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E-grāmata: SQL Server 2019 Revealed: Including Big Data Clusters and Machine Learning

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  • Formāts: EPUB+DRM
  • Izdošanas datums: 18-Oct-2019
  • Izdevniecība: APress
  • Valoda: eng
  • ISBN-13: 9781484254196
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SQL Server 2019 Revealed: Including Big Data Clusters and Machine LearningPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 18-Oct-2019
  • Izdevniecība: APress
  • Valoda: eng
  • ISBN-13: 9781484254196
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Get up to speed on the game-changing developments in SQL Server 2019. No longer just a database engine, SQL Server 2019 is cutting edge with support for machine learning (ML), big data analytics, Linux, containers, Kubernetes, Java, and data virtualization to Azure. This is not a book on traditional database administration for SQL Server. It focuses on all that is new for one of the most successful modernized data platforms in the industry. It is a book for data professionals who already know the fundamentals of SQL Server and want to up their game by building their skills in some of the hottest new areas in technology.

SQL Server 2019 Revealed begins with a look at the project's team goal to integrate the world of big data with SQL Server into a major product release. The book then dives into the details of key new capabilities in SQL Server 2019 using a “learn by example” approach for Intelligent Performance, security, mission-criticalavailability, and features for the modern developer. Also covered are enhancements to SQL Server 2019 for Linux and gain a comprehensive look at SQL Server using containers and Kubernetes clusters.

The book concludes by showing you how to virtualize your data access with Polybase to Oracle, MongoDB, Hadoop, and Azure, allowing you to reduce the need for expensive extract, transform, and load (ETL) applications. You will then learn how to take your knowledge of containers, Kubernetes, and Polybase to build a comprehensive solution called Big Data Clusters, which is a marquee feature of 2019. You will also learn how to gain access to Spark, SQL Server, and HDFS to build intelligence over your own data lake and deploy end-to-end machine learning applications.


What You Will Learn
  • Implement Big Data Clusters with SQL Server, Spark, and HDFS
  • Create a Data Hub with connections to Oracle, Azure, Hadoop, and other sources
  • Combine SQL and Spark to build a machine learning platform for AI applications
  • Boost your performance with no application changes using Intelligent Performance
  • Increase security of your SQL Server through Secure Enclaves and Data Classification
  • Maximize database uptime through online indexing and Accelerated Database Recovery
  • Build new modern applications with Graph, ML Services, and T-SQL Extensibility with Java
  • Improve your ability to deploy SQL Server on Linux
  • Gain in-depth knowledge to run SQL Server with containers and Kubernetes
  • Know all the new database engine features for performance, usability, and diagnostics
  • Use the latest tools and methods to migrate your database to SQL Server 2019
  • Apply your knowledge of SQL Server 2019 to Azure


Who This Book Is For

IT professionals and developers who understand the fundamentals of SQL Server and wish to focus on learning about the new, modern capabilities of SQL Server 2019. The book is for those who want to learn about SQL Server 2019 and the new Big Data Clusters and AI feature set, support for machine learning and Java, how to run SQL Server with containers and Kubernetes, and increased capabilities around Intelligent Performance, advanced security, and high availability. 


Intermediate-Advanced user level
About the Author xiii
About the Technical Reviewer xv
Foreword xvii
Acknowledgments xix
Introduction xxi
Chapter 1 Why SQL Server 2019?
1(18)
Project Seattle
2(1)
Project Aris
3(3)
Seattle Becomes SQL Server 2019
6(1)
Modernizing Your Database with SQL Server 2019
7(8)
Data Virtualization
10(1)
Performance
11(1)
Security
11(1)
Mission-Critical Availability
12(1)
Modern Development Platform
12(1)
Investing in the Platform of Your Choice
13(1)
Azure Data Studio
14(1)
Voice of the Customer
14(1)
Getting Started with SQL Server 2019
15(4)
Download SQL Server 2019
15(1)
Deploy SQL Server 2019
15(1)
Migrate to SQL Server 2019
15(1)
What's New in SQL Server 2019
15(1)
Download Book Code and Sample Databases
16(1)
SQL Server Workshops
16(1)
It Is Your Grandpa's SQL Server?
16(3)
Chapter 2 Intelligent Performance
19(68)
Why Intelligent Performance?
19(1)
Intelligent Query Processing
20(41)
Prerequisites for Using the Examples for Intelligent Query Processing
22(2)
Memory Grant Feedback Row Mode
24(18)
Table Variable Deferred Compilation
42(7)
Batch Mode on Rowstore
49(3)
Scalar UDF Inlining
52(5)
Approximate Count Distinct
57(4)
Lightweight Query Profiling
61(13)
Prerequisites for Using the Examples for Lightweight Query Profiling
62(1)
Should I Kill an Active Query?
63(5)
I Can't Catch It
68(6)
In-Memory Database
74(9)
Memory-Optimized TempDB Metadata
75(6)
Hybrid Buffer Pool
81(1)
Persistent Memory Support
82(1)
Last-Page Insert Contention
83(2)
Summary
85(2)
Chapter 3 New Security Capabilities
87(28)
Enhancing What We Have Built
87(1)
Always Encrypted with Secure Enclaves
88(4)
Why Enclaves?
90(1)
Using Always Encrypted with Enclaves
91(1)
Data Classification
92(19)
Prerequisites for Using the Examples
95(1)
Using Data Classification
96(9)
Auditing and Data Classification
105(6)
Other New Security Features
111(3)
TDE Pause and Resume
111(1)
Certificate Management
112(2)
Summary
114(1)
Chapter 4 Mission-Critical Availability
115(32)
Online Index Maintenance
116(7)
Resumable Index Operations
117(1)
Prerequisites to Using the Example
118(1)
Try Out Resumable Index Creation
118(5)
Online Index Maintenance for Columnstore
123(1)
Enhancing Always On Availability Groups
123(2)
Support for More Synchronous Replicas
124(1)
Secondary to Primary Replica Read/Write Connection Redirection
124(1)
Accelerated Database Recovery
125(20)
The Challenge of Long Active Transactions
126(1)
How Accelerated Database Recovery Works
126(9)
Using Accelerated Database Recovery
135(4)
Accelerate Database Recovery Nuts and Bolts
139(6)
Summary
145(2)
Chapter 5 Modern Development Platform
147(28)
Languages, Drivers, and Platforms
148(3)
Languages and Drivers
148(3)
Platforms and Editions
151(1)
Graph Database
151(5)
What Is a Graph Database in SQL Server?
152(1)
Using a Graph Database in SQL Server
153(2)
Graph Enhancements for SQL Server 2019
155(1)
UTF-8 Support
156(2)
Unicode and SQL Server
157(1)
Why Would You Use UTF-8?
157(1)
SQL Server Machine Learning Services
158(8)
How It Works
159(4)
Security, Isolation, and Governance
163(2)
What's New in SQL Server 2019?
165(1)
Extending the T-SQL Language
166(8)
The Extensibility Framework
167(1)
Extending T-SQL with Java
168(6)
Implementing and Using Other Languages
174(1)
Summary
174(1)
Chapter 6 SQL Server 2019 on Linux
175(20)
The Amazing Story of SQL Server on Linux
175(2)
What Is New for SQL Server 2019 on Linux
177(1)
Platform and Deployment Enhancements
178(4)
Platform Enhancements
178(2)
SQL Server 2019 on Linux Deployment
180(1)
Supporting New Linux Releases
181(1)
Persistent Memory Support
182(1)
SQL Server Replication on Linux
183(1)
Change Data Capture (CDC) on Linux
184(1)
DTC on Linux
184(2)
Active Directory with OpenLDAP
186(1)
SQL Server Machine Learning Services and Extensibility on Linux
187(6)
Deployment of SQL Server ML Services on Linux
187(2)
How It Works
189(3)
The Extensibility Framework and Language Extensions
192(1)
Polybase on Linux
193(1)
Summary
193(2)
Chapter 7 Inside SQL Server Containers
195(54)
Why SQL Server Containers?
195(4)
How SQL Server Containers Work
199(7)
Container Hosting
199(1)
Is Docker Magic?
200(1)
Container Lifecycle
201(2)
The SQL Server Container
203(3)
What Is New for SQL Server 2019
206(4)
Prerequisites for the Examples
210(2)
Deploying a SQL Server Container
212(13)
A New Way to Update SQL Server
225(4)
Deploying Container As an Application
229(7)
The docker-compose.yml File
230(1)
Building Each Container
231(2)
Running the Containers for Replication
233(3)
Deploying SQL Containers in Production
236(7)
Performance
236(2)
Security
238(1)
High Availability
239(1)
Resource Control
239(2)
Server or Database Configuration
241(1)
Using Other Packages
242(1)
Editions and Licensing
242(1)
SQL Server Windows Containers
243(3)
Summary
246(3)
Chapter 8 SQL Server on Kubernetes
249(48)
What Is k8s?
249(4)
References on k8s
250(1)
k8s Objects
250(2)
Comment on Internals of k8s
252(1)
k8s Deployment Options
253(2)
Prerequisites for the Examples
255(2)
Deploying SQL Server on k8s
257(24)
Tips with k8s
273(8)
SQL Server High Availability on k8s
281(6)
Updating SQL Server on k8s
287(5)
Using Helm Charts
292(1)
SQL Server Availability Groups on k8s
292(3)
Summary
295(2)
Chapter 9 SQL Server Data Virilization
297(34)
What Is Poly base?
297(5)
The History of Polybase
298(2)
What Is Data Virilization?
300(2)
How Polybase Works
302(9)
The Polybase Workflow
303(2)
SQL Server 2019 Polybase Architecture
305(1)
How External Tables Work
305(2)
The Polybase Standalone Instance
307(2)
A Polybase Scale-Out Group
309(1)
Query Processing and Polybase
310(1)
How Does It Work on Linux?
310(1)
How Is This Different Than Azure?
310(1)
Prerequisites for the Examples
311(4)
Setting Up and Enabling Polybase
311(2)
Using the Examples
313(2)
Using External Tables
315(12)
Tools and External Tables
315(2)
Using an External Table with Azure SQL Database
317(8)
Using Built-in Connectors for External Tables
325(1)
Using an External Table with HDFS
326(1)
Using External Tables with ODBC Connectors
326(1)
Considerations for External Tables
327(1)
A New Semantic Layer
327(1)
External Tables vs. Linked Servers
328(1)
Restrictions and Limitations
328(1)
Summary
328(3)
Chapter 10 SQL Server Big Data Clusters
331(52)
Why Big Data Clusters?
334(1)
What Comes with Big Data Clusters?
335(3)
SQL Server 2019
335(1)
Polybase
336(1)
Hadoop Distributed File System (HDFS)
336(1)
Spark
336(1)
Data Cache
336(1)
Tools and Services
337(1)
Endpoints
337(1)
Application Deployment
337(1)
Machine Learning
337(1)
Prerequisites for the Examples
338(1)
Deploying Big Data Clusters
339(12)
Plan the Deployment
339(5)
The BDC Deployment Experience
344(2)
Verify the Deployment
346(4)
Configuring Deployment for Production
350(1)
Big Data Cluster Architecture
351(12)
SQL Server Master Instance
353(4)
Controller
357(2)
Storage Pool
359(2)
Compute Pool
361(1)
Data Pool
361(1)
Application Pool
362(1)
Using Big Data Clusters
363(11)
Using Data Virtualization
366(3)
Using the Data Pool
369(1)
Using Spark
369(2)
Deploying and Using Applications
371(1)
Security
372(1)
High Availability
372(1)
Jupyter Books for SQL Server Big Data Clusters
373(1)
Machine Learning and Big Data Clusters
374(2)
Machine Learning Packages
375(1)
Using Examples
375(1)
Managing and Monitoring Big Data Clusters
376(5)
Managing Kubernetes (k8s)
376(1)
Managing and Monitoring Big Data Clusters
377(4)
Summary
381(2)
Chapter 11 The Voice of the Customer and Migration
383(30)
The Voice of the Customer
383(10)
Performance Enhancements
384(2)
User Experience
386(3)
Diagnostics
389(4)
What About Business Intelligence?
393(1)
Migration to SQL Server 2019
394(18)
The Pam and Pedro Show
394(1)
Database Migration Assistant
395(2)
Database Experimentation Assistant
397(2)
Upgrading to SQL Server 2019
399(4)
Database Compatibility
403(4)
Query Tuning Assistant and Post Migration
407(1)
Running in Azure Virtual Machine
408(2)
SQL Server Migration Assistant
410(2)
Summary
412(1)
Index 413
Bob Ward is Principal Architect for the Microsoft Azure Data SQL Server team, which owns the development for all SQL Server releases. He has worked for Microsoft for more than 26 years on every version of SQL Server shipped from OS/2 1.1 to SQL Server 2019 including Azure. Bob is a well-known speaker on SQL Server, often presenting talks on new releases, internals, and performance at events such as PASS Summit, SQLBits, SQLIntersection, Red Hat Summit, Microsoft Inspire, and Microsoft Ignite. You can follow him at @bobwardms or linkedin.com/in/bobwardms. He is the author of the book Pro SQL Server on Linux (Apress).
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