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.
Distributed Query Optimization and Security
Distributed Query Optimization and Security
This course is part of Large-Scale Database Systems Specialization
Instructor: David Silberberg
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Intermediate level
Recommended experience
17 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Learners will gain skills in implementing database security through views, dynamic authorization, and semantic integrity rules.
Students will understand query optimization techniques, cost evaluation, and the creation of optimized query plans to enhance database performance.
Learners will master distributed query optimization, using cost models, MapReduce, and HDFS for efficient data storage, compression, and processing.
Skills you'll gain
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Recently updated!
December 2024
Assessments
9 assignments
Taught in English
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This course is part of the Large-Scale Database Systems Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
This course delves into advanced topics in query processing and data security within distributed databases. Students will learn about semantic data control, including the use of views and authorization techniques, to secure data access. The course also covers distributed query processing, and optimization methods. By understanding these techniques, learners will develop skills to create optimized, secure distributed databases capable of handling complex data queries. This module also introduces another paradigm of large-scale data systems supported by Hadoop, MapReduce and HDFS.
What's included
2 readings
This course provides an in-depth exploration of database security, focusing on defining and implementing views for secure data access. Students will learn how to apply dynamic authorization techniques through cascading grant and revoke policies, and specify semantic integrity rules essential for maintaining security and consistency within distributed database systems.
What's included
3 videos4 readings3 assignments
This course delves into the critical aspects of query optimization in databases, focusing on the motivation behind optimizing queries, evaluating the cost-effectiveness of various query plans, and understanding the steps involved in the optimization process. Students will learn to calculate optimization costs and decompose SQL queries into efficient, optimized query trees, enhancing overall database performance.
What's included
3 videos4 readings3 assignments
This module delves into distributed query optimization techniques, introducing key methodologies to improve performance in distributed systems using cost models and heuristics for near-optimal query plans. Learners will gain practical skills in query optimization, leveraging the semi-join algorithm. Learners will also gain experience in another popular large-scale data approach embodied in MapReduce supported by the Hadoop Distributed File System (HDFS) to efficiently process large datasets. Through hands-on exercises, students will practice writing HDFS code for data storage and manipulation, focusing on data compression and decompression, and employ advanced MapReduce patterns for optimal data processing..
What's included
6 videos6 readings3 assignments
Instructor
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Johns Hopkins University
Johns Hopkins University
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