In-depth, easy-to-follow technical articles on C++, Linux, embedded systems, cloud computing, cloud-native application development, generative AI, and AI-driven live engineering projects, written for software professionals and AI enthusiasts.
Active engineering projects documented in real time, including architecture decisions, code walkthroughs, and lessons learned as they happen.
Practical AI engineering projects built with generative AI, multimodal models, APIs, Python, and end-to-end automation workflows.
Complete, structured technical books published chapter by chapter — starting with “Understanding C++ Templates: A Comprehensive Guide”.
Modern C/C++ articles covering templates, RAII, memory management, and real-world performance engineering.
Kernel programming, Linux internals, drivers, debugging tools, and low-level systems engineering.
Embedded C, board bring-up, device drivers, IoT protocols, microcontrollers, and real embedded workflows.
AWS pipelines, Glue ETL, Lambda automation, monitoring, cloud engineering, and DevOps practices.
Study notes, NLP, ML concepts, training models, GPU compute, Big Data pipelines, and distributed data processing.
Routing, distributed computing, MPI, parallel processing, networking fundamentals, and systems communication.
Large-scale data processing, distributed storage, ETL pipelines, batch and streaming architectures, and Hadoop-ecosystem tools.
BITS Pilani WILP notes, cloud computing summaries, AI/ML semester material, and structured study guides for students and engineers.
The content discusses various partitioning methods for database tables, including range, list, and hash partitioning, along with their applications. It also covers data storage calculations, B-Tree construction, and the nature of B-Trees. Key concepts include composite partitioning, selectivity in data warehouses, and handling duplicate values in B-Tree indexes.
The content discusses various types of encryption, notably symmetric (using a single key) and asymmetric (utilizing a public and private key). It evaluates methods such as AES, homomorphic encryption, and client-server approaches, emphasizing the importance of strong key management practices and the significance of encryption across sectors like healthcare and finance.
The Physical Design Process in database management transforms logical models into practical structures for efficient data handling. It focuses on optimizing data storage, access, and retrieval through methods like indexing, partitioning, and aggregation. This ensures quick query responses and scalability while maintaining a systematic architecture for performance and manageability.
Serverless architecture is a cloud computing model where the provider manages the infrastructure, allowing developers to focus solely on code execution in response to events. This paradigm shift emphasizes event-driven designs, efficient scaling, and cost-effectiveness, reducing operational overhead while highlighting security challenges related to input validation, permissions, and dependency management.
The Shared Responsibility Model outlines security roles between cloud providers and customers across IaaS, PaaS, and SaaS. Providers manage infrastructure security while customers secure their deployed resources. Understanding these responsibilities is crucial to prevent breaches. Effective cloud security frameworks like IAM, API gateways, and VM hardening mitigate risks in various architectures.
OLTP systems require a normalized schema to process everyday transactions efficiently, ensuring data integrity, reducing anomalies, and supporting concurrent users. In contrast, data warehouses use denormalized structures for analytical purposes. The Staging Area facilitates data extraction, cleansing, and integration before loading into the warehouse, vital for decision-making and reporting.
