This slide deck was presented at #DataOnCloud event London. DataOnCloud is an invite-only event for CIOs and top IT innovators. DataOnCloud enables key decision makers to discuss about real life adoption scenarios, challenges and best practices for leveraging Big, Small and Line Of Business Data on Cloud. Aditi Technologies, a 'cloud first' technology services company organized #DataOnCloud, an event series focused on orchestrating data on cloud and navigating the complexity around integration, security, platform selection and technology solutions. Aditi Technologies partnered with Microsoft for this 2-hour, CXO roundtable event in global technology hubs - London, New York, Seattle and San Diego

1. TAME DATA ON CLOUD.

2. Welcome to #DataOnCloud
From this very room Winston Churchill said, “This is the room from which I will direct the war.”
NETWORK. BRAINSTORM. TAME DATA.

3. Agenda
Data On Cloud
Data problems. Why Cloud. Myth busting. Solution Roadmap
By Wade Wegner, CTO, Aditi Technologies
Platform Choices
The latest on Windows Azure
By Simon Karn, Azure Platform Partner, Microsoft
Q&A Panel
Discover Risks, Strategies & Roadmap for Cloud adoption

4. WE HELP OUR CLIENTS…
MOVE THEIR
BUSINESS TO THE
CLOUD
Our Promise

5. You Have a Data Problem
Quality of Data
Derive Valuable
Insights
Massive
Amounts of
Data
Budgets for
Data Growth

6. Are You Experiencing …
High Volume
Data Growth
Quality of
Data
Increased
Frequency of
Data Collection
Data Beyond
Relational

7. From Where Does this Data Come?
Device + Sensors Social Feeds
Relational
Databases
Trading Desks Web Logs
Document StoresNo SQL or Table
Storage
SQL

8. How Do We Use this Data?
KPI
Dashboar
ds
Trading
Stations
Personaliz
ed Web
Business
Alerts
and
Notificati
ons

9. What’s the Opportunity?
Sensor on Plant Floor
10,000 events/sec
Click-Stream Data etc.
Personalized pages
100,000 events/sec.
Fraud Detection
Algorithmic Trading
100,000 events/sec.
Energy Consumption
Smart Grids
100,000 events/sec.

10. So, What Exactly is the Data Problem?
VarietyVelocityVolume Veracity

11. Case STUDY # 1: HealthCare Company Improves
Hospital Hygiene Using Sensor Data
Aggregate and report “Hand Hygiene Compliance” for hospitals
Identify increased patient risk, provide notification in real-time
 Azure based storage for unstructured data - RFID data and video collection
for 100+ doctors
 Azure based computation scalability and push live notifications
 Single sign-on authentication using Active Directory
 Render reports using predefined views
 Storage scalability
 Reduced costs
 Streamlining unstructured data

12. Case STUDY # 2: Big European Travel
Conglomerate Optimizes Product Pricing
 Price their products better based on competitor data using web crawlers
 Collect web logs to analyze customer behavior and deliver better pricing
 Deliver predictive models to forecast future prices of products during
holiday seasons
 Utilize Cloud storage to capture data from web crawlers as raw data
 Use Hadoop to segment and identify best price from logs and crawler data
 Scheduling and computing using the cloud
 Render KPIs using visualizations
 Run machine learning algorithms
 Storage scalability
 Idea to production in six weeks
 High performance computing

14. What Do We Mean By Cloud?
• On-demand self service
• Broad network access
• Resource pooling
• Rapid elasticity
• Measured serviced
• Software as a Service
• Platform as a Service
• Infrastructure as a Service

15. How Does Cloud Solve the 4V’s?
VarietyVelocityVolume Veracity

16. How Cloud Helps Solve the Data Problem
↑ Ability to add storage dynamically
↑ Increase computing power on demand
↑ Use global distributed data centers for
localized processingHigh Volume
Data Growth
VOLUME

17. How Cloud Helps Solve the Data Problem
↑ Use Azure networks to collect data with
very low latency
↑ Leverage CEP on Azure to do real time
event processing
↑ Distribute notifications and alerts
VELOCITY
Increased
Frequency of
Data Collection

18. How Cloud Helps Solve the Data Problem
↑ Azure supports Relational, No SQL and
Blob locally
↑ Ability to process and enrich all kinds of
data using HDInsights
↑ Combine relational and non relational
data in one service
VARIETY
Data Beyond
Relational

19. How Cloud Helps Solve the Data Problem
↑ Ability to add storage dynamically
↑ Increase computing power on demand
↑ Use global distributed data centers for
localized processing
VERACITY
Quality of
Data

21. Approach for USING DATA with the CLOUD

23. Aggregate
Fragmented
information
sources
Non relational
information
Unclean data
DATA SOURCE
Relational
historic data
DATA INJECTION
Use Data hub to load data
into Azure Blob storage
Classify data into
tables, blobs, SQL Azure
Enable the blob storage
as HDFS for HDInsights

24. Enrich
Filter data using
MAPREDUCEREFINE
TRANSFORM
CLEANSE
Apply transformations Segment data based on
multiple variables
Remove duplicates
Eliminate non required information
Leverage HIVE to use
HDInsights as a DW
Prepare and load it into
relational format if required
Load data into
clusters using PIG

25. Analyze
ANALYZE
VISUALIZE
Access HDFS data using
Excel data explorer
Implement Embedded
visualizations using Power view
Leverage machine learning
Deliver alerts and notifications
Implement statistical algorithms
like Naïve baiyes,Clustering
Process real time business
events using StreamInsight

27. Microsoft’s Investment in Data Services

28. Challenges & Mitigations
Compliance ComplexityData Security &
Privacy

30. How Do We Make Sense of this Data?
Right PersonRight TimeRight Data

32. Starting the Journey
Data & Cloud Quickstart
• Half-day with an Architect
• Detailed review of data
challenges and cloud
maturity

33. Additional Quickstarts
• Cloud Application Portfolio Assessment
• HA SQL Server in the Cloud
• Migrating SharePoint Workloads to the Cloud
• Cloud-Based Dev/Test Environments
• Cloud-Based Core Infrastructure
• AD/IAM in the Cloud

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