A discussion of Big Data from a Microsoft perspective looking at the impact of Gartners 3 V's and a case study from Klout

2.  New sources of data
 Social Networks driving change in communication
style and frequency
 Increased use of mobile communications
 Massive increase in digital data
 Legislation – AML and prevention of crime
 Need to understand business risks better
 New Channels
 Hyper competition – fight for share globally
 Targeting use of marketing funds
 Automating reputation analysis

3. Analysts and marketers
need a better way to
manage data

4. Analysts and marketers want
to easily get to the insight
that helps them make better
decisions

6. Volume

7. Velocity

8. Variety

9. Stream
Technologies
Velocity
Variety
Complexity will tend to
push the line back
Big Data
Technologies
Traditional
Data warehousing
Volume

11. Data Engine

12. • Optimise Ad placements and frequency
• Conversion Funnel Analysis / Time to Convert /
Conversion Window Analysis
• Day Part Performance –impressions, clicks, and
conversions fluctuate
• Geographic Performance
• Key Path Analysis – uncover search behaviour patterns

13. HADOOP as a Service HADOOP on Premises Datawarehouse
Low cost SQLServer Archive
Master Slave (s) Slave (s)
Master Storage
Task Task
Task
Tracker Tracker
Task
Tracker Tracker MS BI
PDW
Job
Tracker Job Tracker
MapReduce MapReduce
Name HDFS Name HDFS
Node Node
T SQL Server
SQOOP
T
Data Data Data Data
Node Node Node
Analysis Service
Node
HIVE
SQOOP
SQOOP
Multi-node Azure Cluster