Training on Advanced SQL for Data Analytics

Module 1: SQL Performance Optimization

• Query execution plans and performance analysis
• Indexing strategies and techniques
• Query rewriting and simplification
• Partitioning and sharding for large datasets
• Case Study: Optimizing slow queries in a high-traffic transactional system
• Practical: Analyze execution plans and improve query performance

Module 2: Advanced Data Modeling

• Dimensional and snowflake schemas
• Star and fact tables
• Data warehousing concepts
• Data modeling for analytics and reporting
• Case Study: Designing a data warehouse for business intelligence
• Practical: Create a star schema for a sample dataset

Module 3: Window Functions and Analytical Queries

• Ranking and partitioning data
• Calculating running totals, moving averages, and cumulative sums
• Lead and lag functions for data analysis
• Advanced window functions for complex calculations
• Case Study: Sales trend and performance analysis using window functions
• Practical: Write analytical queries using window functions

Module 4: Recursive Queries and Common Table Expressions (CTEs)

• Hierarchical data and recursive queries
• Creating and using CTEs for complex logic
• Recursive CTEs for tree-like structures
• Case Study: Managing organizational hierarchy data using recursive queries
• Practical: Build recursive queries for hierarchical datasets

Module 5: Regular Expressions and Pattern Matching

• Regular expression syntax and functions
• Text mining and information extraction
• Data cleaning and standardization using regular expressions
• Case Study: Cleaning and standardizing customer data
• Practical: Apply regex for data validation and transformation

Module 6: Advanced Joins and Set Operations

• Beyond inner and outer joins
• Full outer joins and self-joins
• Set operations (UNION, INTERSECT, EXCEPT)
• Advanced join optimization
• Case Study: Combining multiple datasets for comprehensive reporting
• Practical: Perform complex joins and set operations

Module 7: Subqueries and Correlated Subqueries

• Nested queries and correlated subqueries
• EXISTS and NOT EXISTS operators
• IN and ANY/ALL operators
• Case Study: Filtering and segmenting customers using subqueries
• Practical: Write and optimize nested and correlated subqueries

Module 8: SQL for Data Analysis and Business Intelligence

• Creating reports and dashboards
• Data visualization with SQL
• Predictive analytics with SQL
• SQL for machine learning
• Case Study: Building a business intelligence reporting system
• Practical: Generate reports and dashboards using SQL queries

Module 9: SQL for Big Data

• SQL on Hadoop and Spark
• Distributed SQL processing
• Handling large datasets with SQL
• Case Study: Processing big data using distributed SQL systems
• Practical: Run SQL queries on large datasets in a distributed environment

Module 10: Case Studies and Real-world Applications

• In-depth analysis of real-world datasets
• SQL for fraud detection, customer segmentation, and churn analysis
• Performance optimization case studies
• Data modeling and warehousing projects
• Case Study: End-to-end SQL solution for business analytics
• Practical: Complete a full SQL project from data modeling to optimization