microsoft/sql-server-samples

Features

• Relational Database Management Systems - Provides a comprehensive reference library for deploying and managing relational database systems.
• Reference Implementation Libraries - Provides a comprehensive library of reference implementations, T-SQL scripts, and schemas for SQL Server, Azure SQL, and Azure Synapse.
• SQL Server Implementation Patterns - Provides reference implementations and T-SQL patterns for building relational databases and cloud data warehouses.
• SQL-Based Machine Learning - Implements machine learning services and advanced analytics by integrating external language runtimes directly within the database engine.
• Containerized Database Administration - Provides configurations for running and administering SQL database instances within containerized environments.
• Data Warehousing Patterns - Implements standard architectural patterns for data warehousing and transactional processing.
• Database Design Patterns - Uses implementation templates and code examples to standardize database behavior and schema design.
• Database Logic Implementations - Provides reference code and proven implementation patterns for creating functional database logic.
• Enterprise SQL Servers - Includes implementation examples for deploying and managing enterprise-grade SQL Server platforms.
• Procedural Language Runtimes - Integrates external language runtimes directly within the database process for machine learning and advanced analytics.
• SQL Sample Databases - Provides pre-built databases and sample schemas to demonstrate relational workloads for transactions and analytics.
• SQL Server Persistence - Provides reference code and T-SQL patterns for structuring relational databases and warehouses.
• Relational Workload Patterns - Defines relational structures and data warehousing workflows using standardized Transact-SQL scripts.
• Azure SQL Deployment Scripts - Provides configuration scripts and guides for deploying database instances and warehouses in Azure.
• Database Cloud Provisioning - Provides configuration scripts for deploying database instances and data warehouses within Azure.
• SQL Cloud Deployment Automation - Configures and manages database instances and data warehouses within Azure cloud environments.
• Containerized Deployment Runtimes - Packages database engines into container images for isolated execution across different operating systems.
• Containerized Deployments - Runs database instances within containerized environments to ensure consistent deployment across platforms.
• Cloud Architecture Patterns - Provides curated code examples for implementing database features and architectural patterns across cloud services.
• Relational Workload Patterns - Offers standardized Transact-SQL code examples and database structures for building relational databases and cloud data warehouses.
• Database ML Service Examples - Implements machine learning services and language extensions using specialized test databases.
• Storage Latency Analysis - Provides dual storage mode implementations to measure and analyze latency differences between persistent disk and in-memory tables.
• Storage Latency Benchmarks - Implements both disk-based and in-memory configurations to analyze and measure data retrieval latency differences.
• Declarative Data Seeding - Provides pre-defined table structures and data scripts to instantiate reproducible environments for demonstrations.
• Performance Benchmarking - Implements performance testing to measure memory and disk retrieval speeds for optimizing data processing.
• Memory-Optimized Processing - Implements memory-optimized processing techniques to increase transaction speed and reduce system response time.
• In-Memory Databases - Demonstrates performance gains and optimization techniques using in-memory database processing.
• Memory-Optimized Storage Benchmarks - SQL Server compares the speed and throughput of in-memory table technologies against disk-based storage to identify performance gains.
• Real-Time Data Streaming - Processes real-time telemetry from sensors and devices for immediate analytics within the database layer.
• Sample Databases - Provides pre-built database schemas and data dumps to simulate real-world scenarios.
• Engine Capability Showcases - Illustrates advanced capabilities like in-memory processing and integrated analytics using functional code examples.
• Telemetry Data Pipelines - Provides specialized ingestion pipelines for streaming real-time sensor data into relational tables.
• Database Business Scenarios - Builds real-world application contexts using end-to-end sample applications to validate business workflows.
• Container Deployment - Provides guides and examples for deploying database engines within portable container images.
• IoT Data Collection - Collects data from devices and sensors to prepare it for analytical processing within a database.
• IoT Message Ingestion - Captures and routes data from sensors and hardware devices into a relational database environment.
• Database Process Language Hosting - Integrates external language runtimes directly within the database process for machine learning and analytics.
• Database Instance Provisioning - Automates the setup of database instances and schemas using executable SQL and shell scripts.
• Storage Performance Benchmarking - Measures data retrieval speeds and compares in-memory versus disk storage to optimize processing.

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