7. Choose Best Table Distribution Style
All
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
All data on
every node
Key
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
Same key to
same location
Node 1
Slice
1
Slice
2
Node 2
Slice
3
Slice
4
Even
Round robin
distribution
22. Why Amazon Web Services
Our situation
• A preference for
everything-as-a-service
• No own servers
• No IT department
• Ready to innovate
• Streaming data
• A need for Real-time Insights
AWS offering
• Everything as a service
• Pay by usage
• Infrastructure as code
• Self-service for Dev-team
• Low cost piloting
• Streaming technology
• Analytics backend
23. Why Amazon Redshift
Streaming Data
Event & Time Series Analytics
Real-time Insights
Monthly Reports
Amazon
DynamoDB
Amazon
Redshift
Amazon
EMR
Amazon
Redshift
Amazon
Kinesis
Amazon
Redshift
Analytics Software
support
Real-Time Processing
Amazon
S3
+
24. Up stream vs. Down stream aggregations
Access to hot and cold data
Amazon Redshift vs. Spark
Group by & Window Function
Fully Managed
Amazon
Redshift
25. BI
Reflections on the journey
Analytics Big Data Real-Time
Operations
( CX )
Management
A typical organization
What we did
Operations
( CX )
ManagementBIAnalyticsReal-Time Big Data
26. Several iterations later…
More data streams into Redshift
More applications on top of Redshift
More use cases realized
More money saved
More revenue streams
Less EMR
Soon No more EMR
27. Thank You
Anders Bresell, PhD
Telenor Connexion
Head of Technology Development & Data Science
Data is magic!