Webinar: Make Big Data Easy with the Right tools and talent

October 2012

- MetaScale Expertise and Kognitio Analytics Accelerate Hadoop for Organizations Large and Small

Today’s webinar

• 45 minutes with 15 minutes Q&A

• We will email you a link to the slides

• Feel free to use the Q & A feature

Agenda

• Opening introduction• MetaScale Expertise

– Case study – Sears Holdings

• Kognitio Analytics – Hadoop acceleration

explained• Summary• Q&A

Michael HiskeyVP Marketing & Business DevelopmentKognitio

Dr. Phil ShelleyCEO, MetaScaleCTO, Sears Holdings

Presenters

Host

Roger GaskellCTOKognitio

Big Data < > Hadoop

Big Data is high volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision-making

Volume (not only) size Velocity (speed of Input / Output) Variety (lots of data sources) Value – not the SIZE of your data,

but what you can DO with it!

OK, so you’ve decided to put data in Hadoop...

Now what?

Dr. Phil ShelleyCEO – MetaScaleCTO Sears Holdings

Where Did We Start at Sears?

Where Did We Start? Issues with meeting production schedules

Multiple copies of data, no single point of truth

ETL complexity, cost of software and cost to manage

Time take to setup ETL data sources for projects

Latency in data, up to weeks in some cases

Enterprise Data Warehouses unable to handle load

Mainframe workload over consuming capacity

IT Budgets not growing – BUT data volumes escalating

Why Hadoop?

TraditionalDatabases & Warehouses

Hadoop

An Ecosystem

Data Sourcing Connecting to Legacy source systems Loaders and tools (speed considerations) Batch or near-real time

Enterprise Data Model Establish a model and enterprise data strategy early

Data Transformations The End of ETL as we know it

Data re-use Drive re-use of data Single point of truth is now a possibility

Data Consumption and user Interaction Consume data in-place wherever possible Move data only if you have to Exporting to legacy systems can be done, but it duplicates data Loaders and tools (speed considerations) How will your users interact with the data

Enterprise Integration

Rethink Everything

The way you capture dataThe way you store data

The structure of your dataThe way you analyze dataThe costs of data storage

The size of your dataWhat you can analyzeThe speed of analysisThe skills of your team

The way user interact with data

The Learning from our Journey

• Big Data tools are here and ready for the Enterprise

• An Enterprise Data Architecture model is essential

• Hadoop can handle Enterprise workload To reduce strain on legacy platforms

To reduce cost

To bring new business opportunities

• Must be part of an overall data strategy

• Not to be underestimated

• The solution must be an Eco-System There has to be a simple way to consume the data

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Hadoop Strengths & Weaknesses?

• Cost effective platform• Powerful / fast data processing environment• Good at standard reporting• Flexibility: Programmable, Any data type• Huge scalability

• Barriers to entry: lots of engineering and coding• High on-going coding requirements• Difficult to access with standard BI/analytical tools• Ad hoc complex analytics difficult• Too slow for interactive analytics

Reference Architecture

What is an “In-memory” Analytical Platform?

• DBMS where all of the data of interest or specific portions of the data have been permanently pre-loaded into random access memory (RAM)

• Not a large cache– Data is held in structures that take advantage of the properties of

RAM – NOT copies of frequently used disk blocks– The databases query optimiser knows at all times exactly which

data is in memory (and which is not)

In-Memory Analytical Database MangementNot a large cache: • No disk access during query execution

– Temporary tables in RAM– Results sets in RAM

• In-Memory means in high speed RAM – NOT slow flash-based SSDs that mimic

mechanical disks

For more information: • Gartner: “Who's Who in In-Memory DBMSs”

Roxanne Edjlali, Donald Feinberg10 Sept 2012 www.gartner.com/id=2151315

Why In-memory: RAM is Faster Than Disk (Really!)

Actually, this only part of the storyAnalytics completely change the workload characteristics on the databaseworkload

Simple reporting and transactional processing is all about “filtering” the data of interestfiltering

Analytics is all about complex “crunching”of the data once it is filteredcrunching

Crunching needs processing power and consumes CPU cycles

CPU cycles

Storing data on physical disks severely limits therate at which data can be provided to the CPUsstoring

Accessing data directly from RAM allowsmuch more CPU power to be deployedaccess

Analytics is about through Data

• To understand what is happening in the data

“CRUNCHING”

Joins

Sorts

Aggregations

Grouping

AnalyticalFunctionsAnalyticalFunctions

CPU cycle-intensive & CPU-bound

• Analytical platforms are therefore CPU-bound– Assume disk I/O speeds not a bottleneck– In-memory removes the disk I/O bottleneck

More complex analytics

More pronounced this becomes =

For Analytics, the CPU is King

• The key metric of any analytical platform should be GB/CPU– It needs to effectively utilize all available cores– Hyper threads are NOT the equivalent of cores

• Interactive/adhoc analytics: – THINK data to core ratios ≈ 10GB data per CPU core

• Every cycle is precious – CPU cores need to used efficiently– Techniques such as “dynamic machine code generation”

Makes in-memory databases go slowerMakes disk-based databases go faster

Careful – performance impact of compression:

Speed & Scale are the Requirements• Memory & CPU on an individual server = NOWHERE near enough for big data

– Moore’s Law – The power of a processor doubles every two years– Data volumes – Double every year!!

• Every CPU core in• Every server needs to efficiently involved in • Every query

Every

– Data is split across all the CPU cores– All database operations need to be parallelised with no points of

serialisation – This is true MPP

• Combine the RAM of many individual servers• many CPU cores spread across• many CPUs, housed in • many individual computers

Many

• The only way to keep up is to parallelise or scale-out

Hadoop ConnectivityKognitio - External Tables

– Data held on disk in other systems can be seen as non-memory resident tables by Kognitio users.

– Users can select which data they wish to “suck” into memory.• Using GUI or scripts

– Kognitio seamlessly sucks data out of the source system into Kognitio memory.

– All managed via SQL

Kognitio - Hadoop Connectors– Two types

• HDFS Connector• Filter Agent Connector

– Designed for high speed• Multiple parallel load streams• Demonstrable 14TB+/hour load rates

Tight Hadoop integration

HDFS Connector• Connector defines access to hdfs file

system• External table accesses row-based data

in hdfs• Dynamic access or “pin” data into

memory• Complete hdfs file is loaded into memory

Filter Agent Connector• Connector uploads agent to Hadoop

nodes• Query passes selections and relevant

predicates to agent• Data filtering and projection takes

place locally on each Hadoop node• Only data of interest in loaded into

memory via parallel load streams

Not Only SQL

Kognitio V8 External Scripts– Run third party scripts embedded within SQL

• Perl, Python, Java, R, SAS, etc.• One-to-many rows in, zero-to-many rows out, one to one

create interpreter perlinterpcommand '/usr/bin/perl' sends 'csv' receives 'csv' ;

select top 1000 words, count(*)from (external script using environment perlinterp

receives (txt varchar(32000))sends (words varchar(100))script S'endofperl(

while(<>){

chomp();s/[\,\.\!\_\\]//g;foreach $c (split(/ /)){ if($c =~ /^[a-zA-Z]+$/) { print "$c\n”} }

})endofperl'from (select comments from customer_enquiry))dt

group by 1 order by 2 desc;

This reads long comments text from customer enquiry table, in line perl converts long text into output stream of words (one word per row), query selects top 1000 words by frequency using standard SQL aggregation

Hardware Requirements forIn-memory Platforms

• Hadoop = industry standard servers

• Careful to avoid vendor lock-in

• Off the shelf, low cost, servers matchneatly with Hadoop

– Intel or AMD CPU (x86)– No special components

• Ethernet network

• Standard OS

Benefits of an In-memory Analytical Platform

• A seamless in-memory analytical layer on top of your data persistence layer(s):

Analytical queries that used to run in hours and minutes, now run in minutes and seconds (often sub-second)

High query throughput = massively higher concurrency

Flexibility• Enables greater query complexity• Users freely interact with data• Use preferred BI Tools (relational or OLAP)

Reduced complexity• Administration de-skilled• Reduced data duplication

• Big Data tools are here and ready for the Enterprise• An Enterprise Data Architecture model is essential• Hadoop can handle Enterprise workload

To reduce strain on legacy platforms To reduce cost To bring new business opportunities

• Must be part of an overall data strategy• Not to be underestimated• The solution must be an Eco-System

There has to be a simple way to consume the data

Page 26

The Learning from our Journey

www.kognitio.com

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Dr. Phil ShelleyCEO – MetaScaleCTO Sears Holdings

Michael HiskeyVice PresidentMarketing & Business DevelopmentMichael.hiskey@kognitio.com

Phone: +1 (855) KOGNITIO

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