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Pluralsight Real World Big Data in Azure

Pluralsight Real World Big Data in Azure crack

How do you make sense of Big Data? When you’re receiving 100 million events per hour and you need to save them all permanently, but also process key metrics to show real-time dashboards, what technologies and platforms can you use? This course answers those questions using Microsoft Azure, .NET, and Hadoop technologies: Event Hubs, Cloud Services, Web Apps, Blob Storage, SQL Azure, and HDInsight. We build a real solution that can process ten billion events every month, store them for permanent access, and distill key streams of data into powerful real-time visualizations.

 

 

Table of contents 

Understanding Big Data in Azure

 34:20
  • Introducing Big Data in Azure
      2:20
  • Is This Course for Me?
      1:36
  • Building the Phase 1 Solution
      2:43
  • Evolving the Phase 2 Solution
      2:44
  • Introducing the Telemetry API
      1:53
  • Demo: EventsController
      4:13
  • Key Features of the Telemetry API
      2:41
  • Zipping the Event Batch
      2:20
  • Demo: GZipToJsonHandler
      5:02
  • Testing the API
      1:53
  • Demo: IEventSender
      3:45
  • Module Summary
      3:06

Ingesting Data into Event Hubs

 46:57
  • Module Introduction
      1:39
  • Introducing Event Hubs
      1:52
  • Configuring Event Hubs
      3:00
  • Creating Event Hubs
      3:29
  • Automating Event Hub Creation
      2:58
  • Sending Events
      3:17
  • Demo: EventHubEventSender
      3:39
  • Batching Events
      3:13
  • Demo: EventBatchIterator
      4:04
  • Batching and Testing
      2:19
  • Demo: Acceptance Tests
      2:51
  • Understanding Throughput Units
      3:50
  • Demo: Deploy, Monitor & Scale
      3:33
  • Next Steps for Deployment & Monitoring
      2:11
  • Isolating the Event Hub
      2:22
  • Module Summary
      2:34

Storing Event Data for Batch Queries

 51:21
  • Module Introduction
      1:50
  • Aim of Deep Storage
      2:34
  • Concurrency and Partitions
      2:16
  • Demo: IEventProcessor
      3:43
  • Competing Consumers
      3:20
  • Buffering Output
      3:19
  • Demo: DeepStorageEventProcessor
      2:54
  • Level 1: In-Process Memory
      2:38
  • Demo: MemoryEventStore
      2:47
  • Level 2: Local Disk
      2:57
  • Demo: DiskEventStore
      4:27
  • Level 3: Blob Storage
      2:22
  • Demo: BlobStorageEventStore (Part 1)
      2:38
  • Demo: BlobStorageEventStore (Part 2)
      3:20
  • Timed Flushing
      2:46
  • Demo: Deep Storage Worker Role
      3:05
  • Scaling Read with Throughput Units
      1:58
  • Module Summary
      2:20

Querying Batch Data in Deep Storage

 41:27
  • Module Introduction
      2:14
  • About Pig
      2:49
  • Demo: AzCopy and Pig
      4:17
  • Importing Libraries
      3:20
  • Demo: Pig and JSON Data
      3:25
  • HDInsight and Pig
      3:16
  • Demo: Running Pig Scripts in HDInsight
      5:23
  • Streaming Data to Commands
      2:45
  • Demo: Streaming from Pig to .NET
      4:40
  • Visualizing Output from Pig
      3:28
  • Demo: Visualization with D3.js
      2:32
  • Module Summary
      3:12

Normalizing Event Data for Real-Time Queries

 51:08
  • Module Introduction
      2:05
  • Modeling Event Metrics
      3:44
  • Demo: Building the Data Model
      4:02
  • Refactoring the Event Receiver
      4:16
  • Demo: Core.EventProcessor
      4:10
  • Dependency Injection and the EventProcessorHost
      2:36
  • Buffering in the Event Handler
      2:28
  • Demo: ModelBuffer of T
      4:08
  • Handling Event Metrics
      3:01
  • Demo: EventMetricsHandler
      4:40
  • Running On-Premise
      1:59
  • Demo: RealTime.WorkerRole
      3:41
  • Running in Azure
      2:18
  • Demo: Azure Deployment
      4:11
  • Module Summary
      3:42

Building Real-time Dashboards

 39:53
  • Module Introduction
      2:26
  • Demo: Installing Dashing.net
      2:59
  • About Widgets
      3:08
  • Demo: The Number Widget
      2:58
  • About Scheduled Jobs
      3:15
  • Demo: Event Count Job
      4:07
  • How Dashing Pushes Updates
      3:12
  • Adding New Widgets
      2:02
  • Demo: The Pie Widget
      3:19
  • Running Dashing on Azure
      2:20
  • Demo: Deploying Azure Web App
      2:44
  • Demo: Securing the Web App
      3:13
  • Module Summary
      4:04

Using Storm for the Plumbing

 27:00
  • Module Introduction
      2:11
  • Spouts, Bolts, and Topologies
      2:07
  • Parallelism in Storm
      3:06
  • Demo: The Event Hub Spout
      2:41
  • Demo: Connecting to the Event Hub Spout
      2:52
  • Topology for Event Metrics
      2:52
  • Demo: Event Metrics App
      4:03
  • Storm and HSInsight
      1:54
  • Demo: Running in Azure
      2:15
  • Module Summary
      2:56

Using HBase for Storage

 29:50
  • Module Introduction
      1:53
  • About HBase
      2:53
  • Modeling Device Errors in HBase
      3:30
  • Demo: HBase on HDInsight
      2:50
  • HBase Row Key Design
      3:50
  • Demo: Device Errors in Storm
      3:08
  • Demo: Device Errors in HBase
      2:37
  • The HBase .NET SDK
      2:20
  • Demo: Dashing and HBase
      2:15
  • Module Summary
      2:09
  • Course Summary
      2:19

 

 

 

 

 

در این آموزش تصویری با نحوه کار با داده های بزرگ (Big Data) در Azure آشنا می شوید.

این دوره آموزشی محصول موسسه Pluralsight است.

سرفصل های دوره:

  • آشنایی با داده های بزرگ در Azure
  • معرفی Telemetry API
  • ویژگی های Telemetry API
  • تست API
  • پیکربندی Event Hubs
  • ایجاد Event Hubs
  • آشنایی با واحد خروجی
  • نحوه ی ذخیره سازی اطلاعات
  • کار با فرآیند حافظه
  • کار با دیسک محلی
  • ذخیره سازی Blob
  • زمان بندی فلاشینگ
  • کار با Pig و JSON
  • اجرای اسکریپت Pig در HDInsight
  • کار با D3.js
  • رویداد عادی داده ها برای زمان واقعی
  • مدل سازی رویداد Metrics
  • نصب Dashing.net
  • امنیت نرم افزار ها در وب
  • استفاده از HBase برای ذخیره سازی
  • و…
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