Azure Synapse Analytics and Data Warehousing Training Course

 Azure Synapse Analytics and Data Warehousing Training Course 

Introduction 

Azure Synapse Analytics and Data Warehousing Training Course provides comprehensive practical knowledge and technical skills in modern cloud data warehousing, enterprise data integration, big data analytics, and business intelligence. The course explores Microsoft Azure Synapse Analytics as a unified analytics platform that combines enterprise data warehousing, data engineering, big data processing, and advanced analytics. Participants gain practical experience in designing scalable data warehouse architectures, implementing Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) pipelines, managing relational and non-relational data, and optimizing analytical workloads. The training covers Azure Synapse SQL, dedicated SQL pools, serverless SQL pools, Apache Spark, Azure Data Lake Storage, data orchestration, and cloud-based data management to support modern enterprise analytics and digital transformation. 

The course further develops expertise in data warehouse modelling, dimensional modelling, data integration, distributed processing, data governance, and performance optimization. Participants explore real-world applications of Azure Synapse Analytics through practical exercises and international case studies involving financial services, healthcare, retail, telecommunications, and government institutions. Emphasis is placed on building secure, reliable, cost-effective, and high-performance analytical solutions that transform complex datasets into actionable business intelligence. By the end of the training, participants will be equipped to develop enterprise-grade data warehousing solutions, implement modern data architectures, and support data-driven decision-making using Microsoft Azure technologies. 

Course Objectives 

1. Understand Azure Synapse Analytics architecture and cloud data warehousing principles.
2. Design scalable enterprise data warehouse solutions.
3. Implement ETL and ELT data integration pipelines.
4. Configure Azure Synapse dedicated and serverless SQL pools.
5. Develop advanced SQL queries for analytical processing.
6. Apply dimensional modelling and star schema techniques.
7. Integrate Azure Data Lake Storage with Synapse Analytics.
8. Implement Apache Spark for distributed data processing.
9. Develop automated data orchestration workflows.
10. Apply data governance, security, and compliance frameworks.
11. Optimize query performance and warehouse workloads.
12. Integrate Power BI for enterprise business intelligence.
13. Implement cost-effective cloud analytics and data management strategies.

Organizational Benefits
 

1. Improved enterprise data management and accessibility.
2. Reduced infrastructure and data warehousing costs.
3. Faster business intelligence and reporting processes.
4. Enhanced data-driven strategic decision-making.
5. Improved data security and regulatory compliance.
6. Increased efficiency in enterprise data integration.
7. Scalable cloud analytics infrastructure.
8. Better collaboration between data engineering teams.
9. Improved operational performance through automation.
10. Stronger organizational digital transformation capabilities.

Target Audiences
 

1. Data engineers and cloud data architects.
2. Database administrators and SQL developers.
3. Business intelligence and analytics professionals.
4. Data scientists and machine learning practitioners.
5. IT managers and enterprise technology leaders.
6. Cloud solutions architects and consultants.
7. Business analysts and reporting specialists.
8. Data warehouse developers and database specialists.

Course Duration: 10 days

Course Modules

Module 1: Introduction to Azure Synapse Analytics
 

• Overview of Azure Synapse architecture and capabilities.
• Unified analytics and enterprise data warehousing.
• Synapse workspaces and workspace management.
• Azure Synapse Studio navigation and configuration.
• Cloud analytics and modern data architecture.
• Case study: Enterprise analytics transformation at Walmart.

Module 2: Azure Synapse Workspace Administration
 

• Creating and configuring Synapse workspaces.
• Managing workspace resources and access.
• Integrating Azure subscriptions and resource groups.
• Configuring development and production environments.
• Workspace monitoring and administration.
• Case study: Enterprise cloud workspace management at Microsoft.

Module 3: Azure Data Lake Storage Integration
 

• Azure Data Lake Storage Gen2 architecture.
• Storage accounts, containers, and file systems.
• Data ingestion and storage organization.
• Access control and hierarchical namespaces.
• Integrating data lakes with Synapse.
• Case study: Large-scale data lake implementation at Shell.

Module 4: SQL Data Warehousing Fundamentals
 

• Relational database and warehouse concepts.
• Structured and semi-structured data.
• SQL querying and analytical operations.
• Data warehouse architecture and design.
• Database objects and schema management.
• Case study: Retail data warehousing at Tesco.

Module 5: Dedicated SQL Pools
 

• Dedicated SQL pool architecture.
• Creating and configuring SQL pools.
• Table distribution and indexing strategies.
• Loading and querying warehouse data.
• Workload management and resource allocation.
• Case study: Enterprise analytical warehousing in financial services.

Module 6: Serverless SQL Pools
 

• Serverless SQL pool architecture.
• Querying files directly from data lakes.
• OPENROWSET and external data sources.
• Working with CSV, JSON, and Parquet files.
• Query optimization and consumption management.
• Case study: Pay-as-you-go analytics for a retail organization.

Module 7: Data Warehouse Modelling
 

• Dimensional modelling principles.
• Star and snowflake schema design.
• Fact and dimension tables.
• Slowly changing dimensions.
• Data warehouse normalization strategies.
• Case study: Customer analytics modelling at Amazon.

Module 8: ETL and ELT Data Integration
 

• ETL and ELT architecture and workflows.
• Data extraction from multiple sources.
• Transformation and cleansing techniques.
• Loading data into analytical warehouses.
• Incremental loading and change data capture.
• Case study: Enterprise data integration at Unilever.

Module 9: Azure Synapse Pipelines
 

• Pipeline creation and configuration.
• Activities, datasets, and linked services.
• Workflow scheduling and automation.
• Parameterization and pipeline dependencies.
• Monitoring and troubleshooting pipeline execution.
• Case study: Automated enterprise data integration in telecommunications.

Module 10: Apache Spark and Big Data Processing
 

• Apache Spark architecture and fundamentals.
• Spark pools and notebook development.
• PySpark and distributed data processing.
• Data transformation using Spark DataFrames.
• Integrating Spark with Azure Data Lake.
• Case study: Large-scale healthcare data processing.

Module 11: Advanced SQL and Query Optimization
 

• Advanced SQL functions and analytical queries.
• Common table expressions and window functions.
• Query execution plans and performance tuning.
• Partitioning, indexing, and distribution optimization.
• Monitoring query performance and resource consumption.
• Case study: High-performance banking analytics.

Module 12: Data Security and Governance
 

• Azure role-based access control.
• Data encryption and identity management.
• Column-level and row-level security.
• Data classification and auditing.
• Governance, privacy, and compliance management.
• Case study: Secure healthcare data management.

Module 13: Power BI and Business Intelligence Integration
 

• Connecting Power BI to Azure Synapse.
• Developing analytical datasets.
• Building interactive dashboards and reports.
• Implementing enterprise reporting solutions.
• Data visualization and performance optimization.
• Case study: Retail business intelligence at Carrefour.

Module 14: Monitoring, Performance and Cost Management
 

• Monitoring Synapse workspace performance.
• Managing SQL and Spark workloads.
• Identifying performance bottlenecks.
• Cloud resource and cost optimization.
• Troubleshooting and operational maintenance.
• Case study: Cloud infrastructure optimization at a global enterprise.

Module 15: Enterprise Data Warehousing Project
 

• Designing an end-to-end Synapse architecture.
• Integrating multiple enterprise data sources.
• Developing automated ETL and ELT pipelines.
• Implementing analytical models and dashboards.
• Testing, deployment, and solution documentation.
• Case study: Building an integrated enterprise analytics platform for a multinational organization.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using Microsoft Azure Synapse Studio.
• Hands-on exercises involving SQL, Spark, and data integration.
• Real-world enterprise data warehousing case studies.
• Group assignments and collaborative problem-solving.
• Practical project development and performance evaluation.
• Knowledge assessments and participant feedback sessions.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.