qlikview & big data

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QlikView & Big Data Mischa van Werkhoven Senior Solution Architect QlikTech Michael Robertshaw Senior Solution Architect QlikTech

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Slides used during the presentation and demonstration 'QlikView & Big Data' at the Business Discovery World Tour on 9 October 2013 by Mischa van Werkhoven and Michael Robertshaw. Big Data. We've all heard about it. We all think we should do something with it. But do we know exactly what it is and how to create value from it? How reasonable are our expectations? This session focuses on the myths of Big Data, technologies involved as well as how QlikView can be used to add relevance and context to Big Data for the end user.

TRANSCRIPT

Page 1: QlikView & Big Data

QlikView & Big Data

Mischa van Werkhoven

Senior Solution Architect

QlikTech

Michael Robertshaw

Senior Solution Architect

QlikTech

Page 2: QlikView & Big Data

Legal Disclaimer

This Presentation contains forward-looking statements, including, but not limited to, statements regarding the value and

effectiveness of QlikTech's products, the introduction of product enhancements or additional products and QlikTech's growth,

expansion and market leadership, that involve risks, uncertainties, assumptions and other factors which, if they do not

materialize or prove correct, could cause QlikTech's results to differ materially from those expressed or implied by such

forward-looking statements. All statements, other than statements of historical fact, are statements that could be deemed

forward-looking statements, including statements containing the words "predicts," "plan," "expects," "anticipates," "believes,"

"goal," "target," "estimate," "potential," "may", "will," "might," "could," and similar words. QlikTech intends all such forward-

looking statements to be covered by the safe harbor provisions for forward-looking statements contained in Section 21E of

the Exchange Act and the Private Securities Litigation Reform Act of 1995. Actual results may differ materially from those

projected in such statements due to various factors, including but not limited to: risks and uncertainties inherent in our

business; our ability to attract new customers and retain existing customers; our ability to effectively sell, service and support

our products; our ability to manage our international operations; our ability to compete effectively; our ability to develop and

introduce new products and add-ons or enhancements to existing products; our ability to continue to promote and maintain

our brand in a cost-effective manner; our ability to manage growth; our ability to attract and retain key personnel; the scope

and validity of intellectual property rights applicable to our products; adverse economic conditions in general and adverse

economic conditions specifically affecting the markets in which we operate; and other risks more fully described in QlikTech's

publicly available filings with the Securities and Exchange Commission. Past performance is not necessarily indicative of

future results. The forward-looking statements included in this presentation represent QlikTech's views as of the date of this

presentation. QlikTech anticipates that subsequent events and developments will cause its views to change. QlikTech

undertakes no intention or obligation to update or revise any forward-looking statements, whether as a result of new

information, future events or otherwise. These forward-looking statements should not be relied upon as representing

QlikTech's views as of any date subsequent to the date of this presentation.

This Presentation should be read in conjunction with QlikTech's periodic reports filed with the SEC (SEC Information),

including the disclosures therein of certain factors which may affect QlikTech’s future performance. Individual statements

appearing in this Presentation are intended to be read in conjunction with and in the context of the complete SEC Information

documents in which they appear, rather than as stand-alone statements.

© 2013 Qlik Technologies Inc. All rights reserved. QlikTech and QlikView are trademarks or registered trademarks of Qlik

Technologies Inc. or its subsidiaries in the U.S. and other countries. Other company names, product names and company

logos mentioned herein are the trademarks, or registered trademarks of their owners.

Page 3: QlikView & Big Data

Key Takeaways

• The Most Common Purpose of Big Data Is to Produce Small Data

• Big Data is About Relevance and Context

• Know What You Want to Achieve

Page 4: QlikView & Big Data

Agenda

• What is Big Data?

• Myths about Big Data

• Gartner

– Hype Cycle

– Top Challenges

• Who’s doing it?

• What technologies are they using?

• Hadoop Components

• The Bloor Group

– The Intelligent Thing

– Cost vs Benefit

• How to do it using QlikView

• Demonstration

“Big Data Analytics refers to analytics on data that is not able to be

performed on a standard relational data warehouse in a timeframe

and cost that is acceptable for its business purpose”

Page 5: QlikView & Big Data

What is Big Data?

Page 7: QlikView & Big Data

Paper Print Computer Internet

Big Data happens in every part of History

• Medium to write

ideas and

information

• Not enough writers

to disseminate

• Technology to

distribute

information

• No place to store

• Place to store

• Can’t keep up with

computing

requirements

• Distributed

computing globally

• Too many Emails

to read

We always create more than we can consume!

Page 8: QlikView & Big Data

Success characterized by:

Veracity

Visualization

Value

Data characterized by:

Page 9: QlikView & Big Data

The Myth of Big Data

Page 10: QlikView & Big Data

In Many Cases, Reality Looks More Like This

Page 11: QlikView & Big Data

Hype Cycle

Big Data

In-Memory Analytics

Page 12: QlikView & Big Data

Gartner – Top Big Data Challenges

You need to determine

your goals/objectives

QlikView may help you

with these challenges

Page 13: QlikView & Big Data

Who is doing it?

Page 14: QlikView & Big Data

Who is doing it?

Who What Why

Telco Usage and Location Analysis,

Customer Interactions, Services

Data Analysis

Operational Excellence

Financial

Services

Trading Analysis, Portfolio

Analysis

Improve Profit,

Minimize Risk

Utilities Smart Metering Analysis Operational Excellence

Travel and

other Retail

Cross Sell Opportunity

Realisation

Increase Sales

Customer

Behaviour

Click Stream Analysis, Location

Analysis, Social Media

Sentiment Analysis

Customer Experience,

Loyalty, Increase Sales

Page 15: QlikView & Big Data

What technologies are they using?

Page 16: QlikView & Big Data

What Technologies?

• Hadoop

– Cloudera Hadoop

– HortonWorks Hadoop

– Teradata Aster

• Relational Technologies

– Teradata

– HP Vertica

– IBM Netezza

– EMC GreenPlum

– Amazon Redshift (Postgres)

Page 17: QlikView & Big Data

Hadoop Overview ODBC

2.0

ODBC

2.5 Improvement

Hive 3h17m 51s 232x faster

Impala 9m7s 11s 50x faster

Page 18: QlikView & Big Data

Big Data Expectations

Page 19: QlikView & Big Data

How Reasonable are your Expectations?

Notebook

HDD

Server

HDD

SSD

RAM

Hadoop

Tape

Performance

Co

st

Page 20: QlikView & Big Data

The Bloor Group

Hard Disk

Drives (HDD)

Solid State

Storage (SSD)

Random Access

Memory (RAM)

Speed (t/TB) 3300s 1000-300s 1s

Price $/TB $ 50 $ 500 $ 4 500

• Keep data in memory when the value obtained from processing it is high

• Leave data on disk when it is inactive or the value from processing it is low

Page 21: QlikView & Big Data

How to do it using QlikView

Page 22: QlikView & Big Data

The Value in Big Data Comes from Context and Relevance

Machine data, web

data, cloud data

Big Data

cluster

Operational

systems

Data

warehouse

Google

BigQuery

Page 23: QlikView & Big Data

The Value in Big Data Comes from Context and Relevance

Business Discovery is about enabling the users to find their own path

through a pre-defined Dataset.

Structure needs to be defined by a QlikView document developer,

though content could be refreshed periodically (conventionally)

or impacted and triggered by the user (on demand).

Page 24: QlikView & Big Data

The Value in Big Data Comes from Context and Relevance M

ore

His

tory

More Categories

They’re both the same number of bricks!

The same volume of data, same schema.

You choose what is relevant to your analysis.

Page 25: QlikView & Big Data

Using QlikView with Big Data

1. Conventional Reloads with Document Chaining

2. Direct Discovery – Hybrid Approach

3. Reload on Demand

Page 26: QlikView & Big Data

1. Conventional Reloads

• Reload available data into

multiple QVW documents

segmented by Region and

current Financial Year

reloaded Monthly

• Entry Document contains

Details for All Regions for

Current Period only.

Reloaded Daily

• Use Document Chaining to

navigate to/amongst Region-

Year documents

• A lot of Publisher capacity

and Data Replication

Page 27: QlikView & Big Data

2. Direct Discovery

• Reload available data into

multiple QVW documents

segmented by Region and

current Financial Year

reloaded Monthly

• Entry Document provides

Trends for All Regions for

Any Period.

Dimensions reloaded Daily.

QvS generates aggregate

SQL to draw Charts

• Use Document Chaining to

navigate to/amongst

Region-Year documents

containing Detail

• Performance dependent

upon Database

Page 28: QlikView & Big Data

3. On Demand Reloads • Entry Document provides

some Aggregate KPIs for All

Regions, but mostly just

Dimension selection.

• When User selects sufficient

criteria, a Link is enabled to

pass criteria to custom

ASPX page.

• ASPX page causes User

document to be Reloaded

with chosen criteria

• User Document contains

relevant subset entirely in

Memory

• Reload requires a little

patience but then

performance is great.

Page 29: QlikView & Big Data

Demonstration

Page 30: QlikView & Big Data

Demo – Document Chaining

Page 31: QlikView & Big Data

Demo – Hybrid Approach - Direct Discovery

// Direct Discovery v2

DIRECT QUERY

DIMENSION

OrderID,

ProductID

MEASURE

UnitPrice,

Quantity,

Discount

FROM “ Northwind"."dbo"."Order Details";

// Direct Discovery v1

DIRECT SELECT

OrderID,

ProductID

FROM “ Northwind"."dbo"."Order Details";

// Conventional QlikView

[Order Details]:

SQL SELECT

OrderID,

ProductID,

UnitPrice,

Quantity,

Discount

FROM “Northwind"."dbo"."Order Details";

Page 32: QlikView & Big Data

Demo – Hybrid Approach

http://demo.qlikview.com/detail.aspx?appName=American%20Birth%20Statistics.qvw

Page 33: QlikView & Big Data

Key Takeaways

1. The Most Common Purpose of Big Data Is to Produce Small Data

2. Big Data is About Relevance and Context

3. Know What You Want to Achieve

Page 34: QlikView & Big Data

/ 34

Questions?

Business Discovery

World Tour

9 October 2013

Page 35: QlikView & Big Data

Thank You!