Advanced SQL & Data Modeling for BI Professionals

The Advanced SQL & Data Modeling for BI Professionals program is a 5-day intensive corporate training designed to elevate the analytical and data engineering capabilities of business intelligence professionals. The course focuses on advanced SQL techniques, data modeling methodologies, and performance optimization strategies critical for enterprise data analysis, reporting, and decision support systems. Participants will learn to design efficient data architectures, write optimized SQL queries, and model data for scalable analytics environments. The program emphasizes real-world BI scenarios, data warehouse design principles, and integration with visualization tools to empower data-driven business ecosystems.

Objectives of the Training

Master advanced SQL querying techniques for complex data manipulation and analytics.
Learn to design normalized and denormalized data models for BI and reporting systems.
Understand data warehouse architecture, star/snowflake schema design, and dimensional modeling.
Optimize query performance using indexing, execution plans, and partitioning.
Develop robust ETL (Extract, Transform, Load) pipelines for data integration.
Apply best practices for database governance, security, and scalability in enterprise systems.

Prerequisites

Basic to intermediate understanding of SQL.
Familiarity with relational database concepts (tables, joins, keys, normalization).
Exposure to BI tools or analytics workflows is beneficial.
Experience with any RDBMS (SQL Server, MySQL, PostgreSQL, Oracle, etc.) is recommended.

What You Will Learn

Advanced SQL concepts including window functions, CTEs, subqueries, and analytical functions.
Data modeling for OLTP and OLAP systems – normalization, star and snowflake schemas.
Query optimization, indexing, and database performance tuning.
ETL pipeline creation using SQL and integration with BI platforms.
Designing scalable data warehouses and implementing dimensional models.
Building foundation models for analytics dashboards and KPIs.

Target Audience

This course is ideal for Business Intelligence Developers, Data Analysts, Data Engineers, and Database Administrators. It is also valuable for professionals involved in data architecture, data governance, and analytics reporting within enterprise environments.

Detailed 5-Day Curriculum

Day 1 – Advanced SQL Querying & Analytics (6 Hours)

Session 1: Review of SQL Fundamentals and Query Logic Optimization.
Session 2: Working with Subqueries, Common Table Expressions (CTEs), and Window Functions.
Session 3: Advanced Aggregations, Ranking, and Conditional Expressions.
Hands-on: Complex Analytical Queries using SQL Server and PostgreSQL.

Day 2 – Data Modeling Foundations (6 Hours)

Session 1: OLTP vs OLAP – Understanding Transactional and Analytical Systems.
Session 2: Normalization and Denormalization – When and Why.
Session 3: Designing Star and Snowflake Schemas for BI Systems.
Workshop: Building an Analytical Data Model for Sales and Finance Dashboards.

Day 3 – Data Warehouse Design & ETL Processes (6 Hours)

Session 1: Data Warehouse Architecture – Staging, ODS, and Data Mart Layers.
Session 2: ETL Development – Extraction, Transformation, and Loading with SQL.
Session 3: Data Quality, Lineage, and Metadata Management.
Hands-on: Designing ETL Pipelines and Loading Data into a BI-Ready Warehouse.

Day 4 – Query Performance Tuning & Optimization (6 Hours)

Session 1: Understanding Query Execution Plans and Optimizers.
Session 2: Indexing Strategies – Clustered, Non-Clustered, and Composite Indexes.
Session 3: Partitioning, Views, and Materialized Views for Large-Scale Analytics.
Workshop: Optimizing Query Performance for High-Volume Enterprise Data.

Day 5 – Enterprise Integration & Capstone Project (6 Hours)

Session 1: Integrating Data Models with Power BI, Tableau, and Cloud Platforms.
Session 2: Data Governance, Access Control, and Auditability in BI Systems.
Session 3: Capstone Project – Designing a Complete BI Data Model for Enterprise Analytics.
Panel Discussion: The Future of Data Modeling – From Warehouses to Data Lakes and Lakehouses.

Capstone Project

Participants will design and implement a full-scale data model and analytics-ready database schema for a chosen business domain (e.g., retail, finance, or logistics). They will apply normalization, dimensional modeling, and query optimization techniques to ensure high-performance analytics. The final project will include integration with BI tools to demonstrate insights generation through well-structured data architectures.

Future Trends in SQL & Data Modeling

The future of data modeling and SQL is being shaped by the rise of cloud-based architectures, real-time analytics, and AI-driven automation. Technologies such as data lakehouses (e.g., Snowflake, Databricks) and ELT workflows are transforming how enterprises design their analytical ecosystems. SQL continues to evolve with extensions for streaming data, JSON, and AI integrations. Modern BI professionals must master advanced SQL and modeling practices to bridge traditional relational systems with modern cloud-native analytics infrastructures.