We are not short of data and it’s growing at an exponential rate; what we are short of is insight. This talk will cover the latest announcements around advanced analytics, machine learning, and big data technologies to guide you through the future direction of the analytics journey we should all be placing our bets on.
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
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
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
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)