PRACTICAL GRAPH STRUCTURES IN SQL SERVER AND AZURE SQL : enabling deeper insights using highly... connected data

Use the graph table features in Azure SQL that were introduced in SQL Server 2017 and further refined in SQL Server 2019. This book shows you how to create data structures to capture complex connections between items in your data. These connections will help you analyze and draw insights from connections in your data that go beyond classic relationships. The graph examples in the book are useful for analyzing social media relationships, complex product-to-customer relationships, and any other type of data analysis in which indirect connections that otherwise might be missed using conventional techniques can be mined for their insight and business value. Tree structures are covered, with emphasis on a structure commonly used by organizations to aggregate data at different levels of an organization. The book provides code examples of SQL Graph objects as well as an alternate tree implementation technique. Included is sample data (and data generators) for you to test for performance and choose the implementation approach that best suits your needs and that of your application. If your job involves analyzing or storage of data elements that are connected in a networked topology, then this is the book that will help you bring the power of SQL Server to bear on that data and take advantage of your existing knowledge. What You Will Learn Understand the graph model and the associated terms used in graph analysis Store highly connected data in SQL Server and Azure SQL alongside existing relational data Make full use of the graph table feature that is refined and enhanced in SQL Server 2019 Implement high performance tree structures that will make storing and querying tree data possible Report on data associated with a tree structure to aggregate results at different levels Who This Book Is For Table of Contents About the Author About the Technical Reviewer Acknowledgments Preface Chapter 1: Introduction to Graphs Graph Fundamentals Definition Graphs In Computing/Directed Graphs Cyclic and Acyclic Graphs Summary Chapter 2: Data Structures and Algorithms Basic Implementation Acyclic Graphs Trees Other Acyclic Graphs Cyclic Graphs Non-Directed Graphs Summary Chapter 3: SQL Graph Table Basics Object Creation Creating Data Querying Data Node-to-Node Querying Filtering Output Multiple MATCH Expressions Traversing Variable Level Paths Displaying the Last Node in the Path Aggregation Along the Path Controlling Depth of Processing Filtering for One Path Finding All Paths Between Nodes Weighted Graph Calculations Checking Conditions on the Matched Item Summary Chapter 4: SQL Graph Tables: Extended Topics Advanced Data Creation Techniques Building an Interface Layer INSERT UPDATE DELETE Loading Data Using Composable JSON Tags Heterogenous Queries Integrity Constraints and Indexes Edge Constraint Uniqueness Constraints (and Indexes) Additional Constraints Metadata Roundup List Graph Objects in the Database Types of Graph Columns Tools for Fetching Graph Information Summary Chapter 5: Tree Data Structures Creating the Data Structures Base Table Structures Demo Sales Structure Essential Tree Maintenance Code Code To Create New Nodes Reparenting Nodes Deleting a Node Tree Output Code Returning Part of the Tree Determining If a Child Node Exists Aggregating Child Activity at Every Level Summary Chapter 6: Tree Structures, Algorithms, and Performance Alternative Tree Implementation Path Technique Code Table Create Script Insert New Rows Return Hierarchy Check For Child Report Sales Helper Table Kimball Helper Table Code Table Create Script Check For Child Hierarchy Display Helper Code Table Create Script Using the Helper Objects Report Sales Performance Comparison Summary Chapter 7: Other Directed Acyclic Graphs The Problem Set The Example Determining If a Part Is Used in a Build Picking Items for a Build Printing Out the Parts List for a Build Summary Chapter 8: A Graph For Testing The Example Creating the Tables Loading the Data The Queries Find Every Node That Is Connected to a Specific Node Seeing If One Node is Connected to Another Returning All Paths Between Two Nodes Finding All People That a User Connects To At Any Level Where They Share an Interest Finding a Specific User Who a User Connects To at Any Level Where They Share a Specific Interest Finding Connections Through Other Nodes Performance Tuning Results Performance Tuning Roundup Test Index the Internal Columns Employ a Maximum Degree of Parallelism of One Consider Breaking Up Some Queries The End (or Is It the Beginning?) Index