L’obiettivo è quello di fare una panoramica dello stato dell’arte sulle tecnologie a supporto dei database. Alcuni esempi sono la tecnologia in-memory integrata con le funzionalità di analisi operative in tempo reale e della tecnologia Always Encrypted per la protezione dei dati utilizzati in locale o durante gli spostamenti. La tecnologia in-memory consente di migliorare di 30 volte le performance delle transazioni utilizzando hardware standard di settore. Inoltre i Big Data e l'analisi sono diventati un importante fattore di differenziazione competitivo, ma la gestione delle enormi quantità di dati correlate a un tempo di attività 24 ore su 24 continua a essere una sfida per l'IT. Oggi è più importante che mai soddisfare a livello aziendale l'esigenza di prestazioni, disponibilità e sicurezza efficace per gestire carichi di lavoro mission-critical a un costo contenuto. Le soluzioni Microsoft fissano un nuovo standard nelle performance mission-critical.

1. SQL Server 2016
Everything Built-In – Technical Overview

2. SQL Server
2008
SQL Server
2008 R2
SQL Server
2000XML ● KPIs
Compression ● Policy-Based Mgmt ● Programmability
PowerPivot ● SharePoint Integration ● Master Data Services
SQL Server
2012
AlwaysOn ● ColumnStore Index ● Data Quality Services ● Power View ● Cloud Connectivity
SQL Server
2014
In-Memory Across Workloads ● Performance & Scale ● Hybrid Cloud Optimized ● HDInsight ● Cloud BI
Management Studio ● Mirroring
SQL Server
2005
SQL Server
2016
Enhanced Always On ● In-Memory OLTP ● Stretch DB ● Temporal Tables ● Enhanced Backup to Cloud ● Polybase ● Real-
Time Operational Analytics ● Row-Level Security ● Query Store ● R Services● Always Encrypted ● Mobile BI
The evolution of Microsoft SQL Server

3. How we develop SQL
Cloud-first but not cloud-only
Use SQL Database to improve core SQL Server features and cadence
Many interesting and compelling on-premises  cloud scenarios
SQL Server
and APS
Azure
SQL Virtual
Machines
Azure
SQL Database
Azure
SQL Data
Warehouse

4. In-Memory OLTP
enhancements

5. Columnar
index
Inmemory
OLAP
Inmemory
OLTP

6. ALTER TABLE Sales.SalesOrderDetail
ALTER INDEX PK_SalesOrderID
REBUILD
WITH (BUCKET_COUNT=100000000)
ALTER support
Full schema change support: add/alter/drop
column/constraint
Add/drop index supported
Performance
The ALTER TABLE syntax is used for making changes to the table schema, as well as for
adding, deleting, and rebuilding indexes
Indexes are considered part of the table definition
Key advantage is the ability to change the BUCKET_COUNT with an ALTER INDEX
statement

7. CREATE PROCEDURE [dbo].[usp_1]
WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER
AS BEGIN ATOMIC WITH
(
TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE =
N'us_english'
)
SELECT c1, c2 from dbo.T1
END
GO
ALTER PROCEDURE [dbo].[usp_1]
WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER
AS BEGIN ATOMIC WITH
(
TRANSACTION ISOLATION LEVEL = SNAPSHOT, LANGUAGE =
N'us_english'
)
SELECT c1 from dbo.T1
END
GO
You can now perform ALTER
operations on natively compiled
stored procedures using the
ALTER PROCEDURE statement
Use sp_recompile to
recompile stored procedures on
the next execution
Altering natively compiled stored procedures
Performance

8. Transact-SQL
Full support for all Collation and Unicode Support
(var)char columns can use any code page supported by SQL Server
Character columns in index keys can use any SQL Server collation
Expressions in natively compiled modules as well as constraints on memory-
optimized tables can use any SQL Server collation
Scalar User-Defined Functions for In-Memory OLTP
Create, drop, and alter natively compiled, scalar user-defined functions
Native compilation improves performance of the evaluation of UDFs in T-SQL
Performance

9. Cross-Feature Support
System-Versioned Temporal Tables
Query Store
Row-Level Security (RLS)
Multiple Active Result Sets (MARS)
Transparent Data Encryption (TDE)
Performance

10. In SQL Server 2016, the
storage for memory-
optimized tables will be
encrypted as part of enabling
TDE on the database
Simply follow the same steps
as you would for a disk-based
database
Support for Transparent Data Encryption (TDE)
Windows Operating System
Level Data Protection
SQL Server
Instance Level
User Database
Level Database Encryption Key
Service Master Key
DPAPI encrypts the Service Master Key
Master
Database Level Database Encryption Key
Service Master Key Encrypts the Database
master Key for the master Database
Database Master Key of the master Database
creates a certificate in the master database
The certificate encrypts the database
Encryption Key in the user database
The entire user database is secured by the
Datbase Encryption Key (DEK) of the user
database by using transparent database
encryption
Created at a time of SQL Server setup
Statement:
CREAT MASTER KEY…
Statement:
CREATE CERTIFICATE…
Statement:
CREATE DATABASE ENCRYPTION KEY…
Statement:
ALTER DATABSE… SET ENCRYPTION
Performance

11. New Transaction Performance Analysis Overview report
New report replaces the
need to use the
Management Data
Warehouse to analyze
which tables and stored
procedures are candidates
for in-memory
optimization
Performance

12. Temporal Tables
Query back in time

13. Data changes over time
Tracking and analyzing changes is often important
Temporal in DB
Automatically tracks history of data changes
Enables easy querying of historical data states
Advantages over workarounds
Simplifies app development and maintenance
Efficiently handles complex logic in DB engine
Why temporal
Time travel Data audit
Slowly changing
dimensions
Repair record-level
corruptions
Performance

14. No change in programming model New Insights
INSERT / BULK INSERT
UPDATE
DELETE
MERGE
DML SELECT * FROM temporal
Querying
How to start with temporal
CREATE temporal
TABLE PERIOD FOR
SYSTEM_TIME…
ALTER regular_table
TABLE ADD
PERIOD…
DDL
FOR SYSTEM_TIME
AS OF
FROM..TO
BETWEEN..AND
CONTAINED IN
Temporal
Querying
ANSI 2011
compliant
Performance

15. Temporal table (actual data)
Insert / Bulk Insert
* Old versions
Update */ Delete *
How does system-time work?
History table
Performance

16. Temporal table (actual data)
Temporal queries *
(Time travel, etc.)
How does system-time work?
History table
Regular queries
(current data)
* Include historical
version
Performance

17. Always Encrypted

18. Data disclosure
prevention
Client-side encryption of
sensitive data using keys that
are never given to the
database system
Queries on
encrypted data
Support for equality
comparison, including 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.
The need for Always Encrypted
Security

19. trust boundary
"SELECT Name FROM Customers
WHERE SSN = @SSN","111-22-3333"
How it works
Help protect data at rest and in motion, on-premises and in the cloud
Name
Wayne Jefferson
ADO .NET
Name
0x19ca706fbd9a
Result SetResult Set
Client
Name SSN Country
0x19ca706fbd9a 0x7ff654ae6d USA
SQL Server or SQL Database
"SELECT Name FROM Customers
WHERE SSN = @SSN",0x7ff654ae6d
ciphertext
Encrypted sensitive data and corresponding keys
are never seen in plaintext in SQL Server
dbo.Customers
ciphertext
Security

20. Randomized encryption
Encrypt('123-45-6789') = 0x17cfd50a
Repeat: Encrypt('123-45-6789') = 0x9b1fcf32
Allows for transparent retrieval of encrypted
data but NO operations
More secure
Deterministic encryption
Encrypt('123-45-6789') = 0x85a55d3f
Repeat: Encrypt('123-45-6789') = 0x85a55d3f
Allows for transparent retrieval of encrypted
data AND equality comparison
E.g. in WHERE clauses and joins, distinct,
group by
Two types of encryption
available
Randomized encryption uses a method
that encrypts data in a less predictable
manner
Deterministic encryption uses a method
that always generates the same encrypted
value for any given plaintext value
Types of encryption for Always Encrypted
Security

21. Dynamic Data Masking

22. Configuration made easy in new Azure portal
Policy-driven at table and column level, for
defined set of users
Data masking applied in real time to query
results based on policy
Multiple masking functions available, such as full
or partial, for various sensitive data categories
(credit card numbers, SSN)
SQL Database
SQL Server 2016
Table.CreditCardNo
4465-6571-7868-5796
4468-7746-3848-1978
4484-5434-6858-6550
Real-time data masking,
partial masking
Prevent abuse of sensitive data
by hiding it from users
Security
Dynamic Data Masking

23. Regulatory
compliance
Sensitive data
protection
Agility and
transparency
Data is masked on the fly, with
underlying data in database
remaining intact (transparent to
application and applied
according to user privilege)
Limit access to sensitive data by defining policies to obfuscate specific database fields, without
affecting database integrity
Security
Benefits of Dynamic Data Masking

24. Dynamic data masking walkthrough
ALTER TABLE [Employee] ALTER COLUMN [SocialSecurityNumber]
ADD MASKED WITH (FUNCTION = ‘SSN()’)
ALTER TABLE [Employee] ALTER COLUMN [Email]
ADD MASKED WITH (FUNCTION = ‘EMAIL()’)
ALTER TABLE [Employee] ALTER COLUMN [Salary]
ADD MASKED WITH (FUNCTION = ‘RANDOM(1,20000)’)
GRANT UNMASK to admin1
1) Security officer defines dynamic data masking policy in T-SQL over sensitive data in Employee table
2) Application user selects from Employee table
3) Dynamic data masking policy obfuscates the sensitive data in the query results
SELECT [Name],
[SocialSecurityNumber],
[Email],
[Salary]
FROM [Employee]
Security

25. Row-Level Security
SQL Server 2016
SQL Database

26. Nurse
Database
One
Policy manager creates a filter predicate and security policy
Two
App user (e.g., nurse) selects from Patients table
Three
Security Policy transparently rewrites query to apply filter predicate
CREATE FUNCTION dbo.fn_securitypredicate(@wing 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() AND @wing = d.Wing;
CREATE SECURITY POLICY dbo.SecPol
ADD FILTER PREDICATE dbo.fn_securitypredicate(Wing) ON Patients
WITH (STATE = ON)
Filter
Predicate:
INNER
JOIN…
Security
Policy
Application
Patients
SELECT * FROM Patients
SELECT * FROM Patients
SEMIJOIN APPLY dbo.fn_securitypredicate(patients.Wing);
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
RLS in three steps
Policy Manager

27. -- The following syntax creates a security policy with a filter
predicate for the Customer table, and leaves the security policy
disabled
CREATE SECURITY POLICY [FederatedSecurityPolicy]
ADD FILTER PREDICATE
[rls].[fn_securitypredicate]([CustomerId])
ON [dbo].[Customer];
-- Create a new schema and predicate function, which will use the
application user ID stored in CONTEXT_INFO to filter rows.
CREATE FUNCTION rls.fn_securitypredicate (@AppUserId int)
RETURNS TABLE
WITH SCHEMABINDING
AS
RETURN (
SELECT 1 AS fn_securitypredicate_result
WHERE
DATABASE_PRINCIPAL_ID() = DATABASE_PRINCIPAL_ID('dbo') --
application context
AND CONTEXT_INFO() = CONVERT(VARBINARY(128), @AppUserId);
GO
Creates security policy for
row-level security
The following examples
demonstrate use of CREATE
SECURITY POLICY syntax
For an example of a complete
security policy scenario, see
Row-Level Security
Create security policy
Security

28. Capability
Row-Level Security provides fine-grained access control
over rows in a table based on conditions you set up
Benefits
Store data for many users in same databases and tables
while limiting access by other users who share same tables
Security
Summary: Row-Level Security

29. Security enhancements
Built-in tools for enabling compliance: SQL Server audit tools
Create server audits, with server audit specifications (audited events
can be written to event logs or to audit files)
User-defined audit: Allows middle-tier application to write custom events into audit log, which
enables more flexibility to store audit information
Audit filtering: Provides greater flexibility to filter wanted events in audit log
Audit resilience: Audit logging is now tolerant to loss of connectivity to target directory and will
recover automatically once network connection is re-established
Security

30. Azure Key Vault support
Azure Key Vault:
Central key management that leverages hardware security modules (HSMs), separation of key
management from data management
Support for AKV available through SQL Server Connector for AKV
Extensible Key Management (EKM) provider for SQL Server
Leverage Azure Key Vault for managing encryption keys
Both on-premises and SQL Server-in-a-VM users can assume control of encryption keys for
Transparent Data Encryption (TDE), Column Level Encryption (CLE), and Backup Encryption while
leveraging additional security benefits of Azure Key Vault
Security

31. Transparent Data Encryption (TDE)
Encrypt data when it is stored on disk, and decrypt
it when read into memory
Developers can encrypt database files, log files, and backup files without
changing existing applications
Intel AES-NI hardware encryption acceleration
Support for storage of memory-optimized OLTP tables (new)
Exclusive to SQL Server Enterprise edition
Security

32. Transparent Data Encryption (TDE)
Encrypt backup by specifying encryption algorithm and encryptor
Supports on-premises and Azure storage locations
Configurable for Managed Backup to Windows Azure
Backup encryption now supported with compression, using AES-
NI hardware acceleration
Security

33. Summary: Security
Always encrypted: Sensitive data always encrypted (and queryable)
Dynamic Data Masking: Real-time obfuscation of data
Row-Level Security: Fine-grained access control of table rows
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
Security

34. AlwaysOn
 Failover on SQL Server instance level
 Shared storage (SAN/SMB)
 Failover can take minutes based on load
 Multi-node clustering
 Passive secondary nodes
 Failover on database level
 Direct attached storage
 Failover takes seconds
 Multiple secondaries
 Active secondaries
Availability
Failover Cluster Instances
for servers
Availability Groups
for groups of databases

35. WSFC node WSFC node
Failover Cluster Instances
Server failover
Shared storage
Multi-node clustering
Passive secondary nodes
Failover in minutes
SQL Server 2016
Shared storage
SQL Server 2016SQL
Server
Failover
Cluster
Instance
Availability

36. Availability
Group Listener
Availability Groups
Multi-database failover
Direct attached storage
Multiple secondaries
Active secondaries
Failover in seconds
SQL Server 2016
SQL Server 2016SQL Server 2016
Availability

37. Availability Groups + Failover Clustering
AlwaysOn:
Failover Cluster Instances
and Availability Groups
work together to ensure
data is accessible despite
failures
Availability
Windows Server Failover Clustering (WSFC) Cluster
Network Subnet Network Subnet
Node NodeNodeNodeNode
SQL Server
Instance
SQL Server
Instance
SQL Server
Instance
AlwaysOn SQL Server
Failover Cluster Instance
Primary Replica Secondary Replica Secondary Replica Secondary Replica
AlwaysOn Availability Group
Instance
Network Name
WSFC
Configuration
WSFC
Configuration
WSFC
Configuration
WSFC
Configuration
WSFC
Configuration
Instance
Network Name
Instance
Network Name
Instance
Network Name
Availability Group Listener Virtual Network Name
Storage Storage Storage Share Storage

38. PolyBase for
SQL Server 2016

39. Interest in big data spurs customer demand
Adoption of big data technologies like Hadoop
Increase in number and
variety of data sources
that generate large
quantities of data
Realization that data is
“too valuable” to delete
Dramatic decline in
hardware cost,
especially storage
$

40. PolyBase and queries
Provides a scalable, T-SQL-compatible query processing
framework for combining data from both universes
Access any data

41. PolyBase View
Execute T-SQL queries against
relational data in SQL Server and
semi-structured data in Hadoop or
Azure Blob Storage
Leverage existing T-SQL skills and BI
tools to gain insights from different
data stores
Access any data
PolyBase View in SQL Server 2016

42. Access any data
PolyBase use cases

43. Components introduced in SQL Server 2016
PolyBase Engine Service
PolyBase Data Movement Service
(with HDFS Bridge)
External table constructs
MR pushdown computation support
Access any data
Head Node
SQL 2016
PolyBase Engine
PolyBase DMS

44. Step 1: Set up a Hadoop cluster…
Hortonworks or Cloudera distributions
Hadoop 2.0 or above
Linux or Windows
On-premises or in Azure
Access any data
Hadoop Cluster
Namenode Datanode Datanode Datanode Datanode
File
System
AB 01 01 01 01
File
System
File
System
File
System

45. Step 1: …or set up an Azure Storage Blob
Azure Storage Blob (ASB) exposes an HDFS layer
PolyBase reads and writes from ASB using Hadoop
RecordReader/RecordWrite
No compute pushdown support for ASB
Access any data
Azure
Azure
Storage
Volume
Azure
Storage
Volume
Azure
Storage
Volume

46. Access any data
Query relational
and non-relational
data, on-premises
and in Azure
T-SQL query
Apps
SQL Server Hadoop
Summary: PolyBase
Query relational and non-relational data with T-SQL

47. Enterprise-grade
Analysis Services

48. Scale and manage
Increased
productivity
Analysis Services themes for SQL Server 2016
Improved productivity and performance

49. Analysis Services Overview
Analysis Services (SSAS) is an online analytical data engine used in decision support and
business analytics
It provides analytical data for business reports and client applications such as Reporting
Services reports, Power BI, Excel, and other third-party data visualization tools
SSAS supports two modelling modes—multidimensional and tabular
The multidimensional mode also includes a data mining engine

50. Analysis Services Overview
One Semantic Model - Two Ways to Develop
Dimensions and measure groups
Highly scalable and mature
Feature rich and complex
Multidimensional models
Tables and relationships
Fast by design with in-memory
Easy to get started and simple
Tabular models

51. Analysis Services Architectural Overview
BI SEMANTIC MODEL
Data access
Use SQL Server
Data Tools for
Visual Studio to
create BI semantic
models
DATA SOURCES
CLIENT TOOLS
Data model
Business logic
Queries

52. Reporting Services Overview
Reporting Services (SSRS) delivers enterprise, web-enabled reporting functionality
Implemented as a Report Server, in one of two modes—Native and SharePoint—it can
scale to support thousands of users
SSRS 2016 is extended with a new capability to deliver mobile reports, and is enhanced
with a new modern web portal
To contrast classic RDL reports with the new mobile reports, SSRS reports are now referred to as either
paginated or mobile

53. Reporting Services architecture
Web APIs
Report server
database
Web portal
Web browsers
Power BI Mobile
Report Builder
Mobile Report Publisher
Visual Studio
SQL Server
SQL Server
Analysis Services
Oracle Teradata
ODBC, OLE DB More/custom
Processing and
rendering
Scheduling and
delivery
Email File share
SharePoint Power BI
Custom
REPORT VIEWING (PCs, mobile)
REPORT DESIGN (PCs)
REPORT SERVER DATA SOURCES
REPORT DELIVERY DESTINATIONS
Custom apps/LOB

54. SQL Server 2016
Enhanced analysis
Modern reports
Rich tools
Mobile reports
Hybrid BI
SharePoint 2016
support

55. New SSRS Features
Supports the current versions of Microsoft.NET Framework 4
This includes 4.0 and 4.5.1
If no version of .NET Framework 4.x is installed, SQL Server setup installs .NET 4.0 during the feature
installation
Render paginated reports as PowerPoint files
Text boxes and images render as PowerPoint text boxes and images
Data visualizations render as images
The PowerPoint slide layout can be adjusted to suit, by moving and resizing individual objects

56. New SSRS Features
Ability to deliver subscriptions by using a single file share account, reusable across
multiple subscriptions
Pin report items to Power BI dashboards
Requires registering the Report Server with Power BI
Can pin charts, gauge panels, maps and images, and configure how often the data is refreshed
Dashboard tile refresh is achieved with subscriptions
Supported by all SQL Server editions
Support of SharePoint mode for SharePoint 2016

57. New SSRS Features
Paginated report authoring:
Custom parameter pane, allowing
design-time control over report
parameter layout
Two new chart types: Tree Map and
Sunburst charts, which effectively
visualize hierarchical data
Report Builder
Now has a new modern look and feel,
with streamlined UI elements
Supports High DPI (Dots Per Inch)
scaling and devices

58. New SSRS Features
Updated data source support:
Oracle Database 12c (uses ODP.NET)
Oracle Essbase 11
SAP BW 7.5
Teradata 15
Personalized connection strings:
Enables data sources to pass current user name as custom data
Data Source=localhost;Initial Catalog=Sales Analysis;CustomData={{ UserID }}

59. New SSRS Features
Key Performance Indicators (KPIs):
Deliver visual cues that communicate the amount of progress made toward a goal
Typically valuable for teams, managers, and businesses by enabling them to quickly evaluate the
progress made toward measurable goals
Developed in, and displayed by, the new SSRS web portal
Supported by Enterprise edition, and Native mode only

60. New SSRS Features
Key Performance Indicators (KPIs):
Enable defining value, goal, status and trend metrics
The value, goal and status metrics are single values—typically numeric, though text is also possible
The trend metric is designed to use a time series (e.g. monthly values)
Values can be sourced from shared datasets, or by manually entered values
Datasets can be parameterized
Datasets must use caching
Cache plans should be used to schedule the refresh
Can be configured to launch related content—either a mobile report, or a custom URL
The status colors—by default—are green (1), amber (0), or red (-1), and these can be overridden by custom
branding

61. New SSRS Features
The web portal is a new HTML5 application that targets modern web
standards and modern browsers
Report Manager has been removed and the web portal is now the default used to
browse/manage/consume the Report Server catalog
Stores, manages and secures reports and related assets:
Paginated reports
Mobile reports
Key Performance Indicators (KPIs)
Power BI Desktop files
Excel workbooks

62. New SSRS Features
Supports custom branding, allowing the customization of logo, web portal colors and a mobile report
theme

63. New SSRS Features
PDF replaces ActiveX for remote printing (plug-in free printing)
New enhanced subscription management:
Easily enable/disable
Provide a description
User can change the owner

64. Create interactive mobile reports
Responsive experiences for phones and tablets

65. Consume mobile reports
Native apps for all major mobile platforms
One mobile app for Power BI and
SSRS mobile reports
Native mobile experience
optimized for touch
Keep data up-to-date with real-time
query or scheduled data refresh
Interact with mobile reports even
when disconnected

66. R integration

67. Revolution R Enterprise and SQL
Big data analytics platform
Based on open source R
High-performance, scalable, full-featured
Statistical and machine-learning algorithms are performant,
scalable, and distributable
Write once, deploy anywhere
Scripts and models can be executed on a variety of
platforms, including non-Microsoft (Hadoop,
Teradata in-DB)
Integration with the R Ecosystem
Analytic algorithms accessed via R function with similar
syntax for R users (with arbitrary R functions/packages)
Advanced analytics
Data source
integration
Parallel external-memory
algorithm library
Data
Compute context
integration
Resources
Requests
R scripts +
CRAN
algorithms

68. SQL Server 2016 R integration scenario
Exploration
Use Revolution R Enterprise (RRE) from R
integrated development environment
(IDE) to analyze large data sets and build
predictive and embedded models with
compute on SQL Server machine (SQL
Server compute context)
Operationalization
Developer can operationalize R
script/model over SQL Server data by
using T-SQL constructs
DBA can manage resources, plus secure
and govern R runtime execution in SQL
Server
Advanced analytics

69. R script usage from SQL Server
Original R script:
IrisPredict <- function(data, model){
library(e1071)
predicted_species <- predict(model, data)
return(predicted_species)
}
library(RODBC)
conn <- odbcConnect("MySqlAzure", uid = myUser, pwd =
myPassword);
Iris_data <-sqlFetch(conn, "Iris_Data");
Iris_model <-sqlQuery(conn, "select model from my_iris_model");
IrisPredict (Iris_data, model);
Calling R script from SQL Server:
/* Input table schema */
create table Iris_Data (name varchar(100), length int, width int);
/* Model table schema */
create table my_iris_model (model varbinary(max));
declare @iris_model varbinary(max) = (select model from
my_iris_model);
exec sp_execute_external_script
@language = 'R'
, @script = '
IrisPredict <- function(data, model){
library(e1071)
predicted_species <- predict(model, data)
return(predicted_species)
}
IrisPredict(input_data_1, model);
'
, @parallel = default
, @input_data_1 = N'select * from Iris_Data'
, @params = N'@model varbinary(max)'
, @model = @iris_model
with result sets ((name varchar(100), length int, width int
, species varchar(30)));
Values highlighted in yellow are SQL queries embedded in the original R script
Values highlighted in aqua are R variables that bind to SQL variables by name
Advanced analytics

70. Capability
Extensible in-database analytics, integrated with R,
exposed through T-SQL
Centralized enterprise library for analytic models
Benefits
SQL Server
Analytical engines
Integrate with R
Become fully extensible
Data management layer
Relational data
Use T-SQL interface
Stream data in-memory
Analytics library
Share and collaborate
Manage and deploy
R +
Data Scientists
Business
Analysts
Publish algorithms, interact
directly with data
Analyze through T-SQL,
tools, and vetted algorithms
DBAs
Manage storage and
analytics together
Summary: R integration and advanced analytics
Advanced analytics

71. Stretch
Database

72. Ever-growing data, ever-shrinking IT
What to do?
Expand server and storage
Move data elsewhere
Delete
Massive tables (hundreds of
millions/billions of rows, TBs size)
Users want/need to retain data
indefinitely
Cold data infrequently accessed
but must be online
Datacenter consolidation
Maintenance challenges
Business SLAs at risk
Hybrid solutions

73. Capability
Stretch large operational tables
from on-premises to Azure with
the ability to query
Benefits
Stretch SQL Server into Azure
Securely stretch cold tables to Azure with remote query processing
SQL
SERVER
2016
Azure
Hybrid solutions

74. Stretch Database architecture
How it works
Creates a secure linked server definition
in the on-premises SQL Server
Targets remote endpoint with linked
server definition
Provisions remote resources and begins
to migrate eligible data, if migration
is enabled
Queries against tables run against both
local database and remote endpoint
Remote
endpoint
Remote
data
Azure
Internetboundary
Local
database
Local
data
Eligible
data
Linked servers
Hybrid solutions

75. Queries continue working
Business applications continue
working without disruption
DBA scripts and tools work as
before (all controls still held in local
SQL Server)
Developers continue building or
enhancing applications with existing
tools and methods
Hybrid solutions

76. Advanced security features supported
Data in motion always via secure
channels (TLS 1.1/1.2)
Always Encrypted supported if
enabled by user (encryption key
remains on-premises)
Row-Level Security and Auditing
supported
Hybrid solutions

77. Backup and restore benefits
Hybrid solutions
DBAs only back up/restore local
SQL Server hot data
StretchDB ensures remote data is
transactionally consistent with local
Upon completion of local restore,
SQL Server reconciles with remote
using metadata operation, not
data copy
Time of restore for remote not
dependent on size of data

78. 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
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
App
Query
Microsoft
Azure

Jim Gray ox7ff654ae6d 3/18/2005
Hybrid solutions