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2.  A brief history of QlikView
 How Traditional BI works
 How QlikView works
 How QlikView works internally?
 QlikView Competition
 What are the implications for OLAP and the data
warehouse

3.  Founded in Lund, Sweden in 1993 by Björn Berg and Staffan Gestrelius originally as a
consultancy
 Originally called “QuikView” as in “Quality, Understanding, Interaction, Knowledge”
 Product was designed to mimic the way the brain works
 A key aspect was the colour-coding scheme whereby selected values are highlighted in
green, linked values in white, and excluded values in highlighted grey
 First two versions were basically written in Excel using VLOOKUPs
 HåkanWolgé was later hired as lead software engineer to re-architect/re-write QlikView from
the ground up as an in-memory application
 Renamed as “QlikView” in 1996
 IPOed on Nasdaq in 2010 under symbol “QLIK” and had 7th best IPO of 2010
 Now has over 24,000 customers in 100 countries and employs over 1,000 people worldwide
 Market cap: $2.5 billion

4.  Traditional OLAP/cube technologies primarily provide the ability to
drill up and down through “dimension” hierarchies, allowing the
end-user to see pre-aggregated “measures”
 Dimensions and measures must be know a priori
 A small team is usually required to complete a BI project
 A data warehouse or data mart is usually required as a pre-requisite
before OLAP cubes can be built
 This can often lie on the critical path of other data warehouse projects.
Since data warehouse usage cannot be anticipated, a “single version of
the truth” can often bog down development
 ETL is very slow to test, which in turn slows down development time
 If a detail drill down report (e.g. to see all point-of-sale records), a
“drill through” query link is made to the operational data store to
retrieve these data
 Introduces another point-of-failure
 Associations between dimensions are not computed – only resulting
measures (e.g. counts)

5.  The “secret sauce” is: An experienced QlikView can build and test a
dashboard solution (including user acceptance testing) faster than any
other BI tool I have evaluated
 This makes “Agile BI” possible
 Users and developers can remain focused on insights and outcomes
 The resulting dashboards are effectively by-products of the analysis process
 More flexible data model allows normalized data to be imported with
fewer transformations
 ETL development is in-memory. ETL jobs can be tested orders of
magnitude faster than traditional ETL tools
 All data is automatically profiled on import
 QlikView uses the word “associative” to distinguish itself from other BI
vendors
 Associative is a tricky concept to explain, but most people will “get it” when
they see it
 “Associative” puts emphasis on understanding how sets of data relate to one
another
 All those tricky SQL queries involving “NOT EXISTS” or “LEFT/RIGHT
OUTER JOIN” are but a mouse click away

6.  QlikView uses the word
“associative” to distinguish itself
from other BI vendors
• Associative is a tricky concept to
explain, but most people will “get
it” when they see it
• “Associative” puts emphasis on
understanding how sets of data
relate to one another
• All those tricky SQL queries
involving “NOT EXISTS” or
“LEFT/RIGHT OUTER JOIN” are
but a mouse click away

10.  At the centre of QlikView is a large “Multi-Dimensional Cube Table”,
with one column for each table, and each row containing pointers
back to the original table’s row index
 Also uses a: Global Symbol Table; Value Tables; and Data Tables
 The “machine code” most likely refers to bitmap indexes. QlikView
heavily relies on bitmap indexes to perform its JOINs
 QlikView may have the best known solution to Kimball’s “Big JOIN”
problem (JOINing a billion dimensions with a trillion facts), since a
single row is effectively being represented by a single bit
 Consider that a 64 rows can be JOINed in less than a clock cycle
 Intel and AMD now support “Active Vector Extensions” (AVX), which
will allow 256 rows to be JOINed in less than a clock cycle
 Unclear if this architecture lends itself to map/reduce
 The embedded example shows in detail how the indexes work

11.  Only true competitor is Microsoft Power Pivot
 Available as free plug-in for Excel 2010 and can be deployed in SharePoint 2010
 Started as Project Gemini, which was announced 21 months in advanced – the
farthest out for any MS project
 MS has done their best to mimic QlikView’s associative experience
 Will now be rolling out “Power View” as part of SQLServer 2012 SSRS
 Other vendors have greatly simplified the cube/OLAP approach, and can be
considered somewhat Agile, although they lack the “Associative” experience.
Primarily:
 Tableau
 TIBCO SpotFire
 Many vendors have jumped on the “in memory” bandwagon, but ultimately have
just moved their existing cubes “in memory” – effectively just speeding up user
interaction, but offering nothing new in terms of user experience or development
timelines
 Some “big data” analytical DB vendors (e.g. SAP HANA) are feigning competition
with QlikView – but none of these get to the “last mile” of user experience

12.  No longer need to maintain star schemas
 The data warehouse is going through a transition, and will likely be much
simpler to maintain
 Bitemporal data types, which can already be found in TeraData and DB2,
and have been ratified in ISO SQL:2011 will handle all issues related to
Slowly Changing Dimensions, and other time related issues (e.g. when data
was loaded vs. when original transaction occurred)
 Change Data Capture tables should be used to load data warehouse
 Data quality, de-duplication, and fuzzy matching should be treated as
operational issues, e.g. fuzzy matching tables should be maintained
operationally
 Dashboard schemas will be built in tools like QlikView, as needed.
 Star and snowflake schemas are still useful, but should be built as needed
on-the-fly
 The data warehouse should more-or-less be a time invariant mirror of the
ODS, and more-or-less maintain itself