Agile Intelligent Solutions & Services primary focus is to make sure that all specter of solutions we offer can be architected and delivered in the cloud. As cloud-based solutions offer unprecedented scalability and elasticity, they also place specific demands on solutions architecture so the cost of cloud services can be minimized.

1. A T T U N I T Y
PARTNERS
High Performance | Analytics |
Data Integration & BI
On-Premise Cloud
Innovating and Engineering Platforms for
Hybrid

2. Is extremely large data sets that may be
analyzed computationally to reveal patterns,
trends, and associations, especially relating to
human behavior and interactions.
Is an open-source software framework written in
Java for distributed storage and distributed
processing of very large data sets on computer
clusters built from commodity hardware.
Three V’s - Volume, Velocity & Variety
VOLUMEVARIETY
VELOCITY
NETIZENS, VIRTUAL
COMMUNITIES,
SOCIAL NETWORKS,
WEB LOGS...
ARCHIVES, MEDICAL
DEVICES, DIGITAL TV,
E-COMMERCE, SMART
CARDS, COMPUTERS,
MOBILES...
SENSORS, GPS DEVICES,
BAR CODE SCANNERS,
SURVEILLANCE
CAMERAS, SCIENTIFIC
RESEARCH...
EMAILS SENT
EVERY
SECONDS
OF VIDEO
UPLOADED
EVERY MIN
TWEETS
PER DAY
PEOPLE
TO PEOPLE
PEOPLE
TO MACHINE
MACHINE
TO MACHINE 2.9MILLION
20HOURS
50MILLION
>3,500
NORTH AMERICA>2,000
EUROPE
>250
CHINA
>400
JAPAN
>50
INDIA
>200
MIDDLE EAST
>50
LATIN AMERICA
Amount of Big Data
stored across the world
(petabytes)
Big Data Apache Hadoop
Customer-centric outcomes
Optimising operations
Enabling new business models
Risk & financial management
Employee collaboration
Why do companies use
Big data?
38%
26%
18%
16%
2%
Four stages of Big Data
19% 48% 28% 5%
1.Educate 3.Engage2.Explore 4.Execute
Only 5% of UKI companies are using Big
Data in the fullest sense, whilst almost
70% of businesses are still operating
within the first two stages.
What is Big Data?

3. Apache Hadoop Ecosystem
Big Data Enterprises Use case - Of�loading Heavy ETL and Analytics to Hadoop
EDW
BI & Analytics Users
Traditional Operational
Data Sources
EDW
BI & Analytics Users
AFTERBEFORE
ETL
Heavy
Density ETL
Data feed
to hadoop
ETL ETL
DataMart
DataMartDataMart
Analytics
Traditional Operational
Data Sources
New Data Sources
Hadoop based or other
more traditional stores
Sqoop
DataExchange
Flume
LogCollector
Zookeeper
Coordination
Oozie
Workflow
Pig
Scripting
Mahout
MachineLearning
RConnectors
Statistics
Hive
SQLQuery
Hbase
ColumnarStore
Ambari
Provisioning, Managing and Monitoring Hadoop Clusters
Spark (or)
Map Reduce v2
YARN
HDFS
Hadoop Distributed File System
Why Enterprises
need Big Data?

4. SERVICES
STAFFING DATA WAREHOUSING BI APPLICATIONS CLOUD BI MOBILE BI BIG DATA
MASTER DATA MANAGEMENT
W W W . A G I L E I S S . C O M