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Sunilkumar Kakade – Director IT

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1 Sunilkumar Kakade – Director IT
Move to Hadoop, Go Faster and Save Millions - Mainframe Legacy Modernization Sunilkumar Kakade – Director IT Aashish Chandra – DVP, Legacy Modernization Hadoop Summit June 26th, 2013

2 Legacy Rides The Elephant
Hadoop is disrupting the enterprise IT processing. Legacy Rides The Elephant

3 Recognition - Contributors
Our Leaders Ted Rudman Aashish Chandra Team Simon Thomas Sunil Kakade Susan Hsu Bob Pult Kim Havens Murali Nandula Willa Tao Arlene Pynadath Nagamani Banda Tushar Tanna Kesavan Srinivasan Batch workload can be migrated and run anytime in a fraction of the clock-time leveraging Hadoop.

4 The Enterprise Challenge
The Challenge Growing Data Volumes Shortened Processing Windows Escalating Costs Hitting Scalability Ceilings Demanding Business Rqmts ETL Complexity Latency in Data Tight IT Budgets

5 Mainframe Migration - Overview
In spite of recent advances in computing, many core business processes are batch-oriented running on mainframes. Annual Mainframe costs are counted in 6+ figure Dollars per year, potentially growing with capacity needs. In order to tackle the cost challenge, many organization have considered or attempted multi-year mainframe migration/re-hosting strategies. Batch workload can be migrated and run anytime in a fraction of the clock-time leveraging Hadoop.

6 Batch Processing Characteristics
Large amounts of input data are processed and stored (perhaps terabytes or more). Large numbers of records are accessed, and a large volume of output is produced Immediate response time is usually not a requirement, however, must complete within a “batch window” Batch jobs are often designed to run concurrently with online transactions with minimal resource contention. *Ref:. IBM Redbook

7 Batch Processing Characteristics
Key infrastructure requirements: Sufficient data storage Available processor capacity, or cycles job scheduling Programming utilities to process basic operations (Sort/Filter/Split/Copy/Unload etc.)

8 Why Hadoop and Why Now? THE ADVANTAGES: THE CHALLENGE: THE SOLUTION:
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 Batch workload can be migrated and run anytime in a fraction of the clock-time leveraging Hadoop.

9 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

10 MetaScale’ s Hadoop Ecosystem

11 Hadoop based Ecosystem for Legacy System Modernization
MetaScale

12 Mainframe Batch Processing Architecture

13 MetaScale Batch Processing Architecture With Hadoop

14 Typical Batch Processing Units (JCL) on Mainframe

15 Batch Processing Migration With Hadoop
Seamless migration of high MIPS processing jobs with no application alteration

16 Mainframe to Hadoop-PIG conversion example
Mainframe JCL //PZHDC110 EXEC PGM=SORT //SORTIN DD DSN=PZ.THDC100.PLMP.PRC, // DISP=(OLD,DELETE,KEEP) //SORTOUT DD DSN=PZ.THDC110.PLMP.PRC.SRT,LABEL=EXPDT=99000, // DISP=(,CATLG,DELETE), // UNIT=CART, // VOL=(,RETAIN), // RECFM=FB,LRECL=40 //SYSIN DD DSN=KMC.PZ.PARMLIB(PZHDC11A), // DISP=SHR //SYSOUT DD SYSOUT=V //SYSUDUMP DD SYSOUT=D //*__________________________________________________ //* SORT FIELDS=(1,9,CH,A) - 500 Million Records sort took 45 minutes of clock time on A168 mainframe PIG a = LOAD 'data' AS f1:char; b = ORDER a BY f1; - 500 Million Records sort took less than 2 minutes More benchmarking studies in progress 16

17 Mainframe to Hadoop-PIG conversion example
Mainframe JCL //PZHDC110 EXEC PGM=SORT //SORTIN DD DSN=PZ.THDC100.PLMP.PRC, // DISP=(OLD,DELETE,KEEP) //SORTOUT DD DSN=PZ.THDC110.PLMP.PRC.SRT,LABEL=EXPDT=99000, // DISP=(,CATLG,DELETE), // UNIT=CART, // VOL=(,RETAIN), // RECFM=FB,LRECL=40 //SYSIN DD DSN=KMC.PZ.PARMLIB(PZHDC11A), // DISP=SHR //SYSOUT DD SYSOUT=V //SYSUDUMP DD SYSOUT=D //*__________________________________________________ //* SORT FIELDS=(1,9,CH,A) - 500 Million Records sort took 45 minutes of clock time on A168 mainframe PIG a = LOAD 'data' AS f1:char; b = ORDER a BY f1; - 500 Million Records sort took less than 2 minutes More benchmarking studies in progress 17

18 Mainframe Migration – Value Proposition
Cost Savings Open Source Platform Simpler & Easier Code Business Agility Business & IT Transformation Modernized Systems IT Efficiencies Companies can SAVE 60% ~ 80% of their Mainframe Costs with Modernization Optimize High TCO Mainframe Optimization: -5% ~ 10% MIPS Reduction -Quick Wins with Low hanging fruits Mainframe Migration Inert Business Practices Convert Mainframe ONLINE -Tool based Conversion -Convert COBOL & JCL to Java Typically 60% ~ 65% of MIPS are used in Mainframes by BATCH processing Resource Crunch PiG / Hadoop Rewrites Mainframe BATCH -ETL Modernization -Move Batch Processing to Hadoop Estimated 45% of FUNCTIONALITY in mainframes is never used

19 Mainframe Migration – Traditional Approach
Traditional approaches to mainframe elimination call for large initial investments and carry significant risks – It is hard to match Mainframe performance and reliability. Many organizations still utilize mainframe for batch processing applications. Several solutions presented to move expensive mainframe computing to other distributed proprietary platform, most of them rely on end-to-end migration of applications.

20 Mainframe Batch Processing MetaScale Architecture
Using Hadoop, Sears/MetaScale developed an innovative alternative that enables batch processing migration to Hadoop Ecosystem, without the risks, time and costs of other methods. The solution has been adopted in multiple businesses with excellent results and associated cost savings, as Mainframes are physically eliminated or downsized: Millions of dollars in savings based on MIP reductions have been seen.

21 MetaScale Mainframe Migration Methodology
Key to our Approach: allowing users to continue to use familiar consumption interfaces providing inherent HA enabling businesses to unlock previously unusable data Implement a Hadoop-centric reference architecture Move enterprise batch processing to Hadoop Make Hadoop the single point of truth Massively reduce ETL by transforming within Hadoop Move results and aggregates back to legacy systems for consumption Retain, within Hadoop, source files at the finest granularity for re-use 1 2 3 4 5 6

22 Mainframe Migration - Benefits
Cost Savings Significant reduction in ISV costs & mainframe software licenses fees Open Source platform Saved ~ $2MM annually within 13 weeks by MIPS Optimization efforts Reduced MIPS by moving batch processing to Hadoop Transform I.T. Modernized COBOL, JCL, DB2, VSAM, IMS & so on Reduced batch processing in COBOL/JCL from over 6 hrs to less than 10 min in PiG Latin on Hadoop Simpler, and easily maintainable code Massively Parallel Processing Skills & Resources Readily available resources & commodity skills Access to latest technologies IT Operational Efficiencies Moved 7000 lines of COBOL code to under 50 lines in PiG Business Agility Ancient systems no longer bottleneck for business Faster time to Market Mission critical “Item Master” application in COBOL/JCL being converted by our tool in Java (JOBOL) “MetaScale is the market leader in moving mainframe batch processing to Hadoop”

23 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

24 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 HADOOP 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 IMPLEMENTATION 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 UNIQUE VALUE

25 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

26 Legacy Modernization Service Offerings
Leveraging our patent pending and award-winning niche` products, we reduce Mainframe MIPS, Modernize ETL processing and transform business and IT organizations to open source, cloud based, Big Data and agile platform MetaScale Legacy Modernization offers following services – Legacy Modernization Assessment Services Mainframe Migration Services MIPS Reduction Services Mainframe Application Migration Legacy Distributed Modernization ETL Modernization Services Modernize Proprietary Systems and Databases Managed Applications Support Support Transition Services

27 Legacy Modernization Made Easy!
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