Curated, long-form guides on C++, Linux, embedded systems, cloud computing, AI, and more.
Modern C/C++ articles covering templates, RAII, memory management, and real-world performance engineering.
LinuxKernel programming, Linux internals, drivers, debugging tools, and low-level systems engineering.
EmbeddedEmbedded C, board bring-up, device drivers, IoT protocols, microcontrollers, and real embedded workflows.
CloudAWS pipelines, Glue ETL, Lambda automation, monitoring, cloud engineering, and DevOps practices.
AI/MLStudy notes, NLP, ML concepts, training models, GPU compute, Big Data pipelines, and distributed data processing.
NetworksRouting, distributed computing, MPI, parallel processing, networking fundamentals, and systems communication.
Big DataTechnical content focused on large-scale data processing, distributed storage systems, ETL pipelines, data partitioning, batch and streaming architectures, Hadoop-ecosystem tools, and performance optimisation for high-volume data workloads.
NotesBITS Pilani WILP notes, cloud computing summaries, AI/ML semester material, and structured study guides for students and engineers.
This post discusses the importance of efficient data handling in modern C++, exploring methods of passing contiguous data buffers to functions, including raw pointers, std::vector, std::array, and the C++20 feature std::span. It highlights the strengths and weaknesses of each method, emphasizing std::span’s benefits for safe, flexible data management without ownership.
Choosing an appropriate C++ container impacts memory layout, cache efficiency, and access patterns, vital for performance. Common comparisons include std::vector, std::deque, std::array, std::list, std::map, and std::unordered_map. The choice should align with data access and modification requirements, ensuring optimal performance for diverse workloads, from iteration to key-based access.
The study notes cover AI, ML, and deep learning, highlighting key concepts such as pre-trained and fine-tuned models, transfer learning, and their interpretability. It explores cognitive services across platforms like Azure and Google Cloud, emphasizing their applications in AI workflows, NLP, and computer vision tasks, along with practical examples and tools.
Table of Contents Distributed Computing (S1-25_CCZG526) Lab Assignment 1 Lab Assignment 2 Cloud Infrastructure and Systems Software (S1-25_CCZG502) Lab Assignment 1 API-driven Cloud Native Solutions…
Continue reading → BITS PILANI Assignments Second Semester M.Tech in Cloud Computing
The blog discusses the integration of Amazon EventBridge to automate AWS Glue workflows every two minutes, enhancing operational efficiency in data engineering and machine learning tasks. It details steps to create and configure EventBridge rules, set permissions, and verify workflows, emphasizing improvements in responsiveness, agility, and DataOps maturity.
The implemented stages of ingestion, preprocessing, EDA, and feature engineering have transitioned to automation and monitoring, forming a cohesive DataOps layer. By introducing orchestration, the independent Glue jobs become an automated, reliable workflow. Testing confirmed successful execution, paving the way for regular automations to enhance operations and insights from data.
Tech For Talk 