1. 12/2/2017 1:42 AM
Cloud Roadshow [ITPro13] Deliver Mission Critical, Highly Available & Secure Data
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

2. Agenda Cloud First Strategy Modern Mission Critical Platform Overview
12/2/2017 1:42 AM
Agenda
Cloud First Strategy
Modern Mission Critical Platform Overview
In-Memory
Query Store
Always Encrypted, Row-level Security, Dynamic Data Masking
Enhanced AlwaysOn
Stretch Database
Temporal tables
Workload Insights
Summary
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

3. System Center Marketing
12/2/2017
Cloud first
Speed
Agility
Proven
Feedback
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

4. SQL Server 2016 All of this results in a better on-premises SQL Server
12/2/2017 1:42 AM
All of this results in a better on-premises SQL Server
Mission critical performance
Deeper insights across data
Hyperscale cloud
SQL Server 2016
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

5. Availability & Scalability
Modern Mission Critical Platform
Performance
Security
Modern
Availability & Scalability
In-memory OLTP v2
Greater T-SQL surface area, terabytes of memory supported, and greater number of parallel logical processors
Operational Analytics
Insights on operational data; Works with in-memory OLTP and disk-based OLTP
Query Store
Monitor and optimize query plans
Workload Insight
Always encrypted
Sensitive data remains encrypted at all times with ability to query
Row-level security
Apply fine-grained access control to table rows
Dynamic data masking
Real-time obfuscation of data to prevent unauthorized access
Other enhancements
Audit success/failure of database operations
TDE support for storage of in- memory OLTP tables
Enhanced auditing for OLTP with ability to track history of record changes
Temporal database support
Query data as points in time
JSON Support
Built-in JSON data support for modern web application
Polybase
Connect and query Hadoop data
Machine Learning with R integration
Enhanced AlwaysOn
3 synchronous replicas for auto failover across domains
Round robin load balancing of replicas
DTC for transactional integrity across database instances with AlwaysOn
Stretch tables in Azure
Hyperscale table into Azure database without application changes
Support for Windows Server 2016
12TB 16 Sockets

6. In-Memory Faster Transactions Faster Queries IN-MEMORY OLTP
12/2/2017
In-Memory
Faster Transactions
IN-MEMORY OLTP
Faster Queries
IN-MEMORY DW
Up to 30x faster transaction processing with In-Memory OLTP
Over 100x query speed and significant data compression with
In-Memory ColumnStore
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

7. In-memory OLTP Hardware trends Business
Customer Benefits
High performance data operations
Efficient business-logic processing
Frictionless scale-up
Hybrid engine and integrated experience
Architectural Pillars
Main-Memory Optimized
T-SQL Compiled to Machine Code
High Concurrency
SQL Server Integration
Optimized for in-memory data
Indexes (hash and range) exist only in memory
No buffer pool, B-trees
Stream-based storage
T-SQL compiled to machine code via C code generator
Invoking a procedure is just a DLL entry-point
Aggressive compile-time
Multi-version optimistic concurrency control with full ACID support
Core engine uses lock-free algorithms
No lock manager, latches or spinlocks
Same manageability, administration & development experience
Integrated queries & transactions
Integrated HA and backup/restore
Drivers
Hardware trends
Business
Steadily declining memory price, NVRAM
Stalling CPU clock rate
Many-core processors
TCO

8. Columnstore (index) Data stored as rows Data stored as columns
Benefits:
Improved compression:
Data from same domain compress better
Reduced I/O:
Fetch only columns needed
Improved Performance:
More data fits in memory C1 C2 C3 C5 C4 …

9. In-memory column store 2016
12/2/2017 1:42 AM
In-memory column store 2016
Updatable NCCI
In-Memory OLTP + Column-store
Faster batch mode scans using CPU vector instructions
Dynamic Aggregate pushdown
PK/FK enforcement
Offload Reporting to AlwaysOn Secondary Replica
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

10. Operational Analytics
Server & Tools Business
12/2/2017
Operational Analytics
Capability
In-memory Columnar index over in- memory/disk based OLTP tables Enhanced OLTP T-SQL surface area Scale to higher compute and memory
Benefits
Unlike competition, you gain operational analytics & 30x faster transactions & 100x queries
In-memory for more of your applications
In-memory ColumnStore
SQL Server data warehouse
2-24
hrs
ETL
Real-time fraud detection
Fraud detected
In-memory OLTP
SQL Server OLTP
Mission critical performance
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

11. Demo Column store 12/2/2017 1:42 AM
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

12. Query Store = DB flight data recorder
What does it do?
Collects and persists
query texts and compile time stats
plan choices and runtime metrics
Enables you to force plans from history
Advantage
Works in engine, very low overhead
Compact data & high compression
Easy to use interfaces on top
Find and fix plan regressions
Identify top resource consumers
De-risk SQL Server upgrade
Deeply analyze workload patterns
Short-term/tactical
Long-term/strategic

13. Monitoring Performance By Using the Query Store
The query store feature provides DBAs with insight on query plan choice and performance

14. Query Store – Upgrade Made Easy
Upgrade to SQL vNext
Keep 110/120 CompatLevel
Freeze plans (optional)
Run Query Store (establish perf. baseline)
Move to 130 CompatLevel and unfreeze plans
Monitor perf. and fix regressions with plan forcing

15. 12/2/2017 1:42 AM
Demo
Query Store
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

16. Always Encrypted Prevents Data Disclosure Queries on Encrypted Data
12/2/2017
Always Encrypted
Prevents Data Disclosure
Client-side encryption of sensitive data using keys that are never given to the database system.
Queries on Encrypted Data
Support for equality comparison, incl. join, group by and distinct operators.
Application Transparency
Minimal application changes via server and client library enhancements.
Allows customers to securely store sensitive data outside of their trust boundary.
Data remains protected from high-privileged, yet unauthorized users.
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

17. SQL Server or SQL Database
How it Works Help protect data at rest and in motion, on-premises & cloud
Encrypted sensitive data and corresponding keys are never seen in plaintext in SQL Server
SQL Server or SQL Database
trust boundary
Client
"SELECT Name FROM Customers WHERE SSN
" "
"SELECT Name FROM Customers WHERE SSN
0x7ff654ae6d
ciphertext
ADO .NET
Result Set
Result Set
Name
Wayne Jefferson
Name
0x19ca706fbd9a
dbo.Customers
Name
SSN
Country
0x19ca706fbd9a
0x7ff654ae6d
USA
ciphertext

18. Row level security Fine-grained Access Control
12/2/2017
Row level security
Targeting enterprise customers in finance, insurance, healthcare, oil/gas, … sectors
Fine-grained Access Control
Keeping multi-tenant databases secure by limiting access by other users who share the same tables.
Application Transparency
RLS works transparently at query time, no app changes needed.
Compatible with RLS in other leading products.
Centralized Security Logic
Enforcement logic resides inside database and is schema-bound to the table it protects providing greater security. Reduced application maintenance and complexity.
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

19. RLS in 3 steps Security Policy
Microsoft Research 2013
12/2/2017 1:42 AM
RLS in 3 steps
3) Security Policy transparently rewrites query to apply filter predicate
1) Policy manager creates filter predicate and security policy in T-SQL, binding the predicate to the Patients table
2) App user (e.g., nurse) selects from Patients table
Database
Nurse
Policy Manager
Filter Predicate:
INNER JOIN…
Security
Policy
Patients
CREATE FUNCTION int)
RETURNS TABLE WITH SCHEMABINDING AS
return SELECT 1 as [fn_securitypredicate_result] FROM
StaffDuties d INNER JOIN Employees e
ON (d.EmpId = e.EmpId)
WHERE e.UserSID = SUSER_SID() = d.Wing;
CREATE SECURITY POLICY dbo.SecPol
ADD FILTER PREDICATE dbo.fn_securitypredicate(Wing) ON Patients
WITH (STATE = ON)
Application
SELECT * FROM patients
SEMIJOIN APPLY dbo.fn_securitypredicate(patients.Wing);
SELECT * FROM Patients
SELECT Patients.* FROM Patients,
StaffDuties d INNER JOIN Employees e ON (d.EmpId = e.EmpId)
WHERE e.UserSID = SUSER_SID() AND Patients.wing = d.Wing;
© 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

20. Dynamic data masking in SQL database
12/2/2017
Dynamic data masking in SQL database
Limit sensitive data exposure by obfuscating it for non-privileged users
Table.CreditCardNo
On-the-fly obfuscation of data in query results
Policy-driven at the table and column
Multiple masking functions available for various sensitive data categories
Flexibility to define a set of privileged logins for un-masked data access
On-the-fly masking of sensitive data in query results
SQL Server
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

21. Dynamic data masking 2) App user selects from Employee table
Microsoft Research 2013
12/2/2017 1:42 AM
Dynamic data masking
2) App user selects from Employee table
Security officer defines dynamic data masking policy in T-SQL over sensitive data in Employee table
3) Dynamic data masking policy obfuscates the sensitive data in the query results
SELECT Patients.* FROM Patients,
StaffDuties d INNER JOIN Employees e ON (d.EmpId = e.EmpId)
WHERE e.UserSID = SUSER_SID() AND Patients.wing = d.Wing;
Security
Officer
ALTER TABLE [Employee] ALTER COLUMN [SocialSecurityNumber]
ADD MASKED WITH (FUNCTION = ‘SSN()’
ALTER TABLE [Employee] ALTER COLUMN [ ]
ADD MASKED WITH (FUNCTION = ‘ ()’)
ALTER TABLE [Employee] ALTER COLUMN [Salary]
ADD MASKED WITH (FUNCTION = ‘RANDOM(1,20000)’)
GRANT UNMASK to admin1
admin1 login
other login
SELECT [Name],
[SocialSecurityNumber],
[ ],
[Salary]
FROM [Employee]
SELECT * FROM patients
SEMIJOIN APPLY dbo.fn_securitypredicate(patients.Wing);
© 2013 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

22. Readable Secondary load balancing
12/2/2017
Readable Secondary load balancing
READ_ONLY_ROUTING_LIST= ((‘COMPUTER2’,’COMPUTER3’,’COMPUTER4’),’COMPUTER5’)
DR Site
Primary Site
Computer2
Computer1 (Primary)
Computer5
Computer3
Computer4
© 2015 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

23. Server & Tools Business
12/2/2017
Stretch SQL Server into Azure Stretch warm and cold tables to Azure with remote query processing
Capability
Stretch cold database tables from on-premises SQL Server Databases to Azure with remote query processing
Benefits
Cost effective historical data
Entire table is online and remains queryable from on-premises apps
Transparent to applications
Supports Always Encrypted & Row Level Security
Microsoft Azure
Jim Gray
ox7ff654ae6d
3/18/2005
Order history
Name
SSN
Date
Jane Doe
cm61ba906fd
2/28/2005
Jim Gray
ox7ff654ae6d
3/18/2005
John Smith
i2y36cg776rg
4/10/2005
Bill Brown
nx290pldo90l
4/27/2005
Sue Daniels
ypo85ba616rj
5/12/2005
Sarah Jones
bns51ra806fd
5/22/2005
Jake Marks
mci12hh906fj
6/07/2005
Eric Mears
utb76b916gi
6/18/2014
Rachel Hogan
px61hi9306fj
7/1/2014
Sam Johnson
ol43bi506gd
7/12/2014
David Simon
tx83hal916fi
7/29/2014
Michelle Burns
nb95re926gi
8/10/2014
Reed Dean
vc61ira536fe
8/23/2014
Order history
Name
SSN
Date
Jane Doe
cm61ba906fd
2/28/2005
Jim Gray
ox7ff654ae6d
3/18/2005
John Smith
i2y36cg776rg
4/10/2005
Bill Brown
nx290pldo90l
4/27/2005
Customer data
Product data
Order History
Stretch to cloud
Query 
SQL Server
App
Hyperscale cloud
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

24. Why temporal? Real data sources are dynamic Workarounds are…
Historical data may be critical to business success
Traditional databases fail to provide required insights
Workarounds are…
Complex, expensive, limited, inflexible, inefficient
SQL Server 2016 makes life easy
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

25. How to start with temporal
Microsoft Ignite 2015
12/2/2017 1:42 AM
How to start with temporal
no change in programming model
new insights
SELECT * FROM temporal
Querying
FOR SYSTEM_TIME
AS OF
FROM..TO
BETWEEN..AND
CONTAINED IN
Temporal Querying
INSERT / BULK INSERT
UPDATE
DELETE
MERGE
DML
CREATE temporal TABLE PERIOD FOR SYSTEM_TIME…
ALTER regular_table TABLE ADD PERIOD…
DDL
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

26. Temporal with Stretch Facts: Azure SQL Database Solution:
Microsoft Ignite 2015
12/2/2017 1:42 AM
Temporal with Stretch
Facts:
History is much bigger than actual data
Retained between 3 and 10 years
“Warm”: up to a few weeks/months
“Cold”: rarely queried
SELECT * FROM Department
FOR SYSTEM_TIME
AS OF ' '
Azure SQL Database
Solution:
history as a stretch table:
PeriodEnd < “Now - 6 months”
© 2015 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

27. Workload Insight Scale Out Predictable Perf
Powered by Workload Insight
SLA for Business Tx
Backups
My budget for DB infra is $800/month
I need X business Tx/sec to run my business
I need my business Tx to complete under Y ms
I need Z days of backup retention + GeoDR My customers are mostly in US and Europe
Patching
Scale Up/Down
Index/Schema Mgmt.
Monitoring
In Memory
Azure SQL Database Service
HA/DR
Azure SQL Database today:
Platform automatically manages HW/SW stack, backups, HA
Users still need to worry about a lot of DB-specific details
User focus is on how AzureDB platform works
Traditional RDBMS world:
All aspects of building, running and tuning are left up to the users
Requires significant expertise and large investment of time/energy
User focus is on how the RDBMS works
Towards Azure SQL Database as an intelligent service
Platform does the tedious work automatically
Users focus on guiding the platform according to their needs
Hardware
Security

28. Workload Insight in SQL Database
Strike the desired price/perf balance
Optimum service tier for your DB
Optimum elastic pool across your many DBs
Optimize the workload for max perf
Optimize the indexes in your DBs
Recommendations or auto-management
Easily troubleshoot and tune your DB
Query Store and insights on top

29. Learning on hyper-scale cloud Building the learnings into SQL Server
Cloud-First
Azure DB
Learning on hyper-scale cloud
Building the learnings into SQL Server

30. Thank you Lindsey Allen Principal Group Program Manager
SQL Server Core Engine
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.