In dieser Session stellen wir ein Projekt vor, in welchem wir ein umfassendes BI-System mit Hilfe von Azure Blob Storage, Azure SQL, Azure Logic Apps und Azure Analysis Services für und in der Azure Cloud aufgebaut haben. Wir berichten über die Herausforderungen, wie wir diese gelöst haben und welche Learnings und Best Practices wir mitgenommen haben.
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Azure Days 2019: Business Intelligence auf Azure (Marco Amhof & Yves Mauron)
2. Agenda
• The modern data warehouse pattern
• Azure Cloud architecture pillars
• Ingest data (Azure Data Factory, ADF SSIS Runtime Integration, others)
• Store data (Azure SQL, Azure Data Lake, Azure SQL DWH)
• Process data (Azure Databricks)
• Azure SSAS – Advantages and possibilities of a BI semantic layer
• Power BI
• Trivadis Customer Cases
• Case 1 (Lift and Shift on-premise DWH to Azure Cloud)
• Case 2 (BI Solution in Azure Cloud with Data Lake and Databricks Technology)
• Case 3 (Green field a BI Solution from scratch in Azure Cloud)
4. AI built-in | Most secure | Lowest TCO
Data warehouses
Data lakes
Operational databases
Data warehouses
Data lakes
Operational databasesIndustry leader 4 years in a row
#1 TPC-H performance
T-SQL query over any data
70% faster
2x the global reach
99.9% SLA
Easiest lift and shift
with no code changes
The Microsoft offering
SQL Server
Hybrid
Azure Data Services
Security and performanceFlexibility of choiceReason over any data, anywhere
SocialLOB Graph IoTImageCRM
5. The Azure data landscape
Azure
Data
Factory
Azure Import/Export
service
Azure SDKAzure
CLI
Cognitive servicesBot service
Azure Search Azure Data Catalog
Azure ExpressRoute Azure network
security groups
Azure Functions Visual StudioOperations
Management Suite
Azure Active Directory Azure key
management service
Azure Blob Storage Azure Data
Lake Store
Azure IoT Hub Azure event
hubs
Kafka on Azure HDInsight
Azure SQL data warehouseAzure SQL DB Azure Cosmos DB Azure Analysis Services Power BI
Azure
HDInsight
Azure
Databricks
Azure
HDInsight
Azure
Databricks
Azure Stream
Analytics
Azure
ML
Azure
Databricks
ML Server
10
01
SQL
NSG
>_
INGEST STORE PREP MODEL & SERVE
8. Azure Data Factory
• Data Integration Service: Serverless, Scalable, Hybrid
Hybrid Pipeline Model
Seamlessly span: on prem, Azure, other clouds & SaaS
Run on-demand, scheduled, data-availability or on event
Data Movement @Scale
Cloud & Hybrid w/ 80+ connectors provided
Up to 1 GB/s
SSIS Package Execution
Lift existing SQL Server ETL to Azure
Use existing tools (SSMS, SSDT)
Author & Monitor
Programmability w/ multi-language SDK
Visual Tools
Azure
12. Azure SQL Database resource types
Azure SQL Database
Database-scoped
deployment option with
predictable workload
performance
Shared resource model optimized
for greater efficiency of multi-
tenant applications
Best for apps that require resource
guarantee at database level
Best for SaaS apps with multiple
databases that can share resources
at database level, achieving better
cost efficiency
Best for modernization at scale
with low friction and effort
Elastic PoolSingle Managed Instance
Instance-scoped deployment option
with high compatibility with SQL Server
and full PaaS benefits
13. Azure SQL Data Warehouse
Best in class
price-performance
Up to 14X times faster
and 94% less expensive
than cloud competitors
Industry-leading
security
Defense-in-depth
security and 99.9%
financially backed
availability SLA
Intelligent workload
management
Separation of compute
and storage
Prioritize resources for
the most valuable
workloads
Developer productivityData flexibility
21. Azure Databricks
Optimized Databricks Runtime Engine
DATABRICKS I/O SERVERLESS
Collaborative Workspace
Cloud storage
Data warehouses
Hadoop storage
IoT / streaming data
Rest APIs
Machine learning models
BI tools
Data exports
Data warehouses
Azure Databricks
Enhance Productivity
Deploy Production Jobs & Workflows
APACHE SPARK
MULTI-STAGE
PIPELINES
DATA
ENGINEER
JOB SCHEDULER NOTIFICATION & LOGS
DATA
SCIENTIST
BUSINESS ANALYST
Build on secure & trusted cloud Scale without limits
22. Azure Databricks Deployment
Azure Resource
Manager APIs
Azure Portal
Azure
Databricks
Workspace
Managed
Resource
Group
Attached Azure
BLOB (DBFS)
Workspace
VNET
Workspace
NSG rulesCluster Node(s)
Notebooks
Clusters
Jobs
Run on
Interact using UI or Azure Databricks REST API
Integrate with other Azure Services
Azure BLOBs Data Lake
Event Hub IOT Hub Kafka
Cosmos DB SQL DW
Data Factory
24. Why do I also need a cube if I have a data warehouse?
• Semantic layer
• Handle many concurrent users
• Implement complex business logic (DAX)
• Aggregating data for performance
• multidimensional analysis
• No joins or relationships
• Hierarchies, KPI’s
• Row-level Security
• Advanced time-calculations
• Slowly Changing Dimensions (SCD)
• Required for some reporting tools
25. What is Azure Analysis Services?
• Azure Analysis Services is a fully managed platform as a service (PaaS) that provides enterprise-
grade data models in the cloud.
• Use advanced mashup and modeling features to combine data from multiple data sources, define
metrics, and secure your data in a single, trusted tabular semantic data model.
• The data model provides an easier and faster way for users to browse massive amounts of data for
ad hoc data analysis.
26. Business / custom apps
(Structured)
Logs, files and media
(unstructured)
Azure Storage
Polybase
Azure SQL Data Warehouse
Data Factory
Data Factory
Azure Databricks
(Spark)
Analytical dashboards
(PowerBI)
Model & ServePrep & TrainStoreIngest Intelligence
Modern Data Analytics Landscape
AZURE DATA FACTORY ORCHESTRATES DATA PIPELINE ACTIVITY WORKFLOW & SCHEDULING
Azure Analysis ServicesOn Prem, Cloud
Apps & Data