Hadoop 2.0 is gaining adoption among information workers seeking to analyze big data. Since its release, Hadoop has seen increased use by open source developers and vendors looking to leverage its scalability. Hadoop adoption continues to rise and it is entering a phase of maturity. Big data platforms are evolving from standalone tools to integrated platforms with increased capabilities. This reduces programming needs and shifts roles among IT professionals, data scientists, and business analysts.
4. HADOOP
2.0
ž Since
its
release,
Hadoop
has
been
gaining
traction
among
information
workers
seeking
to
wrangle
Big
Data
ž It
has
seen
a
proliferation
of
use
by
both
open-‐source
developers
and
vendors
hoping
to
leverage
its
scalability,
agility
and
processing
ž Adoption
continues
to
rise,
and
Hadoop
is
entering
what
could
be
considered
the
maturity
phase
5. ANALYST:
Rick
Sherman
Managing
Partner,
Athena
IT
Solutions
ANALYST:
Robin
Bloor
Chief
Analyst,
The
Bloor
Group
GUEST:
Lawrence
Weber
Big
Data
Product
Marketing
&
Strategy,
IBM
THE
LINE
UP
14. Hadoop and the ISAM Perspective
— Most OS environments had native key-value stores until
the advent of Unix, Linux, Windows
— Such stores are versatile and useful, and now they are
back – and they are parallel
— Hadoop can act as a foundation for many things (dbms,
analytical engine, parallel processing engine, data lake
and staging area, ETL, etc.)
HADOOP IS A HIGHLY SCALABLE KEY-VALUE STORE
THAT DEFINES ITS TECHNICAL VIRTUES
15. Hadoop Immaturity
Not entirely fault tolerant
It is essentially a batch engine with a primitive scheduling capability
Requires significant skills to install and to program
Rarely used in production environments (except for ETL)
The software ecosystem is still developing
It is parallel but not fast!