#DataOnCloud

TAME DATA ON CLOUD.

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.

Agenda

Data On Cloud Data problems. Why Cloud. Myth busting. Solution RoadmapBy Wade Wegner, CTO, Aditi Technologies

Platform ChoicesThe latest on Windows AzureBy Simon Karn, Azure Platform Partner, Microsoft

Q&A PanelDiscover Risks, Strategies & Roadmap for Cloud adoption

ACCELERATE AND DE-RISK JOURNEY TO CLOUD

DIFFERENTIATE WITH DESIGN AND USER EXPERIENCE

DELIVER SCALE AND AGILITY

WE HELP OUR CLIENTS…

MOVE THEIR

BUSINESS TO THE

CLOUD

Our Promise

You Have a Data Problem

Quality of Data

Derive Valuable Insights

Massive Amounts of Data

Budgets for Data Growth

Are You Experiencing …

High Volume Data Growth

Quality of Data

Increased Frequency of

Data Collection

Data Beyond Relational

From Where Does this Data Come?

Device + Sensors

Social Feeds

Relational Databases

Trading Desks Web Logs

Document Stores

No SQL or Table Storage

SQL

How Do We Use this Data?

KPI Dashboards

Trading Stations

Personalized Web

BusinessAlertsand Notifications

What’s the Opportunity?

Power and Utility

Manufacturing

Sensor on Plant Floor10,000 events/sec

Web Analytics

Click-Stream Data etc.Personalized pages100,000 events/sec.

Financial Services

Fraud DetectionAlgorithmic Trading100,000 events/sec.

Energy ConsumptionSmart Grids100,000 events/sec.

So, What Exactly is the Data Problem?

VarietyVelocityVolume Veracity

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

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

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

Characteristics Service Models

How Does Cloud Solve the 4V’s?

VarietyVelocityVolume Veracity

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

High Volume Data Growth

VOLUME

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

VELOCITYIncreased

Frequency of Data

Collection

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

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

Approach for USING DATA with the CLOUD

Aggregate AnalyzeEnrich

Aggregate

Fragmented information

sourcesNon

relational information

Unclean data DATA SOURCE

Relational historic data

DATA INJECTIONUse Data hub to load data into Azure Blob

storage

Classify data into tables, blobs, SQL

Azure

Enable the blob storage as HDFS for

HDInsights

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

Analyze

ANALYZE

VISUALIZEAccess 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

DEMO

Microsoft’s Investment in Data Services

Power ViewExcel with Data Explorer AlertsPredictive Analytics

APPsLOBCRMERP

SQL Server Relational DW

Statistical models CEP

Devices CrawlersSensors Bots

HDInsights

Challenges & Mitigations

Compliance ComplexityData Security & Privacy

How Do We Make Sense of this Data?

Right PersonRight TimeRight Data

DEMO

Starting the Journey

Data & Cloud Quickstart• Half-day with an Architect• Detailed review of data

challenges and cloud maturity

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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