Welcome to the specialization course of Designing data-intensive applications. This course spans four weeks and is supported by videos and exercises, providing comprehensive knowledge and practical experience in designing and developing high reliable information systems. The course covers a range of essential topics, including:
Learners will gain expertise in proposing, designing, justifying, and developing information systems tailored to the type of data, volume of information, response time, type of processing, and queries, while ensuring scalability, maintainability, security, and reliability using the latest information technologies. Software such as MySQL Workbench, Rapidminer, Hadoop framework, Hortonworks, MongoDB, and VirtualBox (for Mac/iOS operating systems) will be utilized throughout the course.
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This course comprises four modules, each focusing on different aspects of designing data-intensive applications. From designing transaccional and analytical systems to exploring alternatives to relational databases and analytical systems within a data lake, learners will gain comprehensive knowledge and practical experience.
Gain insight into designing transaccional systems, understanding their importance, and learning how to develop and maintain them effectively. This module encompasses real-world use cases and exercises to provide practical experience and in-depth understanding of transaccional systems.
Discover the intricacies of designing analytical systems and learn to develop and maintain them to support data analysis effectively. Through real-world use cases and exercises, learners will gain practical experience and comprehensive knowledge of analytical systems.
Explore alternatives to relational databases and understand their significance in information systems. This module delves into practical exercises and use cases to provide comprehensive knowledge and practical experience in designing and implementing alternative solutions.
Dive into the complexities of designing analytical systems within a data lake and learn to develop and maintain them for effective data analysis. Real-world use cases and exercises offer practical experience and comprehensive knowledge of analytical systems within a data lake.
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