Adam	
  Muise	
  –	
  Solu/on	
  Architect,	
  Hortonworks	
  

ELEPHANT	
  AT	
  THE	
  DOOR:	
  

HADOOP	
  AND	
  NEXT	...
Who	
  am	
  I?	
  
Who	
  is	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  ?	
  
100%	
  Open	
  Source	
  –	
  
Democra/zed	
  Access	
  to	
  
Data	
  

The	
  leaders	
  of	
  Hadoop’s	
  
development...
We	
  do	
  Hadoop	
  successfully.	
  
Support	
  	
  
Training	
  
Professional	
  Services	
  
We	
  do	
  Hadoop	
  successfully	
  
everywhere.	
  
We	
  do	
  Hadoop	
  successfully,	
  
everywhere,	
  with	
  partners.	
  
What	
  is	
  Hadoop?	
  	
  
What	
  is	
  everyone	
  talking	
  about?	
  
Data	
  
“Big	
  Data”	
  is	
  the	
  marke/ng	
  term	
  
of	
  the	
  decade	
  in	
  IT	
  
What	
  lurks	
  behind	
  the	
  hype	
  is	
  
the	
  democra/za/on	
  of	
  Data.	
  
You	
  need	
  data.	
  	
  
But	
  what	
  do	
  you	
  do	
  with	
  your	
  
data	
  now?	
  
We	
  are	
  obsessive	
  compulsive	
  
about	
  collec/ng	
  and	
  structuring	
  
our	
  data.	
  
Put	
  it	
  away,	
  delete	
  it,	
  tweet	
  it,	
  
compress	
  it,	
  shred	
  it,	
  wikileak-­‐it,	
  put	
  
it	
 ...
You	
  need	
  data.	
  Your	
  customers	
  
expect	
  you	
  to	
  know	
  what	
  they	
  want	
  
before	
  they	
  do...
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	
  

Analy/cs	
  Sausage	
  Factory	
  

Dat...
Data	
  begets	
  Data.	
  
What	
  keeps	
  us	
  from	
  our	
  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...
ETL	
  
Data	
  
Data	
  
Data	
  

ETL	
  

ETL	
  

ETL	
  

EDW	
  

Data	
  
Data	
   Data	
  
Data	
   Data	
  
Schem...
What	
  do	
  you	
  want	
  to	
  do	
  with	
  
data?	
  
Marke/ng	
  Analy/cs	
  needs	
  data.	
  
Work	
  with	
  the	
  popula/on,	
  not	
  just	
  a	
  
sample.	
  
Town/City	
  
Middle	
  Income	
  Band	
  

Your	
  segmenta/on	
  today.	
  
Female	
  
Age:	
  25-­‐30	
  

Male	
  
Pro...
GPS	
  coordinates	
  
Looking	
  to	
  start	
  a	
  
business	
  	
  

Walking	
  into	
  
Starbucks	
  right	
  now…	
 ...
Pick	
  up	
  all	
  of	
  that	
  data	
  that	
  was	
  
prohibi/vely	
  expensive	
  to	
  store	
  and	
  
use.	
  	
 ...
Why	
  do	
  viewer	
  surveys…	
  
…when	
  raw	
  data	
  can	
  tell	
  you	
  what	
  
bu^on	
  on	
  the	
  remote	
  was	
  pressed	
  
during	
  what	
...
To	
  approach	
  these	
  use	
  cases	
  you	
  
need	
  an	
  affordable	
  plaForm	
  that	
  
stores,	
  processes,	
 ...
So	
  what	
  is	
  the	
  answer?	
  
Enter	
  the	
  Hadoop.	
  

………	
  
h^p://www.fabulouslybroke.com/2011/05/ninja-­‐elephants-­‐and-­‐other-­‐awesome-­‐sto...
Hadoop	
  was	
  created	
  because	
  
tradi/onal	
  technologies	
  never	
  cut	
  it	
  
for	
  the	
  Internet	
  pro...
Tradi/onal	
  architecture	
  didn’t	
  
scale	
  enough…	
  
App	
   App	
   App	
   App	
  

App	
   App	
   App	
   App...
Databases	
  can	
  become	
  bloated	
  
and	
  useless	
  
$upercompu/ng	
  

Tradi/onal	
  architectures	
  cost	
  too	
  
much	
  at	
  that	
  volume…	
  

$/TB	
  

$pecial	
  ...
So	
  what	
  is	
  the	
  answer?	
  
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	
  
Hadoop	
  2	
  just	
  hit	
  the	
  ground:	
  
Introducing	
  YARN	
  
YARN	
  lets	
  you	
  run	
  more	
  data	
  
apps	
  than	
  ever	
  before	
  
MapReduce	
  V2	
  
MapReduce	
  V?	
   ...
YARN	
  turns	
  Hadoop	
  into	
  a	
  smart	
  
phone:	
  An	
  App	
  Ecosystem	
  
hortonworks.com/yarn/	
  
YARN:	
  	
  
Yeah,	
  we	
  did	
  that	
  too.	
  
hortonworks.com/yarn/	
  
Storm	
  
HDFS	
  

YARN	
  

Pig	
  

MapReduce	
  

Apache	
  Hadoop	
  

HCatalog	
  

Hive	
  
HBase	
  

Ambari	
  

...
Storm	
  

Pig	
  

HDFS	
  

YARN	
  
MapReduce	
  

Hortonworks	
  Data	
  PlaForm	
  
HCatalog	
  

Hive	
  
HBase	
  
...
What	
  else	
  are	
  we	
  working	
  on?	
  
hortonworks.com/labs/	
  
Hadoop	
  is	
  the	
  new	
  Data	
  Opera/ng	
  
System	
  for	
  the	
  Enterprise	
  
There is NO second place

Hortonworks	
  

…the	
  Bull	
  Elephant	
  of	
  Hadoop	
  InnovaDon	
  
© Hortonworks Inc. 20...
Upcoming SlideShare
Loading in...5
×

2013 Dec 9 Data Marketing 2013 - Hadoop

369

Published on

Data Marketing 2013 Presentation of Hadoop. The paradigm shift in 45 minutes or less. No, really.

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
369
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
18
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

2013 Dec 9 Data Marketing 2013 - Hadoop

  1. 1. Adam  Muise  –  Solu/on  Architect,  Hortonworks   ELEPHANT  AT  THE  DOOR:   HADOOP  AND  NEXT  GENERATION  DATA  
  2. 2. Who  am  I?  
  3. 3. Who  is                                        ?  
  4. 4. 100%  Open  Source  –   Democra/zed  Access  to   Data   The  leaders  of  Hadoop’s   development   We  do  Hadoop   Drive  Innova/on  in   the  plaForm  –  We   lead  the  roadmap     Community  driven,     Enterprise  Focused  
  5. 5. We  do  Hadoop  successfully.   Support     Training   Professional  Services  
  6. 6. We  do  Hadoop  successfully   everywhere.  
  7. 7. We  do  Hadoop  successfully,   everywhere,  with  partners.  
  8. 8. What  is  Hadoop?     What  is  everyone  talking  about?  
  9. 9. Data  
  10. 10. “Big  Data”  is  the  marke/ng  term   of  the  decade  in  IT  
  11. 11. What  lurks  behind  the  hype  is   the  democra/za/on  of  Data.  
  12. 12. You  need  data.    
  13. 13. But  what  do  you  do  with  your   data  now?  
  14. 14. We  are  obsessive  compulsive   about  collec/ng  and  structuring   our  data.  
  15. 15. 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…  
  16. 16. You  need  data.  Your  customers   expect  you  to  know  what  they  want   before  they  do.    
  17. 17. Let’s  talk  challenges…  
  18. 18. Volume   Volume   Volume   Volume  
  19. 19. Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume  
  20. 20. 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  
  21. 21. 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  
  22. 22. Storage,  Management,  Processing   all  become  challenges  with  Data  at   Volume  
  23. 23. Tradi/onal  technologies  adopt  a   divide,  drop,  and  conquer  approach  
  24. 24. 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  
  25. 25. 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  
  26. 26. Analyzing  the  data  usually  raises   more  interes/ng  ques/ons…  
  27. 27. …which  leads  to  more  data  
  28. 28. Wait,  you’ve  seen  this  before.   …   Data   Data   Data   Analy/cs  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   Data   Data   Data   Data  
  29. 29. Data  begets  Data.  
  30. 30. What  keeps  us  from  our  Data?  
  31. 31. “Prices,  Stupid  passwords,  and   Boring  Sta/s/cs.”     -­‐  Hans  Rosling   h)p://www.youtube.com/watch?v=hVimVzgtD6w  
  32. 32. 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  
  33. 33. …  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  
  34. 34. 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   Twi^er   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  
  35. 35. 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  Li^le  Data  Empire   Data   ?   Data   ?   Data   Data   Data   Data   Data   ?  ?   Data   Data  
  36. 36. To  resolve  this,  some  people  take   hints  from  Lord  Of  The  Rings...  
  37. 37. …and  create  One-­‐Schema-­‐To-­‐ Rule-­‐Them-­‐All…   EDW   Data   Data   Data   Data   Data   Schema   Data   Data   Data   Data  
  38. 38. 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  
  39. 39. ETL   Data   Data   Data   ETL   ETL   ETL   EDW   Data   Data   Data   Data   Data   Schema   Data   Data   Data   Data   Fragile  workflows  make  suppor/ng  the   analy/cal  models  you  want  expensive  and   /me-­‐consuming.   ETL   Data   Data   Data   ETL   ETL   ETL   EDW   Data   Data   Data   Data   Data   Schema   Data   Data   Data   Data  
  40. 40. What  do  you  want  to  do  with   data?  
  41. 41. Marke/ng  Analy/cs  needs  data.   Work  with  the  popula/on,  not  just  a   sample.  
  42. 42. Town/City   Middle  Income  Band   Your  segmenta/on  today.   Female   Age:  25-­‐30   Male   Product  Category   Preferences  
  43. 43. GPS  coordinates   Looking  to  start  a   business     Walking  into   Starbucks  right  now…   Spent  25  minutes   looking  at  tea  cozies   Unhappy  with  his  cell   phone  plan   $65-­‐68k  per  year   Your  segmenta/on  with   Pregnant   be^er  data.   Tea  Party   Hippie   A  depressed  Toronto   Maple  Leaf’s  Fan   Gene   Expression  for   Risk  Taker   Male   Female   Age:  27  but   feels  old   Product   recommenda/ons   Thinking  about   a  new  house   Products  lek  in   basket  indicate  drunk   amazon  shopper  
  44. 44. Pick  up  all  of  that  data  that  was   prohibi/vely  expensive  to  store  and   use.      
  45. 45. Why  do  viewer  surveys…  
  46. 46. …when  raw  data  can  tell  you  what   bu^on  on  the  remote  was  pressed   during  what  commercial  for  the   en/re  viewer  popula/on?  
  47. 47. To  approach  these  use  cases  you   need  an  affordable  plaForm  that   stores,  processes,  and  analyzes  the   data.    
  48. 48. So  what  is  the  answer?  
  49. 49. Enter  the  Hadoop.   ………   h^p://www.fabulouslybroke.com/2011/05/ninja-­‐elephants-­‐and-­‐other-­‐awesome-­‐stories/  
  50. 50. Hadoop  was  created  because   tradi/onal  technologies  never  cut  it   for  the  Internet  proper/es  like   Google,  Yahoo,  Facebook,  Twi^er,   and  LinkedIn  
  51. 51. 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  
  52. 52. Databases  can  become  bloated   and  useless  
  53. 53. $upercompu/ng   Tradi/onal  architectures  cost  too   much  at  that  volume…   $/TB   $pecial   Hardware  
  54. 54. So  what  is  the  answer?  
  55. 55. If  you  could  design  a  system  that   would  handle  this,  what  would  it   look  like?  
  56. 56. It  would  probably  need  a  highly   resilient,  self-­‐healing,  cost-­‐efficient,   distributed  file  system…   Storage   Storage   Storage   Storage   Storage   Storage   Storage   Storage   Storage  
  57. 57. 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  
  58. 58. It  would  probably  run  on  commodity   hardware,  virtualized  machines,  and   common  OS  plaForms   Processing   Processing  Processing   Storage   Storage   Storage   Processing   Processing  Processing   Storage   Storage   Storage   Processing   Processing  Processing   Storage   Storage   Storage  
  59. 59. It  would  probably  be  open  source  so   innova/on  could  happen  as  quickly   as  possible  
  60. 60. It  would  need  a  cri/cal  mass  of   users  
  61. 61. Hadoop  2  just  hit  the  ground:   Introducing  YARN  
  62. 62. YARN  lets  you  run  more  data   apps  than  ever  before   MapReduce  V2   MapReduce  V?   STORM   Giraph   Tez   YARN   HDFS2   MPI   HBase   …  and   more  
  63. 63. YARN  turns  Hadoop  into  a  smart   phone:  An  App  Ecosystem   hortonworks.com/yarn/  
  64. 64. YARN:     Yeah,  we  did  that  too.   hortonworks.com/yarn/  
  65. 65. Storm   HDFS   YARN   Pig   MapReduce   Apache  Hadoop   HCatalog   Hive   HBase   Ambari   Sqoop   Falcon   Flume  
  66. 66. Storm   Pig   HDFS   YARN   MapReduce   Hortonworks  Data  PlaForm   HCatalog   Hive   HBase   Ambari   Sqoop   Falcon   Flume  
  67. 67. What  else  are  we  working  on?   hortonworks.com/labs/  
  68. 68. Hadoop  is  the  new  Data  Opera/ng   System  for  the  Enterprise  
  69. 69. There is NO second place Hortonworks   …the  Bull  Elephant  of  Hadoop  InnovaDon   © Hortonworks Inc. 2012: DO NOT SHARE. CONTAINS HORTONWORKS CONFIDENTIAL & PROPRIETARY INFORMATION Page  69  
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×