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Modernizing Business with BIG DATA
Modernizing Business with BIG DATA
Big Data fueling Enterprise Agility
Big Data fueling Enterprise Agility
Legacy Rides The Elephant
Legacy Rides The Elephant
Journey to the world with NO Mainframes
Journey to the world with NO Mainframes
Why Hadoop and Why Now
Why Hadoop and Why Now
The Classic Enterprise Challenge
The Classic Enterprise Challenge
The Sears Holdings Approach
The Sears Holdings Approach
The Architecture
The Architecture
The Sears Holdings Architecture
The Sears Holdings Architecture
PiG/Hadoop Ecosystem
PiG/Hadoop Ecosystem
The Learning
The Learning
Some Examples
Some Examples
The Challenge – Use-Case #1
The Challenge – Use-Case #1
The Result – Use-Case #1
The Result – Use-Case #1
The Challenge – Use-Case #2
The Challenge – Use-Case #2
The Result – Use-Case #2
The Result – Use-Case #2
The Challenge – Use-Case #3
The Challenge – Use-Case #3
The Result – Use-Case #3
The Result – Use-Case #3
The Challenge – Use-Case #4
The Challenge – Use-Case #4
The Result – Use-Case #4
The Result – Use-Case #4
Summary of Benefits
Summary of Benefits
Summary
Summary
The Horizon – What do we need next
The Horizon – What do we need next
www
www
Modernizing Business with BIG DATA
Modernizing Business with BIG DATA

Презентация: «Modernizing Business with BIG DATA». Автор: Chuck Jones - Corporate Services. Файл: «Modernizing Business with BIG DATA.ppt». Размер zip-архива: 3768 КБ.

Modernizing Business with BIG DATA

содержание презентации «Modernizing Business with BIG DATA.ppt»
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1 Modernizing Business with BIG DATA

Modernizing Business with BIG DATA

Aashish Chandra Divisional VP, Sears Holdings Global Head, Legacy Modernization, MetaScale

2 Big Data fueling Enterprise Agility

Big Data fueling Enterprise Agility

Harvard Business Review refers Sears Holdings Hadoop use case - Big Data's Management Revolution!

Sears eschews IBM/Oracle for open source and self build

Sears’ Big Data Swap Lesson: Functionality over price?

How banks can benefit from real-time Big Data analytics?

3 Legacy Rides The Elephant

Legacy Rides The Elephant

Hadoop has changed the enterprise big data game. Are you languishing in the past or adopting outdated trends?

4 Journey to the world with NO Mainframes

Journey to the world with NO Mainframes

Cost Savings Open Source Platform Simpler & Easier Code Business Agility Business & IT Transformation Modernized Systems IT Efficiencies

Mainframe Migration

II. Mainframe ONLINE Tool based Conversion Convert COBOL & JCL to Java

I. Mainframe Optimization 5% ~ 10% MIPS Reduction Quick Wins with Low hanging fruits

III. Mainframe BATCH ETL Modernization Move Batch Processing to Hadoop

High TCO

Inert Business Practices

Optimize

Convert

PiG / Hadoop Rewrites

Resource Crunch

5 Why Hadoop and Why Now

Why Hadoop and Why Now

THE ADVANTAGES: Cost reduction Alleviate performance bottlenecks ETL too expensive and complex Mainframe and Data Warehouse processing ? Hadoop THE CHALLENGE: Traditional enterprises lack of awareness THE SOLUTION: Leverage the growing support system for Hadoop Make Hadoop the data hub in the Enterprise Use Hadoop for processing batch and analytic jobs

6 The Classic Enterprise Challenge

The Classic Enterprise Challenge

7 The Sears Holdings Approach

The Sears Holdings Approach

1

2

3

4

5

6

Key to our Approach: allowing users to continue to use familiar consumption interfaces providing inherent HA enabling businesses to unlock previously unusable data

8 The Architecture

The Architecture

Enterprise solutions using Hadoop must be an eco-system Large companies have a complex environment: Transactional system Services EDW and Data marts Reporting tools and needs We needed to build an entire solution

9 The Sears Holdings Architecture

The Sears Holdings Architecture

10 PiG/Hadoop Ecosystem

PiG/Hadoop Ecosystem

MetaScale

11 The Learning

The Learning

Over two years of Hadoop experience using Hadoop for Enterprise legacy workload.

We can dramatically reduce batch processing times for mainframe and EDW We can retain and analyze data at a much more granular level, with longer history Hadoop must be part of an overall solution and eco-system

We can reliably meet our production deliverable time-windows by using Hadoop We can largely eliminate the use of traditional ETL tools New Tools allow improved user experience on very large data sets

We developed tools and skills – The learning curve is not to be underestimated We developed experience in moving workload from expensive, proprietary mainframe and EDW platforms to Hadoop with spectacular results

HADOOP

IMPLEMENTATION

UNIQUE VALUE

12 Some Examples

Some Examples

Use-Cases at Sears Holdings

13 The Challenge – Use-Case #1

The Challenge – Use-Case #1

Intensive computational and large storage requirements Needed to calculate item price elasticity based on 8 billion rows of sales data Could only be run quarterly and on subset of data – Needed more often Business need - React to market conditions and new product launches

Sales: 8.9B Line Items

Price Sync: Daily

Elasticity: 12.6B Parameters

Offers: 1.4B SKUs

Items: 11.3M SKUs

Stores: 3200 Sites

Timing: Weekly

Inventory: 1.8B rows

14 The Result – Use-Case #1

The Result – Use-Case #1

Hadoop

Business Problem:

Intensive computational and large storage requirements Needed to calculate store-item price elasticity based on 8 billion rows of sales data Could only be run quarterly and on subset of data Business missing the opportunity to react to changing market conditions and new product launches

Price elasticity calculated weekly

100% of data set and granularity

Meets all SLAs

New business capability enabled

Sales: 8.9B Line Items

Price Sync: Daily

Elasticity: 12.6B Parameters

Offers: 1.4B SKUs

Items: 11.3M SKUs

Stores: 3200 Sites

Timing: Weekly

Inventory: 1.8B rows

15 The Challenge – Use-Case #2

The Challenge – Use-Case #2

Hadoop

Page 15

Mainframe batch business process would not scale Needed to process 100 times more detail to handle business critical functionality Business need required processing billions of records from 30 input data sources Complex business logic and financial calculations SLA for this cyclic process was 2 hours per run

Mainframe Scalability: Unable to Scale 100 fold

Data Sources: 30+

Mainframe: 100 MIPS on 1% of data

Input Records: Billions

16 The Result – Use-Case #2

The Result – Use-Case #2

Hadoop

Business Problem:

Mainframe batch business process would not scale Needed to process 100 times more detail to handle rollout of high value business critical functionality Time sensitive business need required processing billions of records from 30 input data sources Complex business logic and financial calculations SLA for this cyclic process was 2 hours per run

Teradata & Mainframe Data on Hadoop

JAVA UDFs for financial calculations

Scalable Solution in 8 Weeks

Implemented PIG for Processing

Processing Met Tighter SLA

$600K Annual Savings

6000 Lines Reduced to 400 Lines of PIG

Mainframe Scalability: Unable to Scale 100 fold

Data Sources: 30+

Mainframe: 100 MIPS on 1% of data

Input Records: Billions

17 The Challenge – Use-Case #3

The Challenge – Use-Case #3

Hadoop

Mainframe unable to meet SLAs on growing data volume

Data Storage: Mainframe DB2 Tables

Price Data: 500M Records

Processing Window: 3.5 Hours

Mainframe Jobs: 64

18 The Result – Use-Case #3

The Result – Use-Case #3

Hadoop

Business Problem:

Mainframe unable to meet SLAs on growing data volume

Source Data in Hadoop

$100K in Annual Savings

Maintenance Improvement – <50 Lines PIG code

Job Runs Over 100% faster – Now in 1.5 hours

Data Storage: Mainframe DB2 Tables

Price Data: 500M Records

Processing Window: 3.5 Hours

Mainframe Jobs: 64

19 The Challenge – Use-Case #4

The Challenge – Use-Case #4

Hadoop

Needed to enhance user experience and ability to perform analytics at granular data Restricted availability of data due to space constraint Needed to retain granular data Needed Excel format interaction on data sources of 100 millions of records with agility

Teradata via Business Objects

Transformation: On Teradata

User Experience: Unacceptable

Batch Processing Output: .CSV Files

History Retained: No

New Report Development: Slow

20 The Result – Use-Case #4

The Result – Use-Case #4

Hadoop

Business Problem:

Needed to enhance user experience and ability to perform analytics at granular data Restricted availability of data due to space constraint Needed to retain granular data Needed Excel format interaction on data sources of 100 millions of records with agility

Sourcing Data Directly to Hadoop

Transformation Moved to Hadoop

User Experience Expectations Met

Redundant Storage Eliminated

Business’s Single Source of Truth

Datameer for Additional Analytics

PIG Scripts to Ease Code Maintenance

Granular History Retained

Over 50 Data Sources Retained in Hadoop

Teradata via Business Objects

Transformation: On Teradata

User Experience: Unacceptable

Batch Processing Output: .CSV Files

History Retained: No

New Report Development: Slow

21 Summary of Benefits

Summary of Benefits

22 Summary

Summary

Hadoop can revolutionize Enterprise workload and make business agile Can reduce strain on legacy platforms Can reduce cost Can bring new business opportunities Must be an eco-system Must be part of an data overall strategy Not to be underestimated

23 The Horizon – What do we need next

The Horizon – What do we need next

Automation tools and techniques that ease the Enterprise integration of Hadoop Educate traditional Enterprise IT organizations about the possibilities and reasons to deploy Hadoop Continue development of a reusable framework for legacy workload migration

24 www

www

metascale.com Follow us on Twitter @LegacyModernizationMadeEasy Join us on LinkedIn: .linkedin.com/company/metascale-llc

Legacy Modernization Made Easy!

For more information, visit:

Contact: Kate Kostan National Solutions Kate.Kostan@MetaScale.com

25 Modernizing Business with BIG DATA
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