Large-Scale Database Systems

Johns Hopkins University via Coursera Specialization

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Overview

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The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you'll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems. By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.

Syllabus

Course 1: Foundations of Distributed Database Systems
Course 2: Distributed Query Optimization and Security
Course 3: Reliability, Cloud Computing and Machine Learning

Courses

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17 hours 33 minutes
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The course "Distributed Query Optimization and Security" provides a comprehensive exploration of query optimization and data security in distributed databases. Students will gain in-depth knowledge of how to secure data access through views and dynamic authorization techniques, essential for maintaining the integrity and confidentiality of distributed systems. Learners will also master distributed query processing, understanding how to evaluate, optimize, and implement efficient query plans. The course uniquely blends advanced database security techniques with practical applications of large-scale data systems, such as Hadoop, MapReduce, and HDFS. By completing this course, learners will be equipped with the skills to optimize complex queries, enhance database security, and handle large datasets effectively. With hands-on experience in MapReduce and HDFS, learners will develop the ability to create scalable, optimized, and secure distributed database systems. This course is ideal for professionals seeking to advance their expertise in database management and distributed systems, with a focus on both performance optimization and data protection.
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17 hours 12 minutes
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The course "Foundations of Distributed Database Systems" lays the foundation for understanding distributed database systems, a cornerstone of modern data management. You’ll delve into core principles and architectures, gaining insight into the challenges of managing data across distributed environments. Through hands-on learning, you’ll explore horizontal and vertical partitioning techniques, understanding how to apply them to improve query performance and scalability. By mastering these concepts, you’ll be equipped to design and optimize databases that handle large-scale data efficiently. What makes this course unique is its emphasis on practical implementation, enabling you to translate theoretical knowledge into actionable skills. Whether you're a student, data professional, or developer, this course will empower you to build robust distributed database systems, a critical skill in today’s data-driven world. Prepare to tackle real-world scenarios with confidence and acquire the expertise to manage the complexities of distributed data systems effectively.
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20 hours 23 minutes
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The course "Reliability, Cloud Computing and Machine Learning" explores advanced distributed database concepts, focusing on transaction management, reliability protocols, and data warehousing, while also diving deeper into cloud computing and machine learning. You will develop a solid understanding of transaction principles, concurrency control methods, and how to ensure database consistency during failures using ACID properties and protocols like ARIES. The course uniquely integrates Hadoop, MapReduce, and Accumulo, offering hands-on experience with large-scale data processing and machine learning applications such as collaborative filtering, clustering, and classification. By mastering these advanced topics, you'll gain the skills necessary to work with cutting-edge technologies used in cloud-based data processing and scalable machine learning analysis. With practical applications in both reliability management and machine learning, this course prepares you to tackle complex data management challenges, making you well-equipped for careers in cloud computing, distributed systems, and data science.