The document is a presentation on big data and Hadoop. It introduces the speaker, Adam Muise, and discusses the challenges of dealing with large and diverse datasets. Traditional approaches of separating data into silos are no longer sufficient. The presentation argues that a distributed system like Hadoop is needed to bring all data together and enable it to be analyzed as a whole.

1. Adam
Muise
–
Hortonworks
WELCOME
TO
HADOOP

2. Who
am
I?

3. Why
are
we
here?

4. Data

5. “Big
Data”
is
the
marke=ng
term
of
the
decade

6. What
lurks
behind
the
marke=ng
and
hype
is
a
legi=mate
movement
forward
in
dealing
with
data

7. You
need
to
deal
with
Data

8. Put
it
away,
delete
it,
tweet
it,
compress
it,
shred
it,
wikileak-­‐it,
put
it
in
a
database,
put
it
in
SAN/NAS,
put
in
the
cloud,
hide
it
in
tape…

9. Let’s
talk
challenges…

10. Volume
Volume
Volume
Volume

11. Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume

12. Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
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Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
Volume
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13. Volume
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Volume
Volume
Volume

14. Storage,
Management,
Processing
all
become
challenges
with
Data
at
Volume

15. Tradi=onal
technologies
adopt
a
divide,
drop,
and
conquer
approach

16. Another
EDW
Analy=cal
DB
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
The
solu=on?
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data
OLTP
Data
Data
Data
Data
Data
Data
Data
Data
Data
Yet
Another
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data

17. Another
EDW
Analy=cal
DB
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
OLTP
Ummm…you
dropped
something
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Yet
Another
EDW
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data

18. Analyzing
the
data
usually
raises
more
interes=ng
ques=ons…

19. …which
leads
to
more
data

20. Wait,
you’ve
seen
this
before.
Data
Data
Data
…
Sausage
Factory
Data
Data
Data
Data
Data
Data
Data
Data
Data
…
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data

21. Your
data
silos
are
lonely
places.
EDW
Accounts
Customers
Web
Proper=es
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data

22. …
Data
likes
to
be
together.
EDW
Accounts
Customers
Data
Data
Web
Proper=es
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data

23. New
types
of
data
don’t
quite
fit
your
pris=ne
view
of
the
world
Logs
Data
Data
Data
Data
Data
Data
Data
CDR/SIP
Data
Data
Data
Data
Data
Data
Data
My
LiYle
Data
Empire
Data
?
Data
?
Data
Data
Data
Data
Data
?
?
Data
Data

24. To
resolve
this,
some
people
take
hints
from
Lord
Of
The
Rings..

25. …and
create
One-­‐Schema-­‐To-­‐
Rule-­‐Them-­‐All…
EDW
Data
Data
Data
Data
Data
Schema
Data
Data
Data
Data

26. ETL
Data
Data
Data
ETL
ETL
ETL
EDW
Data
Data
Data
Data
Data
Schema
Data
Data
Data
Data
…but
that
has
its
problems
too.
ETL
Data
Data
Data
ETL
ETL
ETL
EDW
Data
Data
Data
Data
Data
Schema
Data
Data
Data
Data

27. So
what
is
the
answer?

28. Enter
the
Hadoop.
………
hYp://www.fabulouslybroke.com/2011/05/ninja-­‐elephants-­‐and-­‐other-­‐awesome-­‐stories/

29. Hadoop
was
created
because
Big
IT
never
cut
it
for
the
Internet
Proper=es
like
Google,
Yahoo,
Facebook,
TwiYer,
LinkedIn

30. Tradi=onal
architecture
didn’t
scale
enough…
App
App
App
App
App
App
App
App
DB
DB
DB
SAN
App
App
App
App
DB
DB
DB
SAN
DB
DB
DB
SAN

31. $upercompu=ng
Tradi=onal
architectures
cost
too
much
at
that
volume…
$/TB
$pecial
Hardware

32. So
what
is
the
answer?

33. If
you
could
design
a
system
that
would
handle
this,
what
would
it
look
like?

34. It
would
probably
need
a
highly
resilient,
self-­‐healing,
cost-­‐efficient,
distributed
file
system…
Storage
Storage
Storage
Storage
Storage
Storage
Storage
Storage
Storage

35. It
would
probably
need
a
completely
parallel
processing
framework
that
took
tasks
to
the
data…
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage

36. It
would
probably
run
on
commodity
hardware,
virtualized
machines,
and
common
OS
pladorms
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage
Processing
Processing
Processing
Storage
Storage
Storage

37. It
would
probably
be
open
source
so
innova=on
could
happen
as
quickly
as
possible

38. It
would
need
a
cri=cal
mass
of
users

39. {Processing
+
Storage}
=
{MapReduce/YARN+
HDFS}

40. HDFS
stores
data
in
blocks
and
replicates
those
blocks
block1
Processing
Processing
Processing
Storage
Storage
Storage
block2
block2
Processing
Processing
Processing
block1
Storage
Storage
Storage
block3
block2
Processing
Storage
block3
Processing
Processing
block1
Storage
Storage
block3

41. If
a
block
fails
then
HDFS
always
has
the
other
copies
and
heals
itself
block1
Processing
Processing
Processing
block3
Storage
Storage
Storage
block2
block2
Processing
Processing
Processing
block1
Storage
Storage
Storage
block3
block2
Processing
Storage
block3
Processing
Processing
block1
Storage
Storage
X

42. MapReduce
is
a
programming
paradigm
that
completely
parallel
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Mapper
Mapper
Mapper
Mapper
Mapper
Reducer
Data
Data
Data
Reducer
Data
Data
Data
Reducer
Data
Data
Data

43. MapReduce
has
three
phases:
Map,
Sort/Shuffle,
Reduce
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Mapper
Mapper
Key,
Value
Key,
Value
Key,
Value
Reducer
Key,
Value
Key,
Value
Key,
Value
Mapper
Reducer
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Mapper
Reducer
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Key,
Value
Mapper
Key,
Value
Key,
Value
Key,
Value

44. MapReduce
applies
to
a
lot
of
data
processing
problems
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Data
Mapper
Mapper
Mapper
Mapper
Mapper
Reducer
Data
Data
Data
Reducer
Data
Data
Data
Reducer
Data
Data
Data

45. Introducing
YARN

46. YARN
=
Yet
Another
Resource
Nego=ator

47. YARN
abstracts
resource
management
so
you
can
run
more
than
just
MapReduce
MapReduce
V2
MapReduce
V?
STORM
Giraph
Tez
YARN
HDFS2
MPI
HBase
…
and
more

48. YARN
turns
Hadoop
into
a
smart
phone:
An
App
Ecosystem
hortonworks.com/yarn/

49. Check
out
the
book
too…
Preview
at:
hortonworks.com/yarn/

50. YARN
is
an
essen=al
part
of
a
balanced
breakfast
in
Hadoop
2.0
Oct
15
2013:
Apache
Community
releases
Hadoop
2.2.0
Halloween
2013:
Hortonworks
releases
HDP
2.0
GA

51. pict

52. Hadoop
has
other
open
source
projects…

53. Hive
=
{SQL
-­‐>
MapReduce}
SQL-­‐IN-­‐HADOOP

54. Pig
=
{PigLa=n
-­‐>
MapReduce}

55. HCatalog
=
{metadata*
for
MapReduce,
Hive,
Pig,
Hbase,
etc}
*metadata
=
tables,
columns,
par==ons,
types

56. Oozie
=
Job::{Task,
Task,
if
Task,
then
Task,
final
Task}

57. Falcon
Feed
Feed
Feed
Feed
Hadoop
DR
Feed
Replica=on
Feed
Feed
Hadoop
Feed

58. Flume
Files
Flume
JMS
Weblogs
Events
Flume
Flume
Flume
Flume
Flume
Hadoop

59. Sqoop
DB
DB
Sqoop
Hadoop
Sqoop

60. Ambari
=
{install,
manage,
monitor}

61. HBase
=
{real-­‐=me,
distributed-­‐
map,
big-­‐tables}

62. Storm
=
{Complex
Event
Processing,
Near-­‐Real-­‐Time,
Provisioned
by
YARN
}

63. Storm
HDFS
YARN
Pig
MapReduce
Apache
Hadoop
HCatalog
Hive
HBase
Ambari
Sqoop
Falcon
Flume

64. Storm
Pig
HDFS
YARN
MapReduce
Hortonworks
Data
Pladorm
HCatalog
Hive
HBase
Ambari
Sqoop
Falcon
Flume

65. What
else
are
we
working
on?
hortonworks.com/labs/

66. Hadoop
is
the
new
Data
Opera=ng
System
for
the
Enterprise

67. There is NO second place
Hortonworks
…the
Bull
Elephant
of
Hadoop
Innova@on
© Hortonworks Inc. 2012: DO NOT SHARE. CONTAINS HORTONWORKS CONFIDENTIAL & PROPRIETARY INFORMATION
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