Big Data Architect培训




35 小时 通常来说是5天,包括中间休息。


第1天 - 提供重要的Big Data主题领域的高级概述。该模块分为一系列部分,每个部分都附有实践练习。

第2天 - 探讨一系列与Big Data环境相关的分析实践和工具的主题。它没有涉及实现或编程细节,而是将概念保持在概念层面,重点放在使参与者能够全面了解Big Data解决方案提供的常见分析功能和特性的主题上。

第3天 - 概述与Big Data解决方案平台架构相关的基本和必要主题领域。它涵盖了开发Big Data解决方案平台所需的Big Data机制以及用于组装数据处理平台的架构选项。还提供了常见方案,以便基本了解Big Data解决方案平台的使用方式。

第4天 - 通过探索与Big Data解决方案平台架构相关的高级主题,在第3天构建。特别是,介绍和讨论了构成Big Data解决方案平台的不同架构层,包括数据源,数据入口,数据存储,数据处理和安全性。

第5天 - 涵盖了一系列练习和问题,旨在测试代表们应用第3天和第4天所涵盖主题知识的能力。

Machine Translated


Day 1 - Fundamental Big Data

  • Understanding Big Data
  • Fundamental Terminology & Concepts
  • Big Data Business & Technology Drivers
  • Traditional Enterprise Technologies Related to Big Data
  • Characteristics of Data in Big Data Environments
  • Dataset Types in Big Data Environments
  • Fundamental Analysis and Analytics
  • Machine Learning Types
  • Business Intelligence & Big Data
  • Data Visualization & Big Data
  • Big Data Adoption & Planning Considerations

Day 2 - Big Data Analysis & Technology Concepts

  • Big Data Analysis Lifecycle (from business case evaluation to data analysis and visualization)
  • A/B Testing, Correlation
  • Regression, Heat Maps
  • Time Series Analysis
  • Network Analysis
  • Spatial Data Analysis
  • Classification, Clustering
  • Outlier Detection
  • Filtering (including collaborative filtering & content-based filtering)
  • Natural Language Processing
  • Sentiment Analysis, Text Analytics
  • File Systems & Distributed File Systems, NoSQL
  • Distributed & Parallel Data Processing,
  • Processing Workloads, Clusters
  • Cloud Computing & Big Data
  • Foundational Big Data Technology Mechanisms

Day 3 - Fundamental Big Data Architecture

  • New Big Data Mechanisms, including ...
    • Security Engine
    • Cluster Manager 
    • Data Governance Manager
    • Visualization Engine
    • Productivity Portal
  • Data Processing Architectural Models, including ...
    • Shared-Everything and Shared-Nothing Architectures
  • Enterprise Data Warehouse and Big Data Integration Approaches, including ...
    • Series
    • Parallel
    • Big Data Appliance
    • Data Virtualization
  • Architectural Big Data Environments, including ...
    • ETL 
    • Analytics Engine
    • Application Enrichment
  • Cloud Computing & Big Data Architectural Considerations, including ...
    • how Cloud Delivery and Deployment Models can be used to host and process Big Data Solutions

Day 4 - Advanced Big Data Architecture

  • Big Data Solution Architectural Layers including ...
    • Data Sources,
    • Data Ingress and Storage,
    • Event Stream Processing and Complex Event Processing,
    • Egress,
    • Visualization and Utilization,
    • Big Data Architecture and Security,
    • Maintenance and Governance
  • Big Data Solution Design Patterns, including ...
    • Patterns pertaining to Data Ingress,
    • Data Wrangling,
    • Data Storage,
    • Data Processing,
    • Data Analysis,
    • Data Egress,
    • Data Visualization
  • Big Data Architectural Compound Patterns

Day 5 - Big Data Architecture Lab

  • Incorporates a set of detailed exercises that require delegates to solve various inter-related problems, with the goal of fostering a comprehensive understanding of how different data architecture technologies, mechanisms and techniques can be applied to solve problems in Big Data environments.









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