Microsoft SQL Server 2019

Microsoft SQL Server 2019 is the latest iteration of Microsoft’s relational database management system (RDBMS). As a key component of the Microsoft Data Platform, SQL Server provides a comprehensive and integrated platform for managing and analyzing data. Released in late 2019, SQL Server 2019 builds upon the strengths of its predecessors while introducing several notable enhancements and features.

The features in this edition are primarily focused on managing extensive data, particularly in the context of Big Data. Microsoft has notably enhanced PolyBase in this release. This enhancement allows users to establish connections to tables in external databases through External Tables, facilitating TSQL command operations on them. Additionally, SQL Server 2019 introduces new capabilities, allowing connections to a Spark server cluster. It’s important to note that PolyBase technology, while more dynamic than Linked Server in SQL Server, cannot be directly compared in terms of its robustness and speed.

Key Features and Enhancements of Microsoft SQL Server 2019 include

Seamless integration with Apache Spark and Hadoop Distributed File System (HDFS), enabling the processing and analysis of large datasets within the SQL Server environment.

Intelligent Performance Enhancements

The implementation of Intelligent Query Processing (IQP) features aims to enhance query performance. These functionalities include batch mode on rowstore, memory grant feedback, and deferred compilation for table variables. Together, they collaborate to enhance and optimize the overall querying experience.

Container Support

Robust support for containers, facilitating deployment on Docker containers and Kubernetes clusters for enhanced scalability and flexibility.

Machine Learning Services Integration

Integration with machine learning services, supporting both R and Python languages, to embed predictive analytics directly into database applications.

Poly Base for Unified Data Virtualization

PolyBase has undergone enhancements to offer unified data virtualization, enabling effortless querying and analysis of data sourced from a variety of outlets. This includes relational databases, big data clusters, and external databases. These improvements ensure a seamless and integrated approach to data exploration and analysis. They empower users to efficiently derive insights from diverse data sources within a unified framework.

Graph Data Processing

Native support for graph data processing, allowing users to model and analyze complex relationships between entities for applications like social network analysis and fraud detection.