Download - 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
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!