Introduction

In the rapidly evolving landscape of data and analytics, leveraging the immense scalability, flexibility, and cost-effectiveness of cloud platforms has become essential for building modern, agile Business Intelligence (BI) solutions, enabling organizations to democratize data access, accelerate insight delivery, and foster innovation without the burden of managing complex on-premise infrastructure. Cloud-Based BI with Azure and AWS empowers professionals to design, implement, and manage robust analytical ecosystems, harnessing the extensive suite of services offered by the leading cloud providers to ingest, store, process, and visualize data at enterprise scale. This training course is meticulously designed to equip data engineers, BI developers, data architects, solution architects, cloud professionals, and IT managers with cutting-edge knowledge and practical skills in understanding cloud BI architecture patterns, mastering data warehousing and data lake solutions on both Azure and AWS, leveraging cloud-native ETL/ELT services, implementing advanced analytics and machine learning integrations, designing interactive dashboards, and ensuring data governance and security in a cloud environment. Participants will gain a comprehensive understanding of how to build and optimize powerful, scalable, and cost-efficient cloud-based BI platforms that drive transformative business value.

Duration

10 days

Target Audience

• Data Engineers
• Business Intelligence (BI) Developers
• Data Architects
• Solution Architects
• Cloud Professionals
• IT Managers & Directors
• Database Administrators (DBAs)
• DevOps Engineers (Data Focus)
• Enterprise Architects
• Anyone looking to migrate or build BI solutions in the cloud

Objectives

• Understand the fundamental concepts of cloud computing and its advantages for BI.
• Master the core BI services and architectures on both Microsoft Azure and Amazon Web Services (AWS).
• Learn to design and implement cloud data warehousing and data lake solutions.
• Develop proficiency in using cloud-native ETL/ELT services for data ingestion and transformation.
• Understand data governance, security, and cost management in a cloud BI environment.
• Explore advanced analytics and machine learning integration with cloud BI platforms.
• Develop skills in building scalable and performant cloud-based BI solutions.
• Learn about real-time analytics capabilities in Azure and AWS.
• Understand deployment strategies and operational best practices for cloud BI.
• Formulate strategies for migrating existing BI solutions to the cloud.
• Apply cloud BI principles to solve complex, large-scale data challenges.

Course Content

Module 1. Introduction to Cloud BI and Architecture Patterns

• Defining Cloud Computing: IaaS, PaaS, SaaS in BI context
• Advantages of Cloud BI: Scalability, cost-efficiency, agility, global reach
• Overview of Cloud BI architecture patterns: Data Lake, Data Warehouse, Data Lakehouse
• Shared responsibilities model in the cloud
• Introduction to Azure and AWS BI ecosystems

Module 2. Azure Data Storage for BI

• Azure Data Lake Storage (ADLS) Gen2: Concepts, tiers, access
• Azure Blob Storage: Object storage for unstructured data
• Azure SQL Database: Relational database in the cloud
• Azure Cosmos DB: NoSQL database for flexible data
• When to use which Azure storage service for BI data

Module 3. AWS Data Storage for BI

• Amazon S3 (Simple Storage Service): Object storage for data lakes
• Amazon Redshift: Cloud data warehousing service
• Amazon RDS: Managed relational databases (PostgreSQL, MySQL, SQL Server)
• Amazon DynamoDB: NoSQL database service
• When to use which AWS storage service for BI data

Module 4. Azure Data Warehousing: Azure Synapse Analytics

• Introduction to Azure Synapse Analytics: Unified analytics platform
• Synapse SQL Pool (formerly SQL DW): Dedicated data warehouse
• Synapse Serverless SQL Pool: Ad-hoc querying over data lake
• Integrating Synapse with Azure Data Lake Storage
• Best practices for data warehousing in Azure Synapse

Module 5. AWS Data Warehousing: Amazon Redshift

• Introduction to Amazon Redshift: Columnar, MPP data warehouse
• Redshift Architecture: Leader Node, Compute Nodes
• Redshift Spectrum: Querying data in S3 directly
• Workload Management (WLM) and Concurrency Scaling
• Best practices for data warehousing in Redshift

Module 6. Azure ETL/ELT: Azure Data Factory (ADF)

• Introduction to Azure Data Factory: Cloud ETL/ELT service
• ADF Pipelines, Activities, Datasets, Linked Services
• Data Flow (Mapping Data Flow) for code-free data transformation
• Orchestrating data pipelines for BI
• Monitoring and managing ADF pipelines

Module 7. AWS ETL/ELT: AWS Glue

• Introduction to AWS Glue: Serverless ETL service
• Glue Data Catalog: Central metadata repository
• Glue Crawlers for schema discovery
• Glue ETL Jobs: Apache Spark-based transformations
• Workflow management and scheduling in Glue

Module 8. Azure BI Visualization: Power BI and Azure Services

• Power BI Service: Connecting to Azure data sources
• Power BI DirectQuery vs. Import Mode
• Integrating Power BI with Azure Synapse Analytics
• Building real-time dashboards with Azure Stream Analytics and Power BI
• Security and sharing Power BI content from Azure

Module 9. AWS BI Visualization: Amazon QuickSight and Other Tools

• Introduction to Amazon QuickSight: Serverless BI service
• SPICE (Super-fast Parallel In-memory Calculation Engine)
• Connecting QuickSight to Redshift, S3, RDS
• Building interactive dashboards in QuickSight
• Using Tableau/Looker/Power BI with AWS data sources

Module 10. Real-Time BI in Azure

• Azure Event Hubs: Scalable event ingestion service
• Azure Stream Analytics: Real-time stream processing
• Integrating Stream Analytics with Event Hubs, Cosmos DB, Power BI
• Real-time dashboards for operational intelligence
• Use cases for real-time BI in Azure

Module 11. Real-Time BI in AWS

• Amazon Kinesis: Data streams, Firehose, Analytics
• AWS Lambda: Serverless compute for real-time processing
• Integrating Kinesis with Lambda and Redshift/S3
• Real-time dashboards with Kinesis Analytics and QuickSight
• Use cases for real-time BI in AWS

Module 12. Data Governance and Security in Cloud BI

• Identity and Access Management (IAM): Azure AD, AWS IAM
• Data Encryption: At rest and in transit
• Network Security: Virtual Networks, Security Groups, Network ACLs
• Data Masking and Row-Level Security
• Compliance and auditing in cloud BI environments

Module 13. Cloud BI Cost Management and Optimization

• Understanding Cloud Pricing Models: Pay-as-you-go, reserved instances, spot instances
• Cost Optimization Strategies: Resource sizing, auto-scaling
• Monitoring cloud BI costs
• Cloud cost management tools (Azure Cost Management, AWS Cost Explorer)
• Designing for cost-efficiency in cloud BI architectures

Module 14. Advanced Analytics and ML Integration in Cloud BI

• Azure Machine Learning: Integrating ML models with BI workflows
• AWS SageMaker: Building, training, deploying ML models
• Leveraging pre-built AI services (text analytics, vision API) in BI
• Data science notebooks in Azure Synapse and AWS SageMaker
• Building a complete analytics pipeline with ML integration

Module 15. Migration and Deployment Strategies for Cloud BI

• Lift-and-Shift vs. Cloud-Native Re-architecture
• Data Migration Strategies: Offline vs. Online, incremental
• Deployment Automation: Infrastructure as Code (ARM Templates, CloudFormation)
• Monitoring and troubleshooting cloud BI solutions
• Best practices for operating and evolving cloud BI platforms.

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: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute 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: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.