Distributed Query Optimization and Security
Master query optimization and robust security measures in distributed databases for enhanced performance and security.
Master query optimization and robust security measures in distributed databases for enhanced performance and security.
This comprehensive course explores the critical intersection of query optimization and security in distributed database environments. Students will develop expertise in securing database access through views and dynamic authorization techniques, implementing cascading grant and revoke policies, and enforcing semantic integrity rules essential for maintaining data consistency. The course delves deeply into query optimization, teaching learners to evaluate the cost-effectiveness of different query plans, apply transformation rules, and implement optimization algorithms including Ingres and System R. Advanced topics include distributed query processing and optimization using semi-join algorithms and cost models. The course also introduces Hadoop, MapReduce, and HDFS as complementary approaches to large-scale data management, providing hands-on experience with data compression, storage, and processing techniques. Through practical assignments and self-reflective readings, students will gain the skills needed to design and implement efficient, secure distributed database systems capable of handling complex queries across multiple data sources.
Instructors:
English
Powered by
What you'll learn
Implement database security through views and authorization techniques
Apply dynamic authorization with cascading grant and revoke policies
Specify and enforce semantic integrity rules in distributed environments
Evaluate the cost-effectiveness of different query plans
Implement query optimization steps and use statistical information
Apply transformation rules to optimize query execution
Skills you'll gain
This course includes:
2 Hours PreRecorded video
9 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This course provides a comprehensive exploration of two critical aspects of distributed database management: query optimization and security. Organized into four modules, it begins with an introduction to the course themes before diving into semantic data control. Students learn to implement database security through views, dynamic authorization techniques, and semantic integrity rules. The third module focuses on distributed query processing, covering query transformation rules, operation costs, and optimization motivations. The final module explores advanced query optimization techniques including the Ingres and System R algorithms, while also introducing Hadoop, MapReduce, and HDFS as alternative approaches to large-scale data management. Throughout the course, theoretical concepts are reinforced through practical assignments, extensive readings, and self-reflective exercises that develop both technical understanding and critical thinking skills.
Course Introduction
Module 1 · 10 Minutes to complete
Semantic Data Control
Module 2 · 4 Hours to complete
Distributed Query Processing
Module 3 · 5 Hours to complete
Query Optimization and an Introduction to Hadoop, MapReduce and HDFS
Module 4 · 7 Hours to complete
Fee Structure
Instructor
Expert in Distributed Database Systems and Large-Scale Computing
David Silberberg is a distinguished instructor at Johns Hopkins University, specializing in large-scale database systems and distributed computing. He holds a Ph.D. in Computer Science from the University of Maryland and both Master's and Bachelor's degrees in Computer Science from the Massachusetts Institute of Technology. Dr. Silberberg serves as a Principal Professional Staff member at the Johns Hopkins University Applied Physics Laboratory (APL) and is the Research Director of the Johns Hopkins Institute for Assured Autonomy. Dr. Silberberg's expertise spans various areas of computer science, including AI and machine learning algorithms, graph analytics, distributed and large-scale architectures, intelligent access to distributed and heterogeneous database systems, and semantic graph query languages. He is the instructor for the "Large-Scale Database Systems Specialization" on Coursera, which covers advanced topics in distributed database systems, cloud computing, data reliability, and machine learning for large-scale data solutions.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.