Adam	
  Muise	
  –	
  Hortonworks	
  

WELCOME	
  TO	
  HADOOP	
  
Who	
  am	
  I?	
  
Why	
  are	
  we	
  here?	
  
Data	
  
“Big	
  Data”	
  is	
  the	
  marke=ng	
  term	
  
of	
  the	
  decade	
  
What	
  lurks	
  behind	
  the	
  hype	
  is	
  
the	
  democra=za=on	
  of	
  Data.	
  
You	
  need	
  to	
  deal	
  with	
  Data.	
  
You’re	
  probably	
  not	
  as	
  good	
  at	
  
that	
  as	
  you	
  think.	
  
Put	
  it	
  away,	
  delete	
  it,	
  tweet	
  it,	
  
compress	
  it,	
  shred	
  it,	
  wikileak-­‐it,	
  put	
  
it	
 ...
Let’s	
  talk	
  challenges…	
  
Volume	
  
Volume	
  

Volume	
  

Volume	
  
Volume	
   Volume	
  
Volume	
  
Volume	
  
Volume	
  Volume	
  

Volume	
  
Volume	
  
Volume	
  Volume	
  
Volume	
  

V...
Volume	
   Volume	
  
Volume	
  
Volume	
  
Volume	
  Volume	
  
Volume	
  
Volume	
  
Volume	
  Volume	
  
Volume	
  
Vol...
Volume	
  
Volume	
  
Volume	
  
Volume	
  
Volume	
  
Volume	
   Volume	
  
Volume	
  
Volume	
  
Volume	
   Volume	
   V...
Storage,	
  Management,	
  Processing	
  
all	
  become	
  challenges	
  with	
  Data	
  at	
  
Volume	
  
Tradi=onal	
  technologies	
  adopt	
  a	
  
divide,	
  drop,	
  and	
  conquer	
  approach	
  
Another	
  EDW	
  

Analy=cal	
  DB	
  

Data	
  
Data	
   Data	
  
Data	
   Data	
  
Data	
  
Data	
  
Data	
   Data	
  
...
Another	
  EDW	
  

Analy=cal	
  DB	
  

Data	
  
Data	
   Data	
  
Data	
   Data	
  
Data	
  
Data	
  
Data	
   Data	
  
...
Analyzing	
  the	
  data	
  usually	
  raises	
  
more	
  interes=ng	
  ques=ons…	
  
…which	
  leads	
  to	
  more	
  data	
  
Wait,	
  you’ve	
  seen	
  this	
  before.	
  

Data	
  
Data	
  
Data	
  

…	
  

Sausage	
  Factory	
  

Data	
  
Data	
...
Data	
  begets	
  Data.	
  
What	
  keeps	
  us	
  from	
  Data?	
  
“Prices,	
  Stupid	
  passwords,	
  and	
  
Boring	
  Sta=s=cs.”	
  	
  
-­‐	
  Hans	
  Rosling	
  

h"p://www.youtube.com...
Your	
  data	
  silos	
  are	
  lonely	
  places.	
  
EDW	
  

Accounts	
  

Customers	
  

Web	
  Proper=es	
  

Data	
  ...
…	
  Data	
  likes	
  to	
  be	
  together.	
  
EDW	
  

Accounts	
  

Customers	
  
Data	
  
Data	
  
Web	
  Proper=es	
 ...
CDR	
  

Data	
  
Data	
   Data	
   Machine	
  Data	
  
Facebook	
  
Data	
  
Data	
   Data	
  
Data	
  
Data	
  
Data	
  ...
New	
  types	
  of	
  data	
  don’t	
  quite	
  fit	
  into	
  
your	
  pris=ne	
  view	
  of	
  the	
  world.	
  
Logs	
  ...
To	
  resolve	
  this,	
  some	
  people	
  take	
  
hints	
  from	
  Lord	
  Of	
  The	
  Rings...	
  
…and	
  create	
  One-­‐Schema-­‐To-­‐
Rule-­‐Them-­‐All…	
  
EDW	
  

Data	
  
Data	
   Data	
  
Data	
   Data	
  
Schema...
ETL	
  
Data	
  
Data	
  
Data	
  

ETL	
  

ETL	
  

ETL	
  

EDW	
  

Data	
  
Data	
   Data	
  
Data	
   Data	
  
Schem...
So	
  what	
  is	
  the	
  answer?	
  
Enter	
  the	
  Hadoop.	
  

………	
  
hYp://www.fabulouslybroke.com/2011/05/ninja-­‐elephants-­‐and-­‐other-­‐awesome-­‐sto...
Hadoop	
  was	
  created	
  because	
  Big	
  IT	
  
never	
  cut	
  it	
  for	
  the	
  Internet	
  
Proper=es	
  like	
 ...
Tradi=onal	
  architecture	
  didn’t	
  
scale	
  enough…	
  
App	
   App	
   App	
   App	
  

App	
   App	
   App	
   App...
Databases	
  become	
  bloated	
  and	
  
useless	
  
$upercompu=ng	
  

Tradi=onal	
  architectures	
  cost	
  too	
  
much	
  at	
  that	
  volume…	
  

$/TB	
  

$pecial	
  ...
How	
  would	
  you	
  fix	
  this?	
  
If	
  you	
  could	
  design	
  a	
  system	
  that	
  
would	
  handle	
  this,	
  what	
  would	
  it	
  
look	
  like?	...
It	
  would	
  probably	
  need	
  a	
  highly	
  
resilient,	
  self-­‐healing,	
  cost-­‐efficient,	
  
distributed	
  file...
It	
  would	
  probably	
  need	
  a	
  completely	
  
parallel	
  processing	
  framework	
  that	
  
took	
  tasks	
  to...
It	
  would	
  probably	
  run	
  on	
  commodity	
  
hardware,	
  virtualized	
  machines,	
  and	
  
common	
  OS	
  pla...
It	
  would	
  probably	
  be	
  open	
  source	
  so	
  
innova=on	
  could	
  happen	
  as	
  quickly	
  
as	
  possible...
It	
  would	
  need	
  a	
  cri=cal	
  mass	
  of	
  
users	
  
It	
  would	
  be	
  Apache	
  Hadoop	
  
{Processing	
  +	
  Storage}	
  
=	
  
{MapReduce/Tez/YARN+	
  HDFS}	
  
HDFS	
  stores	
  data	
  in	
  blocks	
  and	
  
replicates	
  those	
  blocks	
  
block1	
  
Processing	
   Processing	
...
If	
  a	
  block	
  fails	
  then	
  HDFS	
  always	
  has	
  
the	
  other	
  copies	
  and	
  heals	
  itself	
  
block1...
MapReduce	
  is	
  a	
  programming	
  
paradigm	
  that	
  completely	
  parallel	
  
Data	
  
Data	
  
Data	
  
Data	
  ...
MapReduce	
  has	
  three	
  phases:	
  
Map,	
  Sort/Shuffle,	
  Reduce	
  
Key,	
  Value	
  
Key,	
  Value	
  
Key,	
  Val...
MapReduce	
  applies	
  to	
  a	
  lot	
  of	
  
data	
  processing	
  problems	
  
Data	
  
Data	
  
Data	
  
Data	
  
Da...
MapReduce	
  goes	
  a	
  long	
  way,	
  but	
  
not	
  all	
  data	
  processing	
  and	
  analy=cs	
  
are	
  solved	
 ...
Some=mes	
  your	
  data	
  applica=on	
  
needs	
  parallel	
  processing	
  and	
  inter-­‐
process	
  communica=on	
  
...
…like	
  Complex	
  Event	
  Processing	
  
in	
  Apache	
  Storm	
  
Some=mes	
  your	
  machine	
  learning	
  
data	
  applica=on	
  needs	
  to	
  process	
  in	
  
memory	
  and	
  iterat...
…like	
  in	
  Machine	
  Learning	
  in	
  
Spark	
  
Introducing	
  YARN	
  
YARN	
  =	
  Yet	
  Another	
  Resource	
  
Nego=ator	
  
YARN	
  abstracts	
  resource	
  
management	
  so	
  you	
  can	
  run	
  more	
  
than	
  just	
  MapReduce	
  
MapReduc...
Node	
  Manager	
  

Resource	
  Manager	
  

Container	
  

Scheduler	
  
Pig	
  

AppMaster	
  
Container	
  

Resource	...
YARN	
  turns	
  Hadoop	
  into	
  a	
  smart	
  
phone:	
  An	
  App	
  Ecosystem	
  
hortonworks.com/yarn/	
  
Check	
  out	
  the	
  book	
  too…	
  

Preview	
  at:	
  
hortonworks.com/yarn/	
  
YARN	
  is	
  an	
  essen=al	
  part	
  of	
  a	
  
balanced	
  breakfast	
  in	
  Hadoop	
  2.x	
  
Introducing	
  Tez	
  
Tez	
  is	
  a	
  YARN	
  applica=on,	
  like	
  
MapReduce	
  is	
  a	
  YARN	
  applica=on	
  
Tez	
  is	
  the	
  Lego	
  set	
  for	
  your	
  data	
  
applica=on	
  
Tez	
  provides	
  a	
  layer	
  for	
  abstract	
  
tasks,	
  these	
  could	
  be	
  mappers,	
  
reducers,	
  customize...
Tez	
  can	
  chain	
  tasks	
  together	
  into	
  one	
  
job	
  to	
  get	
  Map	
  –	
  Reduce	
  –	
  Reduce	
  jobs	...
Tez	
  can	
  provide	
  long-­‐running	
  
containers	
  for	
  applica=ons	
  like	
  Hive	
  
to	
  side-­‐step	
  batc...
Hadoop	
  has	
  other	
  open	
  source	
  
projects…	
  
Hive	
  =	
  {SQL	
  -­‐>	
  Tez	
  ||	
  MapReduce}	
  
SQL-­‐IN-­‐HADOOP	
  
Pig	
  =	
  {PigLa=n	
  -­‐>	
  Tez	
  ||	
  
MapReduce}	
  
HCatalog	
  =	
  {metadata*	
  for	
  
MapReduce,	
  Hive,	
  Pig,	
  HBase}	
  

*metadata	
  =	
  tables,	
  columns,	
 ...
Oozie	
  =	
  Job::{Task,	
  Task,	
  if	
  Task,	
  
then	
  Task,	
  final	
  Task}	
  
Falcon	
  
Feed	
   Feed	
  
Feed	
  

Feed	
  

Hadoop	
  

DR	
  

Feed	
  

Replica=on	
  

Feed	
  

Feed	
  

Hadoop	...
Knox	
  
REST	
  
Client	
  
REST	
  
Client	
  

Knox	
  Gateway	
  
REST	
  
Client	
  

Hadoop	
  
Cluster	
  
Hadoop	
...
Flume	
  
Files	
  

Flume	
  
JMS	
  

Weblogs	
  

Events	
  

Flume	
  

Flume	
  

Flume	
  

Flume	
  

Flume	
  

Ha...
Sqoop	
  
DB	
  

DB	
  

Sqoop	
  
Hadoop	
  

Sqoop	
  
Ambari	
  =	
  {install,	
  manage,	
  
monitor}	
  
HBase	
  =	
  {real-­‐=me,	
  distributed-­‐
map,	
  big-­‐tables}	
  
Storm	
  =	
  {Complex	
  Event	
  Processing,	
  
Near-­‐Real-­‐Time,	
  Provisioned	
  by	
  
YARN	
  }	
  
Tez	
  

Storm	
  

YARN	
  

Pig	
  

HDFS	
  

MapReduce	
  

Apache	
  Hadoop	
  

HCatalog	
  

Hive	
  
HBase	
  

Am...
Storm	
  

Tez	
  
Pig	
  

YARN	
  

HDFS	
  

MapReduce	
  

Hortonworks	
  Data	
  Plaeorm	
  
HCatalog	
  

Hive	
  
H...
What	
  else	
  are	
  we	
  working	
  on?	
  
hortonworks.com/labs/	
  
Hadoop	
  is	
  the	
  new	
  Modern	
  Data	
  
Architecture	
  
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2014 feb 5_what_ishadoop_mda

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What Is Hadoop update to include YARN, Tez, Spark, and Storm

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2014 feb 5_what_ishadoop_mda

  1. 1. Adam  Muise  –  Hortonworks   WELCOME  TO  HADOOP  
  2. 2. Who  am  I?  
  3. 3. Why  are  we  here?  
  4. 4. Data  
  5. 5. “Big  Data”  is  the  marke=ng  term   of  the  decade  
  6. 6. What  lurks  behind  the  hype  is   the  democra=za=on  of  Data.  
  7. 7. You  need  to  deal  with  Data.  
  8. 8. You’re  probably  not  as  good  at   that  as  you  think.  
  9. 9. Put  it  away,  delete  it,  tweet  it,   compress  it,  shred  it,  wikileak-­‐it,  put   it  in  a  database,  put  it  in  SAN/NAS,   put  it  in  the  cloud,  hide  it  in  tape…  
  10. 10. Let’s  talk  challenges…  
  11. 11. Volume   Volume   Volume   Volume  
  12. 12. Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume  
  13. 13. Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume  Volume   Volume  
  14. 14. Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume  
  15. 15. Storage,  Management,  Processing   all  become  challenges  with  Data  at   Volume  
  16. 16. Tradi=onal  technologies  adopt  a   divide,  drop,  and  conquer  approach  
  17. 17. 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  
  18. 18. 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  
  19. 19. Analyzing  the  data  usually  raises   more  interes=ng  ques=ons…  
  20. 20. …which  leads  to  more  data  
  21. 21. 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  
  22. 22. Data  begets  Data.  
  23. 23. What  keeps  us  from  Data?  
  24. 24. “Prices,  Stupid  passwords,  and   Boring  Sta=s=cs.”     -­‐  Hans  Rosling   h"p://www.youtube.com/watch?v=hVimVzgtD6w  
  25. 25. 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  
  26. 26. …  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  
  27. 27. CDR   Data   Data   Data   Machine  Data   Facebook   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   Weather  Data   TwiYer   Data   Data  likes  to  socialize  too.   Data   Data   EDW   Data   Data   Data   Data   Data   Data   Accounts   Data   Web  Proper=es   Data   Data   Data   Customers   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  
  28. 28. New  types  of  data  don’t  quite  fit  into   your  pris=ne  view  of  the  world.   Logs   Data   Data   Data   Data   Data  Data   Data   Machine  Data   Data   Data   Data   Data   Data  Data   Data   My  LiYle  Data  Empire   Data   ?   Data   ?   Data   Data   Data   Data   Data   ?  ?   Data   Data  
  29. 29. To  resolve  this,  some  people  take   hints  from  Lord  Of  The  Rings...  
  30. 30. …and  create  One-­‐Schema-­‐To-­‐ Rule-­‐Them-­‐All…   EDW   Data   Data   Data   Data   Data   Schema   Data   Data   Data   Data  
  31. 31. 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  
  32. 32. So  what  is  the  answer?  
  33. 33. Enter  the  Hadoop.   ………   hYp://www.fabulouslybroke.com/2011/05/ninja-­‐elephants-­‐and-­‐other-­‐awesome-­‐stories/  
  34. 34. Hadoop  was  created  because  Big  IT   never  cut  it  for  the  Internet   Proper=es  like  Google,  Yahoo,   Facebook,  TwiYer,  and  LinkedIn  
  35. 35. 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  
  36. 36. Databases  become  bloated  and   useless  
  37. 37. $upercompu=ng   Tradi=onal  architectures  cost  too   much  at  that  volume…   $/TB   $pecial   Hardware  
  38. 38. How  would  you  fix  this?  
  39. 39. If  you  could  design  a  system  that   would  handle  this,  what  would  it   look  like?  
  40. 40. It  would  probably  need  a  highly   resilient,  self-­‐healing,  cost-­‐efficient,   distributed  file  system…   Storage   Storage   Storage   Storage   Storage   Storage   Storage   Storage   Storage  
  41. 41. 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  
  42. 42. It  would  probably  run  on  commodity   hardware,  virtualized  machines,  and   common  OS  plaeorms   Processing   Processing  Processing   Storage   Storage   Storage   Processing   Processing  Processing   Storage   Storage   Storage   Processing   Processing  Processing   Storage   Storage   Storage  
  43. 43. It  would  probably  be  open  source  so   innova=on  could  happen  as  quickly   as  possible  
  44. 44. It  would  need  a  cri=cal  mass  of   users  
  45. 45. It  would  be  Apache  Hadoop  
  46. 46. {Processing  +  Storage}   =   {MapReduce/Tez/YARN+  HDFS}  
  47. 47. 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  
  48. 48. 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
  49. 49. 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  
  50. 50. 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  
  51. 51. 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  
  52. 52. MapReduce  goes  a  long  way,  but   not  all  data  processing  and  analy=cs   are  solved  the  same  way  
  53. 53. Some=mes  your  data  applica=on   needs  parallel  processing  and  inter-­‐ process  communica=on   Data   Data   Data   Data   Data   Data   Process   Data   Data   Data   Process   Data   Data   Data   Data   Data   Data   Data   Data   Data   Process   Process   Data   Data   Data   Data   Data   Data   Data   Data   Data  
  54. 54. …like  Complex  Event  Processing   in  Apache  Storm  
  55. 55. Some=mes  your  machine  learning   data  applica=on  needs  to  process  in   memory  and  iterate     Data   Data   Data   Data   Data   Data   Process   Data   Data   Data   Process   Data   Data   Data   Data   Data   Data   Data   Data   Data   Process   Process   Process   Process   Process   Data   Data   Data   Data   Data   Data  
  56. 56. …like  in  Machine  Learning  in   Spark  
  57. 57. Introducing  YARN  
  58. 58. YARN  =  Yet  Another  Resource   Nego=ator  
  59. 59. 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   Spark  
  60. 60. Node  Manager   Resource  Manager   Container   Scheduler   Pig   AppMaster   Container   Resource  Manager   +   Node  Managers   =  YARN   Node  Manager   Container   Container   Storm   Node  Manager   Node  Manager   MapReduce   AppMaster   Container   Container   Container   Container   Container   AppMaster  
  61. 61. YARN  turns  Hadoop  into  a  smart   phone:  An  App  Ecosystem   hortonworks.com/yarn/  
  62. 62. Check  out  the  book  too…   Preview  at:   hortonworks.com/yarn/  
  63. 63. YARN  is  an  essen=al  part  of  a   balanced  breakfast  in  Hadoop  2.x  
  64. 64. Introducing  Tez  
  65. 65. Tez  is  a  YARN  applica=on,  like   MapReduce  is  a  YARN  applica=on  
  66. 66. Tez  is  the  Lego  set  for  your  data   applica=on  
  67. 67. Tez  provides  a  layer  for  abstract   tasks,  these  could  be  mappers,   reducers,  customized  stream   processes,  in  memory  structures,   etc  
  68. 68. Tez  can  chain  tasks  together  into  one   job  to  get  Map  –  Reduce  –  Reduce  jobs   suitable  for  things  like  Hive  SQL   projec=ons,  group  by,  and  order  by   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   TezMap   TezMap   TezReduce   TezReduce   Data   Data   Data   TezMap   TezReduce   TezReduce   Data   Data   Data   TezReduce   TezReduce   TezMap   TezMap   Data   Data   Data  
  69. 69. Tez  can  provide  long-­‐running   containers  for  applica=ons  like  Hive   to  side-­‐step  batch  process  startups   you  would  have  with  MapReduce  
  70. 70. Hadoop  has  other  open  source   projects…  
  71. 71. Hive  =  {SQL  -­‐>  Tez  ||  MapReduce}   SQL-­‐IN-­‐HADOOP  
  72. 72. Pig  =  {PigLa=n  -­‐>  Tez  ||   MapReduce}  
  73. 73. HCatalog  =  {metadata*  for   MapReduce,  Hive,  Pig,  HBase}   *metadata  =  tables,  columns,  par==ons,  types  
  74. 74. Oozie  =  Job::{Task,  Task,  if  Task,   then  Task,  final  Task}  
  75. 75. Falcon   Feed   Feed   Feed   Feed   Hadoop   DR   Feed   Replica=on   Feed   Feed   Hadoop   Feed  
  76. 76. Knox   REST   Client   REST   Client   Knox  Gateway   REST   Client   Hadoop   Cluster   Hadoop   Cluster   Enterprise   LDAP  
  77. 77. Flume   Files   Flume   JMS   Weblogs   Events   Flume   Flume   Flume   Flume   Flume   Hadoop  
  78. 78. Sqoop   DB   DB   Sqoop   Hadoop   Sqoop  
  79. 79. Ambari  =  {install,  manage,   monitor}  
  80. 80. HBase  =  {real-­‐=me,  distributed-­‐ map,  big-­‐tables}  
  81. 81. Storm  =  {Complex  Event  Processing,   Near-­‐Real-­‐Time,  Provisioned  by   YARN  }  
  82. 82. Tez   Storm   YARN   Pig   HDFS   MapReduce   Apache  Hadoop   HCatalog   Hive   HBase   Ambari   Knox   Sqoop   Falcon   Flume  
  83. 83. Storm   Tez   Pig   YARN   HDFS   MapReduce   Hortonworks  Data  Plaeorm   HCatalog   Hive   HBase   Ambari   Knox   Sqoop   Falcon   Flume  
  84. 84. What  else  are  we  working  on?   hortonworks.com/labs/  
  85. 85. Hadoop  is  the  new  Modern  Data   Architecture  
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