qlikview & big data

QlikView & Big Data

Mischa van Werkhoven

Senior Solution Architect

QlikTech

Michael Robertshaw

Senior Solution Architect

QlikTech

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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

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”

What is Big Data?

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http://www.qlikview.com/us/landing/open-data-challenge

Take Action Open Data Challenge

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!

Success characterized by:

Veracity

Visualization

Value

Data characterized by:

The Myth of Big Data

In Many Cases, Reality Looks More Like This

Hype Cycle

Big Data

In-Memory Analytics

Gartner – Top Big Data Challenges

You need to determine

your goals/objectives

QlikView may help you

with these challenges

Who is doing it?

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

What technologies are they using?

What Technologies?

• Hadoop

– Cloudera Hadoop

– HortonWorks Hadoop

– Teradata Aster

• Relational Technologies

– Teradata

– HP Vertica

– IBM Netezza

– EMC GreenPlum

– Amazon Redshift (Postgres)

Hadoop Overview ODBC

2.0

ODBC

2.5 Improvement

Hive 3h17m 51s 232x faster

Impala 9m7s 11s 50x faster

Big Data Expectations

How Reasonable are your Expectations?

Notebook

HDD

Server

HDD

SSD

RAM

Hadoop

Tape

Performance

Co

st

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

How to do it using QlikView

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

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).

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.

Using QlikView with Big Data

1. Conventional Reloads with Document Chaining

2. Direct Discovery – Hybrid Approach

3. Reload on Demand

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

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

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.

Demonstration

Demo – Document Chaining

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";

Demo – Hybrid Approach

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

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

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Questions?

Business Discovery

World Tour

9 October 2013

Thank You!