Hadoop Summit Amsterdam 2013 - Making Hadoop Ready for Prime Time - Syncsort Lightening Talk

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Lightening talk from the Hadoop Summit 2013 in Amsterdam covering how Syncsort is helping make Hadoop Ready for Prime Time. It includes the pluggable sort contribution - the impact on sort, join, aggregation, merge, filter in hadoopand Syncsort's ability to move mainframe data to hadoop - Big Iron to Big Data.

Technologie

Making Hadoop Ready for Prime Time
Hadoop Summit Amsterdam March 2013
Steve Totman
Director Of Strategy
Syncsort

March 20th 2013

Photo Credit Aaron Sikkink http://www.flickr.com/people/housequakecom/

Syncsort Confidential and Proprietary - do not copy or distribute

3

The Big Data Continuum
Big Data Continuum

Handcoding
nightmare

Integrating Big Data… Smarter

Hand-coding:
SQL, JCL.
Basic ETL Tools

Challenges

Min

Data
Awakening

SQL Migration

Max

Value

Advancing
Traditional
BI
Standardization &

Plateauing

Dynamic

Hitting arch limits + Early Hadoop
Heavy Platforms.
exponential costs. adoption prototyping
Demand for MF data Growing MIPS
& experimentation
Long
development
cycles

Highperformance ETL

Syncsort Confidential and Proprietary - do not copy or distribute

Unsustainable
costs

ETL & Rehosting
Optimization

Hadoop
connectivity &
sort gaps

Hadoop Sort
& Connectivity

Evolved
Big Data is the new
standard for both MF
& open systems data

Efficiency,
ETL &
skills gaps

Hadoop ETL

DMExpress
MFX
4

Mandatory sort steps in MapReduce processing

Syncsort Confidential and Proprietary - do not copy or distribute

5

Syncsort Confidential and Proprietary - do not copy or distribute

6

Smart Contributions to Improve Hadoop
Native Sort:

ᵡ modular
Not
ᵡ
Limited capabilities
ᵡ
Difficult to fine-tune & configure (requires

JIRA Description
4807

Allow MapOutputBuffer to be pluggable

4808

Allow Reduce-side merge to be pluggable

4809

Make classes required for 2454 public

4812

Create reduce input merger plug-in

4842

Shuffle race can hang reducer

2461

HDFS file name globbing in libhdfs

4482

Backport of 2454 to MapReduce 1 & 1.2

coding & compilation)
Native
Sort

Native
Sort

Hadoop Contribution:
Hadoop
Node
Node

 Modular
 Extensible
 Configurable through use of external sorters
on MapReduce nodes
Native
Sort

Native
Sort

Hadoop
Node

Hadoop
Node

First Included - Hadoop distribution, CDH4.2, on February 26th

…and more!!
8

Sy
nc

Benefits to the Community

MATCH

COMPRESSION
MERGE
TeraSort Benchmark
RANK
LOOKUP
Elapsed Time (min)

250
200
150
100
50

0
0

1000

2000
3000
File Size (GB)

JOIN
AGGREGRATION
Syncsort Confidential and Proprietary - do not copy or distribute

4000

5000

CDC
9

Data Access:

Mainframes

Today

Syncsort Confidential and Proprietary - do not copy or distribute

50%

Run

10

Syncsort. A Bridge to Scalable, Cost-effective Big Data
Connect

Pre-process

•HDFS Connectivity
•Mainframe
•Teradata
•Files
•RDBMS, Appliances

•Sort, Join
•Aggregate
•Compress
•Partition

Facilitate
•Graphical UI
•No Manual Coding
•No Tuning

Optimize
•Up to 6x Faster Load
•Up to 2x Faster Sort
•Faster MapReduce
Jobs
•Less Storage

Over 40 Years Solving Big Data
Challenges with Fast. Efficient. Simple.
Cost Effective DI Technology
Syncsort Confidential and Proprietary - do not copy or distribute

11

Hourly Load into comScore’s Hadoop Cluster
SyncSort’s DMExpress saves comScore over 4TB of data per day!
That’s 1460TB a year -1.42 Petabytes
500,000,000,000
450,000,000,000
400,000,000,000
350,000,000,000
300,000,000,000
250,000,000,000
200,000,000,000

150,000,000,000
100,000,000,000
50,000,000,000
1

2

3

4

5

6

7

8

9

10

Input Data in Bytes

© comScore, Inc.

Proprietary.

11

12

13

14

15

16

17

18

19

20

21

22

23

24

Output Data in Bytes

12

comScore’s Daily Trend of Event Volume

5,000,000,000

40,000,000,000

4,000,000,000

30,000,000,000

3,000,000,000

20,000,000,000

2,000,000,000

10,000,000,000

1,000,000,000

0

# of panel records

6,000,000,000

50,000,000,000

# of census records

60,000,000,000

0

Beacon Records

Panel Records

Please Attend Mike Brown’s Session Analyzing 1.4
Trillion Events with Hadoop Tomorrow

© comScore, Inc.

Proprietary.

13

(No elephants were harmed during
the creation of this talk but some
are now a lot faster & meaner)
Please visit our booth to register for a free evaluation
Syncsort Confidential and Proprietary - do
not copy or distribute
© comScore, Inc.

Proprietary.

14

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As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape

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This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

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How to Troubleshoot Apps for the Modern Connected Worker

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Hadoop Summit Amsterdam 2013 - Making Hadoop Ready for Prime Time - Syncsort Lightening Talk

1. Making Hadoop Ready for Prime Time Hadoop Summit Amsterdam March 2013 Steve Totman Director Of Strategy Syncsort March 20th 2013 Photo Credit Aaron Sikkink http://www.flickr.com/people/housequakecom/

2. 2

3. Syncsort Confidential and Proprietary - do not copy or distribute 3

4. The Big Data Continuum Big Data Continuum Handcoding nightmare Integrating Big Data… Smarter Hand-coding: SQL, JCL. Basic ETL Tools Challenges Min Data Awakening SQL Migration Max Value Advancing Traditional BI Standardization & Plateauing Dynamic Hitting arch limits + Early Hadoop Heavy Platforms. exponential costs. adoption prototyping Demand for MF data Growing MIPS & experimentation Long development cycles Highperformance ETL Syncsort Confidential and Proprietary - do not copy or distribute Unsustainable costs ETL & Rehosting Optimization Hadoop connectivity & sort gaps Hadoop Sort & Connectivity Evolved Big Data is the new standard for both MF & open systems data Efficiency, ETL & skills gaps Hadoop ETL DMExpress MFX 4

5. Mandatory sort steps in MapReduce processing Syncsort Confidential and Proprietary - do not copy or distribute 5

6. Syncsort Confidential and Proprietary - do not copy or distribute 6

7. 7

8. Smart Contributions to Improve Hadoop Native Sort: ᵡ modular Not ᵡ Limited capabilities ᵡ Difficult to fine-tune & configure (requires JIRA Description 4807 Allow MapOutputBuffer to be pluggable 4808 Allow Reduce-side merge to be pluggable 4809 Make classes required for 2454 public 4812 Create reduce input merger plug-in 4842 Shuffle race can hang reducer 2461 HDFS file name globbing in libhdfs 4482 Backport of 2454 to MapReduce 1 & 1.2 coding & compilation) Native Sort Native Sort Hadoop Contribution: Hadoop Node Node  Modular  Extensible  Configurable through use of external sorters on MapReduce nodes Native Sort Native Sort Hadoop Node Hadoop Node First Included - Hadoop distribution, CDH4.2, on February 26th …and more!! 8 Sy nc

9. Benefits to the Community MATCH COMPRESSION MERGE TeraSort Benchmark RANK LOOKUP Elapsed Time (min) 250 200 150 100 50 0 0 1000 2000 3000 File Size (GB) JOIN AGGREGRATION Syncsort Confidential and Proprietary - do not copy or distribute 4000 5000 CDC 9

10. Data Access: Mainframes Today Syncsort Confidential and Proprietary - do not copy or distribute 50% Run 10

11. Syncsort. A Bridge to Scalable, Cost-effective Big Data Connect Pre-process •HDFS Connectivity •Mainframe •Teradata •Files •RDBMS, Appliances •Sort, Join •Aggregate •Compress •Partition Facilitate •Graphical UI •No Manual Coding •No Tuning Optimize •Up to 6x Faster Load •Up to 2x Faster Sort •Faster MapReduce Jobs •Less Storage Over 40 Years Solving Big Data Challenges with Fast. Efficient. Simple. Cost Effective DI Technology Syncsort Confidential and Proprietary - do not copy or distribute 11

12. Hourly Load into comScore’s Hadoop Cluster SyncSort’s DMExpress saves comScore over 4TB of data per day! That’s 1460TB a year -1.42 Petabytes 500,000,000,000 450,000,000,000 400,000,000,000 350,000,000,000 300,000,000,000 250,000,000,000 200,000,000,000 150,000,000,000 100,000,000,000 50,000,000,000 1 2 3 4 5 6 7 8 9 10 Input Data in Bytes © comScore, Inc. Proprietary. 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Output Data in Bytes 12

13. comScore’s Daily Trend of Event Volume 5,000,000,000 40,000,000,000 4,000,000,000 30,000,000,000 3,000,000,000 20,000,000,000 2,000,000,000 10,000,000,000 1,000,000,000 0 # of panel records 6,000,000,000 50,000,000,000 # of census records 60,000,000,000 0 Beacon Records Panel Records Please Attend Mike Brown’s Session Analyzing 1.4 Trillion Events with Hadoop Tomorrow © comScore, Inc. Proprietary. 13

14. (No elephants were harmed during the creation of this talk but some are now a lot faster & meaner) Please visit our booth to register for a free evaluation Syncsort Confidential and Proprietary - do not copy or distribute © comScore, Inc. Proprietary. 14

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