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Unfold Latest Features of SQL Server 2017

From Python to graphic database to the various other focused changes, let’s have a look through the major features arriving with SQL Server 2017.

Reporting Services benefited from a big overhaul in SQL Server 2016, and more improvements are coming in SQL Server 2017. This release was primarily referred as SQL Server vNext and later altered to SQL Server 2017. The whole new coming features incorporate components that improve IT abilities to transfer databases back and forth to the cloud.

Fortunately, SQL Server 2017 is going to be the primary version to run on both Linux and Windows. The latest version also maintains Docker containers.

Python power comes to SQL Server 2017

Python is no outsider at Microsoft. It will ride with R as the company’s SQL Server 2017 platform moves to its second CTP. SQL Server Training employs analytics, machine learning models, and handles data-powered work. Python integration within SQL Server is not limited to, enterprise editions, but also will be available in the free-to-use Express edition.

The most conventional application being the execution of Python scripts as normal, with SQL Server as a data source. Microsoft also embeds Python code directly in SQL Server databases by including the code as a T-SQL stored procedure.

Operationalizing R

Microsoft is constantly focusing on enhancing its R capabilities. The company announced it would embrace pre-trained cognitive models within the release of Microsoft R Server 9.1. With this introduction, work of Sparklyr and H2O open source language is installed into R Server.

Along with it, there is also a wider range of algorithms run in a parallel processing mode, supported file formats and Optimized Row Columnar. Microsoft is doing a commendable job in operationalizing R.

Both ‘R’ and Python

Microsoft Machine Learning Services in SQL Server 2017 incorporates both R and Python libraries. These are together used to run AI jobs for acceleration.

Both R and Python find considerable use in analytics,

  • R perhaps favored more by statisticians, and
  • Python is preferred by programmers.

Running both R and Python implementation leads to derive the power of the data management system on both cloud and on premises.

Microsoft touts SQL Server 2017 as ‘first RDBMS with built-in AI’

The introduction of “built-in AI” within SQL Server introduces pre-trained neural network models for featuring images, and sentiment analysis and supports SparkETL, SparklyR, and SparkSQL, along with GPU collaboration.

Threat Detection

Microsoft enhanced its data protection features with the introduction of Azure SQL Database Threat Detection. This feature detects anomalous login monitoring and SQL Injection vulnerabilities. This is brought into effect at the Database level by auditing, authorizing and configuring notifications. The admin will be notified whenever the detection engine notices any unusual performance.

Graph Database

Every user is happier with the employment of a graph database inside the database engine.

With this support, the nodes and edges are introduced.

§ Nodes stand for entities,

§ Edges deal with relationships among two nodes,

Both nodes and edges deal with properties of data. SQL Server 2017 also incorporates extensions to maintain join-less queries. Graph databases are specifically employed in Internet of Things, predictive analytics apps, recommendation engine, and social network.

Adaptive Query Plans

DBA finds it a challenging job in handling system performance. With the change in data, the query optimizer introduces execution plans which sometimes are very little optimal. An adaptive query processing is one such feature that constantly fine-tunes queries to enhance performance.

With this feature, SQL Server can analyze the query runtime and differentiate the existing execution to the former one and also build the execution plan.

The changes made thereby are conservative and incremental. This feature also gets a lead from Azure SQL Database.

Novel graphic data

SQL Server 2017 highlights the ability to deal with graph data objects. This innovation acts as an alternative method to relational methods dealing with data points and, likely opens up a new field of applications.

Graph processing plays a significant role in the detection of fraud, modeling IoT network analysis. This is the place where much of modeling data is generated within cutting-edge applications.

Final Words…

All the above-mentioned enhancements are deliberate to expand the SQL Server’s use in predictive analytics and artificial intelligence abilities.

Any other comments or pointers? Please leave in the comments section below:

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