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SQL Server 2017 Deep Dive - @Ignite 2017
End-to-end mobile BI
on any device
Choice of platform
and language
Most secure
over the last 7 years
0
20
40
60
80
100
120
140
160
180
200
Vulnerabilities(2010-2016)
A fraction of the cost
Self-serviceBIperuser
Only commercial DB
with AI built-in
Microsoft Tableau Oracle
$120
$480
$2,230
Industry-leading
performance
1/10
Most consistent data platform
#1 TPC-H performance
1TB, 10TB, 30TB
#1 TPC-E performance
#1 price/performance
T-SQL
Java
C/C++
C#/VB.NET
PHP
Node.js
Python
Ruby
R
R and Python + in-memory
at massive scale
S Q L S E R V E R 2 0 1 7
I N D U S T R Y - L E A D I N G P E R F O R M A N C E A N D S E C U R I T Y N O W O N L I N U X A N D D O C K E R
Private cloud Public cloud
+ T-SQL
In-memory across all workloads
1/10th the cost of Oracle
F L E X I B L E , R E L I A B L E
D ATA M A N A G E M E N T
SQL Server on the platform of
your choice
Support for RedHat Enterprise Linux (RHEL),
Ubuntu, and SUSE Enterprise Linux (SLES)
Linux and Windows Docker containers
Windows Server / Windows 10
Package-based installation: Yum Install, Apt-Get,
and Zypper
Choice of platform and language
Performance and scale Cross-OS compatibility
Same app code runs across platforms
Native user experience
On Linux and macOS (server & tools)
SQL Server 2017 Deep Dive - @Ignite 2017
SQL Platform Abstraction Layer
(SQLPAL)
DB
Engine
IS AS RS
Windows Linux
Windows
Host Ext.
Linux Host
Extension
SQL Platform Abstraction Layer
(SQLPAL)
Win32-like APIs
Host Extension mapping to OS system calls
(IO, Memory, CPU scheduling)
SQL OS API
SQL OS v2
Everything else
System Resource & Latency
Sensitive Code Paths
SQL Server 2017 Deep Dive - @Ignite 2017
SQL Server 2017 Deep Dive - @Ignite 2017
Choice of platform and language
M I S S I O N C R I T I C A L
AVA I L A B I L I T Y O N
A N Y P L AT F O R M
Always On cross-platform
capabilities
HA and DR for Linux and Windows
Support for clusterless Availability Groups
Ultimate HA with OS-level redundancy
and low-downtime migration
Load balancing of readable secondaries
SQL Server 2017 Deep Dive - @Ignite 2017
Push code Build Test Deploy
Development
Create
dev/test
environments
Consume
dev/test
environment
Push change
Check-in
tests
Scheduled
tests
Create
pre-prod
environment
Pre-
production
tests
Deploy
CI
CD
Development
Create
dev/test
environments
Dependency
Update
SQL Server 2017 Deep Dive - @Ignite 2017
Bring graph data
NEW*
support to
your relational data to store and
analyze new types of relationships
The power to query over any type of data
Graph data support
Quarterly
business
review
Andy
Smith
Mary
Jones
Denny
Usher
Bill
Brown
Rachel
Hogan
Product
dev project
IT
assessment
Eric
Mears
Michelle
Burns
HR team can determine
which staff are working
on which projectsProjects
Managers
Associates
SQL Server 2017 Deep Dive - @Ignite 2017
Value
Data
ActionDecisions
Advanced
Analytics
Predictive &
Prescriptive Analytics
Business
Intelligence
Descriptive &
Diagnostic
Analytics
Intelligent workloads
Intelligent apps need to be able to:
Ingest data in real-time
Query across historical and real-time data
Analyze patterns and make predictions
Ingest real-time train data:
Brakes are hot!
Query across historical data:
They’ve been hot
for 4 hours!
Analyze global trends:
Could lead to accident
SQL Server 2017 Deep Dive - @Ignite 2017
A N N O U N C I N G
S P E C I A L P R I C I N G F O R S Q L S E R V E R O N L I N U X
A N D R E D H AT E N T E R P R I S E L I N U X
Microsoft.com/SQLServer2017
aka.ms/azuredataservices
New capabilities for data
integration in the cloud
Wednesday, September 26
11:00 – 12:15
BRK 2254
Modernize your on-premises
applications with SQL
Database Managed Instances
Wednesday, September 27
10:45 - 12:00
BRK 2217
Azure Cosmos DB: The
globally distributed,
multi-model database
Tuesday, September 26
10:45 - 12:00
BRK3086
How to build ML apps using R
and Python
Thursday, September 28
2:15-3:30
BRK 3298
Dining on data: Consume and
query petabytes of data with
Azure SQL Data Warehouse
Tuesday, September 26
9:00 -10:15
BRK 3242
https://github.com/twright-msft/mssql-test-scripts
https://github.com/twright-msft/contoso-u
https://github.com/tobiassql/samples
http://myignite.microsoft.com
https://aka.ms/ignite.mobileapp
SQL Server 2017 Deep Dive - @Ignite 2017

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SQL Server 2017 Deep Dive - @Ignite 2017

  • 2. End-to-end mobile BI on any device Choice of platform and language Most secure over the last 7 years 0 20 40 60 80 100 120 140 160 180 200 Vulnerabilities(2010-2016) A fraction of the cost Self-serviceBIperuser Only commercial DB with AI built-in Microsoft Tableau Oracle $120 $480 $2,230 Industry-leading performance 1/10 Most consistent data platform #1 TPC-H performance 1TB, 10TB, 30TB #1 TPC-E performance #1 price/performance T-SQL Java C/C++ C#/VB.NET PHP Node.js Python Ruby R R and Python + in-memory at massive scale S Q L S E R V E R 2 0 1 7 I N D U S T R Y - L E A D I N G P E R F O R M A N C E A N D S E C U R I T Y N O W O N L I N U X A N D D O C K E R Private cloud Public cloud + T-SQL In-memory across all workloads 1/10th the cost of Oracle
  • 3. F L E X I B L E , R E L I A B L E D ATA M A N A G E M E N T SQL Server on the platform of your choice Support for RedHat Enterprise Linux (RHEL), Ubuntu, and SUSE Enterprise Linux (SLES) Linux and Windows Docker containers Windows Server / Windows 10 Package-based installation: Yum Install, Apt-Get, and Zypper Choice of platform and language
  • 4. Performance and scale Cross-OS compatibility Same app code runs across platforms Native user experience On Linux and macOS (server & tools)
  • 6. SQL Platform Abstraction Layer (SQLPAL) DB Engine IS AS RS Windows Linux Windows Host Ext. Linux Host Extension SQL Platform Abstraction Layer (SQLPAL) Win32-like APIs Host Extension mapping to OS system calls (IO, Memory, CPU scheduling) SQL OS API SQL OS v2 Everything else System Resource & Latency Sensitive Code Paths
  • 9. Choice of platform and language M I S S I O N C R I T I C A L AVA I L A B I L I T Y O N A N Y P L AT F O R M Always On cross-platform capabilities HA and DR for Linux and Windows Support for clusterless Availability Groups Ultimate HA with OS-level redundancy and low-downtime migration Load balancing of readable secondaries
  • 11. Push code Build Test Deploy
  • 15. Bring graph data NEW* support to your relational data to store and analyze new types of relationships The power to query over any type of data Graph data support Quarterly business review Andy Smith Mary Jones Denny Usher Bill Brown Rachel Hogan Product dev project IT assessment Eric Mears Michelle Burns HR team can determine which staff are working on which projectsProjects Managers Associates
  • 18. Intelligent workloads Intelligent apps need to be able to: Ingest data in real-time Query across historical and real-time data Analyze patterns and make predictions Ingest real-time train data: Brakes are hot! Query across historical data: They’ve been hot for 4 hours! Analyze global trends: Could lead to accident
  • 20. A N N O U N C I N G S P E C I A L P R I C I N G F O R S Q L S E R V E R O N L I N U X A N D R E D H AT E N T E R P R I S E L I N U X
  • 21. Microsoft.com/SQLServer2017 aka.ms/azuredataservices New capabilities for data integration in the cloud Wednesday, September 26 11:00 – 12:15 BRK 2254 Modernize your on-premises applications with SQL Database Managed Instances Wednesday, September 27 10:45 - 12:00 BRK 2217 Azure Cosmos DB: The globally distributed, multi-model database Tuesday, September 26 10:45 - 12:00 BRK3086 How to build ML apps using R and Python Thursday, September 28 2:15-3:30 BRK 3298 Dining on data: Consume and query petabytes of data with Azure SQL Data Warehouse Tuesday, September 26 9:00 -10:15 BRK 3242 https://github.com/twright-msft/mssql-test-scripts https://github.com/twright-msft/contoso-u https://github.com/tobiassql/samples

Editor's Notes

  1. #1 price/performance in TPC-H non-clustered as of 9/1/2017 - http://www.tpc.org/3323 #1 TPC-H non-clustered benchmark as of 9/1/2017 - http://www.tpc.org/3323 #1 TPC-E performance as of 9/1/2017 - http://www.tpc.org/4075
  2. Last but not least, customers need flexibility when it comes to the choice of platform, programming languages & data infrastructure to get from the most from their data.   Why? In most IT environments, platforms, technologies and skills are as diverse as they have ever been, the data platform of the future needs to you to build intelligent applications on any data, any platform, any language on premises and in the cloud.   SQL Server manages your data, across platforms, with any skills, on-premises & cloud Our goal is to meet you where you are with on any platform, anywhere with the tools and languages of your choice. SQL now has support for Windows, Linux & Docker Containers. It allows you to leverage the language of your choice for advanced analytics – R & Python.
  3. Generally only features that “leak” into the OS and performance/scale need work
  4. Mission critical availability on any platform In preparation for the release of SQL Server v.Next, we are enabling the same High Availability (HA) and Disaster Recovery (DR) solutions on all platforms supported by SQL Server, including Windows and Linux. Always On Availability Groups is SQL Server’s flagship solution for HA and DR. Microsoft has released a preview of Always On Availability Groups for Linux in SQL Server v.Next Community Technology Preview (CTP) 1.3. SQL Server Always On availability groups can have up to eight readable secondary replicas. Each of these secondary replicas can have their own replicas as well. When daisy chained together, these readable replicas can create massive scale-out for analytics workloads. This scale-out scenario enables you to replicate around the globe, keeping read replicas close to your Business Analytics users. It’s of particularly big interest to users with large data warehouse implementations. And, it’s also easy to set up. In fact, you can now create availability groups that span Windows and Linux nodes, and scale out your analytics workloads across multiple operating systems. New flexibility to do HA without Windows Server fail over clustering Fail-over clustering with Pacemaker and more through integration scripts and guides Always On availability groups with automatic fail-over, listener, synchronous replication, read-only secondaries Shared disk failover clusters Backup and restore: .bak, .bacpac, and .dacpac Log shipping
  5. New support for Graph Data Full CRUD support to create nodes and edges Query language extension provides multi-hop navigation using join-free pattern matching SQL engine integration enables querying across SQL tables and graph data Existing tools work out of the box with graph data In addition, you can create an external table that maps the two structured and unstructured data and the PolyBase technology available in SQL Server allows customers to query that external table, so the structured and unstructured data can be correlated together.
  6. Slide objective Cover how businesses are using data to help them make actionable decisions more quickly. This slide, based on recognized industry research (from Gartner and IDC), explores this idea in greater detail. The following slide then discusses how R offerings from Microsoft, specifically, support the use of data for making better, faster decisions. Talking points Before we dive into talking about Microsoft SQL Server 2016 R Services and Microsoft R Server, let’s simply talk about data. [CLICK] Exciting, right? Data is the currency of modern business. It is a key strategic business asset. Every device, every customer, every activity―everything that’s happening in the world around us―is producing incredibly rich data that can help us create new experiences, new efficiencies, new business models, and even new inventions. Leveraging this data can be the differentiator for your business. [CLICK] For example, IDC estimates that companies leading the way in using data assets to their advantage will capture $1.6 trillion more in business value than those that lag behind. Yet, while data is pervasive, actionable intelligence from data is elusive. [CLICK] Enterprises want to transform data into intelligent decisions, turn those decisions into action, and reinvent their mostly manual business processes. To do this, they need to analyze massive amounts of data with more ease. [CLICK] The rise of machine learning and advanced analytics today gives them the ability to not only look at historical data to understand “what” happened, [CLICK] But also “why” it happened, [CLICK] And also to harness predictive analytics to peer into the future. [CLICK] Then, using those predictive analytics, they can better understand what is likely to happen and identify what actions should be taken so they can automate outcomes. [CLICK] All of this now means that our data is much more valuable than it was before.
  7. Ingest Query Analyze
  8. Red Hat and Microsoft are offering a special discount on SQL Server on Linux, and Red Hat Enterprise Linux Together we are offering an up to 30% discount on both. And, you get integrated support from both companies.    All in all, a great value for customers who are planning to get started with SQL Server on Linux – the TCO makes it a no-brainer.
  9. And for more information about the topics we discussed today, please join us at one of these Breakout Sessions! Thank you!