• AI, ML & Data Science - Big Data - iPaaS - Analytics & Reporting

    Big Data in 2024: From Hype to AI Powerhouse—What’s the Real Story?

    October 31, 2024 - By 

    Introduction: A Decade of Big Data Blogging When I began writing about Big Data in 2013, it was an exciting new frontier in data management and analytics. My first blog, What’s So BIG About Big Data, introduced the core pillars of Big Data—the “4 Vs”: Volume, Velocity, Variety, and Veracity. As the years passed, I expanded into related topics with posts like Introduction to Hadoop, Hive, and HBase, Data Fabric and Data Mesh, and Introduction to Data Science with R & Python. Each blog marked the evolution of Big Data and reflected the shifting focus in the field as data…

  • AI, ML & Data Science - Analytics & Reporting - Big Data

    Demystifying the World of AI, ML, and Data Science: A New Structured Learning Journey

    October 25, 2024 - By 

    Welcome to an exciting new chapter in exploring the world of AI, Machine Learning (ML), and Data Science! Over the years, I have posted on a variety of topics, covering everything from Python basics to the intricacies of neural networks. But now, it’s time for something bigger—a cohesive, structured series that will demystify these domains, guiding you step-by-step from foundational concepts to advanced applications. In this revamped series, I will reorganize my previously published blogs, presenting them in a logical progression so you can easily follow along, regardless of your current experience level. Alongside these, I’ll also introduce new posts…

  • AI, ML & Data Science

    AI in Today’s World: Machine Learning & Deep Learning Revolution

    October 2, 2024 - By 

    A Brief History of AI Artificial Intelligence (AI) as a concept isn’t new. Its roots trace back to the 1950s when pioneers like Alan Turing began asking if machines could think and how they might do so. The initial focus was on logic and symbolic reasoning, leading to the development of early algorithms designed to mimic human decision-making. However, it wasn’t until the last few decades that AI began to move beyond theoretical applications to practical, everyday use. This progress became feasible with advances in hardware, increased computational power, and the emergence of vast amounts of digital data — setting…