big data bi-mature-oanyc summit
TRANSCRIPT
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Hadoop meets Mature BI: Where the rubber meets the road for Data
Scientists
Michael HiskeyFuturist, + Product Evangelist
VP, Marketing & Business DevelopmentKognitio
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The Data ScientistSexiest job of the 21st Century?
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Key Concept: GraduationProjects will need
to Graduate from the
Data Science Lab and become part
of Business as Usual
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Demand for the Data Scientist
Organizational appetite for tens, not hundreds
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Don’t be a Railroad Stoker!Highly skilled engineering required … but the world innovated around them.
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Business Intelligence
NumbersTablesChartsIndicators
Time - History - Lag
Access - to view (portal) - to data - to depth - Control/Secure
Consumption - digestion
…with ease and simplicity
Straddle IT and Business
FasterLower latency
More granularity
Richer data model
Self service
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What has changed?
More connected-users?
More-connected users?
According to one estimate, mankind created 150 exabytes of data in 2005
(billion gigabytes)
In 2010 this was 1,200 exabytes
Data flow
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Data Variety
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Respondents were asked to choose up to two descriptions about how their organizations view big data from the choices above. Choices have been abbreviated, and selections have been normalized to equal 100%. n=1144
Source: IBM Institute for Business Value/Said Business School Survey
What? New value comes from your existing data
Dark Data
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© 20th Century Fox
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Hadoop ticks many but not all the boxes
a
a a aa a a aa a a a a
a a a a aa a a aa a a a a
a a a a a
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Talk to BI team about plugging into Hadoop--
Should be simple?
No need to pre-process No need to align to schema
No need to triage
New economics = New attitude just grab and retain all datathe data science team will dig into it later
NoSQL is a cool idea for
storage… not so much for our BI
ToolsNull storage concerns
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Machine learning algorithms Dynamic
Simulation
Statistical Analysis
Clustering
Behaviour modelling
The drive for deeper understanding
Reporting & BPMFraud detection
Dynamic Interaction
Technology/Automation
Anal
ytica
l Com
plex
ity
Campaign Management
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Hadoop just too slow for interactive
BI!
…loss of train-of-thought
“while hadoop shines as a processing platform, it is painfully slow as a query tool”
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Analytics needs low latency, no I/O wait
High speed in-memory processing
Analytical Platform: Reference Architecture
AnalyticalPlatform
LayerNear-lineStorage
(optional)
Application &Client Layer
All BI Tools All OLAP Clients Excel
PersistenceLayer
HadoopClusters
Enterprise DataWarehouses
LegacySystems
…
Reporting
Cloud Storage
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The Future
Big DataAdvanced Analytics
In-memory
Logical Data Warehouse
Predictive Analytics
Data Scientists
connect
www.kognitio.com
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NA: +1 855 KOGNITIOEMEA: +44 1344 300 770
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Hadoop meets Mature BI: Where the rubber meets the road for Data
Scientists
• The key challenge for Data Scientists is not the proliferation of their roles, but the ability to ‘graduate’ key Big Data projects from the ‘Data Science Lab’ and production-ize them into their broader organizations.
• Over the next 18 months, "Big Data' will become just "Data"; this means everyone (even business users) will need to have a way to use it - without reinventing the way they interact with their current reporting and analysis.
• To do this requires interactive analysis with existing tools and massively parallel code execution, tightly integrated with Hadoop. Your Data Warehouse is dying; Hadoop will elicit a material shift away from price per TB in persistent data storage.
The new bounty hunters:DrillImpalaPivotalStinger
The No SQL Posse
WantedDead or Alive
SQL
It’s all about getting work done
Bottlenecks
Used to be simple fetch of valueTasks evolving:
Then was calc dynamic aggregate
Now complex algorithms!
Bottlenecks
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create external script LM_PRODUCT_FORECAST environment rsint receives ( SALEDATE DATE, DOW INTEGER, ROW_ID INTEGER, PRODNO INTEGER, DAILYSALES INTEGER ) partition by PRODNO order by PRODNO, ROW_ID sends ( R_OUTPUT varchar ) isolate partitions script S'endofr( # Simple R script to run a linear fit on daily sales
prod1<-read.csv(file=file("stdin"), header=FALSE,row.names=1)colnames(prod1)<-c("DOW","ID","PRODNO","DAILYSALES")dim1<-dim(prod1)daily1<-aggregate(prod1$DAILYSALES, list(DOW = prod1$DOW), median)daily1[,2]<-daily1[,2]/sum(daily1[,2])basesales<-array(0,c(dim1[1],2))basesales[,1]<-prod1$IDbasesales[,2]<-(prod1$DAILYSALES/daily1[prod1$DOW+1,2])colnames(basesales)<-c("ID","BASESALES")fit1=lm(BASESALES ~ ID,as.data.frame(basesales))forecast<-array(0,c(dim1[1]+28,4))colnames(forecast)<-c("ID","ACTUAL","PREDICTED","RESIDUALS")
select Trans_Year, Num_Trans,count(distinct Account_ID) Num_Accts,sum(count( distinct Account_ID)) over (partition by Trans_Year order by Num_Trans) Total_Accts,cast(sum(total_spend)/1000 as int) Total_Spend,cast(sum(total_spend)/1000 as int) / count(distinct Account_ID) Avg_Yearly_Spend,rank() over (partition by Trans_Year order by count(distinct Account_ID) desc) Rank_by_Num_Accts,rank() over (partition by Trans_Year order by sum(total_spend) desc) Rank_by_Total_Spendfrom( select Account_ID,
Extract(Year from Effective_Date) Trans_Year, count(Transaction_ID) Num_Trans, sum(Transaction_Amount) Total_Spend, avg(Transaction_Amount) Avg_Spend
from Transaction_fact where extract(year from Effective_Date)<2009 and Trans_Type='D' and Account_ID<>9025011 and actionid in (select actionid from DEMO_FS.V_FIN_actions where actionoriginid =1) group by Account_ID, Extract(Year from Effective_Date) ) Acc_Summarygroup by Trans_Year, Num_Transorder by Trans_Year desc, Num_Trans;
select dept, sum(sales) from sales_fact Where period between date ‘01-05-2006’ and date ‘31-05-2006’ group by depthaving sum(sales) > 50000;
select sum(sales) from sales_history where year = 2006 and month = 5 and region=1;
select total_sales from summary where year = 2006 and month = 5 and region=1;
Behind the numbers
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For once technology is on our side
First time we have full triumvirate of– Excellent Computing power– Unlimited storage– Fast Networks
…now that RAM is cheap!
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Lots of these
Not so many of these
Hadoop is…
Hadoop inherently disk oriented
Typically low ratio of CPU to Disk