Data Engineering

Table of ContentsWhat are LLMs?Challenges of Working with Large Language Models1. Data Requirements2. Computational Resources3. Expertise in Prompt Engineering4. Bias5. Explainability and TransparencyWhat is LLMOps?LLMOps lifecycleComparison between MLOps and LLMOpsConclusion…

As a data engineer, your job is to create powerful solutions for handling large amounts of data. You start by collecting data from different sources. Then, you clean, organize, and…

Windowing functions allow analysts to divide data into segments, enabling targeted analysis based on time intervals or event sequences. In this article, we will explore different types of windowing functions,…

Over the past two decades, many organizations have focused on creating data warehouses and business intelligence (BI) solutions using relational databases. However, these solutions often struggle to handle unstructured data…

Let's start by answering a fundamental question: Why is Docker important? Think about a situation that every developer can relate to — when you install a new tool on your…

Have you ever wondered why we don’t use Apache Airflow to process data directly? Why is it necessary to integrate Apache Spark with Apache Airflow? Take a moment to think…

The Data Engineer Roadmap helps you become a data engineer in the easiest way. As organizations work with large amounts of data, they need skilled professionals who can create and…

Graph databases are useful for complex queries, like social network analysis, product recommendations, and fraud detection. They can help us discover new insights, solve problems, and uncover fraud. It is…