Big Data!! Simple Solutions!!!

We hear about ChatGPT, Generative AI, and LLMs almost every day, but what actually happens when we ask an LLM a question? Let’s break it down simply 👇 🔹 1. What is an LLM? A Large Language Model is an AI model trained on a massive amount of text to understand patterns in language and generate human like responses. 🔹 2. How does an LLM learn? During training, the model processes huge amounts of text and learns relationships between words, phrases, concepts, and context. At a high level, it learns to predict what comes next based on the information it has seen. 🔹 3. What happens when we ask a question? Our text is converted into tokens . The model processes these tokens, considers the surrounding context, and generates a response token by token. So, an LLM isn't simply "searching a database" for an answer. It is generating a response based on patterns learned during training and the context provided. 🔹 4. Where are LLMs being used? 💻 Code generation 📄 Document summar...

Introduction Artificial Intelligence (AI) has evolved rapidly over the past decade, but one innovation has fundamentally transformed how humans interact with technology: Large Language Models (LLMs) . Unlike traditional software that follows predefined rules, LLMs can understand and generate natural language, summarize information, write and explain code, analyze documents, translate languages, and assist with creative and analytical tasks. Rather than replacing human expertise, they act as intelligent assistants that help individuals and organizations solve problems faster, improve productivity, and make better-informed decisions. The true value of LLMs lies in augmenting human intelligence . By automating repetitive tasks and accelerating access to knowledge, they enable professionals to focus on creativity, critical thinking, strategy, and innovation. What is a Large Language Model? A Large Language Model (LLM) is a deep learning model, typically built on the Transformer architectu...

🏦 Data Lake, Data Warehouse, Data Mart, and Delta Lake Explained through a story you won't forget — using banking data 💳 👨‍💼 Meet Arjun. He works at a bank and wants to understand customer behavior to improve services. So, he starts collecting everything the bank has: ATM withdrawal logs Mobile app clickstream events Loan application forms Voice call recordings with customer care Account transaction history PDFs, emails, scanned documents He stores it all in one central location without worrying about format or structure. 👉 This is a Data Lake – a huge storage system where raw data of all types (structured, semi-structured, unstructured) is dumped for future use. Now, Arjun’s analytics team needs clean, trusted data to build dashboards and generate reports. So, he processes the raw data: He filters out errors, standardizes formats, fills missing values, and organizes it into structured formats (like SQL tables or clean Parquet files) — optimize...

Let's  say we are working on a Customer Analysis Project & have a Jira ticket assigned to us as CA-111 If we are a developer, we would create a feature branch as below : feature/CA-111 and work on it. As soon as we make a git push, and GitHub receives the new code, a pipeline should run which involves below steps : 1. Build   :  Creating a virtual environment & install all the dependencies (In case of python). 2. Test : Run unit test cases / Quality checks. 3. Package : Create a package, can be a zip of code. 4. Deploy : Send the code bundle to edge node using SCP (Secure Copy). If we do all of these manually then it will be a time consuming and error prone. So all of the above steps should run as a automated pipeline step by step.  We can automate it using a automation server such as Jenkins. So anytime a new branch is created, or a git push happens, all of the build -> test -> package -> deploy should run without any manual in...