Apply Advanced SQL Server Analytics and Programming

Overview

By the end of this course, learners will be able to manage semi-structured data using JSON, model complex relationships with graph databases, and apply advanced analytics by integrating R and Python within SQL Server. Learners will gain the ability to query, modify, and analyze diverse data formats directly inside the database environment. This course is designed for database professionals and data practitioners who want to move beyond traditional relational querying and leverage advanced SQL Server capabilities. Through hands-on concepts, learners will understand how to store and manipulate JSON data, build and query graph databases for relationship-driven use cases, and execute R and Python scripts for analytics, data ingestion, and transformation. The course emphasizes practical application, enabling learners to process structured and unstructured data efficiently without moving data outside SQL Server. What makes this course unique is its unified approach to modern data handling within SQL Server—combining JSON, graph databases, R, and Python in a single advanced curriculum. By completing this course, learners will enhance their analytical skill set, improve data-driven decision-making, and be better prepared to handle real-world enterprise data challenges using SQL Server’s advanced features.

Syllabus

JSON Data Management in SQL Server

This module focuses on managing and querying JSON data within SQL Server. Learners explore how JSON supports semi-structured data storage, how to insert and query JSON using built-in SQL Server functions, and how to modify JSON content efficiently. The module emphasizes practical techniques for working with hierarchical data while maintaining data integrity and flexibility in enterprise database environments.

Graph Databases with SQL Server

This module introduces graph database concepts and their implementation in SQL Server. Learners examine how nodes and edges represent entities and relationships, how graph tables are created and modified, and how real-world relationship models are implemented using SQL Server graph features. The module highlights relationship-centric querying for complex data scenarios.

R Integration and Advanced Analytics in SQL Server

This module covers the integration of R with SQL Server to enable advanced analytics directly within the database environment. Learners explore executing R scripts, importing external data sources, processing non-tabular data, and performing analytical joins. The module emphasizes data preprocessing, analysis, and result integration using R.

Python Programming Concepts for SQL Server

This module introduces Python programming concepts as applied within SQL Server. Learners examine how Python scripts are executed, how control flow and operators function, and how core data structures support analytical scripting. The module focuses on foundational Python skills that enhance SQL Server-based analytics.