In this talk, we will compare the most widely used BI tools in the market from the perspective of a mature data organization. The focus of this talk WON’T be on flashy features nor superficial sales talk. We will compare both tools in terms of how well they fit in with DataOps best practices. How do they rank in terms of speed of delivery, governance, robustness, and analytical capabilities.

1. TABLEAU VS
POWER BI
The Ultimate BI Tool
For Mature Data Teams

2. THE DEBATE
YOU ARE
TIRED OF
HEARING!
Existing Comparisons
Outdated
One-sided
BI-centric
Developer's perspective
Mature Data Teams
Robust over easy
NOT BI-centric
Governance in place
Admin's perspective
This Presentation
No hacky approaches
Only documented features
No secondary tools or add-ons
Assumes data maturity

3. DATA CLEANING

4. DATA CLEANING
CREATING DATASETS
SQL-like formulas
New columns only
Basic transformations
No DataOps :(
Limited
Uses M & DAX
Creates columns & tables
Most transformations
No DataOps :(
Confusing

5. DATAOPS
Infrastructure as code Version Control Automated Testing
Containers Orchestration CI/CD
Monitoring Security Governance

7. DATA MODELLING
One Big Table (OBT)

8. DATA MODELLING
CREATING DATASETS
Denormalized and normalized
Blending, joins, relations
Star schema
Filter propagation is hard

9. ARCHITECTURE

11. Flexibility
Cloud or on-prem
Server > Online
Multi-cloud
Linux + Windows
Cloud or on-prem*
Service > Report Server
Azure Only
Windows Only
Terraform
Available, but unofficial
Incomplete
1 contributor
Available, but unofficial
Mostly complete
5 contributors
Access
Multiple authentication stores
License -> Site -> Group -> User -> Object
Azure Active Directory or Microsoft account
Licenses -> Roles
ARCHITECTURE

13. Licenses
€ 70 dev per month
€ 35 or € 12 viewer per month
€ 8.40 or € 16.90 per dev month
Free with Office 365 <= E5
Unlimited Capacity ???????
€ 4.212,30 per capacity
€ 1 to € 32 per hour Power BI embedded
Human Capital
Less time spent in data modelling
More time spent on graphs
More time spent in data modelling
Less time spent in graphs
COSTS

14. Data
Cleaning
Data
Modelling
Architecture
& Security
Microsoft
Integrations
Costs
CONCLUSION