Observation Pattern Theory Hypothesis What will happen? How can
we make it happen? Predictive Analytics Prescriptive Analytics What
happened? Why did it happen? Descriptive Analytics INFORMATION
Diagnostic Analytics OPTIMIZATION Confirmation Theory Hypothesis
Observation
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Implement Data Warehouse Physical Design ETL Development
Reporting & Analytics Development Install and Tune Reporting
& Analytics Design Dimension Modelling ETL Design Setup
Infrastructure Understand Corporate Strategy Data sources ETL BI
and analytic Data warehouse Gather Requirements Business
Requirements Technical Requirements
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Ingest all data regardless of requirements Store all data in
native format without schema definition Do analysis Using analytic
engines like Hadoop Interactive queries Batch queries Machine
Learning Data warehouse Real-time analytics Devices
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What happened? What is happening? Why did it happen? What are
key relationships? What will happen? What if? How risky is it? What
should happen? What is the best option? How can I optimize? Data
sources
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Massive Compute and Storage Deployment expertise Data of all
Volume Variety, Velocity Speed Scale Economics Always Up, Always On
Open and flexible Time to value
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Azure Facts >4 trillion objects in Azure 300,000-1M+
requests per second Double compute and storage every 6 months Azure
Storage HDInsight Data Factory ML Stream Analytics Database
DocumentDB Search Event Hubs
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Microsofts cloud Hadoop offering 100% open source Apache Hadoop
Built on the latest releases across Hadoop (2.6) Up and running in
minutes with no hardware to deploy Harness existing.NET and Java
skills Utilize familiar BI tools for analysis including Microsoft
Excel
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Use CasesWhere? Active Archive / Compliance Reporting
Restricted data = down here. Up there could be considered for other
scenarios. ETL / Data Warehouse Optimization Often has down here
gravity, but cloud-based ETL offload has big payout Smart Meter
AnalysisTypically born up there Single View of Customer May have
heavy down here gravity; unless youre using SaaS apps, then why not
up there? New Data for Product Management Restricted data = down
here. Up there could be considered for many scenarios. Vehicle Data
for Transportation/LogisticsWhy not up there? Vehicle Data for
InsuranceMay have heavy down here gravity (ex. join w/risk data,
etc.)
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Rockwell Automation is partnered with one of the six oil and
gas super majors to build unmanned internet-connected gas
dispensers. Each dispenser emits real-time management metrics
allowing them to detect anomalies and predict when proactive
maintenance needs to occur. Store sensor data every 5 minutes
Temperature, pressure, vibration, etc. Tens of thousands of data
points / second Azure Blobs Azure HDInsight Hive, Pig, Azure SQL DB
Power BI for O365 Mobile Notification Hub Mobile Device Real-time
notification
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JustGiving wanted to harness the power of their data by using
network science to map peoples connections and relationships so
that they could connect people with the causes they care about.
Based on 15 years of data, the JustGiving GiveGraph is the worlds
largest ecosystem of giving behavior. It contains more than 81
million person nodes, thousands of causes and 285 million
connections and is the engine that drives JustGivings social
platform, enabling levels of personalization and engagement that a
traditional infrastructure would be unable to deliver. SQL Server
On-premises Agent Azure Blobs Azure HDInsight Give Graph Azure
Tables Web API Website + Event store Service Bus Serves results
Azure Cache Activity Feeds
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Use Cases Ad Placement and Offers Active Archive ETL Offload
Single View of Customer Recommendation Engine Customer Targeting
and Acquisition New Data for Product Management Vehicle Data Web
Personalization and Experience
