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Advanced Analytics
is where to place your bet
Ric Howe
Data Strategist
AZURE STREAM ANALYTICSHDINSIGHT
Data platform
POWER BI 2.0
AZURE SQL DATABASE
AZURE MACHINE LEARNING
SQL SERVER 2014 DOCUMENT DB
AZURE SQL DATA WAREHOUSE
REVOLUTION R
AZURE DATA FACTORY AZURE DATA LAKE
DATAZEN
Datazen
Free for customers with
SQL Server Enterprise 2008 or later and Software Assurance
Power BI 2.0
Create your own
content packs
Power BI is one place for all your data.
SaaS Data Sources
Azure-resident systems
On-premise systems
Power BI for Developers
Power BI Service
Developing for Power BI in a Nutshell
EmbeddingContent PacksReal-TimePush Data Integrations
Chrome Extension
Send data sets to
Power BI
Enable visualization
and exploration
Quickly add analytics
to existing systems
Add Power BI to
existing application
workflows
Enable immediate
insights for Pro and
non-technical users
Add real-time
monitoring quickly
Scalable architectures
handle smallest to
largest workloads
Explore real-time data
with Power BI,
including QnA
Give instant insights
to your customers
Enable your users to
get more from the
data in your service
Distribute insights to
many users efficiently
Easily add monitoring
and analytics to your
solutions
Enable personalized
data experiences
Leverage Power BI’s
tooling to accelerate
your solution
SSIS Destination
Azure Stream Analytics
Storm
Thank you to our developer community
Power BI Designer vNext
•
•
•
•
•
•
Datasets
GET, POST
API.PowerBI.com/Beta
MyOrg
Dataset Tables
GET
Table
PUT
Rows
POST, DELETE
Get started
quickly
Prebuilt content packs
for popular services
May 2015
June
2015
October
2015
Summer
2016
SQL Server 2016 is here
Interactive
query
Connect live to on-premise data
Live connectivity to SQL Server Analysis Services
Always Encrypted
dbo.Patients
Jane Doe
Name
243-24-9812
SSN
USA
Country
Jim Gray 198-33-0987 USA
John Smith 123-82-1095 USA
dbo.Patients
Jane Doe
Name
1x7fg655se2e
SSN
USA
Jim Gray 0x7ff654ae6d USA
John Smith 0y8fj754ea2c USA
Country
Result Set
Jim Gray
Name
Jane Doe
Name
1x7fg655se2e
SSN
USA
Country
Jim Gray 0x7ff654ae6d USA
John Smith 0y8fj754ea2c USA
dbo.Patients
SQL Server
ciphertext
Query
TrustedApps
SELECT Name FROM
Patients WHERE SSN=@SSN
@SSN='198-33-0987'
Result Set
Jim Gray
Name
SELECT Name FROM
Patients WHERE SSN=@SSN
@SSN=0x7ff654ae6d
Column
Encryption
Key
Enhanced
ADO.NET
Library
Column
Master
Key
Client side
Stretch SQL Server into Azure
Securely stretch cold tables to Azure with remote query processing
Order history
Name Date Item
0x21ba906fdb52 1ba906fd 2ba906f
0x19ca706fbd9a 5re316rl 1da813t
1x59cm676rfd8b 1re306fd 3ha706f
2y36cg776rgd5b 3bg606fl 1ba906i
1t64ce87r6pd7d 5ba616rj 2ra933f
0y16cj676r6fd3e 1ra806fd 3ra806t
3x47cr876r6fd9g 2hh906fj 1sa906f
1x11cj576rf6d3d 6be916gi 3sa523t
2t74ce6676rfd9c 1hi9306fj 2ga906f
0y47cm776rfd1b 3bi506gd 1wa806f
4x32cj6676rfd9y 3ha916fi 2ba913i
0x77cf6676rfd3x 5re926gi 1ba902f
2t22cm676rfd3a 1ra536fe 1ea667i
0x19ca706fbd9a 5re316rl 1da813t
Order history
Name Date Item
0x21ba906fdb52 1ba906fd 2ba906f
0x19ca706fbd9a 5re316rl 1da813t
1x59cm676rfd8b 1re306fd 3ha706f
2y36cg776rgd5b 3bg606fl 1ba906i
0x19ca706fbd9a 5re316rl 1da813t
App
Query
Microsoft Azure

Query
Always Encrypted
0100101010110 SQL Server
OLTP
SQL Server
data warehouse
ETL
In-memory
ColumnStore
In-memory
OLTP
Real-time fraud
detection
Fraud detected
2-24
hrs
R
In-memory enhancements
Operational analytics and enhanced performance now with R
Built-in advanced analytics
In-database analytics at massive scale
Data Scientist
Interact directly with data
Built-in to SQL Server
Data Developer/DBA
Manage data and
analytics together
Example Solutions
• Salesforecasting
• Warehouse efficiency
• Predictive maintenance
Relational Data
Analytic Library
T-SQL Interface
Extensibility
?
R
RIntegration
010010
100100
010101
Microsoft Azure
Marketplace
New R scripts
010010
100100
010101
010010
100100
010101
010010
100100
010101
010010
100100
010101
010010
100100
010101
• Credit risk protection
Polybase
Query relational and non-relational data with T-SQL
T-SQL query
SQL Server Hadoop
Quote:
************************
**********************
*********************
**********************
***********************
$658.39
Jim Gray
Name
11/13/58
DOB
WA
State
Ann Smith 04/29/76 ME
Azure Data Lake
Massively scalable HDFS storage in the cloud
HDFS compatible Unlimited Storage,
Petabyte files
Optimised for massive
throughput
High-frequency, low-
latency,
near-real-time
Native format
Azure SQL Data Warehouse
Instantly and independently scale storage and compute
Scale-out
Relational Data
warehouse
Azure Data Factory
Orchestrate trusted information production in Azure
C#
MapReduce
Trusted data
BI & analytics
Hive
Pig
Stored Procedures
Azure Machine Learning
Beyond business intelligence – machine intelligence
Re-imagined workspace to create
machine learning projects
Single-click to publish as a web
service
Machine Learning Marketplace
APIs to use or create and sell your
own
• Artis
• Avanade
• BlueGranite, Inc.
• Neal Analytics
• Data Science Dojo
• MAX451
• Practical Analytics
• Skyline Technologies
• Matricis Informatique Inc.
• Negotium Technologies
• Altius
• Elastacloud Limited
• Oakton Global
• WARDY IT Solutions
• Softelligence
• Fusionex
• TCS
• Infosys
• CTS
• Dell
• Beyond Data
Executing Launch Activities
What is Streaming Data?
Azure Stream Analytics
Process real-time data in Azure
Point of
Service Devices
Self Checkout
Stations
Kiosks
Smart
Phones
Slates/
Tablets
PCs/
Laptops
Servers
Digital
Signs
Diagnostic
EquipmentRemote Medical
Monitors
Logic
Controllers
Specialized
DevicesThin
Clients
Handhelds
Security
POS
Terminals
Automation
Devices
Vending
Machines
Kinect
ATM
© Microsoft 2015. All rights reserved.

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Advanced Analytics is where to place your bets

  • 1. Advanced Analytics is where to place your bet Ric Howe Data Strategist
  • 2. AZURE STREAM ANALYTICSHDINSIGHT Data platform POWER BI 2.0 AZURE SQL DATABASE AZURE MACHINE LEARNING SQL SERVER 2014 DOCUMENT DB AZURE SQL DATA WAREHOUSE REVOLUTION R AZURE DATA FACTORY AZURE DATA LAKE DATAZEN
  • 3. Datazen Free for customers with SQL Server Enterprise 2008 or later and Software Assurance
  • 4. Power BI 2.0 Create your own content packs
  • 5. Power BI is one place for all your data. SaaS Data Sources Azure-resident systems On-premise systems
  • 6. Power BI for Developers Power BI Service
  • 7. Developing for Power BI in a Nutshell EmbeddingContent PacksReal-TimePush Data Integrations Chrome Extension Send data sets to Power BI Enable visualization and exploration Quickly add analytics to existing systems Add Power BI to existing application workflows Enable immediate insights for Pro and non-technical users Add real-time monitoring quickly Scalable architectures handle smallest to largest workloads Explore real-time data with Power BI, including QnA Give instant insights to your customers Enable your users to get more from the data in your service Distribute insights to many users efficiently Easily add monitoring and analytics to your solutions Enable personalized data experiences Leverage Power BI’s tooling to accelerate your solution SSIS Destination Azure Stream Analytics Storm
  • 8. Thank you to our developer community
  • 11. Get started quickly Prebuilt content packs for popular services
  • 12.
  • 14. Interactive query Connect live to on-premise data Live connectivity to SQL Server Analysis Services
  • 15. Always Encrypted dbo.Patients Jane Doe Name 243-24-9812 SSN USA Country Jim Gray 198-33-0987 USA John Smith 123-82-1095 USA dbo.Patients Jane Doe Name 1x7fg655se2e SSN USA Jim Gray 0x7ff654ae6d USA John Smith 0y8fj754ea2c USA Country Result Set Jim Gray Name Jane Doe Name 1x7fg655se2e SSN USA Country Jim Gray 0x7ff654ae6d USA John Smith 0y8fj754ea2c USA dbo.Patients SQL Server ciphertext Query TrustedApps SELECT Name FROM Patients WHERE SSN=@SSN @SSN='198-33-0987' Result Set Jim Gray Name SELECT Name FROM Patients WHERE SSN=@SSN @SSN=0x7ff654ae6d Column Encryption Key Enhanced ADO.NET Library Column Master Key Client side
  • 16. Stretch SQL Server into Azure Securely stretch cold tables to Azure with remote query processing Order history Name Date Item 0x21ba906fdb52 1ba906fd 2ba906f 0x19ca706fbd9a 5re316rl 1da813t 1x59cm676rfd8b 1re306fd 3ha706f 2y36cg776rgd5b 3bg606fl 1ba906i 1t64ce87r6pd7d 5ba616rj 2ra933f 0y16cj676r6fd3e 1ra806fd 3ra806t 3x47cr876r6fd9g 2hh906fj 1sa906f 1x11cj576rf6d3d 6be916gi 3sa523t 2t74ce6676rfd9c 1hi9306fj 2ga906f 0y47cm776rfd1b 3bi506gd 1wa806f 4x32cj6676rfd9y 3ha916fi 2ba913i 0x77cf6676rfd3x 5re926gi 1ba902f 2t22cm676rfd3a 1ra536fe 1ea667i 0x19ca706fbd9a 5re316rl 1da813t Order history Name Date Item 0x21ba906fdb52 1ba906fd 2ba906f 0x19ca706fbd9a 5re316rl 1da813t 1x59cm676rfd8b 1re306fd 3ha706f 2y36cg776rgd5b 3bg606fl 1ba906i 0x19ca706fbd9a 5re316rl 1da813t App Query Microsoft Azure  Query Always Encrypted
  • 17. 0100101010110 SQL Server OLTP SQL Server data warehouse ETL In-memory ColumnStore In-memory OLTP Real-time fraud detection Fraud detected 2-24 hrs R In-memory enhancements Operational analytics and enhanced performance now with R
  • 18. Built-in advanced analytics In-database analytics at massive scale Data Scientist Interact directly with data Built-in to SQL Server Data Developer/DBA Manage data and analytics together Example Solutions • Salesforecasting • Warehouse efficiency • Predictive maintenance Relational Data Analytic Library T-SQL Interface Extensibility ? R RIntegration 010010 100100 010101 Microsoft Azure Marketplace New R scripts 010010 100100 010101 010010 100100 010101 010010 100100 010101 010010 100100 010101 010010 100100 010101 • Credit risk protection
  • 19. Polybase Query relational and non-relational data with T-SQL T-SQL query SQL Server Hadoop Quote: ************************ ********************** ********************* ********************** *********************** $658.39 Jim Gray Name 11/13/58 DOB WA State Ann Smith 04/29/76 ME
  • 20. Azure Data Lake Massively scalable HDFS storage in the cloud HDFS compatible Unlimited Storage, Petabyte files Optimised for massive throughput High-frequency, low- latency, near-real-time Native format
  • 21. Azure SQL Data Warehouse Instantly and independently scale storage and compute Scale-out Relational Data warehouse
  • 22. Azure Data Factory Orchestrate trusted information production in Azure C# MapReduce Trusted data BI & analytics Hive Pig Stored Procedures
  • 23. Azure Machine Learning Beyond business intelligence – machine intelligence Re-imagined workspace to create machine learning projects Single-click to publish as a web service Machine Learning Marketplace APIs to use or create and sell your own
  • 24. • Artis • Avanade • BlueGranite, Inc. • Neal Analytics • Data Science Dojo • MAX451 • Practical Analytics • Skyline Technologies • Matricis Informatique Inc. • Negotium Technologies • Altius • Elastacloud Limited • Oakton Global • WARDY IT Solutions • Softelligence • Fusionex • TCS • Infosys • CTS • Dell • Beyond Data Executing Launch Activities
  • 25.
  • 26.
  • 27.
  • 29. Azure Stream Analytics Process real-time data in Azure Point of Service Devices Self Checkout Stations Kiosks Smart Phones Slates/ Tablets PCs/ Laptops Servers Digital Signs Diagnostic EquipmentRemote Medical Monitors Logic Controllers Specialized DevicesThin Clients Handhelds Security POS Terminals Automation Devices Vending Machines Kinect ATM
  • 30. © Microsoft 2015. All rights reserved.

Editor's Notes

  1. When it comes mission critical performance one are we are making a continued investment on is our in-memory technology. We are still keeping to the workload optimized approach as customers want to optimize in-memory by workload. When it comes to In-Memory OLTP you will now be able to apply this tuned transaction performance technology to a significantly greater number of applications with expanded T-SQL surface area. In addition to providing up 30x performance gains you will now be able to gain real-time operational insights on your operational data. This data can be in-memory or on disk. Again what is unique here the ability to have speed of in-memory and operational analytics other competing solutions offer one or the other. Not only will OLTP performance be enhanced but you will also see an increase in concurrency where we can now take advantage of even higher parallel compute (double the compute, from 64CPUs to 128) and memory allocations (Terabytes)