The document outlines Azure Databricks as a fast, collaborative analytics platform based on Apache Spark optimized for Azure, offering a seamless environment for data scientists, engineers, and analysts. It emphasizes features such as native integration with Azure services, enterprise-grade security, and enhanced productivity through a unified workspace and collaborative tools. Additionally, it highlights the performance benefits of Spark and various features of the Azure cloud platform for big data analytics.

1. Azure Databricks

2. TrustedProductive IntelligentHybrid
Azure. Cloud for all.

3. 50+Azure
regions
Trusted
Intelligent
Hybrid
Productive
Learn more:
Microsoft.com/datacenter

4. © Microsoft Corporation
Open Source Support
Applications
Infrastructure
Management
Databases &
middleware
App frameworks
& tools
DevOps

5. Modern Data Estate

7. Microsoft Cloud Big Data Building Blocks
Intelligence
Dashboards &
Visualizations
Big Data Stores Machine Learning
and Analytics
Event Hubs
HDInsight
(Hadoop and
Spark)
Stream
Analytics
Data Intelligence Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Bot
Framework
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Warehouse
Data Factory
Machine
Learning
Data Lake Store
Cognitive
Services
Power BI
Data
Sources
Apps
Sensors
and
devices
Data
Information
Management
Azure SQL DB Azure
Databricks
IoT Hub

8. CONTROL EASE OF USE
Azure Data Lake
Analytics
Azure Data Lake Store
Azure Storage
Any Hadoop technology,
any distribution
Workload optimized,
managed clusters
Data Engineering in a
Job-as-a-service model
Azure Marketplace
HDP | CDH | MapR
Azure Data Lake
Analytics
IaaS Clusters Managed Clusters Big Data as-a-service
Azure HDInsight
Frictionless & Optimized
Spark clusters
Azure Databricks
BIGDATA
STORAGE
BIGDATA
ANALYTICS
ReducedAdministration
Bring Big Data to Everyone on the Cloud

9. What is Spark?

10. S P A R K : A B R I E F H I S T O R Y

11. A P A C H E S P A R K
A unified, open source, parallel, data processing framework for Big Data Analytics
Spark Core Engine
Spark SQL
Interactive
Queries
Spark Structured
Streaming
Stream processing
Spark MLlib
Machine
Learning
Yarn Mesos
Standalone
Scheduler
Spark MLlib
Machine
Learning
Spark
Streaming
Stream processing
GraphX
Graph
Computation

12. A P A C H E S P A R K
A unified, open source, parallel, data processing framework for Big Data Analytics

13. A P A C H E S P A R K
Three Spark APIs: Resilient Distributed Datasets, DataFrames, Datasets
Spark Structured
Streaming
Stream processing

14. S P A R K - B E N E F I T S
Performance
Using in-memory computing, Spark is considerably
faster than Hadoop (100x in some tests).
Can be used for batch and real-time data processing.
Developer Productivity
Easy-to-use APIs for processing large datasets.
Includes 100+ operators for transforming.
Ecosystem
Spark has built-in support for many data sources,
rich ecosystem of ISV applications and a large dev
community.
Available on multiple public clouds (AWS, Google
and Azure) and multiple on-premises distributors
Unified Engine
Integrated framework includes higher-level libraries
for interactive SQL queries, Stream Analytics, ML and
graph processing.
A single application can combine all types of
processing.

15. What is Azure Databricks?

16. D A T A B R I C K S - C O M P A N Y O V E R V I E W

17. What is Azure Databricks?
A fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure
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, Blob Storage)
Enterprise-grade Azure security (Active Directory integration, compliance, enterprise -grade SLAs)

18. Get started quickly by launching
your new Spark environment with
one click.
Share your insights in powerful
ways through rich integration with
Power BI.
Improve collaboration amongst
your analytics team through a
unified workspace.
Innovate faster with native
integration with rest of Azure
platform
Simplify security and identity control
with built-in integration with Active
Directory.
Regulate access with fine-grained user
permissions to Azure Databricks’
notebooks, clusters, jobs and data.
Build with confidence on the trusted
cloud backed by unmatched support,
compliance and SLAs.
Operate at massive scale
without limits globally.
Accelerate data processing with
the fastest Spark engine.
ENHANCE PRODUCTIVITY BUILD ON THE MOST COMPLIANT CLOUD SCALE WITHOUT LIMITS
Differentiated Experience on Azure

19. 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
Azure Databricks

22. Engage Microsoft experts for a workshop to help identify
high impact scenarios
Already using Azure? try Azure Databricks now or
create a free Azure account to start using Azure Databricks
Learn more about Azure Databricks www.azure.com/databricks
How to get started

23. Next event: IoT + Data OpenHack in Toronto: Tuesday,
March 5th- Thursday, March 7th, 2019
Learn more at https://openhack.microsoft.com/
Microsoft OpenHacks
OpenHacks are developer-focused events where participants
can learn through hands-on experience

24. Thank you!