Azure Synapse Analytics is an integrated platform that combines big data, data warehousing, and data integration for comprehensive analytics. It offers serverless and dedicated models, advanced workload management, and enhanced security features to ensure efficient resource utilization and compliance. Additionally, it provides tools for machine learning, predictive analytics, and real-time operational insights, democratizing data science across organizations.

1. Azure Synapse Analytics
Hemant Das
Cloud Solution Architect

2. LOB
CRM
Social
IoT & Edge Devices
Media
Logs
Data Sources
Enterprise Data
On-Prem Data Stores
Public Datasets
3rd Party Endpoints
More…
Data Factory
Event Hub
Purview
Data Tools
Data Governance
Azure Tools
Monitor
Data Ingestion Data Storage (Raw & Curated) Data Transformation ML / AI Data Consumption
Storage Account Cosmos DB Stream Analytics Machine Learning Power BI Serverless Functions
IoT Hub
Event Grid
Service Bus
Data Lake Redis Cache
Data Warehouse
/ Synapse
SQL / VM
Azure SQL DB SAP HANA
HDInsight
Cognitive
Services
Databricks Databricks
Synapse Synapse
Data Explorer DS-VM
Analysis Services Kubernetes Services
Power Platform Bot Service
Data Share On-prem
Data Gateway
Elastic Pools Elastic Jobs Data Box Data Migration
Service
IoT Central
Apps
Log Analytics Key Vault Service
Endpoint
Private Link Firewall Metrics Blueprint Lighthouse Cost
Management
Active
Directory
DevOps Security
Center
Batch / Query
Also supports MySQL, PostgreSQL, Mongo DB, and
other open source, some on hyperscale
Azure Data Services Landscape

3. Big data
Experimentation
Fast exploration
Semi-structured
Data science
Relational data
Proven security & privacy
Dependable performance
Structured
Business analytics
Data lake Data warehouse

4. Section 3
BI & DW come together
Azure Synapse Analytics
Azure meets these challenges,
with a single service to provide limitless analytics

5. Azure Synapse is the only unified platform for analytics, blending
big data, data warehousing, and data integration into a single cloud
native service for end-to-end analytics at cloud scale.
The first unified, cloud native platform for converged analytics

6. 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
Azure Synapse Analytics

7. Azure Synapse Analytics
Limitless analytics service with unmatched time to insight
Platform
Platform
Azure
Data Lake Storage
Azure
Data Lake Storage
Common Data Model
Enterprise Security
Optimized for Analytics
METASTORE
SECURITY
MANAGEMENT
MONITORING
DATA INTEGRATION
Analytics Runtimes
DEDICATED SERVERLESS
Form Factors
SQL
Languages
Python .NET Java Scala
Experience
Experience Synapse Analytics Studio
Artificial Intelligence / Machine Learning / Internet of Things
Intelligent Apps / Business Intelligence
METASTORE
SECURITY
MANAGEMENT
MONITORING

8. 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
Azure Synapse Analytics
Synapse SQL
Apache Spark
for Synapse
Synapse Pipelines Synapse Studio

9. Data Warehouse
Push rules for enforcement
to SDKs in data sources
Scalable and secure SQL analytics platform

10. Flexible consumption models
Serverless pay-per-query ideal for ad-hoc data lake
exploration and transformation
Dedicated clusters optimized mission-critical data
warehouse workloads
Serverless Dedicated

11. Fully-managed elastic platform
Elastic compute that can be easily optimized to
different classes of workload
All features available in a single tier
Infinite cost effective PAYG storage

12. SQL Editor
Automatic code completion (Intellisense)
Script collaboration within the Workspace
Built-in visualizations
Easily switch between clusters

13. 11.5
7
62
9.5
28
2.5
30.5
48.5
99
22.5
9
5
11.5
6.5
2 5
27
99.5
18.5 21
95.5
8
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22
TPC-H 1 petabyte execution times
TPH-H and TPC-DS Leader
Price/performance leadership relative to other cloud
data warehouses
“Polaris” is the only query engine to successfully
complete TPC-H at 1PB scale
https://aka.ms/synapse-dqp
$47
$152
$564
$51
DW30000C 4X-Large BigQuery dc2.8xlarge
60N
Test-H 30TB
Price/performance @30TB
($ per query per hour) lower is better
$153
$286 $309
$570
Azure
Synapse
Redshift Snowflake
Enterprise
BigQuery Flat
Rate
Test-DS 30TB
Price/performance comparison (lower is better)
* GigaOm TPC-H benchmark report, January 2019, “GigaOm report: Data Warehouse in the Cloud Benchmark

14. Only platform to compete TPC-H
benchmark at 1 Petabyte
Massive Concurrency
Global Workload Graph
Workload aware query scheduling
https://aka.ms/synapse-dqp

15. Workload Management
Scale in Scale-out
Azure Synapse supports a more diverse set of workload management tools through workload importance, intra-cluster isolation, and elastic clusters.
Workload Importance Workload Isolation
Workload Group B
40%
Elastic Cluster (Scale Up)
2000 cDWU

16. Workload Management Management
• It manages resources, ensures highly efficient resource
utilization, and maximizes return on investment (ROI).
• The three pillars of workload management are
1. Workload Classification – To assign a request to a
workload group and setting importance levels.
2. Workload Importance – To influence the order in
which a request gets access to resources.
3. Workload Isolation – To reserve resources for a
workload group.
Pillars of Workload
Management
Classification
Importance
Isolation

17. Workload classification
• Overview
• Map queries to allocations of resources via pre-determined
rules.
• Use with workload importance to effectively share
resources across different workload types.
• If a query request is not matched to a classifier, it is
assigned to the default workload group.
• Benefits
• Map queries to both Resource
Management and Workload Isolation concepts.
• Monitoring DMVs
• sys.workload_management_workload_classifiers
sys.workload_management_workload_classifier_details
• Query DMVs to view details about all active workload
classifiers.
CREATE WORKLOAD CLASSIFIER classifier_name
WITH
(
WORKLOAD_GROUP = 'name’
, MEMBERNAME = ‘security_account'
[ [ , ] IMPORTANCE = {LOW|BELOW_NORMAL|NORMAL|ABOVE_NORMAL|HIGH} ] )
[ [ , ] WLM_LABEL = 'label' ]
[ [ , ] WLM_CONTEXT = 'name' ]
[ [ , ] START_TIME = 'start_time' ]
[ [ , ] END_TIME = 'end_time' ]
)[ ; ]
WORKLOAD_GROUP: maps to an existing resource class
IMPORTANCE: specifies relative importance of
request
MEMBERNAME: database user, role, AAD login or AAD
group

18. Using Resource Classes to improve Data Ingestion

19. Workload importance
• Overview
• Queries past the concurrency limit enter a FiFo
queue
• By default, queries are released from the queue on
a first-in, first-out basis as resources become
available
• Workload importance allows higher priority
queries to receive resources immediately
regardless of queue
• Example
• State analysts have normal importance.
• National analyst is assigned high importance.
• State analyst queries execute in order of arrival
• When the national analyst’s query arrives, it jumps
to the top of the queue
CREATE WORKLOAD CLASSIFIER National_Analyst
WITH
(
WORKLOAD_GROUP = ‘analyst’
,IMPORTANCE = HIGH
,MEMBERNAME = ‘National_Analyst_Login’)
Azure Synapse
Analytics

20. What if you want to
prioritize the workloads
that get access to
resources?
1 2 10 11
Running Queued
3 4 5 6 7 9
8 12
10
11 12
Scheduler without importance
9 10
Queued
Queued
CEO
CEO
CEO
By default, workloads are run on a first-in first out basis.
By default, workloads are run on a first-in first out basis.
Workload importance

21. With workload
importance,
prioritized workloads
take precedence
1 2 10 11
Running Queued
3 4 5 6 7 9
8 12
Scheduler With Importance Turned On
12
Queued
CEO
CEO
Low
Normal Normal High
CREATE WORKLOAD CLASSIFIER classifier_name
WITH
(
WORKLOAD_GROUP = 'name’ ,
MEMBERNAME = 'security_account' [ [ , ]
IMPORTANCE = { LOW | BELOW_NORMAL | NORMAL (default) | ABOVE_NORMAL | HIGH }])
Workload importance

22. 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
Workload Isolation

23. 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 
Column level encryption 
Authentication
SQL login 
Azure active directory 
Multi-factor authentication 
Network Security
Managed virtual network 
Custom virtual network 
Firewall 
Azure ExpressRoute 
Azure Private Link 
Threat protection
Threat detection 
Auditing 
Vulnerability assessment 
Isolation
Dedicated metadata store 
Hosted in customer tenant 
Best-in-class Security
Customer & System Managed Keys
All data encrypted by default
Up to 3x levels of data encryption at rest
Democratize data at scale with fine-grained ACL
Proactive protection
Comprehensive Compliance

24. HIPAA /
HITECH
IRS 1075 Section 508
VPAT
ISO 27001 PCI DSS Level 1
SOC 1 Type 2 SOC 2 Type 2 ISO 27018
Cloud 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 2
FERPA CJIS
Australian
Signals
Directorate
New
Zealand
GCIO
Industry-leading compliance

25. Eliminate network maintenance
One-click enables automated management of virtual
networks between cluster endpoints
Synapse resources only ever interop with private
endpoints
No management of subnets or IP Ranges
Prevents data exfiltration
Compliance Boundary

26. More than just data security
Native integration with Azure Purview
Automatically discover and classify data assets
End-to-end data lineage

27. Real-time Operational Analytics
Push rules for enforcement
to SDKs in data sources
Eliminate latency and accelerate decision making

28. Real-time operational analytics
One-click enablement in Azure Portal
No data integration pipelines required
Near-zero impact on operational systems
Latency <90s at 99th percentile
Azure Cosmos DB
Analytical Store
Column store optimized
for analytical queries
Transactional Store
Row store optimized for
transactional operations
Azure Synapse
Analytics
Cloud-Native HTAP
Azure Synapse Link

29. Machine Learning
Push rules for enforcement
to SDKs in data sources
Empower everyone with predictive insights

30. Democratize data science to all
Synapse makes predictive analytics accessible to all
Notebooks provides a code authoring experience for
complex predictive models
Automatic ML graphical interface provides a no-
code experience for creating ML models
Native integration with Azure Cognitive Search
provides access to pre-built models
All Code Low/No-Code Pre-built models

31. Code-first ML model development
PySpark, Scala, and C# languages supported
Automatic code completion (Intellisense)
Author multiple languages in a single notebook
Analyze data from the data warehouse, data lake,
and real-time operational data from one place

32. Data + Languages
Languages such as SQL, PySpark, Scala and C# in
support of data science and data warehouse
workloads
The data lake supports and unlimited set of file
formats including Parquet, ORC and Json as well as
audio, image, and video formats
Language
Data

33. All you need is data
Fully automated feature exploration

34. Code-free in Synapse Studio
No-code creation on Machine Learning models
Democratize ML to everyone since no data science
domain knowledge required
Support for ensemble models
Supports classification, regression, and time-series
forecasting

35. Code-free in Synapse Studio
No-code references to Machine Learning models
Democratize ML to everyone since no data science
domain knowledge required
Easily embed in SQL Stored Procedures for
transformation of Views for reporting

36. SELECT d.*, p.Score FROM PREDICT(MODEL = @onnx_model, …
In-engine ML Scoring
Machine Learning models executed using SQL
“In-engine” for performance and scalability
No data leaves the platform for scoring
No additional cost for scoring
T-SQL Language
Synapse SQL
Model Data Predictions

37. Data Integration
Code-free Hybrid Data Integration
Push rules for enforcement
to SDKs in data sources

38. Cloud native ETL/ELT
95+ connectors available
Secure connectivity to on-premise data sources,
other clouds, and SaaS applications
Code-first and low/no code design interfaces
Schedule and Event based triggering
Code-free

39. Code-free data
wrangling
No/low-code data
transformation
Excel-like interface is familiar to users
Transform data to desired shape completely visually
Operationalize into pipelines

40. Real-time operational
analytics
No data integration pipelines required
Near-zero impact on operational systems
Latency <90s at 99th percentile

41. Accelerate time to solution
Azure Open Data sets
Pre-built samples to accelerate development
• SQL Scripts
• Notebooks
• Data Pipelines

42. Build dashboard in Synapse Studio
Code-free experience for development rich
visualizations
One-click publishing to for secure consumption
across the enterprise

44. Questions
Azure Synapse Analytics for Data Engineers | Microsoft Azure