Big Data Concept

  Imagine this: You're a small business owner staring at a spreadsheet bloated with customer data—thousands of rows of sales figures, feedback comments, and market trends. Your eyes glaze over at the thought of sifting through it all, spotting patterns, or even predicting what's next. Coding? Forget it; you're not a data scientist. Years ago, this nightmare was the reality for most of us. Big data felt like an exclusive club for tech wizards with PhDs and supercomputers. But today? Thanks to Google Sheets and its army of AI plugins, that club has thrown open its doors. Anyone with a Gmail account and a curious mind can dive into data analysis like a pro. In this chapter, we're going to unpack how Google Sheets, that humble hero of collaborative spreadsheets, has leveled up with AI smarts. We'll explore what these plugins are, why they matter, and—most importantly—how you can use them to turn your data chaos into actionable insights. No jargon overload, I promise....

  Introduction In the era of Big Data, organizations collect massive volumes of structured and unstructured data from diverse sources. However, raw data is rarely perfect. It often contains errors, missing values, duplicates, or inconsistencies that compromise its quality and reliability. Data cleaning, also known as data preprocessing, is therefore a crucial step in any Big Data project. Traditional approaches to data cleaning are often manual, rule-based, and time-consuming.  With the advent of Agentic AI , a new paradigm is emerging—one that automates, adapts, and optimizes data cleaning at scale. What is Agentic AI? Agentic AI refers to artificial intelligence systems that operate with goal-driven autonomy , capable of perceiving their environment, reasoning about tasks, and taking actions without continuous human oversight. Unlike static machine learning models, Agentic AI agents can dynamically adapt to new conditions, negotiate trade-offs, and optimize workflows i...