Red Hat Forum 2016 Big Data and Analytic Landscape

1. Open
source
ecosystem
– Future
of
Enterprise
IT
ASHNIK
Pte
Ltd.

2. 2
In the last 15 years 52%
of the fortune 500 companies have
disappeared
Since 1995, 89%of those
fortune 500 companies are gone
75
15
0
10
20
30
40
50
60
70
80
1995 2015
Avg.
Life
expectancy
Avg.
Life
expectancy
Did
you
know
Source:
www.constellationr.com

3. Leveraging
the
Power
of
SMAC
to
bring
Transformation
3
Transforming
Business
Transforming
Applications
&
Solutions
Transforming
Processes
Transforming
IT
Infrastructure
Transforming
Data
&
Interaction
SMAC
Framework
Dive
into
unfamiliarity
in
new
geographies
with
new
models
and
different
experiences
with
wider
reach
through
mobility
and
social
powered
by
cloud
Applications
that
cater
to
rapidly
evolving
business
and
workforce
needs,
in
the
hands
of
right
users
powered
via
mobile
and
cloud
Simplified
and
harmonized
processes
across
the
organization
with
context
enmeshed
within
social
and
mobile
Agile
IT
infrastructure
that
caters
to
dynamic
business
needs
,
on
demand
computing,
SaaS,
Paas
– Cloud
and
Analytics
Improved
ways
of
using
data
for
interactions
to
improve
product
and
service
offerings
using
advanced
analytics
and
real
time
insights
from
mobile
and
social

4. Big
Data
&
Analytics
Landscape

5. Enterprise
Data
Warehouse
5

6. Few
Observations
6
Ø Easy
point
to
start
with
Big
Data
initiative.
Few
key
things
to
look
at
ü 2-­‐5
year
TCO
calculation
of
your
existing
data
including
cost
of
integrating
all
that
data.
ü Analysis
of
what
is
the
data
which
can
be
used
for
analytics.
ü Check
out
if
any
of
the
cold
data
needs
to
be
retained
as
that
would
drive
your
storage
Few
factors
to
consider
to
decide
technologies
ü If
analytics
to
be
done
on
Hub.
BI
tools
with
Big
Data
integration.
ü Which
technology
to
choose
for
HUB
and
what
skills
would
be
required.
ü Approach
of
data
integration
and
transformation
required
to
moved
forward
for
analytics.

7. Data
Hub
7

8. Few
Observations
8
Ø When
a
huge
and
increasing
volume
of
data
is
needed
for
business
analytics
with
flexible
requirements
of
latency.
Few
factors
to
consider
to
decide
technologies
ü Data
Integration
tool
and
HUB
which
would
meet
the
enterprise
requirements.
ü BI
tools
with
capabilities
of
integrating
with
Big
Data.

9. HUB
and
ODS
9

10. Few
Observations
10
Ø This
is
pattern
is
helpful
when
you
require
to
quickly
making
data
available
for
analytics
and
secondly
when
you
look
to
source
more
data
for
data
science
Few
key
things
to
look
at
ü When
planning
check
out
the
capability
of
analytics
available
in
your
BI
database
compared
to
what
value
you
want
to
derive
from
that
data.
Few
factors
to
consider
to
decide
technologies
ü Which
BI
DB
to
use.
If
you
one
already
that
can
be
explored
to
use
as
there
will
be
no
learning
curve.
ü If
there
is
a
need
to
build
new
distributed
HUB
or
to
use
existing
data
platform.
ü To
design
the
architecture
for
integration
of
data
and
when
to
land
the
blended
data.

11. Hub
-­‐ Spoke
11

12. Few
Observations
12
Ø This
is
pattern
is
helpful
when
you
want
to
provide
multiple
points
for
analytics.
Ø When
planning
expansion
for
existing
big
data
platform
for
different
business
needs.
Few
key
things
to
look
at
ü Who
owns
the
data?
Few
factors
to
consider
to
decide
technologies
ü What
to
use
to
build
the
HUB.
ü Selecting
right
tools
for
DI
and
Data
blending.

13. Connecting
Hadoop
and
EDB
Postgres
to
Shrink
Big
Data
Challenges.
13
CONFIDENTIAL
©
2011
EnterpriseDB.
All
rights
reserved.
EDB
Postgres
FDW
helps
connecting
external
data
stores
to
Postgres
and
enable
applications
to
query
this
data
with
SQL
as
if
it
is
native
Postgres
data.
Helps
to
easily
combine
data
also
eliminates
complexity
and
risk
and
positions
Postgres
as
a
single,
federated
data
management
solution.
See
more
at:
http://www.enterprisedb.com/postgres-­‐plus-­‐edb-­‐blog/ahsan-­‐hadi/connecting-­‐
hadoop-­‐and-­‐edb-­‐postgres-­‐shrink-­‐big-­‐data-­‐challenges#sthash.uovsLojx.dpuf

14. Open
Source
Technologies
– Business
Transformation
14
Data
Store
Data
Integration
Data
Enrichment
Data
Accuracy
Natural
Language
Processing
– Pyhton,R
,
etc.
Application
Delivary
Platform CONFIDENTIAL
©
2016
Ashnik
Pte
Ltd.
All
rights
reserved.

15. How
can
Ashnik
and
ITG
help!
15
We
partner
with
different
open
source
technologies
q Solution
Consulting
q Services
Consulting
We
bring
the
experience
to
your
team
q Database
q Data
Modelling
q Integration
q Big
Data
Trainings
End-­‐to-­‐end
Project
Engagements
q Consulting
q Services
q Training
CONFIDENTIAL
©
2016
Ashnik
Pte
Ltd.
All
rights
reserved.

16. Ashnik
Technology
Partners
16
CONFIDENTIAL
©
2016
Ashnik
Pte
Ltd.
All
rights
reserved.

17. 17
Stay
in
touch
www.itgroupinc.asia
itgroupincorporated
@itgroupinc
Marketing.ph@itgroupinc.asia
www.ashnik.com
ashnikbiz
@ashnikbiz
success@ashnik.com
CONFIDENTIAL
©
2016
Ashnik
Pte
Ltd.
All
rights
reserved.

18. Thank
You