Microsoft Azure Technical Overview
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5 de Apr de 2017•0 gostou•12,617 visualizações
Microsoft Azure Technical Overview
5 de Apr de 2017•0 gostou•12,617 visualizações
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Technical introduction of Microsoft Azure, from IaaS to PaaS, Serverless, Container Services, DBaaS and Machine Learning.
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Microsoft Azure Technical Overview
- 3. 34Azure regions 2X the number of AWS regions NEWLY ANNOUNCED: France: France Central and France South DoD East and Central
- 10. People Automated Systems Apps Web Mobile Bots Intelligence Capabilities Dashboards & Visualizations Cortana Bot Framework Cognitive Services Power BI Data Sources Apps Sensors and devices Machine Learning and Analytics HDInsight (Hadoop and Spark) Stream Analytics Data Lake Analytics Machine Learning ActionData Intelligence Information Management Event Hubs Data Catalog Data Factory Big Data Stores SQL Data Warehouse Data Lake Store Data Driven Innovation
- 11. Platform Services Infrastructure Services Web Apps Infrastructure Mobile Backends API Management API App Infrastructure Business Process Automation Push Notifications Content Delivery Network (CDN) Live & OD Media Streaming B2B Integration Hybrid Connections Pub/Sub Queuing Simple Queuing Hybrid Operations Server Data Backup Hybrid/Intelligent Data Backup Disaster Recovery Bulk Data Import And Export Relational SQL Database Document Database Service Distributed In-Memory Cache Search Simple Key/Value Store Data Warehouse Directory Health Monitoring Privileged Identity Management Operational Analytics Stateless Compute Scheduled Compute Jobs Virtual App Streaming Distributed Compute Development Tools Application Instrumentation Software Development Kits Software Lifecycle Management Domain Join & Policy Management Big Data Analytics Predictive Analytics Data Stream Analytics Data Pipelines Device Data Collection Mobile Analytics Big Data Storage IoT Device Management Data Source Management Security & Management User/Group Directory Store Multi-Factor Authentication Scheduled Service Management Service Creation & Configuration Encryption Key Store Software/Solution Marketplace Pre-Build VM Images Identity Sign-Up and sign-in Task Scheduler http://aka.ms/azposterapp
- 12. Azure is Open
- 13. Microsoft Azure: an Open Ecosystem
- 14. Azure is Control
- 19. LOWEST PRICE LARGEST SCALE-UP
- 27. ExpressRoute Exchange Provider or WAN Provider Main Corporate Site Site 2 .. N Customer’s connection Traffic to public IP addresses in Azure Traffic to Virtual Networks Microsoft Edge Partner Edge Private WAN Corporate Network Load Balancing Auto Scaling SQL Azure Analytics & Reporting Azure Public Services Web Site Remote Site Public Internet VPN GATEWAY … and many more Point-to-Site VPN Load Balancing Auto Scaling Network Security Groups Azure Private Services VMs Database
- 28. Internet GatewaySubnet 10.10.255.0/27 default 10.10.0.0/24 DemoVNET 10.10.0.0/16 Microsoft Azure On-Prem Microsoft Azure demolinux 10.10.0.4 DemoGateway 10.10.255.1 52.166.200.214 VPN Client 172.16.201.3 (10.85.62.10) Point-to-Site Tunnel (SSTP) 172.16.201.0/24 PRIORITY NAME SOURCE DESTINATION SERVICE ACTION 100 gateway Internet Any Custom (Any/Any) Allow 110 nointranet 10.0.0.0/8 172.16.201.0/24 Custom (Any/Any) Deny INBOUND SECURITY RULES
- 29. GatewaySubnet 10.100.255.0/27 DemoGateway azuregateway-XYZ.cloudapp.net 52.166.200.214 defaultsubnet1 10.10.0.0/24 DMZVNET 10.100.0.0/16 demovm1 10.10.0.4 Network Security Group VNET1 10.10.0.0/16 defaultsubnet2 10.20.0.0/24 demovm2 10.20.0.4 VNET2 10.20.0.0/16 VNET PeeringVNET Peering PRIORITY NAME SOURCE DESTINATION SERVICE ACTION 100 gateway Internet Any Custom (Any/Any) Allow 110 nointranet 10.0.0.0/8 172.16.201.0/24 Custom (Any/Any) Deny INBOUND SECURITY RULES Point-to-Site Tunnel (SSTP) 172.16.201.0/24 VPN Client 2 172.16.201.12 (10.85.60.12) VPN Client 1 172.16.201.11 (10.85.60.11) PROXY Server allow azuregateway-XYZ.cloudapp.net max 128 clients
- 31. DBaaSiPaaSaPaaS
- 32. The Continuum from IaaS to PaaS Cloud Foundry Docker Swarm Mesos Kubernetes
- 34. Event Hub Partition 1 Partition 2 Partition NRevocation List Publisher Policy Throughput Units Event Publisher Event Consumer App 1 Event Consumer App 2
- 37. Fully featured RDBMS Transactional processing RichQuery Managed as a service Elastic scale Internet-accessible http/rest Schema-free data model Arbitrary data formats
- 38. With Azure, you have many managed data store options and you can use the right store for the job Storage Comparison When you need…. Because… But not for… Use … Relational store Transactions, joins, structured data, familiar SQL query Quickly changing data schemas SQL Database NoSQL key-value pair store Low-cost, fast, massive scale Rich query Tables NoSQL JSON document store Flexible schema, familiar SQL query, low latency Complex joins DocumentDB NoSQL wide- column store Open-source, integration with Hadoop analytics Operational simplicity HBase on HDInsight Cache Increasing speed of an app Primary data store Redis Cache Search service Integrating search into an app Primary data store Azure Search
- 39. Goldilocks and the Three Bears by Jan Brett
- 40. Microsoft Service Fabric Kubernetes (Open Shift) Mesosphere DC/OS Cloud Foundry (Pivotal & OSS) Docker Swarm Azure Container Services
- 43. Microservices Azure Windows Server Linux Hosted Clouds Windows Server Linux Service Fabric Private Clouds Windows Server Linux High Availability Hyper-Scale Hybrid Operations High Density Rolling Upgrades Stateful services Low Latency Fast startup & shutdown Container Orchestration & lifecycle management Replication & Failover Simple programming models Load balancing Self-healingData Partitioning Automated Rollback Health Monitoring Placement Constraints
- 47. The agile methodologies are accelerating the construction process ProductionDevelopment Collaboration Backlog Requirements Availability and performance issues are hard to troubleshoot in this fast-changing world with distributed applications Usage should determine the next set of priorities and learnings An automated release pipeline is needed to deliver at the pace of development with full traceability
- 48. ProductionDevelopment Collaboration Backlog Requirements do the RIGHT THING do the THING RIGHT
- 49. Develop Developer Workstation Team Collaboration Build&Test Build/CI Test Deploy Configuration Monitor&Learn Monitor This graphic shows OSS and partner products that are integrated with the Microsoft DevOps solution Mixed Ecosystem Release TFS Workstations - On-Premises| Hybrid | Cloud ALMServices - On-Premises| Hybrid | Cloud DEV TEST QA Environments - On-Premises| Hybrid | Cloud Monitoring- On-Premises | Hybrid | Cloud
- 50. TFS Develop Developer Workstation Team Collaboration Build&Test Build/CI Test Deploy Release Monitor&Learn Monitor Microsoft Ecosystem Workstations - On-Premises| Hybrid | Cloud Monitoring- On-Premises | Hybrid | CloudALMServices - On-Premises| Hybrid | Cloud DEV TEST QA Environments - On-Premises| Hybrid | Cloud
- 51. Visual Studio Web Editor Azure Resource Manager Templates
- 52. Configuration Applied To: Node Configurations (.MOF config document) WebService Compiled Nodes 1…N of these 1…N of these per configuration (+ checksum files for each) 1…N of these per node configuration Via Push or Pull Desired State Configuration (PowerShell DSC)
- 53. SimpleDev SimpleCert SimpleProd Source Code Editor (Visual Studio Code) Delivery Pipeline GIT (on VSTS) Docker Hub Build Automation Visual Studio Team Services Demo Overview
- 55. Delivery Pipeline with Spinnaker https://github.com/Azure/azure-quickstart-templates/tree/master/spinnaker-vm-simple VM Scale Set Kubernetes
- 57. Telemetry is collected at each tier: mobile applications, server applications and browser Telemetry arrives in the Application Insights service in the cloud where it is processed & stored Get a 360° view of the application including availability, performance and usage patterns What is Application Insights?
- 58. Identify & Triage issues. Availablity
- 60. ° view o Export data to manually correlate with external data sources
- 62. ON PREMISES CLOUD Relational DB On Prem HDFS Active Incoming Data Azure DW CONSUMPTIO N Web Portals Power BI Cleansing Analysis Azure Data Factory Azure Data Factory DMG ADL Store ADL Analytics ADL Analytics
- 63. CLOUD Event data ADL Store CONSUMPTIO N HDI Storm HDI R Jupyter Data Science Notebooks Enriching Event data Event data Kafka
- 64. ON PREMISES CLOUD Data Lake Store Data Lake Analytics CONSUMPTION Power BIEventHubs Stream Analytics Data Factory Alerts SQL DB Web Portals Event data Event data Event data
- 65. Azure Data Lake YARN U-SQL Analytics HDInsight Hive R Server WebHDFS Store Store and analyze data of any kind and size Develop faster, debug and optimize smarter Interactively explore patterns in your data No learning curve Managed and supported Dynamically scales to match your business priorities Enterprise-grade security Built on YARN, designed for the cloud
- 66. Debug and Optimize your Big Data programs with ease • Deep integration with Visual Studio, Visual Studio Code, Eclipse, & IntelliJ • Easy for novices to write simple queries • Integrated with U-SQL, Hive, Storm, and Spark • Actively offers recommendations to improve performance and reduce cost • Playback visually displays job run
- 67. CONTROL EASE OF USE Azure Data Lake Analytics Azure Data Lake Store Azure Storage Any Hadoop technology Workload optimized, managed clusters Specific apps in a multi- tenant form factor Azure Marketplace HDP | CDH | MapR Azure Data Lake Analytics IaaS Hadoop Managed Hadoop Big Data as-a-service Azure HDInsight BIGDATA STORAGE BIGDATA ANALYTICS Bringing Big Data to everybody UserAdoption
- 68. Machine Learning Process Model
- 69. Azure Machine Learning Studio APIML STUDIO
- 70. Supervised Unsupervised Reinforcement Learning Anomaly Detection Regression Classification Clustering Agent Based Learning https://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-choice
- 71. Machine Learning Algorithm Cheat Sheet
Notas do Editor
- At the core of Azure is its global infrastructure that spans 38 regions worldwide. We have the largest footprint of any cloud provider – 2X that of AWS. https://azure.microsoft.com/en-us/regions/
- Debug and Optimize your Big Data programs with ease: Debugging failures in cloud distributed programs are now as easy as debugging a program in your personal environment. Our execution environment actively analyzes your programs as they run and offers recommendations to improve performance and reduce cost. For example, if you requested 1000 AUs for your program and only 50 AUs were needed, the system would recommend that you only use 50 AUs resulting in a 20x cost savings.
- The elegance of the solution is in its simplicity – something that has been lacking in the machine learning space The first issue many enterprises face is data ingestion. With the cloud, you can bring in data sources with the ease of a drop down or drop your on-premises data set into the built in storage space. Users can then model in our development environment – Machine Learning Studio where we’re offering R, Python and SQLite as first class citizens in addition to our world-class Microsoft algorithms. The second issue – and often the primary one – is putting finished work into production in a way others can use. Client devices, websites, mobile applications, excel spreadsheets you name it anything that supports HTTP requests. We’ve heard from many data scientists that they model in R on a Linux stack but then have to hand over their work to developers who need to translate that into another language to actually make it work. This time consuming and unnecessary process has been eliminated with our system, as the model is with a click transformed into a web service end-point that can run over any data, anywhere and connect to any solution or client. Next, not only can this model be put into production for your company, it can be made available for the world on our Machine Learning Marketplace. Microsoft hosts your solution and markets it for you, while you have the freedom to brand and monetize as you see fit.
- Microsoft built our machine learning solution as part of Azure so it comes with all the benefits of the cloud. Scalable, no new hardware investments, pay as you go, and as you see value. Let’s review a snapshot of the key benefits here. First and foremost, it’s comes fully managed. No software to install, no hardware to manage, and no updates to worry about. and one portal to view and update. And it’s all based in Azure, so there is one portal to manage, monitor and update the solution. No moving back and forth between technologies or struggling with integrations. It’s Integrated. This is what customers expect from Microsoft and here we deliver in a space where ease of use is lacking today. The visual interface provides simple, drag, drop and connect features. Users can easily access it from anywhere, and share across geographies. Azure Machine Learning is the result of years of innovation, so it comes packed with best in class algorithms like the ones that power Bing and Xbox. There are sample experiments to help customers quickly get started, support for custom R solutions so customers don’t lose investments they’ve already made in machine learning—easily port over their current solutions – no wasted work. And it supports over 350 open source R packages. And uniquely in the market customers can deploy solutions in minutes.
- SVM = Support vector machines