability, and long-term analytical value.

Module 4: Cloud-Native Data Ingestion and Integration

• Designing scalable ingestion architectures for databases, applications, SaaS platforms, APIs, files, events, and enterprise operational systems.

• Applying batch ingestion, incremental loading, change data capture, replication, and event-driven integration patterns.

• Engineering ingestion frameworks for schema validation, data contracts, metadata capture, error handling, retries, and operational resilience.

• Integrating heterogeneous enterprise sources into standardized cloud-native data pipelines and reusable ingestion services.

Module 5: Advanced Data Transformation and Processing

• Designing transformation architectures for batch, incremental, micro-batch, streaming, and distributed data processing workloads.

• Applying SQL-based transformation, distributed processing, ELT, data modeling, enrichment, aggregation, and complex analytical transformations.

• Engineering reusable transformation components with dependency management, testing, version control, and automated deployment.

• Optimizing distributed data processing through partitioning, parallelism, resource management, caching, and workload-aware execution strategies.

Module 6: Data Pipeline Orchestration and Workflow Engineering

• Designing enterprise workflow orchestration architectures for dependency management, scheduling, retries, monitoring, and pipeline execution.

• Applying event-driven orchestration and workflow automation to support complex analytical and data engineering processes.

• Managing pipeline dependencies, parameterization, reusable components, backfills, recovery procedures, and operational controls.

• Integrating orchestration platforms with CI/CD, data quality systems, observability services, metadata platforms, and cloud infrastructure.

Module 7: Analytics Engineering and Modern Data Modeling

• Applying analytical data modeling techniques for cloud warehouses, lakehouses, semantic layers, and enterprise reporting environments.

• Designing dimensional models, analytical marts, wide tables, normalized structures, and purpose-built data models for diverse workloads.

• Establishing transformation standards that improve analytical consistency, maintainability, reuse, and business understanding.

• Integrating analytics engineering practices with version control, testing, documentation, lineage, deployment automation, and data product development.

Module 8: Streaming and Real-Time Data Engineering

• Designing cloud-native streaming architectures for continuous ingestion, real-time processing, event enrichment, and analytical consumption.

• Applying event-driven patterns, stream processing, message brokers, event stores, and real-time analytical platforms.

• Engineering low-latency pipelines for operational intelligence, monitoring, fraud detection, personalization, and real-time decision systems.

• Managing event ordering, replay, delivery guarantees, schema evolution, state management, fault tolerance, and streaming observability.

Module 9: Data Quality, Testing and Reliability Engineering

• Establishing automated data quality frameworks covering accuracy, completeness, consistency, uniqueness, validity, freshness, and reconciliation.

• Designing data testing strategies across ingestion, transformation, storage, analytical models, data products, and consumption layers.

• Implementing quality gates, anomaly detection, validation rules, automated reconciliation, and continuous data reliability monitoring.

• Integrating data reliability engineering with DataOps workflows, incident management, service levels, and continuous platform improvement.

Module 10: Metadata, Lineage and Data Observability

• Designing metadata architectures that capture technical, business, operational, semantic, and governance information across cloud data platforms.

• Implementing automated data lineage to understand dependencies, transformations, impacts, ownership, and downstream analytical relationships.

• Engineering observability for pipeline health, data freshness, schema changes, volume anomalies, infrastructure performance, and service reliability.

• Integrating catalogues, lineage systems, telemetry, alerts, and operational intelligence into a unified data platform observability strategy.

Module 11: Cloud Data Security, Governance and Privacy

• Designing identity, authentication, authorization, encryption, secrets management, and network security for enterprise cloud data platforms.

• Implementing data classification, masking, tokenization, retention, access policies, and privacy controls across distributed data environments.

• Applying policy-as-code and automated governance practices to enforce organizational, regulatory, and security requirements consistently.

• Establishing auditability, lineage, compliance monitoring, risk controls, and governance operating models for cloud-native analytics.

Module 12: Data Platform DevOps, DataOps and Infrastructure Automation

• Applying infrastructure-as-code to automate provisioning, configuration, environment management, and repeatable cloud data platform deployments.

• Designing CI/CD pipelines for data infrastructure, transformation code, analytical models, quality controls, and platform components.

• Integrating automated testing, version control, release management, rollback procedures, and environment promotion into data engineering workflows.

• Establishing DataOps operating models that improve collaboration, deployment frequency, reliability, governance, and engineering productivity.

Module 13: Cloud-Native Analytics and Self-Service Platforms

• Designing governed analytics platforms that enable business users, analysts, engineers, and data scientists to access trusted enterprise information.

• Architecting semantic layers, analytical services, dashboards, notebooks, query platforms, and data APIs for diverse analytical consumers.

• Establishing self-service data access while maintaining security, governance, quality, metadata, and organizational control.

• Integrating business intelligence, advanced analytics, machine learning, and AI workloads with shared cloud-native data foundations.

Module 14: Performance Engineering, Scalability and FinOps

• Optimizing cloud data workloads through query tuning, partitioning, clustering, caching, workload management, and distributed execution strategies.

• Designing elastic architectures that automatically scale data processing and analytical resources according to workload demand.

• Applying FinOps practices for cost allocation, usage monitoring, resource governance, capacity planning, and cloud expenditure optimization.

• Balancing performance, reliability, scalability, operational simplicity, and financial efficiency when engineering cloud-native data platforms.

Module 15: Hybrid, Multi-Cloud and AI-Ready Data Platforms

• Designing hybrid and multi-cloud data architectures supporting interoperability, workload mobility, resilience, data sovereignty, and enterprise continuity.

• Integrating cloud-native data platforms with machine learning, generative AI, vector search, semantic systems, and intelligent applications.

• Architecting shared data services that support analytical workloads alongside emerging AI and real-time processing requirements.

• Evaluating emerging cloud data technologies, open architectures, platform abstractions, and interoperability patterns for future-ready environments.

Module 16: Enterprise Cloud Data Strategy and Implementation

• Developing enterprise strategies for modernizing legacy data platforms and transitioning toward scalable cloud-native engineering architectures.

• Creating target-state architectures, capability models, migration roadmaps, implementation priorities, and platform operating models.

• Establishing organizational responsibilities across data engineering, platform engineering, analytics, governance, security, architecture, and business teams.

• Designing continuous optimization frameworks for platform reliability, technology evolution, performance, cost management, security, and enterprise data value.

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.