The Distributed Databases course develops a structured foundation across four source-defined units. Learners begin with foundations of distributed databases, progress to data distribution, fragmentation, replication and sharding, then move into distributed transactions, concurrency, consistency and recovery, and close with modern distributed database platforms and design decisions. The sequence is supported by theory sessions and course materials, helping learners connect each unit with a consistent learning rhythm.
The course progresses from foundations into fragmentation, replication and sharding, then covers transactions, concurrency, consistency and recovery before closing with modern platforms and design decisions. Designed for students, technical learners and early-career professionals, the course provides a focused six-week online pathway that preserves the supplied module order and topic pattern. The emphasis remains on the skills and concepts named in the source curriculum, with no unsupported modules, tools or outcomes added.
Design distributed data strategies around replication, consistency and recovery
The project submission process helps learners apply Generative AI concepts in a practical and structured way. Therefore, participants complete project work to demonstrate their understanding, originality, and professional application.
First, explore the project topics available in the LMS. Review the project description, learning outcomes, and required skills before selecting your topic.
If required, ask your guide or mentor for support. After that, finalize your topic and begin your project work.
Once your project report is complete, follow the project submission guidelines for formatting, font, spacing, citations, and originality.
Your report will go through a plagiarism check. Therefore, keep the work original, properly structured, and clearly written.
Finally, upload your final PDF report and any supporting files to the LMS.
After submission, your project is evaluated by an organization mentor and an internal faculty supervisor.
The evaluation focuses on report quality, depth of understanding, concept application, problem-solving skills, professional behaviour, and timely submission. In some cases, a viva or presentation may also be required.
Credits may be awarded as per applicable UGC NEP 2020 guidelines. In addition, your internship certificate will be issued after report approval, evaluation completion, and uploading of the required organization certificate.
Thereafter, learners can download the internship completion certificate directly from the LMS.
