Advanced Big Data Systems

本课程将重点讲解高级大数据系统的实现、优化和应用，包括分布式文件系统、MapReduce/Spark、Storm/Spark streaming、Mahout等系统的原理、实现、策略优化。

近年来，人工智能技术正在快速地渗透进各个不同领域。因大数据系统是当今数据驱动人工智能的基础，而变得至关重要。本课程旨在引导学生了解大数据系统的基本概念，包括如何有效地存储、处理和分析数据。课程从分布式系统设计的一般原理出发。之后我们提供了如何在大数据系统中评定存储、计算和网络功能的框架。最后，为了使这些设计原则便于理解，我们的案例研究将使用真实的工业系统来演示基本设计原则如何应用于实际系统，以及该如何分析它们的性能以及局限性。

本课程将重点讲解高级大数据系统的实现、优化和应用，包括分布式文件系统、MapReduce/Spark、Storm/Spark streaming、Mahout等系统的原理、实现、策略优化。

Recent years have witnessed the rapid increase of the penetration of AI technology into different areas in the industry. Big data systems, the foundation that enables today’s data-driven AI, are thus becoming critically important. This course is dedicated to lead students into the basic concepts of big data systems, covering how data is effectively stored, processed and analyzed. We start from the general principles in the design of distributed systems; then we provide frameworks on how storage, computation, and network capabilities are scaled in big data systems; finally, to make such design principles easy to follow, our case studies use real industrial systems to demonstrate how the basic design principles are applied in real-world systems as well as how their performance and limitation are analyzed.

What's inside

Learning objectives

Basic concepts of big data systems
Principelsof designing distributed systems
Frameworks on scaling storage, computaion and network capabilities

Case studeis of recent industrial big data systems, including gfs, mapreduce and spark
Big data processing pipelines such as nosql, streaming, and graph data processing

Basic concepts of big data systems
Principelsof designing distributed systems
Frameworks on scaling storage, computaion and network capabilities
Case studeis of recent industrial big data systems, including gfs, mapreduce and spark
Big data processing pipelines such as nosql, streaming, and graph data processing

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适合高级大数据系统相关学习者学习。

适合想要了解大数据系统基本概念的学习者。

适合想要提高数据存储、处理和分析技能的学习者学习。

授课者具有在大数据系统方面的丰富经验。

课程内容涵盖了大数据系统的设计、实现和应用等核心知识点。

Reviews summary

高级大数据系统核心原理与实践

根据学生反馈，高级大数据系统课程提供了对大数据系统实现、优化和应用的深度解析。学生们普遍认为课程内容系统且深入，特别是在分布式文件系统、MapReduce/Spark等核心原理方面。案例研究和作业设计被认为是理解复杂概念的有效途径，尤其适合有一定基础的专业人士。部分学生提到课程理论性较强，对于初学者或前置知识不足的学员可能难度较大，需要投入大量课后消化时间。然而，近期评论显示，课程内容有所更新和改进，例如增加了实战环节讲解和直播答疑，反映出积极的教学迭代。

作业设计有挑战性，有效检验学习成果。

"作业也很有挑战性，但能学到真东西。"

"作业设计精巧，能真正检验对知识点的掌握。"

"作业量适中，但需要投入时间，这让我对知识掌握得更牢固。"

通过真实案例帮助理解复杂理论。

"尤其是对Spark和MapReduce的案例分析，让我对实际应用有了更深刻的理解。"

"虽然有些理论抽象，但通过案例分析还是能够理解。"

"我学习到了如何在大数据系统中评定存储、计算和网络功能，并通过工业案例了解了实际应用。"

课程深入讲解大数据系统核心原理。

"这门课程内容非常深入，对分布式系统和大数据处理的核心原理讲得很透彻。"

"我从事大数据开发多年，这门课让我对底层原理有了系统性的认识。"

"深入剖析了Hadoop生态和Spark的内部机制。"

讲师根据反馈积极更新教学内容和方式。

"最近的更新似乎加入了更多实战环节的讲解，感觉比我之前上的时候好多了。"

"最近的直播答疑环节非常有益，感觉到老师在积极改进教学方式。"

"我很高兴看到课程正在加入更多实战环节和前沿技术，这让学习体验更好。"

部分学生认为理论内容比实践环节多。

"感觉理论讲解偏多，如果能有更多的实际操作或编程练习就更好了。"

"对于纯粹想上手实践的可能会觉得不够。"

"有些技术栈的讲解感觉和最新的工业实践有脱节，希望实践部分能更贴近前沿。"

对学员的理论基础和学习能力要求较高。

"课程内容确实高级，但对我来说有点吃力，可能是因为我的前置知识不足。"

"课程体系很宏大，但对初学者不友好。我发现很多同学都在抱怨难度太大。"

"讲解速度快，很多概念需要课后花大量时间消化。"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Advanced Big Data Systems | 高级大数据系统 with these activities:

学习 Spark Streaming

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掌握 Spark Streaming 的基本概念和应用，增强对大数据实时处理能力的理解。

Browse courses on Spark Streaming

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查找并参加 Spark Streaming 相关教程
完成教程并练习示例代码
尝试一个小型流数据处理项目

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Career center

Learners who complete Advanced Big Data Systems | 高级大数据系统 will develop knowledge and skills that may be useful to these careers:

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data. This course can help aspiring Quantitative Analysts build a foundation in the concepts and principles of big data systems, including distributed file systems, MapReduce/Spark, Storm/Spark streaming, and Mahout. It also covers the optimization and application of these systems, making you a more well-rounded and effective Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines and infrastructure to support data-driven decision-making. This course can help aspiring Data Engineers build a foundation in the concepts and principles of big data systems, including distributed file systems, MapReduce/Spark, Storm/Spark streaming, and Mahout. It also covers the optimization and application of these systems, making you a more well-rounded and effective Data Engineer.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers design, develop and maintain software systems. This course can help aspiring Software Engineers build a foundation in the concepts and principles of big data systems, including distributed file systems, MapReduce/Spark, Storm/Spark streaming, and Mahout. It also covers the optimization and application of these systems, making you a more well-rounded and effective Software Engineer.

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Cloud Engineer

Cloud Engineers design, build and maintain cloud computing systems and applications. This course can help aspiring Cloud Engineers build a foundation in the concepts and principles of big data systems, including distributed file systems, MapReduce/Spark, Storm/Spark streaming, and Mahout. It also covers the optimization and application of these systems, making you a more well-rounded and effective Cloud Engineer.

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Data Analyst

Data Analysts collect, clean and analyze data to identify trends and patterns. This course can help aspiring Data Analysts build a foundation in the concepts and principles of big data systems, including distributed file systems, MapReduce/Spark, Storm/Spark streaming, and Mahout. It also covers the optimization and application of these systems, making you a more well-rounded and effective Data Analyst.

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Business Intelligence Analyst

Business Intelligence Analysts use data to help businesses make better decisions. This course can help aspiring Business Intelligence Analysts build a foundation in the concepts and principles of big data systems, including distributed file systems, MapReduce/Spark, Storm/Spark streaming, and Mahout. It also covers the optimization and application of these systems, making you a more well-rounded and effective Business Intelligence Analyst.

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Data Mining Analyst

Data Mining Analysts use data mining techniques to extract knowledge and insights from data. This course can help aspiring Data Mining Analysts build a foundation in the concepts and principles of big data systems, including distributed file systems, MapReduce/Spark, Storm/Spark streaming, and Mahout. It also covers the optimization and application of these systems, making you a more well-rounded and effective Data Mining Analyst.

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Machine Learning Engineer

Machine Learning Engineers design, develop and deploy machine learning models to solve real-world problems. This course can help aspiring Machine Learning Engineers understand the underlying principles and architectures of big data systems, which are essential for working with and analyzing large datasets used in machine learning. By taking this course, you will gain a competitive edge in the field of Machine Learning.

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Data Scientist

Data Scientists use scientific methods, processes, algorithms and systems to extract knowledge and insights from data. This course can help aspiring Data Scientists understand the underlying principles and architectures of big data systems, which are essential for working with and analyzing large datasets. By taking this course, you will gain a competitive edge in the field of Data Science.

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Cloud Architect

Cloud Architects design, build and manage cloud computing systems and applications. This course may be useful for aspiring Cloud Architects, as it covers the principles of designing distributed systems, as well as frameworks for scaling storage, computation and network capabilities, which are essential for designing and managing cloud-based systems and applications at scale.

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DevOps Engineer

DevOps Engineers work to bridge the gap between development and operations teams, ensuring that software is built, tested, and deployed efficiently and reliably. This course may be useful for aspiring DevOps Engineers, as it covers the principles of designing distributed systems, as well as frameworks for scaling storage, computation and network capabilities, which are essential for designing and managing the infrastructure that supports software development and deployment.

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Data Architect

Data Architects plan, design, and build an organization's data systems. This course may be useful for aspiring Data Architects, as it will teach you the principles of designing distributed systems, as well as frameworks for scaling storage, computation and network capabilities, which are critical concepts for designing efficient and reliable data systems at scale. Furthermore, this course provides case studies of recent industrial big data systems such as GFS, MapReduce and Spark, which will give you practical insights into the design and implementation of real-world data systems, making you a more competitive candidate for Data Architect roles.

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Systems Administrator

Systems Administrators are responsible for the management and maintenance of computer systems and networks. This course may be useful for aspiring Systems Administrators, as it covers the principles of designing distributed systems, as well as frameworks for scaling storage, computation and network capabilities, which are essential for managing and maintaining complex systems at scale.

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Software Architect

Software Architects design, build and maintain the overall architecture of software systems. This course may be useful for aspiring Software Architects, as it covers the principles of designing distributed systems, as well as frameworks for scaling storage, computation and network capabilities, which are essential for designing and managing complex software systems at scale.

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Database Administrator

Database Administrators are responsible for the design, implementation, maintenance and security of database management systems. This course may be useful for aspiring Database Administrators, as it covers the principles of designing distributed systems, as well as frameworks for scaling storage, computation and network capabilities, which are essential for designing and managing efficient and reliable database systems at scale.

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