Cognos TM1 and Transformer are both OLAP tools used for data analysis and modeling, but they have key differences. TM1 is better suited for large data volumes, real-time reporting needs, and complex models that require on-the-fly calculations. It has faster performance due to in-memory processing and can handle write backs and merging large data sources. Transformer is better for consistent query performance with pre-aggregated cubes, but has limitations on cube size and does not support write backs or dynamic changes to models. TM1 should be chosen for large or changing datasets while Transformer is better for smaller, read-only analysis.

1. Cognos
Transformer Vs TM1
Jasmeet Bhatia S

2. Objective:
S To inform users regarding the differences between
Cognos Transformer and Cognos TM1
S To provide users some key points to consider while
deciding between Cognos TM1 and Transformer

3. Primary OLAP Use:
COGNOS TRANSFORMER COGNOS TM1
Data Analysis / Slice and Dice What-If scenario modeling / Slice
and Dice

4. TM1 – Key Advantages:-
S In-memory processing(faster performance)
S Quickly load and merge large data volumes(intra day or
nightly)
S Load data from multiple sources (ODBC/Flat files)
S Multi cube architecture with shared dimensions
S On the fly changes to hierarchies and self-service ad-hic
analysis
S Can accommodate advanced calculations through rules & TI
S Easily create reports deployed through excel or Web

5. Key Features:-
TM1 Transformer
In memory data; 64 bit architecture 32 bit architecture leads to
leads to efficient memory use cube size limitations
Cubes calculate and aggregate on fly; Cubes are pre aggregated
Calculated cells are cached
Large data or calculation impact first Since aggregations happen
time load times during build, query
performance is consistent
Users can alter views, hierarchies or No write backs
contribute data on the fly and
recalculate
Pre-cached views lead to better query Query performance remains
performance consistent

6. When to choose:-
TM1 Transformer
Large data volumes Relatively smaller data footprint
Real time reporting expected Consistent query performance
expected
Write back capability required High number of concurrent ‘read’
users
Complex models; on the fly Pre-calculated data
calculations