Training course
Overview
Cloud
Data Analytics for Professionals is a comprehensive professional training
course designed to strengthen the practical and technical capabilities of
professionals responsible for analyzing, integrating, managing, and delivering
insights from data in modern cloud environments. The course provides a
structured understanding of cloud analytics architectures, data ingestion,
cloud storage, data transformation, analytical modeling, SQL, Python,
visualization, governance, security, and operational practices. Participants
learn how cloud-based analytics platforms can be applied to real-world
organizational requirements while developing professional approaches to data
accuracy, scalability, reliability, and business value.
The
course follows the professional cloud data analytics lifecycle from
understanding business and analytical requirements through data acquisition,
processing, storage, analysis, visualization, and reporting. Participants work
with structured and semi-structured data from databases, files, APIs, and
operational systems while applying source-to-target mapping, data profiling,
transformation, validation, and analytical modeling techniques. Practical
exercises and case studies provide experience with SQL, Python, cloud data
warehouses, data lakes, notebooks, business intelligence tools, and
cloud-native data services, helping participants connect technical activities
with professional delivery standards.
Cloud
Data Analytics for Professionals also develops the skills required to operate
cloud analytics solutions effectively within organizational environments.
Participants examine data quality management, metadata, lineage, governance,
identity and access management, encryption, privacy, monitoring, observability,
performance optimization, and cost management. The course introduces practical
use of tools and technologies such as cloud storage, SQL workbenches, Python,
Apache Airflow, Git, Apache Spark, cloud data warehouses, and visualization
platforms, while emphasizing appropriate architecture patterns, documentation,
testing, version control, and operational controls.
By
the end of the course, participants will be able to design, develop, test,
analyze, document, secure, monitor, and improve cloud-based analytics solutions
using professional data engineering and analytics practices. The training
integrates cloud architecture principles, data management practices, analytical
techniques, governance controls, security standards, DataOps concepts, and
performance management approaches to support dependable professional delivery.
A practical capstone enables participants to apply the complete workflow to a
realistic organizational scenario and produce an end-to-end cloud data
analytics solution that addresses business requirements, technical constraints,
data quality, security, performance, and operational needs.
Course
Duration
5
Days (40 Hours)
Target
Participants
This
course is suitable for:
•
Data Analysts and Business Intelligence Professionals seeking practical cloud
analytics capabilities.
•
Data Engineers responsible for developing and supporting cloud-based data
pipelines and analytics platforms.
•
Analytics Engineers working with data transformation, modeling, and cloud
analytical environments.
•
Database Administrators and SQL Developers transitioning from traditional
database environments to cloud platforms.
•
Business Intelligence Developers responsible for analytical reporting,
dashboards, and visualization solutions.
•
Data Scientists who need practical knowledge of cloud data platforms and
analytics workflows.
•
Cloud and IT Professionals involved in data migration, integration, analytics,
and digital transformation projects.
•
Data Governance and Data Management Professionals responsible for cloud data
quality, metadata, and controls.
•
Technical Specialists and Team Leads supporting cloud data analytics delivery
and operational activities.
•
Professionals seeking to develop or strengthen their capabilities in
professional cloud data analytics roles.
Course
Objectives
By
the end of the training, participants will be able to:
•
Explain the role of cloud computing and cloud analytics within modern
professional data environments.
•
Identify major cloud analytics architecture components, services, workloads,
and deployment approaches.
•
Assess business requirements and translate them into practical cloud data
analytics solutions.
•
Work with cloud storage, databases, data warehouses, data lakes, and lakehouse
environments.
•
Extract and integrate data from databases, files, APIs, applications, and other
operational sources.
•
Use SQL and Python to query, transform, cleanse, validate, and analyze
cloud-based data.
•
Develop practical ETL and ELT workflows using cloud-native data integration
approaches.
•
Apply data profiling, quality validation, reconciliation, and
exception-management techniques.
•
Design analytical data models, dimensional structures, data marts, and reusable
analytical datasets.
•
Build professional dashboards, reports, KPIs, and data visualizations that
communicate actionable insights.
•
Apply cloud data governance, metadata management, lineage, cataloging, and
stewardship practices.
•
Implement professional cloud data security practices including identity,
access, encryption, and privacy controls.
•
Apply monitoring, logging, observability, testing, and operational support
practices to cloud analytics workflows.
•
Optimize cloud analytics workloads for query performance, scalability,
reliability, and resource efficiency.
•
Apply cloud cost-management and FinOps principles to analytics workloads and
resources.
•
Use Git, documentation, testing, and deployment practices to support
professional analytics development.
•
Understand DataOps and CI/CD practices for reliable and repeatable cloud
analytics delivery.
•
Evaluate common cloud analytics architectures and services according to
business and technical requirements.
•
Develop and present an end-to-end professional cloud data analytics solution
through a practical capstone.
Course
Content
Day
1: Professional Cloud Data Analytics Foundations, Requirements, and
Architecture
Module:
Establishing Professional Cloud Data Analytics Capabilities
Topics
- Introduction
to Cloud Data Analytics and the Professional Analytics Lifecycle
- Cloud
Computing Fundamentals, Service Models, Deployment Models, and Shared
Responsibility
- Professional
Cloud Analytics Requirements: Business Objectives, Users, Data Needs, and
Success Measures
- Cloud Data
Architecture Components, Data Flows, Analytical Workloads, and Integration
Patterns
- Cloud
Storage, Databases, Data Warehouses, Data Lakes, and Lakehouse
Environments
- Comparing
AWS, Microsoft Azure, and Google Cloud Analytics Capabilities and Services
- Assessing
Data Sources, Data Structures, Metadata, Dependencies, and Source-System
Constraints
- Professional
Data Profiling, Source Assessment, Data Mapping, and Analytical
Requirements Documentation
- Case Study:
Assessing Cloud Analytics Requirements for a Multi-Department Organization
- Practical
Exercise: Developing a Professional Cloud Analytics Architecture and
Initial Solution Design
Day
2: Cloud Data Ingestion, Transformation, Quality, and Pipeline Development
Module:
Developing Professional Cloud Data Integration and Processing Workflows
Topics
- Cloud Data
Ingestion Patterns for Batch, Incremental, Scheduled, and Near-Real-Time
Processing
- Extracting
Data from Relational Databases, Files, APIs, Applications, and External
Sources
- Cloud Object
Storage, Staging Areas, Data Lake Zones, and Data Organization Practices
- SQL for
Professional Cloud Analytics: Joins, Aggregations, CTEs, Window Functions,
and Analytical Queries
- Python for
Cloud Data Processing, Automation, APIs, and Reusable Data Workflows
- ETL and ELT
Transformation Patterns, Business Rules, Standardization, and Data
Enrichment
- Data
Cleansing, Duplicate Management, Missing Values, Invalid Records, and
Format Standardization
- Data Quality
Validation, Reconciliation, Exception Handling, and Practical Quality
Controls
- Workflow
Orchestration, Scheduling, Dependencies, Monitoring, and Pipeline
Automation with Tools such as Apache Airflow
- Practical
Exercise: Building, Testing, and Documenting a Professional Cloud Data
Pipeline
Day
3: Analytical Data Modeling, SQL Analytics, Visualization, and Business
Intelligence
Module:
Delivering Professional Cloud-Based Analytical Insights
Topics
- Analytical
Data Modeling, Dimensional Modeling, Facts, Dimensions, and Star Schema
Design
- Cloud Data
Warehouse Structures, Data Marts, Semantic Layers, and Reusable Analytical
Datasets
- Advanced SQL
for Professional Analytics: Time Series, Ranking, Segmentation, and
Complex Business Metrics
- Data
Exploration and Analysis Using Cloud Notebooks, SQL Workbenches, and
Python
- Statistical
Analysis, Trend Analysis, Correlation, Distribution, and Business Data
Interpretation
- Business
Intelligence Platforms, Dashboard Development, Reporting, and Self-Service
Analytics
- Professional
Data Visualization, KPI Development, Interactive Reports, and Analytical
Storytelling
- Data
Preparation for Forecasting, Predictive Analytics, and Machine Learning
Workloads
- Case Study:
Converting Cloud-Based Operational Data into an Executive Business
Intelligence Solution
- Practical
Exercise: Developing an Analytical Model, Performing Analysis, and
Building a Professional Cloud Dashboard
Day
4: Governance, Security, Performance, Monitoring, and Professional Operations
Module:
Managing Secure, Reliable, and High-Quality Cloud Analytics Environments
Topics
- Professional
Cloud Data Governance, Policies, Standards, Roles, Stewardship, and
Accountability
- Metadata
Management, Data Catalogs, Data Lineage, Classification, and Data
Discoverability
- Data Quality
Management Frameworks, Validation Rules, Quality Metrics, and Continuous
Quality Improvement
- Cloud
Identity and Access Management, Authentication, Authorization, Least
Privilege, and Role Management
- Encryption,
Key Management, Privacy, Sensitive Data Protection, and Compliance
Requirements
- Cloud
Analytics Performance Optimization, Query Tuning, Partitioning, Caching,
and Workload Management
- Monitoring,
Logging, Observability, Alerts, Service-Level Indicators, and Operational
Dashboards
- ETL and
Analytics Testing: Unit Testing, Integration Testing, Regression Testing,
and Data Reconciliation
- Cloud Cost
Management and FinOps: Budgeting, Resource Monitoring, Forecasting, and
Optimization
- Real-World
Scenario: Troubleshooting a Cloud Analytics Environment with Data Quality,
Security, Performance, and Cost Issues
Day
5: Advanced Professional Practices, DataOps, Modernization, and Capstone
Module:
Applying Professional Standards to Enterprise Cloud Data Analytics
Topics
- Advanced
Cloud Analytics Architecture Patterns and Enterprise Data Platform
Practices
- DataOps
Principles, Collaboration, Automation, Continuous Integration, and
Continuous Analytics Delivery
- Git-Based
Version Control, CI/CD, Automated Testing, Release Management, and
Deployment Practices
- Apache Spark
and Distributed Processing for Large-Scale Cloud Data Analytics
- Advanced
Incremental Processing, Change Data Capture, Schema Evolution, and Data
Integration
- Cloud
Analytics Reliability, Backup, Disaster Recovery, Business Continuity, and
Operational Readiness
- Cloud Data
Warehouse and Legacy Analytics Modernization, Migration, and Platform
Improvement
- Professional
Documentation, Data Lineage, Technical Standards, Runbooks, and Knowledge
Management
- Capstone
Exercise: Designing, Building, Testing, Securing, Monitoring, and
Documenting an End-to-End Cloud Analytics Solution
- Capstone
Presentation, Technical Review, Business Requirements Assessment, Lessons
Learned, and Professional Improvement Roadmap