Taming data lake - scalable metrics model

www.globalbigdataconference.com
Twitter : @bigdataconf

Intended for Knowledge Sharing only
Disclaimer:
Participation in this summit is purely on personal basis and not representing VISA in any form or
matter. The talk is based on learnings from work across industries and firms. Care has been taken to
ensure no proprietary or work related info of any firm is used in any material.
Director, Insights at Visa, Inc.
Enable Decision Making at the Executives/
Product/Marketing level via actionable
insights derived from Data.
RAMKUMAR RAVICHANDRAN
Data Warehouse Architect at Visa, Inc.
Architect a data-shop in Hadoop to get 360-
degree view of the interaction. Technology
interface for the Data Stakeholder Community.
BHARATHIRAJA CHANDRASEKHARAN

Quick recap of what it is
Data Lakes – the concept

AS THEY ARE ENVISIONED TODAY…
Source: http://www.tangerine.co.th/tag/how-do-data-lake-work/
5

DOES IT RING A BELL?
*only satiric to wake you up and not indicative of anyone or anything- any similarity is purely coincidental! 6

& DOES THIS TOO?
*only satiric to wake you up and not indicative of anyone or anything- any similarity is purely coincidental! 7

SO WHAT DO WE HEAR FROM OUR USERS?
We often hear these statements in the context of data lakes…
Success criteria was engineering specific – Storage/Scalability cost saving, etc
Expensive Change Management
Complex for the end users to deal with
Analytical performance issues
Data Governance, Lineage and Management complexities
“Although the cost of Storage went down, actual cost of utilizing the data has shot up”
8

Taking a step back

DATA REALLY HAS GOTTEN BIG – VOLUME, VARIETY, VELOCITY & VERACITY
Each of the data source is critical either across all or multiple functions….
…and are consumed either as reports, analytical deep dive insights, forward looking projections, etc.
TRANSACTION DATA
CLICK STREAM DATA (MOBILE
& WEB)
SENTIMENT/SOCIAL DATA
• Are overall txns going up/down; where the txns are happening,
etc..
• How are Consumers interacting with the website/app – drop-offs,
clicks, Time spent, etc..
• Social Media, NPS surveys, Media mentions helps in gauging true
Consumer reactions
DATA SOURCES TYPES OF INSIGHTS
SERVER LOGS DATA
• How are consumers reacting with various functions on the front
end?
LOCATION DATA • Are consumers using the product in-store or on the move?
PROMOTIONS DATA • How are consumers reacting to various marketing campaigns?
INDUSTRY DATA • Benchmarking against industry performance
10

EVERYONE NEEDS DATA…
How are we doing today?
BI
Where will be tomorrow?
What if we do this?
What can we do?
ANALYTICS
Did the initiative work?
A/B TESTING
How do Customers feel
about us?
USER RESEARCH
Where should we invest?
STRATEGY
11

…AND DISTRIBUTED DATA SYSTEMS HAD THEIR OWN ISSUES
Inconsistent (and/or conflicting) definitions of data and numbers
Varying granularities
Multiple methodologies
Different BU = (different KPIs or same KPIs different priorities)
Lack of visibility/understanding outside of the BUs
“Slow & inefficient, Non-scalable,
Difficulties rolling up, Trust issues,
Cascading mistakes”
12

AND IT THEN JUST HAPPENED…
TRANSACTION DATA
CLICK STREAM DATA (MOBILE
& WEB)
SENTIMENT DATA
DATA SOURCES
SERVER LOGS DATA
LOCATION DATA
CAMPAIGN DATA
INDUSTRY DATA
Source: http://www.adamadiouf.com/2013/03/22/bigdata-vs-enterprise-data-warehouse/
As if all prayers were answered Hadoop arrived in a big way & poof all problems seemed to disappear…
13

A stroke of luck or was it?

WE FOCUSED ON OUR SPOUSE BUT FORGOT THE IN-LAWS…
Inform
Reports on KPIs
with high level
drilldowns
Act
Deep dives via
Business
Analytics
Predict
Identify Causal
relationships
via Advanced
Analytics
Optimize
Experiments to
verify which
one works via
A/B Testing
Maturity phases of Analytics Practice
ValueAddition
Mine
Machine
Learning
Focus on the 20% Data consumers (Reports) and assumption was that 80% Data Consumers will either
love it or at least figure it out…
5%
50%
15%
20%
10%
15

HIGH DEVELOPMENT/MODIFICATION COSTS
Rigid Structure and scale of operations make dynamism difficult…
16
Data Modeling/Schema
ETL; Metadata
Raw Data

NOT ONLY IS THE AUDIENCE CHANGING…
Stakeholders Needs
Reports, Insights
& Drilldowns
Datamart Documentation
Executives
- Reports
- High level drilldown
- Unified summary
- “On the go*”
Marketing & PR
- Campaign performance
- Infographics
- Deep dives
- Testing
Sales / RM
- Sales performance
- Prospecting
- Competitive
- Infographics
Product
- Product performance
- Deep dive
- Mining
- Testing
- Research
Technology / AE /
Operations
- Platform performance
- Deep dive
- Forecasting
- Real time alerting
FP & A
- Consolidated Initiative
readouts (E2E)
- Deduping
- Drill downs
- Forecasting
17

…BUT ALSO THE NEEDS ARE EVER CHANGING
“In mail”
Recommendations
with supporting
graphs, tables, etc.
“Story Deck”
Full deck with the pitch
and supporting arguments,
numbers, graphs, charts
“On-the-go”
-Mobile App, On the
Cloud, Subscriptions
-Reports, Dashboards,
Infographics
Algorithm/Model
Ready to be deployed
How to decide? Customer needs;
Turnaround Speed; One time/reuse;
Deployment on Front end; Strategic
Doc; Quick read/research doc
18

Getting to the point – what do we propose?

WE BRING TO YOU THE SCALABLE METRICS MODEL (SMM)…
EDW
Aggregated
Cubes
Every attempt to bring the best of the most used models…
20
ACID, Fast, Stable
Rigid, Cost, Resourcing
Scalable
Metrics Model
(Pre-Aggregated
Metrics + Primary-
Foreign Keys)
Cost, Flexibility,
Scalability
Performance, Reliability
Performance, Easy to
understand
Reporting only

TACTICAL DETAILS: WHERE DO WE START?
An illustrative example from Retail domain…
21
• Defined Granularity & associated Info: Determined by Core Objectives,
e.g., Customer level table for Customer Engagement team
• Defined Foreign Keys & Common Dimensions: For extensibility
• Defined Metrics: KPIs as required
• Identify Value Add Metrics: recommendation, forecasting etc
CUSTOMER
•Primary Key: Customer id
•Foreign Keys: Sign Up Partner,
Promotion Id, First Txn id
•Customer Level Info: Email, Phone,
Number, Geo, etc.
•Metrics:
•Lifetime Spend, Txns
•Behavioral Bucket
•RFM Bucket
•Recommended Action items:
•Next Best Product
•CLV
•Target Offers
•Call Center Agent Reco

TACTICAL DETAILS: DATA MODEL
22
id Dimensions foreign_keys metrics
Customer_id
Name
Email
Address,etc.
signup_partner_id
promotion_id
Lifetime Spend, Txns
Behavioral Bucket
RFM Bucket
Recommended Action items:
Next Best Product
CLV
Target Offers
Call Center Agent Reco
11234
{"name":"John",
"Email" :
"john@email.co
m" ,
"Address":"123
nowhereblvd"}
{"signup_partner_id
":"666YYY",
"promotion" :
"YAH123" }
{"Lifetime Spend":"3400",
"Txns":"150",
"Behavioural Bucket" : "repeat
user" ,
"RFM Bucket":"",
"recommended Product
id":"PRD789",
"CLV":"??",
"Target Offer":"OFF789",
"CallCenterAgentReco":"1234"}
WhatitcontainsSampledata

TACTICAL DETAILS: ETL FRAMEWORK
23
STEP I:
QUERIES
STEP II:
FRAMEWORK
RUNS
•Write separate queries/code to get metrics on the defined granularity
•Put those queries into the framework
STEP III:
IMPLEMENT
MODULARITY
STEP IV:
USER
INTERFACE
•Adding a new metric is just adding a new query/code for that metric alone
•Can change an existing logic for a metric will impact that metric alone
•Create physical impala tables for interactive querying
•Create views for abstraction and end-user access
•Exporting data to reporting tools like Tableau/QlikView brings a high level
of analysis capability to this model.
•Framework runs each of these queries and populate respective keys

ETL framework
• Divide and conquer
– Write separate queries/code to get metrics on the defined
granularity
– Put those queries into the framework
• Framework runs each of these queries and populate
respective keys
• Modularity
– Adding a new metric is just adding a new query/code for that metric
alone
– Can change an existing logic for a metric will impact that metric
alone
24

Reporting and presentation
• Map data-types are hard for the users for access
• Three options
– Create physical impala tables for interactive querying
– Create views for abstraction and end-user access
• Reporting layer (like Tableau)
– Brings a different level of accessibility and analysis capability to this
model.
• Faster (if data is cached)
• Create report level calculations
• Data blending
• Using metrics as a dimension – like customer buckets on transaction size
• Visualization
25

DATA BUS EXTENSIBILITY
CUSTOMER
•Primary Key: Customer id
•Foreign Keys: Sign Up Partner,
Promotion Id, First Txn id
•Customer Level Info: Email,
Phone, Number, Geo, etc.
•Metrics:
•Lifetime Spend, Txns
•Behavioral Bucket
•RFM Bucket
•CLV
•Target Offers
•Call Center Agent Reco
SELLERS
•Primary Key: Seller id
•Foreign Keys: Product id,
Operating Channel
•Customer Level Info: Name,
Operating Region, Annual Sales
•Metrics:
•Lifetime Sales, Txns
•Performance Bucket
•Special Category Flag
•Next Co-Marketing
•RM action
TXNS
•Primary Key: Txn id
•Foreign Keys: Custid, Sellerid,
Channel,
•Txn Level Info: Amt, Type,
Date,
•Flags:
•Buyer/Seller Type
•Deviation Metrics
•Fraud/Good
•Agent Verification
•Next Best Offer
CLICKSTREAM
PROMOTIONS
PARTNERS
PRODUCTS
SENTIMENT
LOGS
3rd PARTY
ETC ETC…
Common
Dimensions or
Foreign Keys

Business
requirements
Design
DataModel
ETL
Framework
Reporting
Layer
Use and
learn
27

THE SALIENT FEATURES
28
• Fit for wide variety of Solution Sets & audiences: Optimal data model to
support all three needs – Reporting, Analytics & Data Mining.
• Best of all worlds: Scalable Metrics Model is a hybrid approach,
• ACID Strengths: performance, stability and reliability of RDBMS.
• Non ACID Strengths: scalability, flexibility, versatility of Hadoop.
• Needs Optimized Model: Highest premium is provided to needs of the user – easy
to incorporate changes as they come along (view like). Refresh cycle is easy and
changed logics easily get incorporated in the next run.
• Data Governance & Lineage: Operates with a modular approach – break down
complex problems into smaller items and integrate in a bigger scheme of things. This
eases better Data Governance and Lineage.
• Extensibility:
• Caching: Easy integration with buffering technologies to optimize on
performance.
• Visualization: Easier integration with visualization tools like Tableau.
• Coding Interface: Additional drilldowns, analyses, data analysis via HIVE/SAS/R.
● MODULAR ● EXTENDABLE ● UPDATABLE ● SCALABLE

FOUR DIMENSIONS OF SUCCESSFUL EXECUTION
29
PEOPLE
• Business Analysts: Details on Business needs like Timing(Immediate/
near/medium/long term), Priority (Critical/Urgent/Important/Good to have),
Frequency (Regular/once-in-a-while/rare), Real-time, Delivery & Users.
• Technical Architects: Understand the raw data structure, flow mechanisms &
pipelines, security/legal/storage/resourcing constraints, feasibility
assessments.
PROCESS
• Matching & Gap Analysis: Is the technology available to handle all business
needs (possible/not enough RoI/deferred); Contingency, resourcing & budgeting.
• Project Planning: Milestone based delivery, Deep Stakeholder involvement in
development & validation, Communications Management
• Execution: Schema on read efficient, Aggregates, Tight Metadata,
reporting/analytics layer, Tables/Partitions/File types/Compression, Metadata
TECH
• PIG: ETL
• HIVE/Impala: Schema & Table creation
• Java/Streaming:
• SAS/Python/R: Statistical Modeling
CULTURE
• Customer Needs Focused
• Need for a smart vision, sound planning and able change management
• Outcome Focused Organization (common business goal)
• SAS/Python/R: Statistical Modeling

WHY DO WE THINK THE TIME IS NOW?
Evolution in the value prop of Analysts:
What/where/how much -> what can happen ->what should we do ?
Audience has broadened (A numbers middle man -> Front line Managers)
Luxury of time has evaporated
Nature of questions have drastically changed (Expectation of being able to
connect the dots in “Data Lake” world).
Overselling potential before getting “there”
30
KPI of Analytics has changed from Turn-Around-Time (TAT) to Time-to-
Action (TTA)

Putting it all together

SWOT ANALYSIS OF SMM
STRENGTHS
OPPORTUNITIES
WEAKNESSES
THREATS
• Need sensitive model
• Cost of development, modification & refresh
reduced
• Easy for Analysts/End Users to understand and
play with
• Data Governance & Lineage: Break down
bigger problems into smaller manageable
• Integration with front end tools that can
simplify UX.
• Tools that buffer the backend data to
ensure speedy delivery.
• Good vision of future Analytical
requirements is paramount.
• Full refresh every time it runs again.
• Maximum granularity needs to be pre-
fixed.
• Learning Curve on Coding
language/syntax.
• Non-normalized data model.
• Not for real-time insights delivery
• No Slowly Changing Dimensions
32

THE FIVE COMMANDMENTS
33
• “Know” that it caters to most frequent and not all needs.
• “Must have” as good & farther as possible Analytics vision/needs and Outcome
Focused approach.
• “Ensure” Deeper Stakeholder involvement in the development. Test & Learn
approach must. And be ready to modify if needed.
• “Develop” modularity in delivery.
• “Prepare” for ever more increasing dependencies from Analytics and other
stakeholders.

Appendix

THANK YOU!
Would love to hear from you on any of the following forums…
https://twitter.com/decisions_2_0
http://www.slideshare.net/RamkumarRavichandran
https://www.youtube.com/channel/UCODSVC0WQws607clv0k8mQA/videos
http://www.odbms.org/2015/01/ramkumar-ravichandran-visa/
https://www.linkedin.com/pub/ramkumar-ravichandran/10/545/67a
https://www.linkedin.com/in/dataisbig
http://bigdatadw.blogspot.com/
BHARATHIRAJA CHANDRASEKHARAN
RAMKUMAR RAVICHANDRAN
35

36
RESEARCH/LEARNING RESOURCES
• Alternative approach by Martin Fowler:
http://martinfowler.com/bliki/DataLake.html
• Teradata/Hortonworks Data Lake Whitepaper:
http://hortonworks.com/wp-
content/uploads/2014/05/TeradataHortonworks_Datalake_White-Paper_20140410.pdf
• Teradata/Hortonworks Data Lake Whitepaper:
http://hortonworks.com/wp-
content/uploads/2014/05/TeradataHortonworks_Datalake_White-Paper_20140410.pdf
• EMC Data Lake:
https://www.youtube.com/watch?v=o2fs02h_LEo
36

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