AI News, Data Science with Microsoft SQL Server 2016 – Free eBook
- On Wednesday, June 6, 2018
- By Read More
Data Science with Microsoft SQL Server 2016 – Free eBook
The world around us – every business and nearly every industry – is being transformed by technology.
This disruption is driven in part by the intersection of three trends: a massive explosion of data, intelligence from machine learning and advanced analytics, and the economics and agility of cloud computing.
They support very low latency advanced analytics and machine learning, such as forecasting and predictive models, on the same data, so that applications can easily embed data-driven intelligence.
It supports hybrid transactional/analytical processing, advanced analytics and machine learning, mobile BI, data integration, always-encrypted query processing capabilities, and in-memory transactions with persistence.
By combining the performance of SQL Server in-memory Online Transaction Processing (OLTP) technology as well as in-memory columnstores with R and machine learning, applications can achieve extraordinary analytical performance in production, all while taking advantage of the throughput, parallelism, security, reliability, compliance certifications, and manageability of an industrial-strength database engine.
SQL Server 2016: The database for mission-critical intelligence
Microsoft is the only company recognized as a leader across data platforms and cloud by Gartner in both vision and execution, in database, business intelligence, advanced analytics, data warehouse, cloud infrastructure and cloud application platforms.
In addition, as the only commercial database that leads simultaneously in both transaction processing (per the TPC-E benchmark) and data warehousing (per the TPC-H benchmark), SQL Server allows customers to realize incredible performance against massive data sets and gain real-time insights – across all workloads, new and existing applications.
Several capabilities in SQL Server 2016 help protect data at rest and in memory (Always Encrypted), encrypt all user data with low performance overhead (Transparent Data Encryption), mitigate attacks with support for Transport Layer Security version 1.2, and allow developers to build applications that restrict access and protect data from specific users with Dynamic Data Masking (DDM) and Row Level Security (RLS).
With all of these capabilities built-in, SQL Server 2016 delivers not just a relational database, but an entire data platform for your business with incredible TCO. Today customers can save up to $10 million over three years versus Oracle, running transactional, data warehouse, data integration, business intelligence and advanced analytics workloads.* Judson Althoff, president of Microsoft North America, announced a new program to help more customers adopt SQL Server 2016 and save. Specifically, customers currently running applications or workloads on non-Microsoft paid commercial RDBMS platform will be able to migrate their existing applications with free SQL Server licenses.** Microsoft is delivering on a vision that no other company can match across data, intelligence and cloud.
Delivering AI with data: the next generation of the Microsoft data platform
This post was authored by Joseph Sirosh, Corporate Vice President, Microsoft Data Group Leveraging intelligence out of the ever-increasing amounts of data can make the difference between being the next market disruptor or being relegated to the pages of history.
We are delivering a comprehensive data platform for developers and businesses to create the next generation of intelligent applications that drive new efficiencies, help create better products, and improve customer experiences.
encourage you to attend the live broadcast of the Data Amp event, starting at 8 AM Pacific, where Scott Guthrie, executive VP of Cloud and Enterprise, and I will describe product innovations that integrate data and artificial intelligence (AI) to transform your applications and your business.
The third is flexibility—the flexibility for developers to compose multiple cloud services into various design patterns for AI, and the flexibility to leverage Windows, Linux, Python, R, Spark, Hadoop, and other open source tools in building such systems.
In the past, a common application pattern was to create statistical and analytical models outside the database in the application layer or in specialty statistical tools, and deploy these models in custom-built production systems.
In addition, developers can use all the rich features of the database management system for concurrency, high-availability, encryption, security, and compliance to build and deploy robust enterprise-grade AI applications.
In addition to several advanced machine learning algorithms from Microsoft, R Server 9.1 introduces pretrained neural network models for sentiment analysis and image featurization, supports SparklyR, SparkETL, and SparkSQL, and GPU for deep neural networks.
Here are some of the different types of intelligence that cognitive services can bring to your application: Azure Data Lake Analytics (ADLA) is a breakthrough serverless analytics job service where you can easily develop and run massively parallel petabyte-scale data transformation programs that compose U-SQL, R, Python, and .NET.
This enables what I call “Big Cognition—it’s not just extracting one piece of cognitive information at a time, and not just about understanding an emotion or whether there’s an object in an individual image, but rather it’s about integrating all the extracted cognitive data with other types of data, so you can perform powerful joins, analytics, and integrated AI.
Designed as such from the ground up, it allows customers to distribute their data across any number of Azure regions worldwide, guarantees low read and write latencies, and offers comprehensive SLAs for data-loss, latency, availability, consistency, and throughput.
I’m also proud to announce that the upcoming version of SQL Server will run just as fast on Linux as on Windows, as you’ll see in the newly published 1TB TPC-H benchmark world record nonclustered data warehouse performance achieved with SQL Server 2017 on Red Hat Enterprise Linux and HPE ProLiant hardware.
With up to 100x faster analytical queries using in-memory Columnstores, PolyBase for single T-SQL querying across relational and Hadoop systems, capability to scale to hundreds of terabytes of data, modern reporting, plus mobile BI and more, it provides a powerful integrated data platform for all your enterprise analytics needs.
Threat Detection in Azure SQL Database works around the clock, using machine learning to detect anomalous database activities indicating unusual and potentially harmful attempts to access or exploit databases.
These solutions templates have been built on best practice designs motivated by real-world customer implementations done by our engineering team, and include Personalized Offers (for example, for retail applications), Quality Assurance (for example, for manufacturing applications), and Demand Forecasting.
- On Thursday, January 17, 2019
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