This document discusses architectures for using Snowflake and Power BI together. It begins by describing the benefits of each technology. It then outlines several architectural scenarios for connecting Snowflake to Power BI, including using a Power BI gateway, without a gateway, and connecting to Analysis Services. The document also provides examples of usage scenarios and developer best practices. It concludes with a section on data governance considerations for architectures with and without a Power BI gateway.

1. SNOWFLAKE AND
POWER BI
ANGEL ABUNDEZ
VP SOLUTIONS ARCHITECTURE, DESIGNMIND
2/18/2020

2. AGENDA
 Benefits of each technology
 Architectural Scenarios
 Usage scenarios
 Developer Best Practices
 Data Governance

3. © 2018 Snowflake Computing Inc. All Rights Reserved

4. © 2018 Snowflake Computing Inc. All Rights Reserved
A NEW ARCHITECTURE FOR DATA WAREHOUSING
Multi-cluster, shared data, in the cloud
4
Traditional Architectures Snowflake
Shared storage
Single cluster
Shared-disk
Decentralized, local storage
Single cluster
Shared-nothing Multi-cluster, shared data
• Centralized, scale-out storage
• Multiple, independent compute clusters

5. © 2018 Snowflake Computing Inc. All Rights Reserved
SEPARATE COMPUTE, SAME DATA
5
Data science
ETL
Dev/QA
BI/Visualization
(Auto scaling)
Elastic scaling for storage
Low-cost cloud storage, fully replicated and resilient
Elastic scaling for compute
Virtual warehouses scale up & down instantly without
downtime to support workload needs
Dedicated performance SLAs
Each warehouse can access the same tables at the same
time without performance penalty (including ETL)
Test/Dev/Staging/QA
Reference objects in multiple databases with
one SQL statement
Elastic scaling for concurrency
Auto-scaling maintains constant query performance

6. © 2018 Snowflake Computing Inc. All Rights Reserved 6
SECURE BY DESIGN
Authentication Access control Data encryption External validation
• Embedded multi-
factor authentication
• Federated
authentication
available
• Role-based access
control model
• Granular privileges on
all objects & actions
• All data encrypted,
always, end-to-end
• Encryption keys
managed
automatically
• Certified against
enterprise-class
requirements

7. © 2018 Snowflake Computing Inc. All Rights Reserved

8. Any data, any way, anywhere
Power BI
© 2017 MICROSOFT. ALL RIGHTS RESERVED.

9. CREATE  COLLABORATE  DISTRIBUTE
App Workspaces
Co-owned Dashboards
Co-owned Reports
Co-owned Datasets
Co-owned Apps
Distribute
Sharing
App Workspaces / Apps
Mobile apps
Embedding in apps
Static: PPT, Email
Other: Cortana, Publish to
web, Static (Alerts, Print)
Development Production
App Workspaces
Co-owned Dashboards
Co-owned Reports
Co-owned Datasets
Co-owned Apps

10. IT Pro
Existing
Data
LOB
Applications
FilesData Marts
End Users
Power BI/Excel Power Tools
Power BI/SharePoint
Analysis
Cubes
Data
Warehouse
A balanced approach…

11. Power BI Delivery Approaches
Business-Led
Self-Service BI
Bottom-Up Approach
IT-Managed
Self-Service BI
Blended Approach
Corporate BI
Top-Down Approach
Analysis using any type of
data source; emphasis on
data exploration and
freedom to innovate
Ownership:
Business supports all
elements of the solution
Scope of Power BI use
by business users:
Data preparation, data
modeling, report creation
& execution
Governed by:
Business
A “managed” approach
wherein reporting utilizes
only predefined/governed
data sources
Ownership:
IT: data + semantic layer
Business: reports
Scope of Power BI use
by business users:
Creation of reports and
dashboards
Governed by:
IT: data + semantic layer
Business: reports
Utilization of reports and
dashboards published by
IT for business users to
consume
Ownership:
IT supports all elements
of the solution
Scope of Power BI use
by business users:
Execution of
published reports
Governed by:
IT
Ownership Transfer
Over time, certain self-service solutions deemed as critical to the business may transfer ownership
and maintenance to IT. It’s also possible for business users to adopt a prototype created by IT.

12. POWER BI ARCHITECTURE

13. USAGE SCENARIO DEMOS
TRADITIONAL STAR SCHEMA, DENORMALIZED DATASETS, SHARED DATASETS/DATAFLOWS

14. PUTTING IT TOGETHER

15. ANALYTICS ARCH W/ GATEWAY
Pros
 Better governance on service accounts used
 Dedicated resources for scheduled refresh or direct
query
 Control refresh or direct query performance
 Low cost per node
Cons
 Additional maintenance of Gateway Cluster
 Workspace contributors or higher need access to
Gateway Data Sources

16. NOW THINGS ARE SIMPLER… OR ARE THEY?

17. ANALYTICS ARCH WITHOUT GATEWAY
Pros
 No infrastructure dependency
 Less steps to deploy new datasets
 Zero resource cost to integrate
 Share datasets across workspaces
Cons
 Cannot enforce credentials used by datasets
 Can introduce inconsistent deployments leading to:
 Dataset sprawl
 Unauthorized access
 Varying Terminology for the same metrics

18. WANT EVEN BETTER PERFORMANCE?

19. ANALYTICS ARCH W/ ANALYSIS SERVICES
Pros
 Best performance possible
 Shared dataset across any workspaces
 Gets around the 1GB / file limitation
Cons
 Relies on AAD and/or AD
 Need to learn Visual Studio
 Different UI
 Different deployment process
 Need to learn Analysis Services
 Performance best practices
 Processing options
 Monitoring and Maintenance

20. DEVELOPER BEST PRACTICES
 Make sure you are Query Folding (pushing down Native queries)
 Create DATE dimension in Snowflake with Fiscal Calendar
 If report is in Direct Query, double-check the Scheduled Cache Refresh Setting is set to Weekly or Monthly. Otherwise YOU
WILL GET CHARGED!
 Use Certification process in Power BI for Certified Datasets

21. DATA GOVERNANCE
w/ Gateway w/o Gateway