Best practices working with Big Data on Power BI. Create aggregations, and combine direct query, dual, and import storage modes

1. Gaston Cruz - MVP Data Platform
Data Analytics Architect
Slalom, Seattle.
/gastoncruz
@gastonfcruz
gastoncruz
gastoncruz@outlook.com
www.gastoncruz.com
Power BI with Big Data
Composite Models and Aggregations
Seattle Modern Excel + Power BI User Group
(MEUG)

2. Performance Factors
• Created summarized tables w/ filter for large DQ tables
(Full aggregate functionality available soon)
• Unloaded and removed irrelevant tables and columns
• Document relationship info & measures & calculated columns
• Changed semi-relevant tables from import to DQ
• Turned off auto date/time (Current file/Global)
• Removed bookmarks and moved calculated measures to
the model
• Removed custom visuals

3. Composite Model
Power BI Architecture (Ad-Hoc
Model)
• Transactional Views in
DW
• Best Practice to build
pre calculated columns
Power BI Desktop
Power BI Desktop
Dual
On-Prem
Gateway
DirectQuery Import
Power BI Desktop
Power BI Desktop
Gateway
Data Sources
Dashboard
Report
Reports
Publish
Periodic Data Refresh
Periodic
Data Refresh
In the first level and to improve performance a View is created in DW with pre calculated columns
When reports and dashboards are viewed, Power BI queries its copy of the data stored within the dataset (an in-memory
columnstore database) instead of going back to the original data source. This happens with Composite model using Direct
Query to DW transactional table and Aggregated Table in Power BI with Import type of connection. Dimensional Tables
are going to set via Dual connectivity (DQ with Transactional and Import with Aggregated Table)
Publish to PBI Premium Capacity for computing purposes
1
1
2
3
2
Power BI Desktop
Composite Model
Power BI Service with Premium Capacity
Source Data
Source Data
Source Data
Systems Data Warehouse
Publish
PBIX
Premium App Workspace
3Dual
DirectQuery Import
Connection via Direct Query to Transactional
Tables/Views in Snowflake and then implement
Aggregated Tables with Import connection to
improve performance.

4. Power BI Architecture (Ad-Hoc Model)

5. Power BI Architecture (Ad-Hoc Model)

6. Power BI Architecture (Ad-Hoc
Model) – Best Practices

7. Tools
DAX Studio by SQLBI
https://www.sqlbi.com/tools/dax-studio/
VertiPaq Analyzer by SQLBI
https://www.sqlbi.com/tools/vertipaq-analyzer/
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Power BI Helper by RADACAD
https://radacad.com/power-bi-helper

8. Business logic & metrics
Data modeling
Security
Azure Analysis Services
Server
Lifecycle management
In-memory
cache
Enterprise Data Analytics

9. Thanks! And see you on the next
meetup!!!
@gastonfcruz
"Improve the Process"