1.
news.trivadis.com/blog@trivadis
Data Warehouse – (high) added value in Azure
Cloud
yves.mauron@trivadis.com
marco.amhof@trivadis.com

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)

3.
© Microsoft Corporation
Modern data warehousing pattern
Advanced Analytics
Social
LOB
Graph
IoT
Image
CRM
INGEST STORE PREP MODEL & SERVE
(& store)
Data orchestration
and monitoring
Big data store Transform & Clean Data warehouse
AI
BI + Reporting

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

6.
Ingest

7.
ETL vs. ELT
vs.

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

9.
Pipeline foreach (…)Trigger
Linked
Service

10.
© Microsoft Corporation
No-code data transformation @ scale
Mapping Data Flow
Data cleansing, transformation, aggregation, conversion, etc.
Cloud scale via Spark execution
Easily build resilient data flows

11.
Store

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

14.
Azure SQL Data Warehouse MPP Architecture

15.
© Microsoft Corporation
Data ingestion using external data sources
Polybase
-- Create Azure DataLake Gen2 Storage reference
CREATE EXTERNAL DATA SOURCE AzureStorage with
(
TYPE = HADOOP,
LOCATION='abfss://<container>@<storageaccnt>.blob.core.windows.net'
,
CREDENTIAL = AzureStorageCredential –- not required if using
managed identity
);
-- Type of format in Hadoop (CSV, RCFILE , ORC, PARQUET).
CREATE EXTERNAL FILE FORMAT TextFileFormat WITH
(
FORMAT_TYPE = DELIMITEDTEXT,
FORMAT_OPTIONS (FIELD_TERMINATOR ='|', USE_TYPE_DEFAULT =
TRUE)
)
-- LOCATION: path to file or directory that contains data
CREATE EXTERNAL TABLE [dbo].[CarSensor_Data]
(
[SensorKey] int NOT NULL,
[Speed] float NOT NULL,
[YearMeasured] int NOT NULL
)
WITH (LOCATION='/Demo/’, DATA_SOURCE = AzureStorage,
FILE_FORMAT = TextFileFormat
);
Overview
Polybase supports querying files (Parquet,
Delimited Text) stored in a Hadoop File System
(HDFS), Azure Blob storage, or Azure Data Lake
Store.
To query files, users create three objects:
External data source, external file format,
external table.
Starting in SQL Server 2019, you can now use
PolyBase to access external data in SQL
Server, Oracle, Teradata, and MongoDB.

16.
DWU’s (CPU, memory, and IO) in $$$
SQL Azure Data Warehouse (a preview)

17.
© Microsoft Corporation
It is a central storage repository that holds data coming from many sources in a raw, granular format. It can store structured,
semi-structured, or unstructured data, which means data ingested quickly and can be kept in a more flexible format for
future use cases.
What is a Data Lake?
Characteristics
• Schema-on-read
(ELT)
• Collection of data,
not a platform
• Perfect place for
evolving data
Benefits
• Quickly ingest high
volumes of diverse
data structures
• Enable advanced
analytics and data
exploration
• Scalability and
storage cost
reduction
BestPractices
• Data Governance
needed to avoid
Data Swamp
• Security
considerations
• Design your Data
Lake
• Metadata
management

18.
© Microsoft Corporation
Data Lake Design Considerations
Data Lake Zones
Transient Landing Zone
Temporary storage of data to meet regulatory and quality control
requirements. Limited access. May not be required depending on
requirements.
Raw Zone
Original source of data ready for consumption. Metadata publicly
available but access to data still limited.
Trusted Zone
Standardized and enriched datasets ready for consumption to
those with appropriate role-based access. Metadata available to
all.
Curated/Refined Zone
Data transformed from Trusted Zone to meet specific business
requirements.
Sandbox Zone
Playground for Data Scientists for ad hoc exploratory use cases.
Data Governance Considerations
Security and Compliance
Access Control
Encryption
Row-Level Security
Metadata Management
Data Quality
Metadata Management
Lifecycle Management

19.
Transform & Clean

20.
© Microsoft Corporation
A fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure
Azure Databricks
Best of Databricks Best of Microsoft
Designed in collaboration with the founders of Apache Spark
One-click set up; streamlined workflows
Interactive workspace that enables collaboration between data scientists, data engineers, and business
analysts.
Native integration with Azure services (Power BI, SQL DW, Cosmos DB, ADLS, Azure Storage, Azure Data
Factory, Azure AD, Event Hub, IoT Hub, HDInsight Kafka, SQL DB)
Enterprise-grade Azure security (Active Directory integration, compliance, enterprise-grade SLAs)

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

23.
Model & Serve

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

27.
BI & Reporting

28.
Power BI
Power BI Report Server (on-premise)

29.
Customer success stories

30.
Case 1 – «on-premise» to Azure Cloud
Customer

31.
Case 1 - Approach

32.
Case 1 – Timeline

33.
Case 2 – Modern Data Warehouse – (Delta) Lake

34.
Case 3 – Management Reporting
PowerBI
Excel
…
Sub1
Sub2
Sub n
Structures
Messaging
Model & ServePrep / TrainStoreIngest
SQL DatabaseBLOB Storage
Logic App Function App
Analysis Services
Azure AD

35.
DEMO

36.
Questions?