Training course
Overview
Practical
Cloud Data Analytics is a hands-on professional training course designed to
develop practical skills for collecting, integrating, processing, analyzing,
visualizing, and managing data within modern cloud environments. The course
takes participants through the complete cloud data analytics lifecycle, from
connecting to data sources and building ingestion workflows to transforming
datasets, creating analytical models, developing dashboards, monitoring
pipelines, and delivering actionable business insights. Emphasis is placed on
practical implementation using SQL, Python, cloud-native data services, ETL and
ELT techniques, data warehouses, data lakes, lakehouses, and business
intelligence tools.
The
course provides structured practical exposure to major cloud data analytics
concepts and technologies across platforms such as AWS, Microsoft Azure, and
Google Cloud. Participants work with cloud storage, databases, data warehouses,
data lakes, analytical engines, data integration services, notebooks, APIs, and
visualization platforms while learning how these components work together in
real-world analytics pipelines. Through guided exercises, practical labs, case
studies, and scenario-based activities, participants gain experience designing
and implementing cloud analytics solutions that are scalable, reliable, secure,
and aligned with business requirements.
Strong
emphasis is placed on data preparation, quality, governance, security,
performance, monitoring, automation, and cost management. Participants apply
practical approaches based on established practices and frameworks, including
data quality principles, DAMA-DMBOK concepts, cloud security
shared-responsibility models, NIST Cybersecurity Framework concepts, ISO/IEC
27001 principles, FinOps practices, Git-based version control, CI/CD concepts,
and DataOps approaches. Practical activities include source profiling, SQL
transformations, Python-based processing, pipeline testing, data validation,
access control, performance optimization, cost analysis, monitoring,
troubleshooting, and operational documentation.
By
the end of this 5-day Practical Cloud Data Analytics training course,
participants will be able to build and operate practical cloud analytics
workflows, integrate multiple data sources, transform and validate data,
develop analytical datasets, create business intelligence dashboards, implement
quality and security controls, monitor cloud analytics pipelines, and optimize
performance and cost. The course culminates in a practical capstone project
where participants design and implement an end-to-end cloud data analytics
solution based on a realistic organizational scenario, combining ingestion,
transformation, modeling, analysis, visualization, governance, monitoring, and
operational best practices.
Course
Duration
5
Days (40 Hours)
Target
Participants
This
course is suitable for:
•
Data analysts and business intelligence analysts
•
Data engineers and analytics engineers
•
Cloud engineers and database professionals
•
Business intelligence developers and reporting specialists
•
Data scientists who need practical cloud analytics capabilities
•
Database administrators transitioning to cloud analytics environments
•
IT professionals responsible for cloud-based data platforms
•
Analytics and reporting professionals working with SQL and Python
•
Technical project team members implementing cloud data solutions
•
Professionals seeking practical experience with modern cloud data analytics
tools and workflows
Course
Objectives
By
the end of the training, participants will be able to:
•
Explain the architecture and components of modern cloud data analytics
platforms
•
Connect cloud analytics environments to databases, files, APIs, and other data
sources
•
Configure practical cloud storage and analytical data environments
•
Build data ingestion workflows using ETL and ELT techniques
•
Use SQL and Python to extract, transform, clean, validate, and analyze cloud
data
•
Design analytical datasets using data warehouse, data lake, and lakehouse
principles
•
Build practical data pipelines with scheduling, automation, testing, and error
handling
•
Develop analytical dashboards and visualizations using business intelligence
tools
•
Apply practical data quality, governance, security, and access-control
practices
•
Monitor, troubleshoot, and optimize cloud analytics pipelines and workloads
•
Apply cloud cost-management and FinOps principles to analytics workloads
•
Use Git, CI/CD, DataOps, and automation practices to improve analytics delivery
•
Design and implement an end-to-end cloud data analytics solution through a
practical capstone project
Course
Content
Day
1: Cloud Data Analytics Foundations, Data Sources, Storage, and Initial
Pipeline Development
Module:
Practical Cloud Analytics Environment Setup and Data Ingestion
Topics
- Introduction
to Practical Cloud Data Analytics
- Cloud
analytics lifecycle and practical workflow
- Analytics
architecture components
- Relationship
between data engineering, analytics, and business intelligence
- Common cloud
analytics use cases
- Practical
exercise: mapping a business requirement to an analytics workflow
- Cloud
Analytics Platforms and Working Environments
- AWS,
Microsoft Azure, and Google Cloud analytics ecosystems
- Cloud
consoles, command-line tools, notebooks, and development environments
- Managed
versus self-managed analytics services
- Selecting
services for practical workloads
- Lab:
navigating a cloud analytics environment
- Cloud Storage
for Analytics
- Object
storage concepts
- Buckets,
containers, folders, and logical data zones
- Structured,
semi-structured, and unstructured data
- File formats
including CSV, JSON, Parquet, and Avro
- Lab: creating
and organizing a cloud data storage environment
- Connecting to
Databases and Operational Data Sources
- Relational
and non-relational data sources
- Database
connectivity and connection parameters
- Source
schemas and table structures
- Extracting
data using SQL
- Exercise:
connecting to a sample operational database
- Working With
APIs, Files, and External Data Sources
- REST APIs and
JSON responses
- Authentication
and secure credentials
- File-based
ingestion
- Handling
external datasets
- Lab:
extracting data from an API and storing it in cloud storage
- Data
Profiling and Source Assessment
- Understanding
source structures
- Identifying
missing, duplicate, invalid, and inconsistent records
- Data types
and field-level profiling
- Source
reliability assessment
- Practical
exercise: profiling a real-world dataset
- SQL for Cloud
Data Analytics
- SELECT,
filtering, sorting, grouping, and aggregation
- JOIN
operations
- Common table
expressions
- Window
functions
- Lab:
developing analytical SQL queries
- Python for
Cloud Data Analytics
- Python
analytics workflow
- Data
structures and functions
- Pandas-based
data manipulation
- Reading and
writing cloud datasets
- Lab:
analyzing a cloud-hosted dataset using Python
- Designing
Source-to-Target Mappings
- Source and
target structures
- Field
mappings and transformation rules
- Business
definitions and data requirements
- Data lineage
considerations
- Exercise:
developing a source-to-target mapping specification
- Building the
First Cloud Data Ingestion Pipeline
- Pipeline
components and dependencies
- Extracting
source data
- Loading data
into cloud storage
- Logging and
basic error handling
- Practical
lab: implementing an initial ingestion workflow
Day
2: Data Transformation, Quality, Integration, and Analytical Data Modeling
Module:
Practical Cloud Data Preparation and Analytical Data Engineering
Topics
- Cloud ETL and
ELT Processing
- ETL versus
ELT approaches
- Transformation
location and workload considerations
- Cloud-native
transformation services
- Pipeline
design patterns
- Lab:
implementing an ETL/ELT workflow
- Data Cleaning
and Standardization
- Handling
missing values
- Duplicate
identification and removal
- Standardizing
formats and values
- Data type
conversion
- Practical
exercise: cleaning a business dataset
- Advanced SQL
Transformations
- Complex joins
- Conditional
logic
- Window
functions
- Common table
expressions
- Aggregations
and analytical calculations
- Lab:
developing reusable transformation queries
- Python-Based
Data Transformation
- Pandas
transformations
- Filtering and
reshaping datasets
- String and
date processing
- Custom
transformation functions
- Lab: building
a Python transformation workflow
- Data
Validation and Quality Controls
- Accuracy,
completeness, consistency, validity, and timeliness
- Business-rule
validation
- Referential
integrity
- Automated
data-quality checks
- Exercise:
creating a practical data-quality checklist
- Integrating
Multiple Data Sources
- Database-to-cloud
integration
- File and API
integration
- Schema
alignment
- Key matching
and record integration
- Case study:
integrating customer, sales, and operational data
- Incremental
Processing and Change Data Capture
- Full versus
incremental loads
- Watermark
strategies
- Timestamp-based
processing
- Change data
capture concepts
- Lab:
implementing incremental data loading
- Data
Warehouses, Data Lakes, and Lakehouses
- Analytical
storage patterns
- Bronze,
silver, and gold data layers
- Workload
separation
- Data
accessibility and governance
- Practical
exercise: designing a layered analytical environment
- Analytical
Data Modeling
- Fact and
dimension concepts
- Star and
snowflake schemas
- Keys and
relationships
- Slowly
changing dimensions
- Lab: creating
an analytical data model
- Integrated
Data Preparation Case Study
- Source
profiling and transformation requirements
- Multi-source
integration
- Quality
validation
- Analytical
model development
- Practical
case study: producing a trusted analytical dataset
Day
3: Cloud Analytics, Visualization, Automation, and Business Intelligence
Module:
Practical Analytical Modeling, Visualization, and Pipeline Automation
Topics
- Cloud
Analytical Warehouses and Query Engines
- Cloud
warehouse architecture
- Query engines
and analytical workloads
- Tables,
views, and materialized views
- Workload
organization
- Lab: loading
and querying an analytical warehouse
- Advanced
Analytical SQL
- Complex
aggregations
- Window
functions
- Ranking and
segmentation
- Time-series
calculations
- Performance-aware
query development
- Lab:
developing executive and operational analytics queries
- Building
Analytical Data Marts
- Departmental
and subject-oriented datasets
- Sales,
finance, customer, and operational data marts
- Reusable
analytical datasets
- Business
definitions and semantic consistency
- Exercise:
designing a departmental data mart
- Business
Intelligence and Cloud Visualization
- Connecting BI
tools to cloud data
- Dataset and
semantic model preparation
- Dashboard
architecture
- Interactive
reporting
- Lab:
connecting a BI tool to cloud analytical data
- Dashboard and
Data Visualization Design
- Selecting
appropriate visualizations
- KPI cards and
performance indicators
- Charts,
filters, drill-downs, and interactive elements
- Data
storytelling
- Exercise:
designing a business performance dashboard
- Practical
Business Analytics and KPI Development
- Defining
measurable business indicators
- Revenue,
customer, operational, and financial KPIs
- Trends,
variances, and comparisons
- Translating
analytical results into business insights
- Case study:
building a sales-performance analytics solution
- Workflow
Orchestration and Scheduling
- Pipeline
dependencies
- Scheduling
and triggers
- Airflow and
cloud-native orchestration concepts
- Retry and
failure handling
- Lab: creating
a scheduled analytics workflow
- Version
Control and Collaborative Analytics Development
- Git
fundamentals
- Branching and
merging
- Repository
organization
- Code review
and documentation
- Practical
exercise: version-controlling an analytics project
- Automated
Testing and Data Pipeline Reliability
- Unit and
integration testing
- Data-quality
tests
- Schema
validation
- Pipeline
failure scenarios
- Lab:
implementing automated validation and pipeline tests
- End-to-End
Cloud Analytics Workflow Exercise
- Ingestion to
transformation
- Analytical
modeling
- BI dashboard
development
- Automated
execution and testing
- Practical
scenario: delivering an operational analytics workflow
Day
4: Security, Governance, Performance, Monitoring, and Cloud Cost Optimization
Module:
Practical Cloud Analytics Operations, Control, and Optimization
Topics
- Cloud
Analytics Security Fundamentals
- Shared
responsibility model
- Identity and
access management
- Authentication
and authorization
- Least-privilege
access
- Lab:
configuring practical analytics access controls
- Data
Encryption and Secure Data Handling
- Encryption at
rest and in transit
- Key
management concepts
- Secrets and
credential management
- Secure
data-transfer practices
- Practical
exercise: reviewing a secure data-handling workflow
- Data
Governance and Metadata Management
- Data
ownership and stewardship
- Data catalogs
and business glossaries
- Metadata and
lineage
- Data
classification
- DAMA-DMBOK-aligned
governance practices
- Privacy and
Compliance in Cloud Analytics
- Personal and
sensitive data
- Data
minimization and retention
- Access
controls and auditability
- Regulatory
and contractual requirements
- Case study:
handling sensitive customer information
- Cloud
Analytics Monitoring and Observability
- Pipeline
monitoring
- Logs,
metrics, and alerts
- Data
freshness monitoring
- Failure
detection and operational dashboards
- Lab: creating
practical pipeline monitoring controls
- Troubleshooting
and Incident Response
- Identifying
failed pipeline stages
- Log analysis
- Data-quality
incidents
- Recovery and
rerun strategies
- Scenario
exercise: resolving a failed production pipeline
- Cloud
Analytics Performance Optimization
- Query
optimization
- Partitioning
and clustering
- Data
compression and file formats
- Caching and
workload management
- Lab:
improving an inefficient analytical workload
- Cloud Cost
Management and FinOps Practices
- Compute and
storage cost drivers
- Data-transfer
considerations
- Resource
utilization
- Budgets,
alerts, and cost allocation
- Practical
exercise: optimizing analytics workload costs
- Reliability,
Backup, Recovery, and Business Continuity
- High
availability concepts
- Backup
strategies
- Recovery
objectives
- Failure
isolation and resilience
- Case study:
designing recovery procedures for a critical analytics platform
- Production
Readiness and Operational Review
- Security
readiness
- Data quality
and governance readiness
- Performance
and cost review
- Monitoring
and recovery readiness
- Practical
production-readiness assessment
Day
5: Advanced Cloud Analytics Engineering, DataOps, Modernization, and Capstone
Module:
Advanced Practical Cloud Analytics Implementation and Capstone
Topics
- Advanced
Cloud Analytics Architecture
- Cloud-native
analytical architectures
- Lakehouse and
modern data platform patterns
- Distributed
processing
- Decoupled
storage and compute
- Architecture
design exercise
- Scalable Data
Processing With Spark
- Distributed
processing concepts
- Spark
DataFrames
- Partitioning
and parallel processing
- Large-scale
transformation workloads
- Practical
lab: processing a large analytical dataset
- Real-Time and
Streaming Analytics
- Streaming
versus batch processing
- Event-driven
analytics
- Streaming
ingestion concepts
- Real-time
dashboards and alerts
- Scenario
exercise: implementing near-real-time operational analytics
- Advanced
Pipeline Reliability and Idempotency
- Idempotent
pipeline design
- Retry
strategies
- Checkpointing
- Error
isolation
- Practical
exercise: strengthening a production pipeline
- Schema
Evolution and Data Contracts
- Schema
changes and compatibility
- Data
contracts between producers and consumers
- Versioning
strategies
- Breaking-change
management
- Case study:
managing evolving source systems
- DataOps,
CI/CD, and Analytics Automation
- DataOps
principles
- Automated
deployment
- Continuous
integration and testing
- Environment
management
- Lab:
designing a CI/CD workflow for analytics pipelines
- Advanced
Governance, Quality, and Observability
- Automated
governance controls
- Data lineage
and impact analysis
- Quality
monitoring
- Service-level
objectives
- Practical
exercise: creating an analytics observability framework
- Cloud
Analytics Modernization and Advanced Use Cases
- Legacy
platform modernization
- Migration and
transformation patterns
- Machine
learning integration
- Generative AI
and natural-language analytics concepts
- Case study:
modernizing an enterprise analytics platform
- Practical
Capstone: End-to-End Cloud Data Analytics Solution
- Business
requirements and source assessment
- Cloud storage
and ingestion
- ETL/ELT
transformation and quality controls
- Analytical
modeling and BI dashboard development
- Monitoring,
security, performance, and cost optimization
- Capstone
Presentation, Testing, and Operational Handover
- End-to-end
solution testing
- Data-quality
and security validation
- Performance
and cost review
- Dashboard and
analytical insight presentation
- Final
practical demonstration, documentation, and production handover