Technology Comparison of BI Architectures
Transcript of Technology Comparison of BI Architectures
7/28/2019 Technology Comparison of BI Architectures
http://slidepdf.com/reader/full/technology-comparison-of-bi-architectures 1/1
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.