7/28/2019 Technology Comparison of BI Architectures

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Business Intelligence Technologies Overview and Comparison

QlikView ROLAP OLAP Reporting

Examples: QlikView

MicroStrategy, Oracle

Discoverer 

Cognos, Hyperion,

Panorama, Microsoft Analytical Services

Business Objects, Crystal

Reports, Oracle Reports

Technology:

 Associative Database

Layer: Data stored in

 Associative database in

memory (RAM) and all

aggregations/calculations

created dynamically as

needed.

On Demand Queries:

Complex queries broken

into simpler SQL queries

and pushed down to source

database in real time.

OLAP Cubes: Static cubes

built to store pre-

aggregated data on hard

discs.

Static Database Queries:

Predefined queries run

against source database.

Impact on Source

DB:

Best - Records pulled

straight across with minimal

processing, data stored in a

snapshot until refreshed.

Worst - Every new view of 

the data creates a separate

and involved query that

runs against the source

database, data not stored

and must be requeried

every time it is accessed.

 Average - Cubes must be

refreshed but data is

processed (aggregated) on

server which impacts DB

server, data stored in a

snapshot until refreshed..

Poor - Each report contains

one or more queries that

must be executed against

the source database every

time an instance of a report

is refreshed, data is stored

in each individual report file

until refreshed.

Performance -

Refreshing the

Snapshot:

Best - Data is pulled from

the ODBC or OLE/DB

connection with minimal

processing. The limiting

factor is typically the speed

at which records can pulled

out of the ODBC or OLE/DB

connection.

N/A - Data not stored in a

snapshot. Every new

request directly hits the

source database servers.

Worst - Very complex

demanding queries must be

run against the source

databases in order to pre-

aggregate the data. This

data must then be written

out to a huge

multidimensional cube

stored on a hard disk.

Poor - Each report must be

refreshed individually one

after the other. Calculations

are performed at the time

the reports are refreshed.

Performance -

Analyzing Data:

Best - Data is stored in

memory (RAM) and

calculated as neededwithout any disc-reads or 

network traffic.

Worst - Each change in

view of the data results in a

query that will have to be

executed on the database

server and the results

transferred back across the

network to the analysis tool.

Queries are complicated

and can take a long time to

run.

Best - Data is stored in a

cube in a pre-aggregated

format and displayed as

needed.

Worst - Data is static and

can not be analyzed

interactively. Each report

must be rerun against the

source database servers to

be updated.

Flexibility - Adding

Dimensions and

Measures

Best - Any field in the

source data is can be

added as a dimension

instantly. Likewise new

measures can be added on

the fly.

Best - Any field in the

source data can be added

as a dimension instantly.

Likewise new measures can

be added on the fly.

Worst - New dimensions

and measures must be hard

coded in to the cube

definitions (an IT task) and

then the cubes must be

refreshed.

Worst - New aggregations

must be hard coded into

reports (an IT task) and

reports must be refreshed.

Offline Analysis

Best - QlikView applications

compress data to less than

8% - 3% of original source

data size. QlikView apps

can be analyzed

disconnected from the

network and many apps are

small enough to be sent via

email.

Worst - ROLAP needs to

execute queries against the

source database in real

time. Offline analysis is

impossible.

Worst- OLAP cubes

exponentially increase the

data size from source data.

Some vendors will provide

tiny trivial cubes and claim

offline analysis capabilities.

True offline analysis is not

pratical. Drill down to

detailed data impossible.

Poor - Reports are generally

availible offline but are

static and do not allow

interactive analysis.