大数据架构大师课：Lambda、Kappa、Data Mesh系统设计全解析

本课程摒弃碎片化的工具教学，以框架优先的方式系统讲解大数据架构设计的核心思维。课程围绕一套通用的大数据参考架构模型展开，涵盖Lambda、Kappa、微服务及Data Mesh等主流架构模式，帮助学员理解每个组件在系统中的定位与取舍。内容涉及数据采集、批流处理、存储方案选择、安全治理、基础设施规划及工作流编排等全流程，并结合Kafka、Spark、Airflow、Hadoop生态等实际工具进行映射。课程适合希望从数据工程师晋升为架构师、或跨入大数据领域的技术人员，通过掌握可复用的架构蓝图，具备在不同行业和部署环境下独立设计可扩展、高可靠大数据系统的能力。

MP4格式 | 视频：h264，1280×720 | 级别：所有级别 | 语言：英语 | 时长：54节课（3小时31分钟） | 文件大小：997 MB

Big Data Architecture Masterclass – Complete Course – 2026

Big Data architecture: Design Scalable Lambda, Kappa, Data Mesh Systems Using Kafka, Spark, Airflow & Hadoop Ecosystem

This course contains the use of artificial intelligence.

Are you designing Big Data systems but feeling lost in a sea of tools, frameworks, and conflicting architectures?

Most courses teach you tools. This course teaches you how to think like a Big Data Architect.

Whether you’re a data engineer trying to move up, a software architect expanding into data, or a student building toward a career in Big Data — this is the structured, framework-first course the industry has been missing.

Why this course is different

Unlike bootcamps that throw Spark, Kafka, and Hadoop at you and call it architecture, this course gives you a universal reference model — a standardized Big Data blueprint that works across industries, deployment environments, and technology stacks. Healthcare, finance, e-commerce, IoT — one framework to rule them all.

You won’t just learn what the components are. You’ll learn why each exists, when to use which architecture pattern, and how to make strategic trade-offs like a senior architect.

What You Will Be Able to Do After This Course

• Design a complete, scalable Big Data system from scratch — adapting it to any industry or business model

• Choose confidently between Lambda, Kappa, and Microservices architectures based on real project requirements

• Architect robust data pipelines covering ingestion, ETL/ELT, batch and stream processing, analytics, and visualization

• Select the right storage solution — Data Lake, Data Warehouse, Data Lakehouse, NoSQL, or SQL — for each use case

• Understand and apply Data Mesh principles for decentralized, scalable data governance

• Make infrastructure decisions around scalability, reliability, security, and performance

• Communicate architecture decisions clearly to technical and non-technical stakeholders

What Makes This Architecture Framework Universal

This course is built around a standardized Big Data Reference Architecture — a logical model inspired by NIST principles that maps every component of a Big Data system into a coherent, reusable blueprint. This is the foundation used by Big Data teams in enterprise environments across the world.

Every technology you encounter in the real world — Kafka, Spark, Airflow, Hive, Flink — has a place in this model. Once you understand the architecture, the tools become obvious choices rather than confusing options.

Course Content Overview

High-Level Architecture (Conceptual Layer) The 4 Vs of Big Data (Volume, Velocity, Variety, Variability + Value) — Data Sources & Providers — Ingestion (Batch & Stream) — ETL/ELT & Preprocessing — Distributed Processing — Analytics Engines (Descriptive, Predictive, Prescriptive, ML/AI) — Visualization — Workload Orchestration — Data Lifecycle Management

Big Data IT Infrastructure Storage, Compute & Networking — Horizontal vs. Vertical Scaling — System Resource Management

Security & Governance Big Data Security Principles — Privacy Frameworks — Data Governance Best Practices

Low-Level Technical Architecture Lambda Architecture — Kappa Architecture — Microservices — Apache Kafka, Flume & Amazon Kinesis — Data Lakes, Warehouses & Lakehouses (Hadoop, S3, Hive) — Batch & Stream Processing: MapReduce, Apache Spark, Flink, Storm — Querying: Apache Hive, PIG, Presto, SparkSQL — Orchestration: Apache Airflow, Oozie, Luigi — Visualization: Kibana, Superset, Apache Zeppelin

Modern & Emerging Topics Data Mesh Architecture — Cloud Big Data Infrastructure — Edge Computing — Distributed Computing Deep Dive — Vector Databases & Feature Stores (context for ML integration)

Why Enroll Now?

• Framework-first approach: Learn the architecture, then map any tool to it — future-proof your skills

• Industry-agnostic: The same blueprint works for healthcare, fintech, retail, IoT, and more

• Career-oriented: Designed to build the mindset of architects, not just practitioners

• Constantly updated: Content reflects the 2024–2025 Big Data landscape including Data Mesh, cloud-native patterns, and modern orchestration

Stop learning isolated tools. Start thinking in systems. Enroll now and build the architectural foundation that will define your entire data career.

What you’ll learn
✓ Understand the core components of any robust big data architecture for efficient data processing and analytics.
✓ Learn to design scalable and secure data pipelines using popular big data tools and technologies.
✓ Gain expertise in integrating diverse data sources into a unified and well-structured big data ecosystem.
✓ Understand Big Data Reference Architecture Components from Data Sources to Data Consumers
✓ Become a successful Big Data Architect and Data Engineer
✓ Learn how to design a performant, scalable distributed system that handles big data.
✓ Practice Big Data software engineering fundamentals; Data Ingestion, Data Loading & preprocessing, ETL, Batch & Stream processing, Analytic Engine, Visualizion
✓ Architect and create a big data or distributed system based on ultimate reference architecture
✓ How Big data architectures can be set up in a generic and repetitive way
✓ Work out a blueprint for a Big data architecture for any project in any industry landscapse
✓ Familiarity with big data storage systems: Data Lake, Warehouse, NoSQL & Relational Databases, data mesh and how to best apply it
✓ Master the Big Data conceptual model with views and activities performed by the functional components
✓ Big Data architecture High-level and Low-level overview
✓ Ability to design, implement, and maintain any big data architecture that meets the organization’s requirements
✓ Understanding of Big Data Security and Privacy
✓ Understand the role of System Workload Orchestrator and Data Life Cycle management for the Big Data Software Application pipeline
✓ Master Data Architecture

Requirements
● Basic Knowledge of Big Data and data Architecture terminology
● Desire to master Big Data , Data Architecture and Become Big Data Architect Expert
● Basic knowledge of data concepts and computer systems will be helpful
● This course welcomes learners from diverse backgrounds.