Analytics in a Day Virtual Workshop

Analytics in a Day
Cloud analytics in the age of
self-service and data science

Housekeeping
Please message Sami
with any questions,
concerns or if you
need assistance during
this workshop.
Please mute your line!
We will be applying mute.
This session will be
recorded.
If you do not want to be
recorded, please disconnect at
this time.
Links:
See chat window.
Worksheet:
See handouts.
To make presentation
larger, draw the
bottom half of screen
‘up’.

Agenda
9:00 – 10:00 The heart of analytics – why modernize the data warehouse?
10:00 – 11:00 Optimizing analytics with Azure Synapse Analytics
11:00 – 11:30 Data Integration in Azure Synapse Analytics
11:30 – 12:00 Insights for all with Power BI + Azure Synapse
Times are approximate and will be fluid with the workshop

Virtual Introductions
▸ Name, Company & Title
▸ What do you hope to get out of today’s workshop?

James McAuliffe,
Cloud Solution Architect
James McAuliffe is a Cloud Solution Architect with over 20 years of technology
industry experience. During this journey into data and analytics, he’s held all
of the traditional Business Intelligence Solution project roles, ranging from
design and development to complete life cycle BI implementations. He is a
Microsoft Preferred Partner Solutions expert and has worked with clients of all
sizes, from local businesses to Fortune 500 companies.
And I like old Italian cars.
linkedin.com/in/jamesmcauliffesql/

Analytics Strategy
• Data Governance Solution
• Data Privacy Solution
• Analytics Roadmap Solution
Services
• Health Assessments
• Analytics Roadmaps
• Data Governance
• Data Privacy
• Master Data Management
• Metadata Management
Analytics and Insights
• Customer Intelligence Solution
• Visualization & Reporting Solutions
Services
• Dashboards & Visualizations
• Operational Reporting
• Data Exploration
• Customer Insights
• Marketing Analytics
Data Science
• Machine Learning Solution
• Model As A Service
Services
• Predictive Analytics
• Prescriptive Analytics
• Azure Cognitive Services
• Natural Language Processing
• Computer Vision / Image
• ML Ops
• Data Mining
• Data Science Enablement
• Data Science Roadmap
• Data Science Center of Excellence
Services and Solutions
Data Management
• Platform Modernization Solution
• Cloud Migration Solution
• Cloud Management Solution
Services
• DR/BC
• Security
• Azure Governance
• Data Warehousing
• Data Integration
• Data Architecture
• PowerApps

Why Modern Data Estate?
Why Unified Data Platform?

10%
of organizations are expected
to have a highly profitable
business unit specifically for
productizing and
commercializing data by 2020
$100M
The most digitally transformed
enterprises generate on
average $100 million in
additional operating income
each year
5,247GB
Approximate amount of data
for every man, woman and
child on earth in 2020
Data is a key strategic asset

Data Landscape – Volume and Pressure
IDC Data Age 2025 - The Digitization of the World

Data Landscape - Different Types of Data
• Mobile
• Social
• Scanners
• Sensors
• RFID
• Devices - IoT
• Feeds/APIs
• Other, non-traditional sources
85%

The heart of analytics
Section 1
Data businesses need
data warehouses
Section 2
Data warehouses &
data lakes come together
Section 3
BI & DW come together
Section 4
The cloud for modern analytics
Section 5
A new class of analytics

Section 1
Data businesses need
data warehouses

Is the data warehouse
still relevant?
What’s changed since 1988?
A 30-year-old architecture, still going strong
Commerce and technology
The data warehouse itself

Today, all businesses are
data businesses
Data is the lifeblood of modern work

All data businesses need
to be analytic businesses
Without analytics data is a cost center,
not a resource

Analytic businesses need
to evolve data science
Every business has opportunities to make
analytics faster, easier, and more insightful

Store
Data Ingestion Big Data Data Warehousing
The cloud data warehouse in the data-driven business

Store

Store
Cloud data
SaaS data
On-premises data
Devices data

Azure Synapse Analytics
Store

Store

Section 2
Data warehouses &
data lakes come together

80%
report struggling to
become mature users
of data*
55%
report data silos and
data management
difficulties as roadblocks*
* Harvard Business Review (2019), Understanding why analytics strategies fall short for some, but not for others
Analytics & AI is the #1 investment for business leaders,
however they struggle to maximize ROI

Big data
Experimentation
Fast exploration
Semi-structured
Data science
OR
Relational data
Proven security & privacy
Dependable performance
Structured
Business analytics
Data lake Data warehouse
Businesses are forced to maintain
two critical, yet independent analytics systems

©Microsoft Corporation
Azure
It’s a challenge to integrate these areas with security
Big data
Experimentation
Fast exploration
Semi-structured
Data science
OR
Relational data
Structured
Business analytics
Securing a business-critical
environment needs enterprise-
class features such as row and
column security
Relying on views only increases the
number of artifacts to be managed

Azure
Big data
Experimentation
Fast exploration
Semi-structured
Data science
OR
Relational data
Structured
Business analytics
column security
Securing a platform used for
both experimentation and
production demands the ability
to discover sensitive data
You can’t secure what you don’t know

Azure
Big data
Experimentation
Fast exploration
Semi-structured
Data science
OR
Relational data
Structured
Business analytics
column security
Securing a platform used for
both experimentation and
production demands the ability
to discover sensitive data
You can’t secure what you don’t know
User management in an
environment with diverse use
cases requires integrated
enterprise authentication

Azure
It’s a challenge to manage these diverse workloads
Big data
Experimentation
Fast exploration
Semi-structured
Data science
OR
Relational data
Structured
Business analytics
In this mixed
environment, not all
workloads have the
same priority
Mission-critical warehouse jobs are demanding and
predictable
Exploratory analytics and data science are important
but unpredictable
Executive queries are high-profile, although rare

Azure
It’s a challenge to manage these diverse workloads
Big data
Experimentation
Fast exploration
Semi-structured
Data science
OR
Relational data
Structured
Business analytics
In this mixed
environment, not all
workloads have the
same priority
Mission-critical warehouse jobs are demanding and
predictable
Exploratory analytics and data science are important
but unpredictable
Executive queries are high-profile, although rare
Throwing multiple
clusters at these
scenarios is easy,
especially in
lightweight scenarios,
however:
Each cluster has a cost
Bringing a cluster online has a lag, which can impact
important, if rare, workloads
It takes compute to maintain large caches

Azure
It’s a challenge to build integrated lifecycle management
Big data
Experimentation
Fast exploration
Semi-structured
Data science
OR
Relational data
Structured
Business analytics
Data Scientists
need a real
data lake
Query over open file formats such as Parquet and
ORC natively without loading the data to a
proprietary cluster
Enable data science platforms and the EDW to share a
common data set

Azure
It’s a challenge to build integrated lifecycle management
Big data
Experimentation
Fast exploration
Semi-structured
Data science
OR
Relational data
Structured
Business analytics
Developers need
real development
tools
Version control
Continuous integration and deployment
Unit testing, integration testing and load testing
Data Scientists
need a real
data lake
Query over open file formats such as Parquet and
ORC natively without loading the data to a
proprietary cluster
Enable data science platforms and the EDW to share a
common data set

Azure
Welcome to limitless
Ease of use
Fast exploration
Quick to start
Proven security
Airtight privacy
Data warehousing & big data analytics—all in one service
Azure meets these challenges,
with a single service to provide limitless analytics

Section 3
Azure meets these challenges,
with a single service to provide limitless analytics

Section 3

• “Relational” stores.
• Most work is on gathering from other disparate stores, and known, structured files, from 3NF into Dimensional (star)
• Typically there is an OLAP (cube, semantic) solution in the mix, consumed by a reporting layer
• Typically these are on-premise, but not always, and can be cloud based
• Technologies vary, but are usually OLEDB, ODBC, File connections. Typically interacting with some form of
• LOE varies, and tools can be disparate
Traditional RDBMS Approach to Data Warehouse Reporting
ata Warehouse nal sis e orting

Advanced, Mature (Legacy) Hub and Spoke Architecture

Basic Reporting Unit - Star “Platform” or Star “Fabric”

The new economy
thrives on data literacy
Communicating with data is a critical skill in
the new economy

Users and IT must come
together in the new enterprise
Get over the IT / business divide

Governance and self-service
enhance decision-making
Governance is not about making the right decisions,
it is about making decisions the right way

The importance of data models
BI models Power BI
• Built and maintained by business users or BI developers
• Use enterprise models, departmental data, and external sources
• Focused on a single subject area, but often widely shared
Machine Learning
models
Azure Synapse
Analytics
• Built and maintained by data scientists
• Mostly developed from raw sources in the data lake
• Often experimental, needing a data engineer for production use
Azure Synapse
AnalyticsEnterprise models
• Built and maintained by IT architects
• Consolidated data from many systems
• Centralized as an authoritative source for reporting and analysis

Enterprise models in the
self-service environment
If business users
are tech-smart and
data literate, why
do they need
enterprise models?
Consistency
Some business processes can be built once and shared as a
corporate standard
Governance
Certain data sets need complex security and privacy controls
Efficiency
No need to repeat design, preparing, and loading or securing
Line-of-business sources
Data ingestion &
transformation
Enterprise models
Azure Synapse
Analytics
Power BI

BI models in the enterprise
environment
If enterprise
models are so
important, why do
users need self-
service BI models?
Flexibility
Some data sets are temporary, external, or ad-hoc don’t need to
be consolidated
Efficiency
Tech-smart business users have fresh and innovative ideas they need to
explore with agility
Ad-hoc, departmental and
external sources
Line-of-business sources
Data ingestion &
transformation
Power BI
Enterprise models
Azure Synapse
Analytics
BI models

Section 4
Data science models in the
enterprise environment
What is the role of
the data
warehouse with
data science?
Integrating results with enterprise models
Making the results of data science easily available for business functions
Serving enterprise data for data scientists
Helps ensure consistency across diverse analyses
Power BI
Azure Synapse
Analytics
Azure
Databricks
Enterprise models
Azure Synapse
Analytics
Data science results

Section 4

Cloud Statistics
• Cloud data centers will process 94% of workloads in 2021 (Source: Cisco)
• Main reason for cloud adoption (Source: Sysgroup)
o Access to data anytime (42%)
o Disaster recovery (38%)
o Flexibility (37%)
• The US is the most significant public cloud market with an expected spending of $124.6 billion in 2019 (Source:
IDC)
1. United States – $124.6 billion
2. China – $10.5 billion
3. UK – $10 billion
4. Germany – $9.5 billion
5. Japan – $7.4 billion

Modern businesses
succeed in the cloud
The cloud is the default environment for
new technology initiatives

Cloud security offers a
new level of protection
Businesses benefit from built-in security
found only in the cloud

Price, performance, and agility
A cloud analytics platform
is an economic breakthrough

Structured, unstructured, and streaming data
integrated in a single, scalable, environment
A cloud analytics platform
is the hub for all data models

BI
Bring together the best of both worlds with the market-
leading BI service and the industry-leading analytics platform
Power BI can analyze and visualize
massive volumes of data
Azure Synapse Analytics provides a
scalable platform to enable real-time BI
Analytics

Section 5
Power BI can analyze and
visualize massive volumes of data
provides a scalable platform
to enable real-time BI
Azure Machine Learning natively
integrates with Azure Synapse &
Power BI to democratize AI across
your business
BI Analytics Machine learning

Section 5

Is the data warehouse
still relevant?
The data warehouse itself
Commerce and technology
What’s changed since 1988?
A 30-year-old architecture, still going strong

Unified experience
Azure Synapse Studio
Integration Management Monitoring Security
Analytics runtimes
SQL
Azure Data Lake Storage
Azure Machine
Learning
On-premises data
Cloud data
SaaS data
Streaming data
Power BI
Azure Synapse lies at the heart of business, AI, and BI

Unified experienceAzure Synapse Studio
Integration Management Monitoring SecuritySQL
Azure Machine
Learning
On-premises
data
Cloud
data
SaaS data
Streaming
data
Cloud analytics has taken a leap forward
with a unified, unmatched platform
Power BI

Limitless analytics service with unmatched
time to insight

Introducing Azure
Synapse Analytics
A limitless analytics service with unmatched
time to insight, that delivers insights from all
your data, across data warehouses and big
data analytics systems, with blazing speed
Simply put, Azure Synapse is Azure SQL Data
Warehouse evolved
We have taken the same industry leading data
warehouse and elevated it to a whole new level of
performance and capabilities

Azure Synapse
Analytics
Snowflake
Standard
Amazon
Redshift
Google
BigQuery
per byte
$33
$103
$48
…$564
94% less
TPC-H benchmark comparison
Price-performance | Lower is better
* GigaOm TPC-H benchmark report, January 2019, “GigaOm report: Data Warehouse in the Cloud Benchmark
With the best price-performance
in the business
Up to 14x faster and costs 94%
less than other cloud providers
A breakthrough in the cost of enterprise analytics

Data consolidation using
Migration to the cloud for
efficient business operations
Using Azure Synapse Analytics
for predictive analytics
Organizations that fully harness their data outperform

t the core of all use cases is…Azure Synapse Analytics
Real-time
analytics
Modern data
warehousing
Advanced
analytics
"We want to analyze
data coming from
multiple sources and
in varied formats"
"We want to leverage
the analytics platform
for advanced fraud
detection"
“We’re trying to get
insights from our
devices in real-time”
Cloud-scale analytics

Store
Ingest Transform Model & serve Visualize
Modern Data Warehouse

Synapse SQL
Apache Spark
for Synapse
Synapse Pipelines Synapse Studio

Query and analyze data with
T-SQL using both provisioned
and serverless models
Quickly create notebooks with
your choice of Python, Scala,
SparkSQL, and .NET for
Apache Spark
Build end-to-end workflows
for your data movement and
data processing scenarios
Execute all data tasks with a
simple UI and unified
environment
Synapse SQL
Apache Spark
for Synapse
Synapse Pipelines Synapse Studio

Integrated analytics platform for AI, BI, and continuous intelligence
Platform
Azure
Data Lake Storage
Common Data Model
Enterprise Security
Optimized for Analytics
Data lake integrated and Common Data
Model aware
METASTORE
SECURITY
MANAGEMENT
MONITORING
Integrated platform services
for, management, security, monitoring,
and metastore
DATA INTEGRATION
Analytics Runtimes
Integrated analytics runtimes available
provisioned and serverless
Synapse SQL offering T-SQL for batch,
streaming, and interactive processing
Synapse Spark for big data processing
with Python, Scala, R and .NET
PROVISIONED (DW) SERVERLESS
Form Factors
SQL
Languages
Python .NET Java Scala R
Multiple languages suited to different
analytics workloads
Experience Synapse Studio
SaaS developer experiences for code
free and code first
Artificial Intelligence / Machine Learning / Internet of Things
Intelligent Apps / Business Intelligence
Designed for analytics workloads at any
scale

Integrated analytics platform for AI, BI, and continuous intelligence
Platform
Azure
Data Lake Storage
Common Data Model
Enterprise Security
Optimized for Analytics
METASTORE
SECURITY
MANAGEMENT
MONITORING
DATA INTEGRATION
Analytics Runtimes
PROVISIONED (DW) SERVERLESS
Form Factors
SQL
Languages
Python .NET Java Scala R
Experience Synapse Studio
Artificial Intelligence / Machine Learning / Internet of Things
Intelligent Apps / Business Intelligence
Connected Services
Azure Data Catalog
Azure Data Share
Azure Databricks
Azure HDInsight
Azure Machine Learning
Power BI
3rd Party Integration

Synapse Studio

Synapse Studio is divided into
Activity hubs
Hubs organize the tasks needed for
building analytics solutions
Synapse Studio
Overview Data
Monitor Manage
Quick-access to common
gestures, most-recently used
items, and links to tutorials
and documentation.
Explore structured and
unstructured data
Centralized view of all resource
usage and activities in the
workspace.
Configure the workspace,
pool, access to artifacts
Develop
Write code and the define
business logic of the pipeline
via notebooks, SQL scripts,
Data flows, etc.
Orchestrate
Design pipelines that that
move and transform data.

Start coding immediately
Begin with SQL scripts, notebook,
data flow and more
Overview hub

Explore data inside the workspace
and in linked storage accounts
Data Hub

Explore data inside the workspace
and in linked storage accounts
Data Hub
ADLS Gen2 Account
Container (filesystem)
Filepath

Preview a sample of your data
Data Hub –
Storage accounts

Manage access and configure
standard POSIX ACLs on files and
folders
Data Hub –
Storage accounts

Analyze SQL scripts or notebooks
with two simple actions
Autogenerate T-SQL or PySpark
Data Hub –
Storage accounts

SQL pool
SQL serverless
Apache Spark
Explore workspace databases
Databases

Author SQL Scripts
Execute SQL script on provisioned SQL
Pool or SQL Serverless
Publish individual SQL script or multiple
SQL scripts through Publish all feature
Support for languages and Intellisense
Develop hub -
SQL scripts

View results in table or chart form and
export results in several popular formats
Develop hub -
SQL scripts

Data flows are a visual way of
specifying how to transform data,
providing a code-free experience
Develop hub -
Data flows

Develop hub –
Power BI
Create Power BI reports in the workspace
Provide access to published reports in the
workspace
Update reports in real time from Synapse
workspace and show on Power BI service
Visually explore and analyze data

Synapse SQL

Best-in-class
Price-performance is calculated by GigaOm as the TPC-H metric of cost of ownership divided by composite query.
Results based on GigaOm’s TPC-H results, published in January 2019
Leader in price per performance

Amazon Redshift
$0
$10
$20
$30
$40
$50
$60
$550
$600
$40
$33
$47
$54
$48
$51
$564
Price-performance @ 30TB
Lower is Better
Google BigQueryAzure Synapse Analytics Snowflake
$103
$110
$152
$80
$100
$120
$140
Best-in-class

Price-performance @ 30TB
Lower is Better
Amazon
Redshift
Google BigQuery
Flat Rate
Azure Synapse
Analytics
Google BigQuery
Flat Rate
Snowflake
Standard
$1310
$570
$309
$206
$286
$153
$0
$100
$200
$300
$400
$500
$600
Snowflake
Standard
Best-in-class

Benchmark
Data Warehouse in the Cloud Benchmark

--T-SQL syntax for scoring data in SQL DW
SELECT
d.*, p.Score
FROM PREDICT(MODEL = @onnx_model, DATA = dbo.mytable AS
d)
WITH (Score float) AS p;
Upload
models
Machine learning
enabled DW
Native PREDICT-ion
T-SQL based experience
(interactive/batch scoring)
Interoperability with other
models built elsewhere
Scoring executed where the
data lives
T-SQL Language
Data Warehouse
Data
+
Score models
Model Predictions
=
Synapse SQL
Create models

Event Hubs
IoT Hub
T-SQL language
Built-in streaming ingestion & analytics
Streaming Ingestion Data Warehouse
Synapse SQL
Heterogenous
data preparation
and ingestion
Native SQL streaming
High throughput ingestion
(up to 200MB/sec)
Delivery latencies in seconds
Ingestion throughput scales with
compute scale
Analytics capabilities

Empower more users
per data warehouse
Leverage up to 128 concurrent
slots, simultaneously, on a single
data warehouse
Number of simultaneous workloads
increases with data warehouse capacity
Utilize preset functions to allocate
resources that need them the most

Intra cluster workload isolation
(Scale in)
Marketing
CREATE WORKLOAD GROUP Sales
WITH
(
[ MIN_PERCENTAGE_RESOURCE = 60 ]
[ CAP_PERCENTAGE_RESOURCE = 100 ]
[ MAX_CONCURRENCY = 6 ] )
40%
Data
warehouse
Local In-Memory + SSD Cache
Compute
1000c DWU
60%
Sales
60%
100%
Workload aware
query execution
Workload isolation
Multiple workloads share
deployed resources
Reservation or shared resource
configuration
Online changes to workload policies

Cluster N
Multi-clusters
(Scale out)
Sales Marketing
Finance
Data Warehouses
Workload
Management
Scale-out Clusters
Independent elasticity,
pause, and resume
Highest performance
Physical workload isolation
Highest concurrency
Chargeback per cluster

Benefits:
• Most predictable cost
• Most efficient for unpredictable workloads
• No cache eviction for scaling (no performance cliff)
• Workload isolation
• Single endpoint (auto isolation with classification)
Benefits:
• Maximize cluster throughput
• Workload aware query scheduling
• Fine grained cluster scaling
Benefits:
• Best performance
• Physical workload isolation
• Chargeback
• Highest concurrency
Intra-cluster workload isolation
(scale in)
Marketing
Sales
60%
40%
Data
Warehouse
Autonomous workload balancing
Cluster
1
Cluster
2
Cluster
3
Data
Warehouse
Cluster
N
Multi-clusters
(scale out)
Data
Warehouse

CREATE MATERIALZIED VIEW vw_ProductSales
WITH (DISTRIBUTION = HASH(ProductKey))
AS
SELECT
ProductName
ProductKey,
SUM(Amount) AS TotalSales
FROM
FactSales fs
INNER JOIN DimProduct dp ON fs.prodkey = dp.prodkey
GROUP BY
ProductName,
ProductKey
See more by scaling
to petabytes

ProductName ProductKey TotalSales
Product A 5453 784,943.00
Product B 763 48,723.00
… … …
FactSales
Table
10B Records
DimProduct
Table
1,000 Records
Materialized View
(1000 Records)
See more by scaling
to petabytes
FactInventory
Table
mvw_ProductSales
1,000 Records
CREATE MATERIALZIED VIEW
mvw_ProductSales
WITH (DISTRIBUTION = HASH(ProductKey))
AS
SELECT
ProductName
ProductKey,
FROM
FactSales fs
INNER JOIN DimProduct dp
ON fs.prodkey = dp.prodkey
GROUP BY
ProductName,
ProductKey
SELECT
<COLUMNS>
FROM FactSales fs
INNER JOIN
SELECT
ProductName
ProductKey,
FROM
FactSales fs
INNER JOIN DimProduct dp
GROUP BY
ProductName,
ProductKey ) ps
INNER JOIN FactInventory
GROUP BY …

Execution 2
Cache Hit
~.2 seconds
Execution 1
Cache Miss
Regular
Execution
SELECT
ProductName
ProductKey,
FROM
Fact Sales
INNER JOIN DimProduct
GROUP BY
ProductName,
ProductKey
Build confidence in your
data with result set cache
Data
Warehouse
Resultset
Cache

Most secure data
warehouse in the cloud
Multiple levels of security between the
user and the data warehouse
...at no additional cost
Threat Protection
Network Security
Authentication
Access Control
Data Protection
Customer Data

Comprehensive security
Category Feature
Data protection
Data in transit
Data encryption at rest
Data discovery and classification
Access control
Object level security (tables/views)
Row level security
Column level security
Dynamic data masking
SQL login
Authentication Azure active directory
Multi-factor authentication
Virtual networks
Network
Ssecurity
Firewall
Azure ExpressRoute
Threat detection
Threat protection Auditing
Vulnerability assessment

Synapse SQL (serverless)

Discovery and
exploration
What’s in this file? How many rows are there? What’s the max value?
SQL serverless reduces data lake exploration to the right-click
Data
transformation
How to convert CSVs to Parquet quickly? How to transform the raw data?
Use the full power of T-SQL to transform the data in the data lake

Overview
An interactive query service that provides T-SQL
queries over high scale data in Azure Storage.
Benefits
Serverless
No infrastructure
Pay only for query execution
No ETL
Offers security
Data integration with Databricks, HDInsight
T-SQL syntax to query data
Supports data in various formats
(Parquet, CSV, JSON)
Support for BI ecosystem
Azure Storage
SQL
Serverless
Query
Power BI
Azure Data
Studio
SSMS
DW
Read and write
data files
Curate and
transform data
Sync table
definitions
Read and write
data files
Azure Synapse Analytics > SQL > SQL serverless

Apache Spark for Synapse

Allows multiple languages in one notebook
%%<Name of language>
Offers use of temporary tables across languages
Support for syntax highlight, syntax error, syntax code
completion, smart indent, and code folding
Export results
Quickly create &
configure notebooks

As notebook cells run, the underlying
Apache Spark application status is
shown, providing immediate feedback
and progress tracking.
Quickly create &
configure notebooks

Data Integration and Synapse Pipelines

Overview
Linked services defines the connection
information needed for pipelines to connect to
external resources
Benefits
Offers 85+ pre-built connectors
Allows easy cross platform data migration
Represents data store or compute resources

Prep and transform data
Mapping dataflow
Code free data transformation at scale
Wrangling dataflow
Code free data preparation at scale

Handle upserts,
updates, deletes
on sql sinks
Add new partition
methods
Add schema
drift support
Add file handling (move
files after read, write files
to file names described
in rows, etc.)
New inventory of
functions (e.g. Hash
functions for row
comparison)
Commonly used ETL
patterns (Sequence
generator/Lookup
transformation/SCD…)
Data lineage – Capturing
sink column lineage &
impact analysis
(invaluable if this is for
enterprise deployment)
Implement commonly
used ETL patterns as
templates (SCD type1,
type2, data vault)
Data flow
Capabilities

Insights for all with
Power BI + Azure
Power up your BI with Azure Synapse

2020 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms

Where do you find yourself on the curve?
Hindsight Insight Foresight
Value
Difficulty
What happened?
Descriptive Analysis
Why did it happen?
Diagnostic Analysis
What will happen?
Predictive Analysis
How can we make it happen?
Prescriptive Analysis

Where do you find yourself on the curve?
Value
Difficulty
What happened?
Why did it happen?
Diagnostic Analysis
What will happen?
Predictive Analysis
BI

BI + Analytics unlock the door to AI, machine learning, and
real-time insights
Value
Difficulty
What happened?
Why did it happen?
Diagnostic Analysis
What will happen?
Predictive Analysis
AnalyticsBI

Power BI can analyze and
visualize massive volumes of data
provides a scalable platform
to enable real-time BI
Azure Machine Learning natively
integrates with Azure Synapse &
Power BI to democratize AI across
your business
BI Analytics Machine learning

Accelerate business value with a powerful analytics platform
Business analysts IT professionals Data scientists
Frictionless
collaboration
Unified
analytics platform
Advanced analytics
and AI
Powerful visualization and
reporting
Unmatched
capabilities
Business value
Common Data Model on Azure Data Lake StorageUnified data
Azure Synapse AnalyticsPower BI
Powerful and
integrated
tooling
Azure Machine Learning

Visualize and
report
Power BI
Model &
serve
Azure Synapse
Analytics
CDM folders
Azure Data Lake
Storage
Respond instantly
Enable instant response times with
Power BI Aggregations on massive
datasets when querying at the
aggregated level
Get granular with your data
Queries at the granular level are
sent to Azure Synapse Analytics
with DirectQuery leveraging its
industry-leading performance
Save money with industry-
leading performance
Azure Synapse Analytics is up to
14x faster and 94% cheaper than
other cloud providers
View reports with a single pane
of glass
Skip the configuration when
connecting to Power BI with
integrated Power BI-authoring
directly in the Azure Synapse Studio
Accelerate business value with a powerful analytics platform

Customers using Azure Synapse & Power BI today
are transforming their business with purpose
27%
Faster time
to insights
271% Average ROI
26%
Lower total cost
of ownership
60%
Increased customer
satisfaction
* Forrester, October 2019, “The Total Economic Impact of Microsoft Azure Analytics with Power BI”

Build Power BI dashboards directly
from Azure Synapse
Azure Synapse + Power BI integration

View published reports in Power BI workspace
Azure Synapse + Power BI

Edit reports in Synapse workspace

Real-time publish on save

A Walk Around Azure Synapse
Studio
My Azure Synapse Studio

Hands-on lab – Coming Soon!
Build an end-to-end analytics solution in the Azure Synapse Studio
Join Us July 9 at 10:00 AM

Exercise 1 - Explore the data lake with Azure Synapse SQL On-demand and Azure Synapse Spark
Exercise 2 - Build a Modern Data Warehouse with Azure Synapse Pipelines
Exercise 3 - Power BI integration
Exercise 4 - High Performance Analysis with Azure Synapse SQL Pools
Exercise 5 - Data Science with Azure Synapse Spark

Analytics in a Day
Thank You!
James McAuliffe
jmcauliffe@ccganalytics.com
https://www.linkedin.com/in/jamesmcauliffesql/
https://ccganalytics.com/

Get Started Today
Create a free Azure account and get started with Azure Synapse Analytics:
https://azure.microsoft.com/en-us/free/synapse-analytics/
Get in touch with us:
https://info.microsoft.com/ww-landing-contact-me-azure-analytics.html
Learn more:
https://aka.ms/synapse
Get the Azure Synapse Analytics Toolkit

Power BI COVID Crisis Response Resources
Power BI & COVID-19
Keeping citizens informed
Find out more at: https://aka.ms/pbicovid19
Crisis Communications App
https://aka.ms/crisis-communication-app-docs
Emergency Response Solution
https://aka.ms/emergency-response-doc

The Ignite Book of News
https://news.microsoft.com/wp-content/uploads/prod/sites/563/2019/11/Ignite-2019-Book-of-News-2.pdf

Get the Azure Synapse Analytics Toolkit
Azure Synapse is Azure SQL Data Warehouse evolved
Analytics Primer in 60 minutes with Microsoft Azure
Accelerate Time to Analytics with Azure Synapse Analytics
Build 2020
Data Warehouse in the Cloud Benchmark
Overview of Microsoft Azure compliance
Microsoft Compliance Offerings
2020 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms
The Digitization of the World from Edge to Core
The Total Economic Impact of Microsoft Azure Analytics with Power BI
Azure Data Factory Overview
Power BI Governance Admin
References and Links

Microsoft Gold Partner – HQ in Tampa, FL
Flexible with
High Touch
Services and
Measurable
Outcomes
Emphasis on
Business
Outcomes,
Communication
and Strategy
Industry
focus: Retail,
Financial
Services,
Manufacturing,
and Professional
Services
Deep
Expertise in
Analytics,
Cloud and
Information
Management
with expert
SMEs
CCG helps organizations become more insights-driven, solve complex business challenges
and accelerate growth.

HIPAA /
HITECH
IRS 1075 Section 508
VPAT
ISO 27001 PCI DSS Level 1SOC 1 Type 2 SOC 2 Type 2 ISO 27018Cloud Controls
Matrix
Content Delivery and
Security Association
Singapore
MTCS Level 3
United
Kingdom
G-Cloud
China Multi
Layer Protection
Scheme
China
CCCPPF
China
GB 18030
European Union
Model Clauses
EU Safe
Harbor
ENISA
IAF
Shared
Assessments
ITAR-ready
Japan
Financial Services
FedRAMP JAB
P-ATO
FIPS 140-2 21 CFR
Part 11
DISA Level 2FERPA CJIS
Australian
Signals
Directorate
New Zealand
GCIO
Industry-leading compliance

Threat Protection
Threat Protection - Business requirements
Network Security
Authentication
Access Control
Data ProtectionHow do we enumerate
and track potential SQL
vulnerabilities?
To mitigate any security
misconfigurations before they
become a serious issue.
How do we discover and
alert on suspicious
database activity?
To detect and resolve any data
exfiltration or SQL injection attacks.

✓ Automatic discovery of columns with
sensitive data
✓ Add persistent sensitive data labels
✓ Audit and detect access to the sensitive data
✓ Manage labels for your entire Azure tenant
using Azure Security Center
SQL Data Discovery & Classification
Discover, classify, protect and track access to sensitive data

SQL Data Discovery & Classification - setup
Step 1: Enable Advanced Data Security
on the logical SQL Server
Step 2: Use recommendations and/or manual classification to
classify all the sensitive columns in your tables

SQL Data Discovery & Classification – audit sensitive data access
Step 1: Configure auditing for your target Data warehouse. This can be
configured for just a single data warehouse or all databases on a server.
Step 2: Navigate to audit logs in storage account and
download ‘xel’ log files to local machine.
Step 3: Open logs using extended events viewer in SSMS.
Configure viewer to include ‘data_sensitivity_information’ column

Single Sign-On
Implicit authentication - User provides
login credentials once to access Azure
Synapse Workspace
AAD authentication - Azure Synapse
Studio will request token to access each
linked services as user. A separate token is
acquired for each of the below services:
1. ADLS Gen2
2. Azure Synapse Analytics
3. Power BI
4. Spark – Spark Livy API
5. management.azure.com – resource
provisioning
6. Develop artifacts – dev.workspace.net
7. Graph endpoints
MSI authentication - Orchestration uses
MSI auth for automation

The data warehouse in the data-driven business
Azure Synapse
Analytics
Azure
Databricks
Azure Data
Lake Storage
Business
services
Power BI
Transform
and enrich
PrepareIngest
Azure
Data Factory

F’s execution engine
• Data movement
• Pipeline activity execution
• SSIS package execution
Azure
Integration runtime
Self-hosted
Integration runtime
Cloud services
Apps & Data
Pipeline SSIS package
Command
and control
LEGEND
Data
Integration Runtime (IR)
Azure Data Factory v2 Service Scheduling | Orchestration | Monitoring
UX & SDK Authoring | Monitoring/Management

Serverless, scalable, hybrid data integration service
Lift existing SQL Server ETL
to Azure
Use existing tools
(SSMS, SSDT)
Azure Data Factory
Cloud and hybrid w/
80+ connectors
Up to 2 GB/s ETL/ELT
in the cloud
Seamlessly span on-prem,
Azure, other clouds, SaaS
Run on-demand, scheduled,
or on-event data-availability
Programmability with
multi-language SDK
Visual tools
Data movement
and transformation
at scale
Hybrid
pipeline model
Author
and monitor
SSIS package
execution

No-code data transformation at scale
Focus on building business
logic and transforming data
• Data cleansing, transformation,
aggregation, conversion, etc.
• Cloud scale via Spark execution
• Resilient data flows with ease

Wrangling dataflow
Code-free data
preparation @scale

Best-in-class monitoring and management
Monitor pipeline and
activity runs
Query runs with rich language
Operational lineage between
parent-child pipelines
Azure Monitor Integration
• Diagnostics logging
• Metrics and alerts
• Events
Restate pipeline and activities

Use templates to quickly get started
Quickly build data
integration solutions
Avoid rebuilding workflows—
instantiate a template
Improve developer productivity
and reducing development
time for repeat processes

Pipelines
Overview
It provides ability to load data from storage
account to desired linked service. Load data by
manual execution of pipeline or by
orchestration
Benefits
Supports common loading patterns
Fully parallel loading into data lake or SQL
tables
Graphical development experience

Triggers
Overview
Triggers represent a unit of processing that
determines when a pipeline execution needs to be
kicked off.
Data Integration offers 3 trigger types as –
1. Schedule – gets fired at a schedule with
information of start date, recurrence, end date
2. Event – gets fired on specified event
3. Tumbling window – gets fired at a periodic time
interval from a specified start date, while
retaining state
It also provides ability to monitor pipeline runs and
control trigger execution.

Prep & Transform Data
Overview
It offers data cleansing, transformation,
aggregation, conversion, etc
Benefits
Cloud scale via Spark execution
Guided experience to easily build resilient data
flows
Flexibility to transform data per user’s comfort
Monitor and manage dataflows from a single
pane of glass

Coming Later This Summer
Synapse will collect query patterns in order to create materialized views
Composite Models
Microsoft Information Protection improvements

Power BI service
Cloud-based SaaS solutions
Get started quickly
Secure, live connection to your data sources,
on-premises and in the cloud
Auto insights and intuitive data exploration using
natural language query
Deliver insights through other services such as
SharePoint, PowerApps & Teams
Pre-built dashboards and reports for popular SaaS
solutions
Sharing and collaboration of dashboards, reports & datasets
Live, real-time dashboard updates

Deliver insights through other services
Collaborate and share insights with teams in your
organization using existing services
Fully interactive reports integrated into your service

Data Connectivity Modes in Power BI Desktop
Import DirectQuery Live/Exploration
Overview
• ETL
• Data download
• Select specific tables
• No data download
• Queries triggered from
Report visuals
• Explore source objects from
Report surface
• No data download
• Queries triggered from
Report visuals
Supported Data Sources • All sources (>80 sources)
• SQL Server
• Azure SQL Database
• Azure SQL Data Warehouse
• SAP HANA
• Oracle
• Teradata
• SQL Server Analysis Services
(Tabular & Multidimensional)
Max # of data sources per report • Unlimited • One One
Data Transformations • All transformations (100’s)
• Partial support
(varies by data source)
None
Mashup Capabilities
• Merge (Joins)
• Append (Union)
• Parameterized queries
• Merge (Joins)
• Append (Union)
None
Modeling Capabilities
• Relationships
• Calculated Columns & Tables
• Measures
• Hierarchies
• Calculated Columns
• Measures
• Change Column Types
None
With Power BI Desktop,
you can connect to
your data in three ways:
• Import
• DirectQuery
• LiveConnect

Dedicated resources in the cloud
Flexibility to license by capacity
Greater scale and performance
Extending on-premises capabilities
Premium capacity – P3
My workspace
User 2
My workspace
User 3
App workspace
Marketing
App workspace
Sales
My workspace
User 1
APIs
Custom app
Power BI service – Contoso organization
Power BI Premium

Analytics in a Day Virtual Workshop

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