SQL Server 2019

SQL Server 2019
Name	SQL Server 2019
Developer	Microsoft
Initial release	2019
Latest release	2019 RTM
Written in	C++
Operating system	Windows Server, Linux
Genre	Relational database management system
License	Proprietary, free editions available

SQL Server 2019 Microsoft's 2019 relational database platform introduced enhancements to analytics, scalability, and interoperability with container and big data technologies. It integrates traditional relational processing with data virtualization, machine learning, and distributed compute, appealing to enterprises, research institutions, and cloud providers. Adoption spans industries that rely on platforms from Amazon Web Services and Google Cloud Platform to private deployments managed by organizations such as Deutsche Bank, Walmart, Pfizer, NASA, and Siemens.

Overview

SQL Server 2019 is a release of Microsoft's enterprise database family positioned alongside competing systems from Oracle Corporation, IBM, PostgreSQL Global Development Group, and MongoDB, Inc.. It continues a lineage that includes earlier releases associated with corporations such as Microsoft Corporation and platforms like Windows Server 2016. The product targets transactional workloads, online analytical processing used by firms like Goldman Sachs and Citigroup, and data science workflows favored by groups including DeepMind and OpenAI. It interoperates with ecosystems driven by Kubernetes, Docker, and cloud providers such as Microsoft Azure.

Editions and Licensing

Editions of the product include free and paid tiers analogous to offerings from Red Hat, Ubuntu, and SUSE Linux Enterprise Server distributions. Licensing models reflect enterprise practices similar to those used by SAP SE and VMware, Inc., supporting cores and server CALs comparable to schemes in use at IBM and Hewlett Packard Enterprise. The product provides a free Express edition for small deployments and Developer edition for testing, while Standard and Enterprise editions align with requirements of corporations such as General Electric, Ford Motor Company, and AT&T.

Features and Improvements

Key additions mirror trends in modern platforms championed by projects like Apache Hadoop and Apache Spark, and by companies such as Cloudera and Confluent. Notable features include data virtualization that connects to sources like Oracle Database, MySQL, and PostgreSQL; integration with machine learning frameworks used by TensorFlow, PyTorch, and libraries from Scikit-learn; and support for big data clusters leveraging orchestration systems like Kubernetes and container runtimes from Docker Inc.. Enhancements also parallel advances in query processing and indexing seen in research at institutions such as Massachusetts Institute of Technology and Stanford University.

Architecture and Components

The architecture combines a relational engine, storage engine, and extensibility layers influenced by designs used by SAP HANA and Snowflake (company). Components include the Database Engine, SQL Server Agent, and tools resembling those from Microsoft Visual Studio and SQL Server Management Studio. The product's big data cluster feature bundles Apache components such as Hadoop Distributed File System, Spark (software), and integration points to data catalogs similar in concept to Apache Atlas. Deployment models reflect patterns used across Amazon EC2, Google Compute Engine, and Microsoft Azure Virtual Machines.

Performance and Scalability

Performance optimizations borrow from hardware and software strategies employed by firms such as Intel Corporation, AMD, and NVIDIA. Features like intelligent query processing and adaptive joins align with optimization research from University of California, Berkeley and Carnegie Mellon University. Scale-out capabilities for analytics use patterns similar to distributed systems developed by Facebook and Twitter; parallel processing and in-memory technologies echo work by SAP and Oracle on columnstore and in-memory engines.

Security and Compliance

Security features incorporate authentication and authorization mechanisms consistent with standards promoted by National Institute of Standards and Technology, ISO/IEC, and regulatory regimes enforced by entities such as European Commission and U.S. Department of Health and Human Services. Capabilities include encryption-at-rest and in-transit, row-level security, dynamic data masking, and auditing tools comparable to those used in enterprise environments at Bank of America and JPMorgan Chase. Compliance guidance addresses frameworks like General Data Protection Regulation and industry standards applied by Health Level Seven International.

Deployment and Administration

Administration leverages management tools and automation approaches found in ecosystems from Ansible, Puppet, and Chef (software), and integrates with monitoring systems from Prometheus (software) and Grafana. Containerized and Kubernetes-based deployments reflect practices advocated by the Cloud Native Computing Foundation and enterprises such as Netflix and Uber Technologies. Backup, high-availability, and disaster recovery features support architectures used by critical infrastructure providers including AT&T, Verizon Communications, and Boeing.

Category:Microsoft database software