MICROSOFT SQL SERVER 2022 - IMPLEMENTING DATA WAREHOUSE SOLUTIONS WITH SQL SERVER 2016/2014
The Microsoft SQL Server 2022 - Implementing Data Warehouse Solutions with SQL Server 2016/2014 course is a professional, data engineering-focused program designed to equip participants with the skills required to design, build, populate, and manage enterprise data warehouse solutions using Microsoft SQL Server technologies. Built on data platform solutions from Microsoft, this course focuses on data warehousing concepts, dimensional modeling, ETL processes, data integration, and performance optimization using SQL Server-based tools and techniques commonly deployed in enterprise environments. While SQL Server 2022 provides the modern platform context, the course covers data warehouse implementations compatible with SQL Server 2016 and 2014, reflecting real-world enterprise deployments. The program is ideal for professionals involved in business intelligence (BI), analytics, reporting, and enterprise data management initiatives. Course Objectives By the end of this course, participants will be able to: Understand data warehousing concepts and architectures Design dimensional data models for analytics and reporting Implement data warehouses using SQL Server Develop ETL processes for data extraction and transformation Optimize data warehouse performance and scalability Manage and maintain enterprise data warehouse environments
Course Curriculum
1
- Data warehousing concepts and terminology
- OLTP vs OLAP systems
- Data warehouse architectures
- Role of data warehouses in enterprise analytics
2
- SQL Server data platform overview
- Data warehouse components in SQL Server
- Compatibility considerations (2016/2014)
- Platform design considerations
3
- Enterprise data warehouse architectures
- Staging, integration, and presentation layers
- Data flow design
- Scalability and extensibility considerations
4
- Dimensional modeling concepts
- Facts and dimensions
- Star schema design
- Snowflake schema design
5
- Identifying business processes
- Grain definition
- Measures and aggregations
- Additive, semi-additive, and non-additive facts
6
- Conformed dimensions
- Slowly Changing Dimensions (SCD)
- Handling hierarchies and attributes
- Dimension key strategies
7
- ETL architecture and workflows
- Data extraction strategies
- Data transformation techniques
- Load and validation processes
8
- ETL implementation concepts
- Data cleansing and transformation logic
- Error handling and logging
- Performance considerations in ETL design
9
- Data quality challenges
- Validation and reconciliation techniques
- Managing data consistency
- Handling data anomalies
10
- Indexing strategies for data warehouses
- Partitioning concepts
- Query performance optimization
- Managing large data volumes
11
- Security and access control
- Managing user access and permissions
- Data protection considerations
- Auditing and compliance overview
12
- Incremental data loading strategies
- Managing late-arriving data
- Change detection techniques
- Data refresh scheduling
13
- Data warehouse maintenance tasks
- Monitoring performance and usage
- Capacity and growth planning
- Operational best practices
14
- Designing data warehouses for reporting
- Supporting BI and analytics tools (overview)
- Optimizing schemas for query workloads
- User access patterns
15
- Designing a complete data warehouse solution
- Implementing dimensional models
- Building ETL workflows
- Performance tuning and validation exercises
16
- Data warehouse developers
- Business intelligence professionals
- Database administrators
- Data analysts and data engineers
17
- Understanding of relational database concepts
- Familiarity with SQL and basic querying
- Exposure to SQL Server environments is beneficial
This course includes
- 17+ Activity Modules
- 40 hours + lessons
- Lifetime access
- Certificate of completion
- Available on desktop and mobile
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