1.
BIG DATA INFRASTRUCTURE AND
ANALYTICS SOLUTION
Erdenebayar Erdenebileg, Oyun-Erdene Namsrai
School of Information Technology, National University of Mongolia
erdenebayar.erdenebileg@gmail.com, oyunerdene@num.edu.mn

2.
Overview
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Introduction
Methods
Proposed methods
Experimental results
Related work
Discussion
Future work
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

3.
Introduction
• BIG DATA is coming from structured
and unstructured information (Web
data, market purchases, Credit card
transactions …)
• BIG DATA: 10% is structured data, But
90% is unstructured data
• Nowadays, almost every organization
is facing BIG DATA problems in
Mongolia.
• They need to analyze and predict their
valuable information
School of Information Technology, National University of Mongolia
Why?
How?
FITAT/ISPM 2013

4.
Why?
Why we are facing BIG DATA problem?

5.
Big Data: 3V’s
We are facing big data problem
with Volume, Variety, Velocity
reasons:
• Transactional data is growing day
by day
• Storing different types of data
• Need to be processed fast
Real Time
Data Velocity
(Fast analyzing requirement)
Near Real Time
Periodic
Batch
Unstructured
Video
Table
Database
GB
Web
Social
Data Variety
MB
Photo
Audio
Mobile
TB
PB
(Many types of data)
Data Volume
(Large amount of data)
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

6.
How?
How to solve the BIG DATA problem?

7.
How to solve problem?
To provide BI and Analytic tool
Full solution is
1. To construct BIG DATA
infrastructure
2. To find and develop data
transmission tools
3. To implement warehousing and
mining tools and techniques
4. To provide BI and Analytic tool
To implement warehousing and
mining tools and techniques
To construct BIG DATA
infrastructure
School of Information Technology, National University of Mongolia
To find and develop data
transmission tools
Data Sources
(Structured,
Semi-structured,
Unstructured)
FITAT/ISPM 2013

8.
Methods and Comparison?
RDBMS versus NoSQL database?

9.
RDMBS based infrastructure
From my experimental :
• Optimization requires more
cost (Licenses and Server), but
open source RDBMS is not
fitted with license
• RDBMS is not good with more
than gigabyte data
• It is not compatible to store
unstructured data (video, audio
etc…)
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

10.
HADOOP based infrastructure
From the biggest companies
experience (Facebook, Yahoo,
Twitter …), main advantages
are :
• Distributed File System
paradigm
• Powerful parallel computing
framework (MapReduce)
• It can be store any type of
data, which are structured,
semi-structured, unstructured
data
• It is Open source and easy to
integrate Hadoop related
products
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

11.
Brief introduction: HDFS Architecture
NameNode
BackupNode
Balancing, Replication, Failover
DataNode
DataNode
DataNode
DataNode
Data Node stores in local disks
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

12.
Brief introduction : MapReduce framework
Job Tracker
2010
2011
2012
2013
1. We have a big GREEN data
3. Aggregation and calculation data
2. Data will separate to the different
server
4. Consolidated result to the client
Task Tracker /
Server
Task Tracker /
Server
School of Information Technology, National University of Mongolia
Task Tracker /
Server
Task Tracker /
Server
FITAT/ISPM 2013

13.
Proposed method & solution
It is Hadoop and open source technologies

14.
Proposed method selection (Hadoop stacks)
Proposed method selected with following reason:
• Data should be stored in Distributed system
• Aggregation and calculation should be done in parallel computing
paradigm
• Data type is structured and unstructured data, which are mobile
call detailed record
• Data size is about 20TB
• Method should be Open source technologies
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

15.
Full Infrastructure (3 main method)
Client Machine (Jasper Business Intelligence)
Client software
(Reporting tool)
JasperRepors Server
Hive connector
Machine 1 (Slave Hadoop)
HBase connector
Machine 2 (Master Hadoop)
Clustered Big Data Infrastructure and Data Processing
Physical Machine (Resources)
Data Sender
Data resources
Sensor Data (Phone, Web Log, Camera etc…)
Structured Data
Big Data
Infrastructure
Semi -Unstructured Data
School of Information Technology, National University of Mongolia
Unstructured Data
FITAT/ISPM 2013

16.
Method 1: Clustered Big Data Infrastructure and Data Processing
• First task is configuring BIG DATA infrastructure with Analytic products
• This configuration clustered with TWO machine (Physical machine)
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

17.
Method 2: Data transmission way
• Data resources consist RDBMS
and unstructured data (CDR file,
video …)
• If structured data stores such as
Relational databases, we need
Sqoop product for bulk data
transfer
• If unstructured data stores such
as video and file, we need custom
application development using
HDFS client (SSH)
•
•
School of Information Technology, National University of Mongolia
Manual data transfer way
Automatic data transfer way
(Custom application)
FITAT/ISPM 2013

18.
Method 3: Analytics solution over the BIG DATA
This is the main method and trying to solve
following concepts
Predictive Analytics
They are focusing now
Prediction
(What will happen?)
Complexity
Business Intelligence
Almost every
organizations are
doing now
Monitoring
(What is happening now?)
Analysis
(Why did it happen?)
Reporting
(What happened?)
Business value
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

19.
Method 3: Analytics solution over the BIG DATA
• This is describes how to Reporting, Analyzing, Monitoring and Predict over the
BIG DATA infrastructure
Hadoop Distributed File System (Resources)
Sensor
Data
Hive
Table
HBase
Table
Hive Warehouse Data
Hive Table
Summarization
(Reporting, Analyzing,and analysis
Creation
Monitoring)
Hive Query
Language (HQL)
Direct Access To
HDFS
HBase table
management
HBase Table
Creation
(Reporting,
Analyzing,
Monitoring)
Aggregated data
Ad-hoc query
Sensor
Data
Mined
Data
Mahout Machine
Mahout Machine
LearningMining) Data
and
Learning (Data
Thrift
Server
HBase query
Mining
(Prediction)
Direct Access To
HDFS
End User (Analytic Tool)
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

20.
Experimental results
Testing, Monitoring, Working

21.
Experimental results
Experimental work focused on following main job:
1. Install and configure BIG DATA infrastructure (Clustered 2
physical machine)
2. Import sample unstructured data to the HDFS using SSH (to the
Big data infrastructure)
3. Ran sample HiveQL query, HBase query and Mahout job over
the MapReduce framework
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

22.
Running and monitoring HDFS and MapReduce framework
Sample results: HDFS and MapReduce
Master Machine:
DataNode, JobTracker,
NameNode, SNN,
TaskTracker are running
Slave Machine:
DataNode, TaskTracker
are running
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

23.
Running and working Hive warehouse
Sample results: Hive warehouse and HiveQL
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

24.
Running and working HBase table management
Sample results: HBase table management and Rest-ful web service
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

25.
Future work and Conclusion
Keep continue data mining research

26.
Future work
Keep continue my research work about BIG DATA
and Analytic solution:
1. Validate proposed infrastructure with real world data
(Mobile call logs, Camera sensor)
2. Keep research new technology to support to our
architecture
3. Predict and analyze real data over the infrastructure
(Market basket analyze, recommendation etc…)
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

27.
Conclusion
1. This is the full analytics solution for Analyzing big data
over the Hadoop Distributed File System:
-
Reporting (What happened?) (Hive)
-
Analysis (Why did it happen?) (Hive, HBase)
-
Monitoring (What happening now?) (Hive)
-
Predict (What will happen?) (Mahout)
School of Information Technology, National University of Mongolia
FITAT/ISPM 2013

28.
Thank you
Questions?