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Modernizing Business with BIG DATA
Modernizing Business with BIG DATA
Modernizing Business with BIG DATA
Modernizing Business with BIG DATA
Modernizing Business with BIG DATA
Modernizing Business with BIG DATA
Modernizing Business with BIG DATA
Modernizing Business with BIG DATA
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Legacy Rides The Elephant
Legacy Rides The Elephant
Legacy Rides The Elephant
Legacy Rides The Elephant
Legacy Rides The Elephant
Legacy Rides The Elephant
Legacy Rides The Elephant
Legacy Rides The Elephant
Legacy Rides The Elephant
Legacy Rides The Elephant
Legacy Rides The Elephant
Legacy Rides The Elephant
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Sears Holdings Architecture
The Sears Holdings Architecture
The Result – Use-Case #1
The Result – Use-Case #1
www
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Modernizing Business with BIG DATA

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1Modernizing Business with BIG DATA. 15Page 15. Mainframe batch business process
Aashish Chandra Divisional VP, Sears would not scale Needed to process 100
Holdings Global Head, Legacy times more detail to handle business
Modernization, MetaScale. critical functionality Business need
2Big Data fueling Enterprise Agility. required processing billions of records
Harvard Business Review refers Sears from 30 input data sources Complex
Holdings Hadoop use case - Big Data's business logic and financial calculations
Management Revolution! Sears eschews SLA for this cyclic process was 2 hours
IBM/Oracle for open source and self build. per run. Mainframe Scalability: Unable to
Sears’ Big Data Swap Lesson: Functionality Scale 100 fold. Data Sources: 30+.
over price? How banks can benefit from Mainframe: 100 MIPS on 1% of data. Input
real-time Big Data analytics? Records: Billions.
3Legacy Rides The Elephant. Hadoop has 16The Result – Use-Case #2. Hadoop.
changed the enterprise big data game. Are Business Problem: Mainframe batch business
you languishing in the past or adopting process would not scale Needed to process
outdated trends? 100 times more detail to handle rollout of
4Journey to the world with NO high value business critical functionality
Mainframes.. Cost Savings Open Source Time sensitive business need required
Platform Simpler & Easier Code processing billions of records from 30
Business Agility Business & IT input data sources Complex business logic
Transformation Modernized Systems IT and financial calculations SLA for this
Efficiencies. Mainframe Migration. II. cyclic process was 2 hours per run.
Mainframe ONLINE Tool based Conversion Teradata & Mainframe Data on Hadoop.
Convert COBOL & JCL to Java. I. JAVA UDFs for financial calculations.
Mainframe Optimization 5% ~ 10% MIPS Scalable Solution in 8 Weeks. Implemented
Reduction Quick Wins with Low hanging PIG for Processing. Processing Met Tighter
fruits. III. Mainframe BATCH ETL SLA. $600K Annual Savings. 6000 Lines
Modernization Move Batch Processing to Reduced to 400 Lines of PIG. Mainframe
Hadoop. High TCO. Inert Business Scalability: Unable to Scale 100 fold.
Practices. Optimize. Convert. PiG / Hadoop Data Sources: 30+. Mainframe: 100 MIPS on
Rewrites. Resource Crunch. 1% of data. Input Records: Billions.
5Why Hadoop and Why Now? THE 17The Challenge – Use-Case #3. Hadoop.
ADVANTAGES: Cost reduction Alleviate Mainframe unable to meet SLAs on growing
performance bottlenecks ETL too expensive data volume. Data Storage: Mainframe DB2
and complex Mainframe and Data Warehouse Tables. Price Data: 500M Records.
processing ? Hadoop THE CHALLENGE: Processing Window: 3.5 Hours. Mainframe
Traditional enterprises lack of awareness Jobs: 64.
THE SOLUTION: Leverage the growing support 18The Result – Use-Case #3. Hadoop.
system for Hadoop Make Hadoop the data hub Business Problem: Mainframe unable to meet
in the Enterprise Use Hadoop for SLAs on growing data volume. Source Data
processing batch and analytic jobs. in Hadoop. $100K in Annual Savings.
6The Classic Enterprise Challenge. Maintenance Improvement – <50 Lines PIG
7The Sears Holdings Approach. 1. 2. 3. code. Job Runs Over 100% faster – Now in
4. 5. 6. Key to our Approach: allowing 1.5 hours. Data Storage: Mainframe DB2
users to continue to use familiar Tables. Price Data: 500M Records.
consumption interfaces providing inherent Processing Window: 3.5 Hours. Mainframe
HA enabling businesses to unlock Jobs: 64.
previously unusable data. 19The Challenge – Use-Case #4. Hadoop.
8The Architecture. Enterprise solutions Needed to enhance user experience and
using Hadoop must be an eco-system Large ability to perform analytics at granular
companies have a complex environment: data Restricted availability of data due
Transactional system Services EDW and Data to space constraint Needed to retain
marts Reporting tools and needs We needed granular data Needed Excel format
to build an entire solution. interaction on data sources of 100
9The Sears Holdings Architecture. millions of records with agility. Teradata
10PiG/Hadoop Ecosystem. MetaScale. via Business Objects. Transformation: On
11The Learning. Over two years of Hadoop Teradata. User Experience: Unacceptable.
experience using Hadoop for Enterprise Batch Processing Output: .CSV Files.
legacy workload. We can dramatically History Retained: No. New Report
reduce batch processing times for Development: Slow.
mainframe and EDW We can retain and 20The Result – Use-Case #4. Hadoop.
analyze data at a much more granular Business Problem: Needed to enhance user
level, with longer history Hadoop must be experience and ability to perform
part of an overall solution and analytics at granular data Restricted
eco-system. We can reliably meet our availability of data due to space
production deliverable time-windows by constraint Needed to retain granular data
using Hadoop We can largely eliminate the Needed Excel format interaction on data
use of traditional ETL tools New Tools sources of 100 millions of records with
allow improved user experience on very agility. Sourcing Data Directly to Hadoop.
large data sets. We developed tools and Transformation Moved to Hadoop. User
skills – The learning curve is not to be Experience Expectations Met. Redundant
underestimated We developed experience in Storage Eliminated. Business’s Single
moving workload from expensive, Source of Truth. Datameer for Additional
proprietary mainframe and EDW platforms to Analytics. PIG Scripts to Ease Code
Hadoop with spectacular results. HADOOP. Maintenance. Granular History Retained.
IMPLEMENTATION. UNIQUE VALUE. Over 50 Data Sources Retained in Hadoop.
12Some Examples. Use-Cases at Sears Teradata via Business Objects.
Holdings. Transformation: On Teradata. User
13The Challenge – Use-Case #1. Intensive Experience: Unacceptable. Batch Processing
computational and large storage Output: .CSV Files. History Retained: No.
requirements Needed to calculate item New Report Development: Slow.
price elasticity based on 8 billion rows 21Summary of Benefits.
of sales data Could only be run quarterly 22Summary. Hadoop can revolutionize
and on subset of data – Needed more often Enterprise workload and make business
Business need - React to market conditions agile Can reduce strain on legacy
and new product launches. Sales: 8.9B Line platforms Can reduce cost Can bring new
Items. Price Sync: Daily. Elasticity: business opportunities Must be an
12.6B Parameters. Offers: 1.4B SKUs. eco-system Must be part of an data overall
Items: 11.3M SKUs. Stores: 3200 Sites. strategy Not to be underestimated.
Timing: Weekly. Inventory: 1.8B rows. 23The Horizon – What do we need next?
14The Result – Use-Case #1. Hadoop. Automation tools and techniques that ease
Business Problem: Intensive computational the Enterprise integration of Hadoop
and large storage requirements Needed to Educate traditional Enterprise IT
calculate store-item price elasticity organizations about the possibilities and
based on 8 billion rows of sales data reasons to deploy Hadoop Continue
Could only be run quarterly and on subset development of a reusable framework for
of data Business missing the opportunity legacy workload migration.
to react to changing market conditions and 24www.metascale.com Follow us on Twitter
new product launches. Price elasticity @LegacyModernizationMadeEasy Join us on
calculated weekly. 100% of data set and LinkedIn:
granularity. Meets all SLAs. New business www.linkedin.com/company/metascale-llc.
capability enabled. Sales: 8.9B Line Legacy Modernization Made Easy! For more
Items. Price Sync: Daily. Elasticity: information, visit: Contact: Kate Kostan
12.6B Parameters. Offers: 1.4B SKUs. National Solutions
Items: 11.3M SKUs. Stores: 3200 Sites. Kate.Kostan@MetaScale.com.
Timing: Weekly. Inventory: 1.8B rows. 25
15The Challenge – Use-Case #2. Hadoop.
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Modernizing Business with BIG DATA

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