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Big Data for Smart City
Furqonuddin Ramdhani
www.icion-leadership.com
22
Smart City
Definition, objectives, Big Data Use Cases for Smart City
Big Data
Definition, Landscape, Ecosystem, Hadoop, Life Cycle
Big Data Use Case
RecommendationEngine, Risk Management, Data Warehouse Optimization
Outline
3
Smart City
Smart City
44
In Smart Cities, digital technologies translate into better public services for
citizens, better use of resources and less impact on the environment.
5
Big Data Use Cases for Smart City
• Social Media Analysis / Sentiment Analysis, aims to analyze and study of people’s
opinions, attitudes and emotions toward an entity in text of specific subjects.
• Retail Analytics, recommendation engine to predict next best offers of products to
customers.
6
Introduction to Big Data
7
What is Big Data ?
8
Big data enabled by five ‘Digital Economy’ drivers
www.slideproject.com 9
10
Hadoop
• “Scalable”, “Fault Tolerant” platform for large amount data storage & processing,
distributed across machines.
• Based on Google File System (GFS) & MapReduce
11
12
MapR Hadoop Ecosystem
13
Hadoop
1414
Big Data Life Cycle
15
Copy Files in HDFS
16
Import RDBMS Data
17
Stream data into Hadoop using Flume
18
Streams using Kafka
19
Process Data
20
Data Processing
• Transform the data
• Enrich the data
• Examples :
• Store data in multiple formats
• Aggregate data
• Build recommendations
• …
21
MapReduce Processing Model
• Define mappers
• Shuffling is automatic
• Define reducers
22
Apache Spark : Fast Big Data
• Fast to Run
• General execution graphs
• In-memory storage
• Rich APIs in Java, Scala, Python
23
Apache Drill
24
Use Case 1
Financial Services : RecommendationEngine &
Realtime Targeting
25
Financial Services : RecommendationEngine & Realtime Targeting
Making personalized real-time offers to credit card customers
26
Making personalized real-time offers to credit card customers
Financial Services : RecommendationEngine & Realtime Targeting
27
Use Case 2
Security & Risk Management
28
Security & Risk Management
High Performance machine-learning to detect fraud patterns
29
Security & Risk Management
30
Use Case 3
Data Warehouse Optimization
31
TDWI : Evolving Data Warehouse Architectures
32
Data Warehouse Optimization
Improve data services to customers while reducing enterprise architecture costs
33
Data Warehouse Optimization
Thank	you	
Support	our	CISSP	CLASS	FROM	25	TO	29	APRIL	2016
Contact	to	shindy@advancedtechpac.com
www.advancedtechpac.com
• www.icion-leadership.com
• LOOK FORWARD TO YOUR SUPPORT IN 2017
• CISSP COMMUNITY EVENT FEB IN PONDOK INDAH
• https://www.youtube.com/watch?v=fqUjXIlCcfM

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