If you are an engineering student looking to make a mark in this field, consider undertaking projects that focus on distributed and parallel computing systems. These projects could involve developing algorithms for parallel processing, optimizing system performance, and implementing machine learning techniques. Few Engineering Parallel and Distributed Computing Project ideas: • Apache Hadoop: This is a popular open-source framework that is used to store and process large datasets in parallel across a distributed network of computers. Hadoop provides a scalable and fault-tolerant way to handle big data. • Apache Spark: This is another popular open-source framework that is used for big data processing. Spark provides a more generalized computing model than Hadoop and can be used for a variety of tasks such as machine learning, graph processing, and stream processing. • MPI (Message Passing Interface): This is a standard protocol used for communication between parallel processes running on different computers. MPI is widely used in scientific and engineering applications that require parallel computing. • OpenMP: This is a programming interface for shared-memory parallel computing. OpenMP allows developers to parallelize their code by adding simple directives to their code.