Cloud Data Analytics

Training course

Overview

Cloud Data Analytics is a comprehensive professional training course designed to equip participants with the knowledge and practical skills required to collect, store, process, analyze, visualize, and govern data using modern cloud-based analytics platforms. The course explores how cloud computing enables scalable, flexible, and cost-effective analytics environments while introducing participants to core concepts such as cloud data architecture, data lakes, data warehouses, lakehouses, cloud storage, distributed processing, analytics services, and business intelligence. Participants develop an understanding of how cloud analytics capabilities can support operational reporting, advanced analytics, forecasting, decision-making, and enterprise data strategies.

The course provides practical coverage of the complete cloud analytics lifecycle, beginning with cloud data sources and ingestion before progressing through storage, transformation, data modeling, querying, analysis, visualization, and reporting. Participants learn how to work with structured, semi-structured, and unstructured data while applying SQL, Python, cloud-native data services, and modern data engineering techniques. Practical exercises and case studies demonstrate how organizations can build scalable analytical environments using platforms and technologies such as Amazon Web Services, Microsoft Azure, Google Cloud, cloud data warehouses, object storage, distributed processing frameworks, and business intelligence tools.

Cloud Data Analytics also develops the ability to design secure, reliable, performant, and cost-conscious cloud analytics solutions. Participants explore cloud data governance, identity and access management, encryption, privacy, metadata, data quality, lineage, monitoring, observability, performance optimization, and FinOps principles. The training introduces practical analytics workflows using SQL, Python, notebooks, dashboards, machine learning concepts, and automated data pipelines while emphasizing recognized cloud architecture and data management practices. Real-world scenarios enable participants to evaluate architectural decisions and select appropriate cloud analytics services according to business, technical, security, and financial requirements.

By the end of the course, participants will be able to design, implement, analyze, optimize, secure, and manage practical cloud-based analytics solutions aligned with organizational objectives. The training integrates cloud architecture principles, data engineering practices, analytics techniques, governance frameworks, security controls, and operational best practices to support sustainable enterprise analytics environments. A practical capstone enables participants to combine cloud storage, data ingestion, transformation, analytical modeling, visualization, governance, and optimization techniques into an end-to-end cloud analytics solution based on a realistic business scenario.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Data Analysts and Business Intelligence Professionals working with cloud-based analytics platforms.

• Data Engineers responsible for building cloud data pipelines, warehouses, lakes, and analytics environments.

• Cloud Engineers and Solutions Architects involved in designing cloud data and analytics architectures.

• Database Administrators and SQL Developers transitioning to cloud-based data platforms.

• Business Intelligence Developers responsible for analytical models, dashboards, and reporting solutions.

• Data Scientists who need to understand cloud data platforms and scalable analytics environments.

• IT Professionals involved in cloud migration, data modernization, analytics, and digital transformation.

• Data Governance and Data Management Professionals responsible for cloud data quality, security, and compliance.

• Managers, supervisors, and technical leads responsible for cloud analytics projects and data initiatives.

• Professionals transitioning into cloud data analytics and modern data platform roles.

Course Objectives

By the end of the training, participants will be able to:

• Explain the foundations of cloud computing and its role in modern data analytics.

• Describe cloud data architectures including data warehouses, data lakes, lakehouses, and modern analytical platforms.

• Identify appropriate cloud storage, database, ingestion, processing, and analytics services for different business requirements.

• Design practical cloud data ingestion and integration workflows for structured and semi-structured data.

• Use SQL and Python to query, transform, analyze, and prepare data within cloud environments.

• Develop analytical data models suitable for reporting, business intelligence, and advanced analytics.

• Implement cloud-based data pipelines using appropriate orchestration and automation techniques.

• Apply data quality, metadata, lineage, cataloging, and governance practices to cloud analytics environments.

• Implement cloud security practices including identity and access management, encryption, network controls, and data protection.

• Optimize cloud analytics workloads for performance, scalability, reliability, and cost efficiency.

• Apply monitoring, logging, observability, and operational management techniques to cloud data platforms.

• Use cloud-based notebooks, analytical tools, visualization platforms, and business intelligence technologies.

• Develop dashboards and analytical reports that communicate meaningful business insights.

• Understand the application of machine learning and advanced analytics within cloud data environments.

• Apply cloud architecture and data management best practices to real-world analytics scenarios.

• Evaluate cloud analytics platforms and services according to business, technical, security, and financial requirements.

• Apply FinOps principles to monitor, manage, forecast, and optimize cloud analytics expenditure.

• Design practical cloud analytics solutions that support scalability, governance, resilience, and continuous improvement.

• Complete an end-to-end cloud data analytics capstone using realistic business data and analytical requirements.

Course Content

Day 1: Cloud Data Analytics Foundations, Architecture, and Data Platforms

Module: Establishing Cloud Data Analytics Foundations and Architecture

Topics

  1. Introduction to Cloud Data Analytics and the Modern Cloud Data Ecosystem
  2. Cloud Computing Fundamentals, Service Models, Deployment Models, and Shared Responsibility
  3. Cloud Data Architecture Principles, Components, Layers, and Analytical Workloads
  4. Cloud Object Storage, Databases, Data Warehouses, Data Lakes, and Lakehouse Architectures
  5. Comparing AWS, Microsoft Azure, and Google Cloud Data Analytics Services
  6. Structured, Semi-Structured, and Unstructured Data in Cloud Analytics Environments
  7. Cloud Data Sources, Data Ingestion Patterns, APIs, Files, Databases, and Application Data
  8. Cloud Data Architecture Frameworks, Best Practices, and Enterprise Design Principles
  9. Case Study: Designing a Cloud Analytics Architecture for a Multi-Source Enterprise Environment
  10. Practical Exercise: Creating a Cloud Data Architecture and Selecting Appropriate Cloud Analytics Services

Day 2: Cloud Data Ingestion, Storage, Transformation, and Data Engineering

Module: Building Practical Cloud Data Pipelines and Analytical Data Stores

Topics

  1. Cloud Data Ingestion Strategies for Batch, Incremental, Streaming, and Event-Driven Processing
  2. Loading Data into Cloud Object Storage, Data Lakes, and Cloud Data Warehouses
  3. SQL for Cloud Analytics: Queries, Joins, Aggregations, Window Functions, and Common Table Expressions
  4. Python for Cloud Data Processing, Automation, APIs, and Analytical Workflows
  5. Data Transformation, Cleansing, Standardization, Validation, and Enrichment in Cloud Environments
  6. ETL and ELT Patterns for Cloud Analytics and Modern Data Engineering
  7. Cloud Data Pipeline Orchestration, Scheduling, Dependencies, Monitoring, and Automation
  8. Incremental Processing, Change Data Capture, Partitioning, and Efficient Data Loading
  9. Practical Data Quality Management, Reconciliation, Error Handling, and Exception Processing
  10. Exercise: Building a Cloud Data Pipeline from Source Ingestion through Transformation and Analytical Storage

Day 3: Cloud Data Modeling, Analytics, Visualization, and Business Intelligence

Module: Developing Analytical Models and Business Insights in the Cloud

Topics

  1. Analytical Data Modeling, Dimensional Modeling, Facts, Dimensions, and Star Schemas
  2. Cloud Data Warehouse Design, Semantic Layers, Data Marts, and Analytical Structures
  3. Advanced SQL Analytics for Aggregation, Ranking, Time-Series Analysis, and Business Metrics
  4. Cloud-Based Data Exploration Using Notebooks, SQL Workbenches, and Analytical Tools
  5. Exploratory Data Analysis, Statistical Techniques, Trend Analysis, and Pattern Identification
  6. Business Intelligence Integration, Dashboard Design, Reporting, and Self-Service Analytics
  7. Data Visualization Principles, KPI Design, Interactive Dashboards, and Analytical Storytelling
  8. Cloud Analytics for Forecasting, Predictive Analysis, and Machine Learning Workloads
  9. Case Study: Developing an Executive Analytics Solution from Cloud Data to Business Dashboard
  10. Practical Exercise: Building an Analytical Data Model, Performing Analysis, and Creating a Cloud-Based Dashboard

Day 4: Cloud Data Governance, Security, Performance, and Cost Optimization

Module: Managing Secure, Reliable, Governed, and Cost-Efficient Cloud Analytics

Topics

  1. Cloud Data Governance Frameworks, Policies, Ownership, Stewardship, and Accountability
  2. Metadata Management, Data Cataloging, Data Lineage, Classification, and Discoverability
  3. Cloud Identity and Access Management, Roles, Permissions, Authentication, and Authorization
  4. Encryption, Key Management, Network Security, Privacy, and Sensitive Data Protection
  5. Data Quality Monitoring, Validation Controls, Completeness, Accuracy, Consistency, and Timeliness
  6. Cloud Analytics Performance Optimization, Query Tuning, Indexing, Partitioning, and Caching
  7. Scalability, Elastic Compute, Workload Management, Resource Allocation, and Reliability
  8. Monitoring, Logging, Observability, Alerts, Service Levels, and Operational Support
  9. FinOps for Cloud Analytics: Cost Drivers, Budgets, Forecasting, Resource Optimization, and Cost Controls
  10. Real-World Scenario: Diagnosing Performance, Security, Governance, and Cost Issues in a Cloud Analytics Environment

Day 5: Advanced Cloud Analytics, Modernization, DataOps, and Capstone

Module: Designing Advanced and Enterprise-Ready Cloud Analytics Solutions

Topics

  1. Advanced Cloud Analytics Architecture Patterns for Enterprise Data and Decision-Making
  2. Modern Data Lakehouse Concepts, Unified Analytics, and Open Data Architecture Principles
  3. Distributed Cloud Processing with Apache Spark and Large-Scale Analytical Workloads
  4. Real-Time and Near-Real-Time Cloud Analytics Using Streaming and Event-Driven Architectures
  5. Advanced DataOps, CI/CD, Automated Testing, Deployment, and Analytics Lifecycle Management
  6. Cloud Analytics Reliability, Disaster Recovery, Business Continuity, and Resilience Engineering
  7. Cloud Migration and Modernization Strategies for Legacy Data Warehouses and Analytics Platforms
  8. Advanced Governance, Security, Compliance, Data Contracts, and Enterprise Analytics Controls
  9. Capstone Exercise: Designing and Implementing an End-to-End Cloud Data Analytics Solution
  10. Capstone Presentation, Solution Evaluation, Performance Review, Lessons Learned, and Continuous Improvement Planning

 

Course Schedules:

Dates Fees Location Apply