© 2014 Ellen Friedman 1 
Seeing With Your Eyes Closed 
Ellen Friedman 
No SQL Matters Barcelona 
22 November 2014
© 2014 Ellen Friedman 2 
Contact Information 
Ellen Friedman 
Solutions Consultant and Commentator 
Apache Mahout committe...
© 2014 Ellen Friedman 3 
Thinking With Your Eyes Closed 
When some people think… 
© 2014 Ellen Friedman 
… they close thei...
© 2014 Ellen Friedman 4 
Getting Past the Details 
• Look at your data with an open mind 
• Listen to what data tells you ...
© 2014 Ellen Friedman 5 
NoSQL 
• Founded on discovery 
• Solution-driven 
• Don’t be bound by the tool 
• Flexibility is ...
! ! ! ! !Basic idea: 
! !“Eyes open” ! !“Eyes closed” 
! ! !D e tails ! ! ! !Discovery! 
© 2014 Ellen Friedman 6
© 2014 Ellen Friedman 7 
Imagination, technology and 
careful reasoning 
Think where this may take you.
Things don’t always turn out the way you predict… 
With exploration into new frontiers, you may 
meet your goal in surpris...
Big Data and Open Source in the 19th Century 
Here’s a story with the power of vision (eyes closed thinking) plus 
keen ob...
© 2014 Ellen Friedman 10 
Here’s the story!
© 2014 Ellen Friedman 11 
Matthew Fountain Maury was a sailor 
in the 1830s. 
Injured at sea, the US Navy gave 
him a “des...
© 2014 Ellen Friedman 12 
Time Series Data – An Old Idea 
Captain’s log book entry 
for the Steam Ship Bear, 
1884 trip to...
© 2014 Ellen Friedman 13 
Time Series Data – An Old Idea 
The basis of a time series is the repeated measurement of parame...
© 2014 Ellen Friedman 14 
Time Series Data – An Old Idea 
At his desk job in the U.S. Navy Office of Charts, Maury 
discov...
Big data project: Bring the data together 
• Using the log data, Maury and his team built maps to indicate wind, 
temperat...
Big data project: Maury’s Wind and Currents charts 
At first, no body was 
interested in them… 
© 2014 Ellen Friedman 16
© 2014 Ellen Friedman 17 
Maury’s Wind and Currents charts 
Using Maury’s carefully compiled data, Captain Jackson got bac...
© 2014 Ellen Friedman 18 
Maury’s Wind and Currents charts 
Now everybody wanted one of his charts. 
Here’s where the open...
Maury’s Open Source Project: The Abstract Log 
Maury wanted better data from the ship’s captains. To get one of 
Maury’s W...
Data-Drive Decisions Set a World Record 
• In 1853, clipper ship Flying Cloud set record for fastest sailing 
from New Yor...
© 2014 Ellen Friedman 21 
Key Lessons from Maury’s Work 
• Give to get 
– Give the Abstract Log to captains, get data coll...
© 2014 Ellen Friedman 22 
Where exploration is 
taking us now!
© 2014 Ellen Friedman 23 
Exploration takes you to surprising places 
The really scary part is knowing the amount of 
comp...
© 2014 Ellen Friedman 24 
Computing power in familiar objects 
For comparison: SIM chip in smart card similar 
to the SIM ...
© 2014 Ellen Friedman 25 
Computing power in familiar objects 
SIM chip in smart card similar to the SIM chip in a 
cell p...
© 2014 Ellen Friedman 26 
Computing power in familiar objects 
Arduino is a little microprocessor with enough 
power to in...
Things may not turn out the way you predict 
© 2014 Ellen Friedman 27 
Surprising use for a 
microprocessor: 
Family cat e...
© 2014 Ellen Friedman 28 
Who Needs Time Series Data? 
Utility providers use 
smart meters to monitor 
very short term cha...
© 2014 Ellen Friedman 29 
Who Needs Time Series Data? 
Manufacturers who monitor 
equipment on the assembly 
line 
Manufac...
© 2014 Ellen Friedman 30 
Unmanned Ocean Robot: Wave Glider 
• Made by Liquid Robotics 
http://liquidr.com/technology/wave...
© 2014 Ellen Friedman 31 
Environmental Monitoring 
• Big trend and growing 
• Companies to collect, store and analyze dat...
© 2014 Ellen Friedman 32 
Smart Shirt 
• Sensors embedded in fabric 
– Measures heart rate & movement 
– Includes time sta...
© 2014 Ellen Friedman 33 
Cityzen Data 
• Spin-off from consortium Cityzen Sciences 
• Provides data platform for storage ...
When is a NoSQL time series database useful? 
© 2014 Ellen Friedman 34 
Build a NoSQL time series database when 
• Most of...
© 2014 Ellen Friedman 35 
!
© 2014 Ellen Friedman 36 
Lesson: 
It’s scary to go the Moon with the 
computing power of a credit card!
© 2014 Ellen Friedman 37 
Lesson: 
Modern computing + NoSQL methods = 
enormous potential!
© 2014 Ellen Friedman 38 
Communication matters… 
!
Like monkeys trying to describe a Capybara… 
© 2014 Ellen Friedman 39 
Seen on Twitter: https://twitter.com/rudytheelder/s...
Getting Past the Details 
It’s no longer acceptable for technical and non-technical teams to be 
unable to communicate 
• ...
© 2014 Ellen Friedman 41 
Basic idea: 
Seeing key concepts leads to 
discovery and implementation!
Time Series Databases 
by Ted Dunning and Ellen Friedman © Oct 2014 (published by O’Reilly) 
© 2014 Ellen Friedman 42 
How...
Innovations in Recommendation 
by Ted Dunning and Ellen Friedman © Feb 2014 (published by O’Reilly) 
© 2014 Ellen Friedman...
A New Look at Anomaly Detection 
by Ted Dunning and Ellen Friedman © June 2014 (published by O’Reilly) 
© 2014 Ellen Fried...
! ! ! ! !Basic idea: 
! !“Eyes open” ! !“Eyes closed” 
! ! !P r esent ! ! ! ! !Future! 
© 2014 Ellen Friedman 45
Flexibility is a key aspect of 
© 2014 Ellen Friedman 46 
NoSQL!
How would you like to be able to… 
• Query multiple data types including JSON or Parquet with SQL? 
• Use directory name a...
Apache Drill 
• Low latency SQL query engine for Apache Hadoop and NoSQL 
• Extremely flexible: 
– 1st and only distribute...
Real SQL instead of “SQL-like” 
• May be surprising to boast in a NoSQL conference, but flexibility 
is important – find s...
© 2014 Ellen Friedman 50 
Schema-less distributed SQL engine 
• Save weeks or months 
– would have been spent on defining ...
Query complex, semi-structured data “as is” 
• No need to flatten or transform data prior to query execution 
• Intuitive ...
Apache Drill 
• Open source, open opportunities 
• What would you use Drill to do? 
• Best use case will be featured in up...
© 2014 Ellen Friedman 53 
Looking Forward: 
Apache Drill SQL on NoSQL!
Big Impact on Society! 
© 2014 Ellen Friedman 54
What if you needed to uniquely 
identify every person in India?! 
A ll 1.2 billion of them?! 
© 2014 Ellen Friedman 55
PEOPLE © 2014 Ellen Friedman 56 
1.2 B 
Largest Biometric Database in the World 
PPEEOOPPLLEE 
The Aadhaar Project: 
• Uni...
© 2014 Ellen Friedman 57 
A Day in the Life of the Aadhaar Project 
Data platform must handle: 
• 1 million new enrollment...
© 2014 Ellen Friedman 58 
What does Aadhaar mean for India? 
• Better delivery of welfare services 
• More open society 
–...
! ! ! ! !Basic idea: 
! !“Eyes open” ! !“Eyes closed” 
! !Implementation ! ! !Vision! 
© 2014 Ellen Friedman 59
© 2014 Ellen Friedman 60 
Exploration takes you to surprising places 
Buzz Aldrin steps onto Moon 
photo by Neil Armstrong...
© 2014 Ellen Friedman 61 
India’s Space Program: Mission to Mars 
• India’s ISRO gets Mars orbit on 1st try 
• US NASA & I...
© 2014 Ellen Friedman 62 
India’s Women Engineers at ISRO 
• ISRO and NASA have many women 
engineers 
• Very cool
European Space Agency: Rosetta Mission to Comet 
• Mission took 10 years, 8 mo, 19 days 
• Philae lander touched down on c...
What do I predict for the NoSQL 
© 2014 Ellen Friedman 64 
future?!
What future do you want to build?! 
© 2014 Ellen Friedman 65
© 2014 Ellen Friedman 66 
Contact Information 
Ellen Friedman 
Solutions Consultant and Commentator 
Apache Mahout committ...
© 2014 Ellen Friedman 67 
Thank you!!
Nächste SlideShare
Wird geladen in …5
×

Ellen Friedman - Keynote NoSQL matters Barcelona 2014

1.355 Aufrufe

Veröffentlicht am

Ellen Friedman – Thinking With Your Eyes Closed

NoSQL is one of the most creative movements to emerge in database technology of recent history. How can you continue to foster this creative strength at the same time as you attend to the demands of NoSQL growing into mainstream, enterprise-ready technology? This talk will trace the connections between careful, disciplined work and leaps of insight and advancement in fields ranging from molecular biology to oceanography to the manufacture of modern “smart” textiles. We will examine current trends in the NoSQL movement including increasingly widespread use of NoSQL techniques to implement large scale time-series databases and the paradoxical emergence of SQL in NoSQL environments. Finally, rather than trying to predict the future, we’ll look at what future you choose to build.

Veröffentlicht in: Daten & Analysen
0 Kommentare
1 Gefällt mir
Statistik
Notizen
  • Als Erste(r) kommentieren

Keine Downloads
Aufrufe
Aufrufe insgesamt
1.355
Auf SlideShare
0
Aus Einbettungen
0
Anzahl an Einbettungen
4
Aktionen
Geteilt
0
Downloads
25
Kommentare
0
Gefällt mir
1
Einbettungen 0
Keine Einbettungen

Keine Notizen für die Folie

Ellen Friedman - Keynote NoSQL matters Barcelona 2014

  1. 1. © 2014 Ellen Friedman 1 Seeing With Your Eyes Closed Ellen Friedman No SQL Matters Barcelona 22 November 2014
  2. 2. © 2014 Ellen Friedman 2 Contact Information Ellen Friedman Solutions Consultant and Commentator Apache Mahout committer, Apache Drill contributor Email ellenf@apache.org efriedman@maprtech.com Twitter @Ellen_Friedman @ApacheDrill Hashtag today: #NoSQL14
  3. 3. © 2014 Ellen Friedman 3 Thinking With Your Eyes Closed When some people think… © 2014 Ellen Friedman … they close their eyes in order to “see”.
  4. 4. © 2014 Ellen Friedman 4 Getting Past the Details • Look at your data with an open mind • Listen to what data tells you • Find the key concepts in what you do • Give yourself an opportunity for discovery
  5. 5. © 2014 Ellen Friedman 5 NoSQL • Founded on discovery • Solution-driven • Don’t be bound by the tool • Flexibility is important • How do you keep your ability for invention?
  6. 6. ! ! ! ! !Basic idea: ! !“Eyes open” ! !“Eyes closed” ! ! !D e tails ! ! ! !Discovery! © 2014 Ellen Friedman 6
  7. 7. © 2014 Ellen Friedman 7 Imagination, technology and careful reasoning Think where this may take you.
  8. 8. Things don’t always turn out the way you predict… With exploration into new frontiers, you may meet your goal in surprising ways. © 2014 Ellen Friedman 8 Spanish explorers came to the Americas in search for riches. They were looking for gold and silver. They found cochineal. Red dye worth a fortune. A Perfect Red, by Amy Butler Greenfield
  9. 9. Big Data and Open Source in the 19th Century Here’s a story with the power of vision (eyes closed thinking) plus keen observation and attention to detail (eyes open thinking) It’s got: • Adventure on the high seas • Time series data (a hot topic in the NoSQL world today) • Clever community building for open source participation • World speed record • (but no pirates) © 2014 Ellen Friedman 9
  10. 10. © 2014 Ellen Friedman 10 Here’s the story!
  11. 11. © 2014 Ellen Friedman 11 Matthew Fountain Maury was a sailor in the 1830s. Injured at sea, the US Navy gave him a “desk job”. Oddly, that’s where the real adventure starts.!
  12. 12. © 2014 Ellen Friedman 12 Time Series Data – An Old Idea Captain’s log book entry for the Steam Ship Bear, 1884 trip to Arctic From image digitized by www.oldweather.org and provided via www.naval-history.net . Image modified by Ellen Friedman and Ted Dunning. Ship captains kept log books with various comments plus measurements recorded at specific times.
  13. 13. © 2014 Ellen Friedman 13 Time Series Data – An Old Idea The basis of a time series is the repeated measurement of parameters over time, together with the times at which the measurements were made.
  14. 14. © 2014 Ellen Friedman 14 Time Series Data – An Old Idea At his desk job in the U.S. Navy Office of Charts, Maury discovered boxes with hundreds of ship’s logs, largely forgotten.
  15. 15. Big data project: Bring the data together • Using the log data, Maury and his team built maps to indicate wind, temperature, currents – They extracted, transformed and aggregated this huge volume of data – By hand! • Mariners would be able to predict conditions on various routes at © 2014 Ellen Friedman 15 different times of the year • His theory was that this would help navigation • Maury published his Winds and Currents charts to be widely available
  16. 16. Big data project: Maury’s Wind and Currents charts At first, no body was interested in them… © 2014 Ellen Friedman 16
  17. 17. © 2014 Ellen Friedman 17 Maury’s Wind and Currents charts Using Maury’s carefully compiled data, Captain Jackson got back one month early on a trip from Baltimore in the US to Rio de Janeiro in Brazil.
  18. 18. © 2014 Ellen Friedman 18 Maury’s Wind and Currents charts Now everybody wanted one of his charts. Here’s where the open source parts comes in…
  19. 19. Maury’s Open Source Project: The Abstract Log Maury wanted better data from the ship’s captains. To get one of Maury’s Winds and Currents charts: • Captains first had to fill in a special template for one of their trips • They returned the template, called Abstract Log, to Maury and got a chart • Maury’s team collected new data that was better than before: regular and systematic time series data © 2014 Ellen Friedman 19
  20. 20. Data-Drive Decisions Set a World Record • In 1853, clipper ship Flying Cloud set record for fastest sailing from New York City to San Francisco • Maury’s charts played a key role in the navigator’s expert, data-driven © 2014 Ellen Friedman 20 decisions about the route • Surprisingly, the navigator was a woman, Eleanor Creesy
  21. 21. © 2014 Ellen Friedman 21 Key Lessons from Maury’s Work • Give to get – Give the Abstract Log to captains, get data collected in careful way • Big data consortium wins – Merging data gives pictures nobody else can see • Building open source community is valuable – The collective effort builds the basis for exploration and discovery • Lessons like today: Just 150 years before everybody else
  22. 22. © 2014 Ellen Friedman 22 Where exploration is taking us now!
  23. 23. © 2014 Ellen Friedman 23 Exploration takes you to surprising places The really scary part is knowing the amount of computing power in the Apollo 11 guidance system… Buzz Aldrin steps onto Moon photo by Neil Armstrong, Apollo 11 20 July 1969 NASA photo http://1.usa.gov/1uXi53U
  24. 24. © 2014 Ellen Friedman 24 Computing power in familiar objects For comparison: SIM chip in smart card similar to the SIM chip in a cell phone Has about 0.5 kilobytes RAM 16.0 kilobytes ROM Only a little less than Apollo…
  25. 25. © 2014 Ellen Friedman 25 Computing power in familiar objects SIM chip in smart card similar to the SIM chip in a cell phone Has about 0.5 kilobytes RAM 16.0 kilobytes ROM Phone processor is very powerful: 1.3 GHz, dual core,1 GB of RAM Much more powerful than Apollo
  26. 26. © 2014 Ellen Friedman 26 Computing power in familiar objects Arduino is a little microprocessor with enough power to interact with sensors in the IoT The question is, what can you use these powerful, compact technologies to do?
  27. 27. Things may not turn out the way you predict © 2014 Ellen Friedman 27 Surprising use for a microprocessor: Family cat equipped with “smart collar” investigates neighborhood and reveals weak security for local wi-fi Humorous glimpse at the potential for IoT https://www.mapr.com/blog/the-internet-of-cat-toys
  28. 28. © 2014 Ellen Friedman 28 Who Needs Time Series Data? Utility providers use smart meters to monitor very short term changes in energy usage
  29. 29. © 2014 Ellen Friedman 29 Who Needs Time Series Data? Manufacturers who monitor equipment on the assembly line Manufacturers who produce “smart parts” that report back after the parts are in operation
  30. 30. © 2014 Ellen Friedman 30 Unmanned Ocean Robot: Wave Glider • Made by Liquid Robotics http://liquidr.com/technology/waveglider/how-it-works.html • Powered by wave motion • Onboard sensors solar powered • Travelled from San Francisco to Hawaii, Japan & Australia • Survived shark attack and typhoon • Cool
  31. 31. © 2014 Ellen Friedman 31 Environmental Monitoring • Big trend and growing • Companies to collect, store and analyze data • Example: Planet OS – Multi-sensor, machine data – Time series + spatial data – https://planetos.com
  32. 32. © 2014 Ellen Friedman 32 Smart Shirt • Sensors embedded in fabric – Measures heart rate & movement – Includes time stamp and geo data • Smart fabric uses smart phone as hub • Fabric also used for other industries • Made by Smart Sensing, part of Cityzen Sciences Consortium • Also cool. Feb 2014 article in gizmag http://www.gizmag.com/cityzen-smart-shirt-sensing- fabric-health-monitoring/30428/
  33. 33. © 2014 Ellen Friedman 33 Cityzen Data • Spin-off from consortium Cityzen Sciences • Provides data platform for storage & analysis of sensor data inc smart shirt • http://www.cityzendata.com • Presentation by Cityzen Data CTO Mathias Herberts “From Thread to API” (Feb 2014 ) https://www.youtube.com/watch?v=RV_Wgc-0yOs • Presentation in Silicon Valley in June 2014 http://www.slideshare.net/Mathias-Herberts/20140611-io-tsiliconvalley
  34. 34. When is a NoSQL time series database useful? © 2014 Ellen Friedman 34 Build a NoSQL time series database when • Most of your scans are based on a time range • Data is at large scale
  35. 35. © 2014 Ellen Friedman 35 !
  36. 36. © 2014 Ellen Friedman 36 Lesson: It’s scary to go the Moon with the computing power of a credit card!
  37. 37. © 2014 Ellen Friedman 37 Lesson: Modern computing + NoSQL methods = enormous potential!
  38. 38. © 2014 Ellen Friedman 38 Communication matters… !
  39. 39. Like monkeys trying to describe a Capybara… © 2014 Ellen Friedman 39 Seen on Twitter: https://twitter.com/rudytheelder/status/500471789042954240
  40. 40. Getting Past the Details It’s no longer acceptable for technical and non-technical teams to be unable to communicate • Data science team needs to clearly exchange ideas about project © 2014 Ellen Friedman 40 goals, resources and planning with domain experts • Find a new language to describe your work appropriately • Find the key concepts in what you do • Describe them in a way that makes sense to your audience
  41. 41. © 2014 Ellen Friedman 41 Basic idea: Seeing key concepts leads to discovery and implementation!
  42. 42. Time Series Databases by Ted Dunning and Ellen Friedman © Oct 2014 (published by O’Reilly) © 2014 Ellen Friedman 42 How to store & access time series data using NoSQL database (HBase or MapR-DB) e-books currently available courtesy of MapR http://bit.ly/1GMk9yY
  43. 43. Innovations in Recommendation by Ted Dunning and Ellen Friedman © Feb 2014 (published by O’Reilly) © 2014 Ellen Friedman 43
  44. 44. A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman © June 2014 (published by O’Reilly) © 2014 Ellen Friedman 44
  45. 45. ! ! ! ! !Basic idea: ! !“Eyes open” ! !“Eyes closed” ! ! !P r esent ! ! ! ! !Future! © 2014 Ellen Friedman 45
  46. 46. Flexibility is a key aspect of © 2014 Ellen Friedman 46 NoSQL!
  47. 47. How would you like to be able to… • Query multiple data types including JSON or Parquet with SQL? • Use directory name as a table name when you query so you don’t have to © 2014 Ellen Friedman 47 know in advance the files you’re going for? • Use standard SQL query on Hadoop or NoSQL, with low-latency? • Go schema-less !? (shocking!) • Reduce the distance to your data? • This is where Apache Drill comes in… • That’s where Drill comes in…
  48. 48. Apache Drill • Low latency SQL query engine for Apache Hadoop and NoSQL • Extremely flexible: – 1st and only distributed SQL query engine that does not require schema – Uses wide range of data types including nested, JSON, Parquet © 2014 Ellen Friedman 48 • Convenient: – Uses familiar ANSI SQL commands – Lets you continue to use standard BI tools • Open source community: – Approaching graduation
  49. 49. Real SQL instead of “SQL-like” • May be surprising to boast in a NoSQL conference, but flexibility is important – find solutions, not bound by one tool • Sample TPC-H SQL benchmark query that Drill can run “as is”: © 2014 Ellen Friedman 49
  50. 50. © 2014 Ellen Friedman 50 Schema-less distributed SQL engine • Save weeks or months – would have been spent on defining schema, ETL and maintaining schema • Drill automatically understands the structure of data • Simply point Drill at data and run queries – Works on file, directory, Hbase or MapR-DB, table etc.
  51. 51. Query complex, semi-structured data “as is” • No need to flatten or transform data prior to query execution • Intuitive extensions to SQL to work with nested data • Here is simple query on a JSON file: © 2014 Ellen Friedman 51
  52. 52. Apache Drill • Open source, open opportunities • What would you use Drill to do? • Best use case will be featured in upcoming book on Drill © 2014 Ellen Friedman 52
  53. 53. © 2014 Ellen Friedman 53 Looking Forward: Apache Drill SQL on NoSQL!
  54. 54. Big Impact on Society! © 2014 Ellen Friedman 54
  55. 55. What if you needed to uniquely identify every person in India?! A ll 1.2 billion of them?! © 2014 Ellen Friedman 55
  56. 56. PEOPLE © 2014 Ellen Friedman 56 1.2 B Largest Biometric Database in the World PPEEOOPPLLEE The Aadhaar Project: • Unique 12 – digit number for each person in India • Proof of identity and address, authenticated anytime, anywhere • Runs on NoSQL database MapR-DB
  57. 57. © 2014 Ellen Friedman 57 A Day in the Life of the Aadhaar Project Data platform must handle: • 1 million new enrollments /day – After 4 years, ~ 600 million of the 1.2 billion already enrolled – 4+ PB of raw data • Each new enrollment needs de-duplication – 100s of millions of transaction over billions of records doing 100s of trillions of biometric matches/day • Online sub-second authentications – as many as 100 million per day From Pramod Varma, Chief Architect of UIDAI at Strata / Hadoop World NYC Oct 2014 http://strataconf.com/stratany2014/public/schedule/detail/36305 Official website of Unique Identification Authority of India (UIDAI) http://uidai.gov.in
  58. 58. © 2014 Ellen Friedman 58 What does Aadhaar mean for India? • Better delivery of welfare services • More open society – Identification without regard to cast, creed, religion or geography • Reduction in embezzlement – save billions in government funds • NoSQL is changing society for the better
  59. 59. ! ! ! ! !Basic idea: ! !“Eyes open” ! !“Eyes closed” ! !Implementation ! ! !Vision! © 2014 Ellen Friedman 59
  60. 60. © 2014 Ellen Friedman 60 Exploration takes you to surprising places Buzz Aldrin steps onto Moon photo by Neil Armstrong, Apollo 11 20 July 1969 NASA photo http://1.usa.gov/1uXi53U
  61. 61. © 2014 Ellen Friedman 61 India’s Space Program: Mission to Mars • India’s ISRO gets Mars orbit on 1st try • US NASA & India’s ISRO look forward to collaboration (while @MarsOrbiter chats with @MarsCuriosity) • Also cool
  62. 62. © 2014 Ellen Friedman 62 India’s Women Engineers at ISRO • ISRO and NASA have many women engineers • Very cool
  63. 63. European Space Agency: Rosetta Mission to Comet • Mission took 10 years, 8 mo, 19 days • Philae lander touched down on comet on 12 November 2014 • Outrageously cool! © 2014 Ellen Friedman 63
  64. 64. What do I predict for the NoSQL © 2014 Ellen Friedman 64 future?!
  65. 65. What future do you want to build?! © 2014 Ellen Friedman 65
  66. 66. © 2014 Ellen Friedman 66 Contact Information Ellen Friedman Solutions Consultant and Commentator Apache Mahout committer, Apache Drill contributor Email ellenf@apache.org efriedman@maprtech.com Twitter @Ellen_Friedman @ApacheDrill Hashtag today: #NoSQL14
  67. 67. © 2014 Ellen Friedman 67 Thank you!!

×