big data mgmt

7/28/2019 Big Data Mgmt

1/23

Big Data MGMTBig Data MGMT

Tweets and Stats

By Tweet Category for iPad

Date: 04/02/2013

Analysis of the session 'Big Data MGMT' created with Tweet Category


2/23

Session Big Data MGMT

Introduction: Report made with the iPad app tweet category: stats and tweets in categories. If you want to make reports like this one download today the app from

http://www.TweetCategory.com We created several categories for this event, one per each answer and also positive tweets and other.

Statistics

Potential impact

Potential reach

Categories

Category Total

tweets

% Original

Tweets

RT Users

Links 240 33 143 97 100

Other 170 23 87 83 58

Replies 51 7 51 0 20

Answer 2 36 5 26 10 12

Answer 6 33 5 17 16 14

Answer 3 32 4 15 17 13

Answer 5 32 4 19 13 13

Answer 1 28 4 15 13 10

Rest of categories 111 15 64 47 58

Charts

num.

tweets

time

16

13:2027 mar

586

01:3428 mar

18

13:48

50

02:0229 mar

9

14:17

41

02:3130 mar

5

14:45

7

03:5931 mar

0

16:13

num.

users

num.

followers

15

0-50

23

50-100

12

100-150

8

150-200

6

200-250

4

250-300

5

300-400

8

400-500

12

500-750

10

750-1000

8

1000-1500

23

1500-5000

5

5000-10000

7

>10000

num.

tweets

per user

num. users

971

172

43

54

15

22>5

Most Active Users

tweets

followers

tweets

followers

tweets

followers

tweets

followers

tweets

followers

tweets

followers

tweets

followers



3/23

Other

Links

Other

Links

Links

Other

Links

Other

Links

Other

Charts

Category Total

tweets

% Original

Tweets

RT Users Impressions Potential Reach Tweets/

User

Followers/

User

Links 240 33 143 97 100 804.870 178.364 2,4 1.783Other 170 23 87 83 58 846.389 251.955 2,9 4.344

Replies 51 7 51 0 20 248.269 61.137 2,5 3.056

Answer 2 36 5 26 10 12 115.578 22.354 3,0 1.862

Answer 6 33 5 17 16 14 127.768 27.087 2,4 1.934

Answer 3 32 4 15 17 13 124.246 35.978 2,5 2.767

Answer 5 32 4 19 13 13 130.020 47.221 2,5 3.632

Answer 1 28 4 15 13 10 78.334 26.441 2,8 2.644

Positive 25 3 16 9 11 135.377 34.948 2,3 3.177

Questions 24 3 16 8 14 103.895 37.450 1,7 2.675

Answer 7 22 3 10 12 13 95.607 38.249 1,7 2.942

Answer 8 20 3 9 11 9 83.706 31.685 2,2 3.520

Answer 4 19 3 12 7 10 69.040 36.056 1,9 3.605

Pictures 1 0 1 0 1 1.792 1.792 1,0 1.792



4/23

11,2 5,0 20.307

IBMbigdata

137tweets

Natasha_D_G

74tweets

BTRG_MikeMartin

46tweets

tweets

tweets

GlenGilmore

133.839followers

Timothy_Hughes

30.755followers

IBMSoftware

16.996followers

followers

followers

IBMbigdata

1.657.015impressions

jameskobielus

225.690impressions

furrier

208.995impressions

impressions

impressions

IBMbigdata

13num. categories

Natasha_D_G

13num. categories

jeffreyfkelly

13num. categories

num. categories

num. categories

IBMbigdata

38num. of RTs

Natasha_D_G

37num. of RTs

BigDataAlex

21num. of RTs

num. of RTs

num. of RTs

IBMbigdata

99original tweets

Natasha_D_G

37original tweets

BTRG_MikeMartin

32original tweets

original tweets

original tweets

2565

2

Very low

0 to 10

followers

13

Low

10 to 50

followers

43

Medium-low

50 to 200

followers

23

Medium

200 to 500

followers

22

Medium-high

500 to 1000

followers

31

High

1000 to 5000

followers

12

Very high

>5000

followers



5/23Analysis of the session 'Big Data MGMT' created with Tweet Category

IBMbigdata

137

Natasha_D_G

74

BTRG_MikeMartin46

jeffreyfkelly

42

BigDataAlex

41

jameskobielus

30

zacharyjeans

27

Dmattcarter

22

InfoMgmtExec

19

cristianmolaro

17

furrier

15

dvellante

14

katsnelson

12

johncrupi

11

rkeshavmurthy

10

dfloyer

8

tomjkunkel

8

IBM_InfoSphere

7

TerraEchos

7

BTRGIG

6

CuneytG

6

susvis

6

kdnuggets

5

IBM_DB2

4

IBM_InfoMgmt_SE

4

TheSocialPitt

4

tmustacchio

4

troycoleman4

Ercan__Yilmaz

3

IBMRedbooks

3

StacyLeidwinger

3

timoelliott

3

CrystaAnderson

2

Ellen_Friedman

2

GCSResearch

2

IBMSmrtrCmptng

2

IBM_Guardium

2

IBMinfomgtFR

2

K_Orovboni

2

MoserMaCH

2

PWIndustries

2

abaum67

2

camilo_rojas

2

easysoft

2

gzim

2

ibm_iod

2

jasebell

2

karthik_ph

2

nige25

2

ASUG365

1

AVialBoukobza

1

AdvaiyaInc

1

BButlerNWW1

BIABAYCOM

1

BigDataCoaltion

1

BostjanKozuh

1

CGOC_Council

1

CenturyLinkBiz

1

ChristopheGC

1

ForsythMAlexand

1

FransBouma

1

GlenGilmore

1

IBMOptim4Oracle

1

IBMPartnerPlan

1

IBMPowerSystems

1

IBMSoftware

1

IBM_DWAnalytics

1

IBMdatamag

1

ITredux

1

InfoMgmtPartner

1

JaneTHoye

1

Javier_A_Soto

1

JohnEvans_IBM

1

KeithBraswell

1

LifeisData

1

LubicaT

1

MDI_LLC

1

MULTILINKGIRL1

MattRMorrison

1

Mbs_craig

1

MhdKarneeb

1

NicolasJMorales

1

PR_KBrosey

1

PabloJMoralesG

1

PaminaPiegsa

1

PlottingSuccess

1

ReneeLivsey

1

Storagecreep

1

TT_Nicole

1

TarekAbouAli1

1

TheJillT

1

Timothy_Hughes

1

VinGAbr

1

VinnieCardoso12

1

WebhelpTSC

1

annettefranz

1

annickLEBER

1

anupam_gaur

1

battymarc

1

bhurtibm

1



bigdatasci

1

billramo

1

brunokilian

1

cate

1

claverieberge1

day_dree

1

edd

1

euclid_project

1

fooisms

1

heppenstance

1

ibmpartners

1

ideeHO

1

jacqilevy

1

jaumebp

1

jennifer_dubow

1

jsgarano

1

jvfaulks

1

kirstengraham

1

ktwiter99

1

malhotrayush

1

marcusborba

1

matt_parkerZT

1

mervynvk

1

mytek

1

nigelwallis

1

nkalaima

1

padma8376

1

paulawilesigmon

1

piersgrundy

1

plankers

1

resilvajr

1

roger_barnard1

sauravpoudel

1

stephloverde

1

stevengustafson

1

storageio

1

strataconf

1

suvimarias

1

swatzmystery

1

swissjohnny

1

tctjr

1

theRab

1

tinagroves

1

tinyclues

1

yeahsathish

1

zaxar16

1



@furrierJohn Furrier

where are all the big data apps? they are already here. Analytics

& in memory make them better #bigdatamgmt

Cat.: Questions @zacharyjeansZachary Jeans

Is @Spotify an in memory application? #BigDataMgmt

Cat.: Questions

@troycolemanTroy Coleman

Do you see any in-memory databases running on z/OS?

#bigdatamgmt

Cat.: Questions @IBMbigdataIBM big data

Great #bigdatamgmt contributions from @katsnelson @InfoMgmtExec

@johncrupi @timoelliott @zacharyjeans @dfloyer @jasebell

Cat.: Positive

@CrystaAndersonCrysta Anderson

Great #bigdatamgmt chat! Very interesting conversations. Thanks for

herding, @thesocialpitt @ibmbigdata

Cat.: Positive @johncrupiJohn Crupi

In a year, will we still be talking about in-memory as a separate thing.

Or will it just become in-memory analytics. #bigdatamgmt

Cat.: Other


one issue is counterfeit Flash NAND devices data recovery not

possible is a healthy industry of counterfeiting Flash NAND

#bigdatamgmt

Cat.: Other @dfloyerDavid Floyer

#BigDataMgmt Using flash in conjunction with DRAM increases the

scope of problems tackled and improves recoverability dramatically

Cat.: Other

@johncrupiJohn Crupi

#m2m #IndustrialInternet analytics is the killer use case for in-memory,

IMO. #bigdatamgmt

Cat.: Other @johncrupiJohn Crupi

We have to treat in-memory as the new architectural tier for real-time

analytic apps #bigdatamgmt

Cat.: Other

@InfoMgmtExec

Richard R. Lee

#bigdatamgmt Info Mgmt has always been about "managing the

bottlenecks". A major one has always been the db itself. In-Memory

helps a lot.

Cat.: Other @IBMbigdata

IBM big data

Welcome to the chat @GCSResearch! Glad to have you!

#bigdatamgmt

Cat.: Other

@cristianmolaroCristian Molaro

A8 main role should be to accelerate access in relevant chunks...

#bigdata is too big to be contained in memory... #bigdatamgmt

Cat.: Answer 8 @dvellanteDave Vellante

A8. But no IO is expensive so in-memory in #bigdata has to be used

judiciously #bigdatamgmt

Cat.: Answer 8

@Ercan__YilmazErcan Yilmaz

A7. #spark uses in memory querying of data #bigdatamgmt

Cat.: Answer 7 @dvellanteDave Vellante

A7. yes and @jeffreyfkelly - interesting Aerospike - that's an extensionof memory using flash #bigdatamgmt

Cat.: Answer 7



@dvellanteDave Vellante

A6. Ask any DW practitioner and they'll tell you a story of "chasing the

chips" #the_need_for_speed #bigdatamgmt

Cat.: Answer 6 @zacharyjeansZachary Jeans

A6: I don't know the answer. What are the stability issues with long

term storage on physical media vs In Memory solutions?

#BigDataMgmt

Cat.: Answer 6

@katsnelsonLeon Katsnelson

A5 right cost model for the right type of data. Nothing is cheap or

expensive on its own. Too expensive for something #bigdatamgmt

Cat.: Answer 5 @BigDataAlexAlex Philp

A5: It would take only one shelf of a flash-based storage system.

#bigdatamgmt

Cat.: Answer 5

@Natasha_D_GNatasha Bishop

A4: When data scientists can find answers 2 questions they didnt

THINK to ask its a win #bigdatamgmt


A4: Fire Scientists in Montana are using in-memory computing to

better understand wild land fire given a changing climate.

#bigdatamgmt

Cat.: Answer 4

@jeffreyfkellyJeff Kelly

A3 any transaction workload that requires real-time response in order

to win/save/upsell the customer is in-memory candidate #bigdatamgmt

Cat.: Answer 3 @CuneytGCuneyt Goksu

A3 all oltp apps need to be fast. n memory is fast too. So any oltp app

is in the scope of inmemory #bigdatamgmt

Cat.: Answer 3


A2 When you remove the I/O constraints by going on-memory you will

hit the next performance wall: CPU #bigdatamgmt

Cat.: Answer 2 @Natasha_D_GNatasha Bishop

A2: In-memory tech = gold in #CX tactics and can drive proactive

#custserv: up-sell, cross sell #bigdatamgmt #cxo

Cat.: Answer 2


A1 faster data access enables real-time massive data processing: real-

time #bigdata #bigdatamgmt


A1: IMC reduces power and storage costs, revolutionizing access.

#bigdatamgmt

Cat.: Answer 1


9/23

Annexes



Category

Answer 2Tweets to question 2

26 12 10 22.354 115.578 2,2tweets users retweets potential reach potential

impact

tweets / user

Most Active

Users

1. jeffreyfkelly2. IBMbigdata

3.Natasha_D_G4.cristianmolaro5.zacharyjeans

Tweets from this category

@IBMbigdataIBM big data

Q2 What are the killer apps of in-memory tech? Share examples for

good reference models #bigdatamgmt

@BigDataAlexAlex Philp

A2: Working with streaming data to analyze audio processing in real-

time 32 petabytes a day burn rate. #bigdatamgmt


A2 anything requiring speed-of-thought response time - allows for

exploration of large data sets in near real-tim #bigdatamgmt


Q2 Call Detail Records processing in memory. 9 bilion CDRs per day.

Can't think of a better case for memory #bigdatamgmt


A2: In-memory tech = gold in #CX tactics and can drive proactive

#custserv: up-sell, cross sell #bigdatamgmt #cxo


Nice RT @katsnelson: Q2 Call Detail Recs processing in memory. 9

bilion CDRs per day. Can't think of a better case for memory

#bigdatamgmt


Nice RT @katsnelson: Q2 Call Detail Records processing in memory.

9 bilion CDRs per day. Cant think of a better case for memory#bigdatamgmt


A2 I cannot think about any application that would not take advantage

of faster processing... #bigdatamgmt


Cash! RT @Natasha_D_G: A2: In-memory tech = gold in #CX tactics

and can drive proactive #custserv: up-sell, cross sell #cxo

#bigdatamgmt

@InfoMgmtExecRichard R. Lee

#bigdatamgmt A2 - Apps such as High Frequency Trading and Real-

time Risk/Fraud Analysis come to mind as strong users In-Memory.

Many more.


A2 When you remove the I/O constraints by going on-memory you will

hit the next performance wall: CPU #bigdatamgmt


Good 1s RT @InfoMgmtExec: #bigdatamgmt A2 - Apps such as High

Frequency Trading and Real-time Risk/Fraud Analysis come to mind

#bigdatamgmt


A2 smart meter analytics #bigdatamgmt


A2 many apps where data is not valuable enough to even store on

disk. In Streams we process stuff in memory and discard

#bigdatamgmt

@CuneytGCuneyt Goksu

A2 fraud detection and investigation is a good candidate

#bigdatamgmt

@zacharyjeansZachary Jeans

.@InfoMgmtExec: #bigdatamgmt A2 - Apps such as High Frequency

Trading and Real-time Risk/Fraud Analysis come to mind

#bigdatamgmt




Then what? RT @cristianmolaro: A2 When you remove I/O constraints

by going on-memory you hit next performance wall: CPU

#bigdatamgmt

@TerraEchosTerraEchos, Inc.

Definitely has great security applications! RT @CuneytG: A2 fraud

detection and investigation is a good candidate #bigdatamgmt


Good for #finserv & #insurance RT @CuneytG: A2 fraud

detection and investigation is a good candidate #bigdatamgmt


A2 investigating network traffic issues, finding bottlenecks

#bigdatamgmt


A2 on-memory allows applications to fully exploit today's more and

more powerful CPUs... good news for #bigdata! #bigdatamgmt


A2 analyzing high-velocity financial data in trading scenarios - no time

to lose in this use case! #bigdatamgmt


A2 iterate, iterate, iterate #bigdatamgmt


Then iterate again RT @jeffreyfkelly: A2 iterate, iterate, iterate

#bigdatamgmt


A2: Logistics. SAP HANA reduced a chinese bottled water company's

calculation time from 24 hours to under a minute. #BigDataMgmt


A2 in-memory allows Data Scientists to ask more questions, to quickly

refine questions, and to more quickly find answers #bigdatamgmt



Category



impact

tweets / user

Most Active

Users

1. IBMbigdata2.cristianmolaro

3.BigDataAlex4.Natasha_D_G5. jeffreyfkelly



Q6 How does in-memory support or supplement data warehousing?

#edw

#bigdatamgmt


A6: IMC can help folks leverage their data warehouse - rewire the

house for speed. #bigdatamgmt


A6 back to economics - don't need your entire DW in-memory - use in-

memory to supplement trad DW workloads #bigdatamgmt


A6. DW/BI for years has been like a "snake swallowing a basketball" -

in memory is critical to solve this problem #bigdatamgmt


I feel the need! RT @BigDataAlex: A6: IMC can help folks leverage

their data warehouse - rewire the house for speed. #bigdatamgmt


A6: I don't know the answer. What are the stability issues with long

term storage on physical media vs In Memory solutions?

#BigDataMgmt


A6. Ask any DW practitioner and they'll tell you a story of "chasing the

chips" #the_need_for_speed #bigdatamgmt


#bigdatamgmt A6 In Memory EDW is Holy Grail. Makes EDW more of

"real-time repository" that can better serve Operational &Analytical needs.


A6 in memory analytics is needed if you expect fast reply from dw

supported by hadoop #bigdatamgmt


A6 some data warehousing appliances take advantage of in-memory

processing of data. An example is IBM IDAA #bigdatamgmt


A6: People need to save money in building and supporting their

warehouse. IMC is one way to get there. #bigdatamgmt


A6 in-memory processing has been for ages THE performance

strategy of every database management system... bufferpools?

#bigdatamgmt


A6 must balance biz value of better performance via in-memory versus

cost as applied to DW workloads - all workloads really #bigdatamgmt


A6: #Forbes is writing about In Memory Computing - paradigm

shifting. #bigdatamgmt


Funny....RT @BigDataAlex: A6: #Forbes is writing about In Memory

Computing - paradigm shifting. #bigdatamgmt


A6 often computer systems are CPU rich and Memory poor... in some

cases adding more memory can be the best performance upgrade

#bigdatamgmt




A6 a huge amount of memory is not necessarily a recipe for great

performance: the system has to divide info to conquer #bigdata

#bigdatamgmt



Category



impact

tweets / user

Most Active

Users

1. IBMbigdata2.BigDataAlex

3.Natasha_D_G4. InfoMgmtExec5. jeffreyfkelly



Q3 in a minute #bigdatamgmt


Q3 What are in-memory's applications in transactional computing?

#bigdatamgmt


#bigdatamgmt A3 Orgs want entire Customer Base, Product Sku's

& Pricing in Memory for rapid transaction processing. Customers

will not wait!


A3 ad tech - analyzing user data, real-time bidding, delivering

persnalized content - in milliseconds #bigdatamgmt


A3 many Streams apps are transactional and Streams is always in

memory. #bigdatamgmt


Pondering Q3, I see. What are in-memory's applications in

transactional computing? #bigdatamgmt


A3 all oltp apps need to be fast. n memory is fast too. So any oltp app

is in the scope of inmemory #bigdatamgmt


A3:Connecting the Internet of Things - IP addressable sensors to real-

time calibrate our models for better predictive analytics #bigdatamgmt


Impatient souls RT @InfoMgmtExec: #bigdatamgmt A3 Orgs want

entire Customer Base, Product Skus & Pricing in Memory

#bigdatamgmt


Needed in our "instant" mrkt RT @InfoMgmtExec: #bigdatamgmt A3

Orgs want entire Customer Base, Product Skus & Pricing in

Memory #bigdatamgmt


Customization RT @jeffreyfkelly: A3 ad tech, analyzing user data, real-

time bidding, persnalized content in millisecs #bigdatamgmt


A3 any transaction workload that requires real-time response in order

to win/save/upsell the customer is in-memory candidate #bigdatamgmt


A3:Working in the oil and gas industry-energy exploration requires

millions of transactions a day for discovery of new resource

#bigdatamgmt


A3: memory is making up for disk speed & is now becoming more

important in software models-big oppty #bigdatamgmt


#bigdatamgmt A3. In-Memory db will allow Predictive Models to be

deployed into Transactional Work Flows for real-time scoring &

prediction



Category



impact

tweets / user

Most Active

Users

1. IBMbigdata2.Natasha_D_G

3.zacharyjeans4.BigDataAlex5.dvellante



Q5 What are the economics? Is in-memory more expensive? Where

does it make sense? #bigdatamgmt


A5: Flash memory is cheap, and getting cheaper. #bigdatamgmt


A5: In-Memory must either serve a mission critical system, or profit the

company via efficiency gain. #BigDataMgmt


A5: It takes 4 racks of disk storage to create a system capable of 1

million IOPS, or input/output operations per second. #bigdatamgmt


MT @jameskobielus: #bigdatamgmt A5: Yes, in-mem more expensive

acq than HDD, but coming down. Cost per IOPS, though, in-mem cost-

effective


A5: It would take only one shelf of a flash-based storage system.

#bigdatamgmt


A5 hybrid approach - in-memory/disk - often needed to make

economics work #bigdatamgmt


Reductions in latency well worth the cost factors. @jameskobielus:

@IBMbigdata #bigdatamgmt A5: Yes, in-mem more expensive acqthan HDD.


A5: We wouldn't even be talking In Memory solutions today if the price

for RAM wasn't becoming so reasonable. #BigDataMgmt


A5 right cost model for the right type of data. Nothing is cheap or

expensive on its own. Too expensive for something #bigdatamgmt


Nice! RT @BigDataAlex: A5: It would take only one shelf of a flash-

based storage system. #bigdatamgmt


A5. Isn't it really a balance? - hierarchy of media from in-memory-

>flash->spinning rust #bigdatamgmt


Gd point RT @katsnelson: A5 right cost model 4 right type data.

Nothing cheap or expensive on its own. 2 expensive 4 something

#bigdatamgmt


Rust - nice! RT @dvellante: A5. Isnt it really a balance? - hierarchy of

media from in-memory->flash->spinning rust #bigdatamgmt


A5: opensource impacts the economics when talking mission critical;

sw written to live in-memory is paradigm shift #disruption

#bigdatamgmt


A5. Best economic solution is intelligence in file sys where active data

svcd fm fast memory and slow data is in the bit bucket #bigdatamgmt




#bigdatamgmt A5 Economics self-evident. Living in real-time world

using tools that are not real-time. Reducing Latency to Zero is end

game.


A5. imho less a matter of $ + more case of biz impact. If biz

case=excellent $ of in-mem is irrelevant #bigdatamgmt


A5: #BigDataMgmt #CXO #Leadfromwithin #LeadWithGiants

#CXOTalk are all great twitter chats. #cmgrhangout



Category



impact

tweets / user

Most Active

Users

1.Natasha_D_G2.BigDataAlex

3. IBMbigdata4.BTRGIG5.Dmattcarter



Reminder: Use A1, A2, etc. as you respond to signify which question

you are addressing, and include #bigdatamgmt


Q1 What is in-memory tech? How does it enable real-time speed-of-

thought #analytics? #bigdatamgmt


A1: In-Memory Computing (IMC) utilizes RAM-DRAM for extremely

fast I/O, moving us away from slow, underutilized spinning disk

#bigdatamgmt


A1:In-memory tech enables biz 2 utilize data stored in main memory vs

fragmented/siloed trad databases #bigdatamgmt


A1 in-memory refers to storing data in main memory (DRAM) rather

than spinning disk #bigdatamgmt


Nobody likes slow RT @BigDataAlex: A1: In-Memory Computing

(IMC) utilizes RAM-DRAM for extremely fast I/O #bigdatamgmt


A1: In simplest form: open book exams vs memorizing ans. Time it

takes to search for answers test is over! #bigdatamgmt


A1: IMC reduces power and storage costs, revolutionizing access.

#bigdatamgmt


A1 much faster to pull data from memory than disk - response time

much quicker than spinning rusty metal allows #bigdatamgmt


Ha ha! RT @Natasha_D_G: A1: In simplest form: open book exams vs

memorizing ans. #bigdatamgmt


A1 memory access is way faster than disk I/O... even against SSD

#bigdatamgmt


A1 faster data access enables real-time massive data processing: real-

time #bigdata #bigdatamgmt

@jameskobielusjameskobielus

#bigdatamgmt A1: Speed of thought is any tech that doesnt have any

architectural bottlenecks that arbitrarily slow people's explorations


A1 #bigdatamgmt inmemory means fast access to data #bigdatamgmt


A1: Memory makes diff! Ability 2 deliver accurate answer w/o pregnant

pauses impacts biz agility #bigdatamgmt



Category



impact

tweets / user

Most Active

Users

1. IBMbigdata2. jeffreyfkelly

3.BTRG_MikeMartin4. InfoMgmtExec5.Natasha_D_G



Q7 Can in-memory techniques be applied to non-relational databases

and/or #Hadoop? #bigdatamgmt


A7 yes - see @aerospike, NoSQL, flash-optimized in-memory DB

#bigdatamgmt


#bigdatamgmt A7 -Time Series db's(Informix) will benefit substantially

from In Memory. Critical to Smart Metering and Smart Grid strategies.

@dfloyerDavid Floyer

#BigDataMgmt A7 Of course! Databases such as Couchbase

(Memcache) & Aerospike (Flash) use KV pairs in memory

extensively for transactions


A7. yes and @jeffreyfkelly - interesting Aerospike - that's an extension

of memory using flash #bigdatamgmt


A7 MT @katsnelson: Hadoop is about data on disk. Streams does

opposite i.e processes in-memory. IBM bundles Hadoop and Streams

#bigdatamgmt


A7 like the DW question, in-memory DB can supplement Hadoop

batch analytics w/ real-time analytic queries #bigdatamgmt


A7: InfoSphere #Streams brings "database" functions into IMC in real

time for continuous query and calculations #bigdatamgmt


A7. #spark uses in memory querying of data #bigdatamgmt


A7 I believe there are in-memory instances of #Cassandra - anybody

have info? #bigdatamgmt



Category



impact

tweets / user

Most Active

Users

1. IBMbigdata2.dvellante

3.cristianmolaro4.BTRG_MikeMartin5.Natasha_D_G



Last ? - Q8 What is main role for in-memory in #bigdata

infrastructures? Where does flash memory fit? #bigdatamgmt


A8. The best IO is no IO #bigdatamgmt


A8. But no IO is expensive so in-memory in #bigdata has to be used

judiciously #bigdatamgmt


No IO! No IO! No IO! RT @dvellante: A8. The best IO is no IO

#bigdatamgmt


A8 main role should be to accelerate access in relevant chunks...

#bigdata is too big to be contained in memory... #bigdatamgmt


HA! RT @BTRG_MikeMartin: RT Favorite comment so far @dvellante:

A8. The best IO is no IO #bigdatamgmt


A8 in-memory should be used strategically in #BigData infrastructure

where speed, performance gains outweigh costs #bigdatamgmt


A8 I like the concept of multi-temperature storage: the hottest data

stored on the faster (and more expensive) storage device#bigdatamgmt


A8 not all the #bigdata has the same requirements for access

performance: keep the hot data close to you and in memory

#bigdatamgmt



Category



impact

tweets / user

Most Active

Users

1.Natasha_D_G2. IBMbigdata

3.BigDataAlex4. InfoMgmtExec5. jeffreyfkelly



Q4 How does in-memory support greater data scientist productivity?

#bigdatamgmt


A4: HPC, next gen chip design, less I/O disk functions in our code,

converging toward better scientific computing. #bigdatamgmt


A4 less trips to the watercooler waiting for query response

#bigdatamgmt


Indeed! RT @jeffreyfkelly: A4 less trips to the watercooler waiting for

query response #bigdatamgmt


A4: Data scientist gain major advantage when they can access &

digest massive amts data in secs. #bigdatamgmt


#bigdatamgmt A4 - Decision Scientists spend way too much time

today conditioning & gathering data. In Memory can have it all in

one place.


A4: Fire Scientists in Montana are using in-memory computing to

better understand wild land fire given a changing climate.#bigdatamgmt


A4: When data scientists can find answers 2 questions they didnt

THINK to ask its a win #bigdatamgmt


Nice point RT @Natasha_D_G: A4: When data scientists can find

answers 2 questions they didnt THINK to ask its a win #bigdatamgmt


A4. To the effect that it improves data munging and visualization, it

helps #bigdatamgmt


#bigdatamgmt A4 - In-Memory allows DS to create a "Memory Palace"

for Models, A/B Tests, Algorithms in development, etc. All in real-time.


A palace! RT InfoMgmtExec: A4 - In-Memory allows DS to create a

"Memory Palace" for Models, A/B Tests, Algorithms in dev

#bigdatamgmt


21/23

Glossary



Page 1: General Overview: This page shows at a glance the evolution and the global statistics of the session.

Statistics

Number of Tweets: Total number of Tweets sent during the session, RTs and replies included. It is shown below, a

breakdown of the types of tweets: original tweets (those containing text only) tweets with links, retweets, conversations

(tweets as part of a conversation between several users), check-ins and photos.

- Number of users: Total number of users who participated in the session using the given hashtag. It also includes userswho only sent RTs.

- Potential Impact: Number of impressions of the hashtag, which is the number of times that people could have seen the

hashtag. This is important because it tells you how many times it has been possible to visualize the hashtag. This number is

calculated by multiplying the number of followers of each user by the number of number of Tweets and adding those results.

Example: If a user sends 2 tweets and he has 100 followers, the number of impressions generated by the users would be

200. If another user sends 3 tweets and he has 50 followers, the number of impressions generated by this person would be

150 which would make a total of 350 impressions of the session.

- Potential Reach: Number of users who have been unable to see the hashtag and could have been impacted by the

hashtag. This number is calculated by adding all the followers of each user who participated in the session. Using the

previous example, if the session had 2 users, one with 100 followers and the other one with 50, the reach will be 150

followers, regardless of the number of tweets sent. IMPORTANT: both the impact and reach are 'potential' because not

everyone may have seen the hashtag and users can have other users in common.

- Average number of Tweets per user: this number is the average of Tweets sent per each user. This number is calculated

by dividing the number of tweets between the number of users who have participated. RTs included.

- Average followers per user: the average number of followers that users of the session have. This figure indicates how

influential are the participants in our session. Given that the average number of followers that a Twitter user has is about

250, you can calculate if participants in your session exceed that average. This number is calculated by dividing the sum of

followers by the number of users who have participated.

- Difference between Total Tweets and Tweets: The Total Tweets include RTs, links, replies, links and 'Tweets'.

'Tweets' are the ones containing only text.

ChaRTs

There are different types of graphs in the report Tweet Category:

Temporal Evolution: shows the time evolution of the tweets sent by users. Tweet Category takes the first and last tweet

and draws the timeline of the session. Thanks to this chart you will be able to identify the moment people tweeted the most

or the least.

Influence of Users: shows the influence of the users who participated in the session. On the vertical axis you will find the

total number of users and on the horizontal one, the number of followers of those users. As we move to the right part of the

graph you will see the users who have a greater number of followers and therefore influence. The higher the columns on the

right, the higher influence of your users.

User activity: shows the number of tweets sent by users. The vertical axis shows the number of tweets sent and the

horizontal one the number of users who have participated.

Page 2: Statistics of the categories: on this page you will find the detailed statistics for each category.

Rankings of categories:

This ranking shows which categories have reached the top 5 according to several statistics. It is interesting to note that

although a category may have a greater number of tweets that another one, it could have a minor number of impressions

(lower impact). The rankings show the categories with the highest reach, impact, number of users, number of tweets and

number of RTs.


23/23

ChaRTs:

Impact by category: This graph shows which category has the highest number of impressions and therefore the highest

impact.

Tweets by category Chart: This chart shows which category has the highest number of total tweets.

Users by category Chart: This chart shows which category has the highest number of users.

Table of categories:

This table shows detailed statistics for each category; these statistics are the same variables as the global statistics of the

session but applied to each of the category. Thus you can see which category gets more impact, more users, and so on. It is

worth taking a second to consider this table as very interesting conclusions can be obtained from it.

Page 3: User Rankings

Tweet Category offers different kinds of user rankings:

Most active users: the ones who tweeted the most using the hashtag. RTs included.

Most popular users: the ones who have the highest number of followers in the session.

Users with the highest impact: the ones who generated the highest number of impressions.

Most participative users: the ones who participated in more categories.

Most retweeter users: the ones who sent the highest number of RTs.

Most original users: the ones who sent the highest number of original tweets (No RTs).

big data mgmt

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