Learnitweb

Author: Editorial Team

  • Hive Architecture

    —

    by

    Editorial Team
    in

    In this tutorial, we are going to deeply understand the architecture of Hive, how its components are structured, and how a query flows through the system from start to finish in a real execution scenario. Introduction to Hive Architecture Hive architecture consists of five major components, and one of these components is tightly integrated with…

  • Hive vs SQL

    —

    by

    Editorial Team
    in

    Hive is similar to SQL in syntax, but fundamentally very different in architecture, purpose, and usage. In this tutorial, we will carefully explore these differences so that you develop a clear and accurate understanding. Why Do People Get Confused? The confusion arises because Hive queries look very similar to SQL queries, using familiar keywords like…

  • Introduction to Apache Hive

    —

    by

    Editorial Team
    in

    Welcome to the first lecture of this course. Before we start writing queries or diving into hands-on examples, it is very important to build a strong conceptual foundation of what Apache Hive is, how it works, and where it should (and should not) be used. This tutorial will walk you through Hive in a structured…

  • Multi-Line Statements and Multi-Line Strings in Python

    —

    by

    Editorial Team
    in

    Introduction When we write Python programs, we often think of code simply as lines written one after another in a file, but internally, Python does not interpret code strictly based on how we physically write it. Instead, Python distinguishes between what we see as lines of code and what it actually executes as logical statements.…

  • Python Type Hierarchy

    —

    by

    Editorial Team
    in

    1. Introduction When we begin learning Python, we usually start with simple data types like integers, lists, or dictionaries, and we learn how to use them in isolation without thinking much about how they relate to each other internally. However, as we progress toward becoming an advanced Python developer, it becomes extremely important to understand…

  • Numeric Data Types in ClickHouse

    —

    by

    Editorial Team
    in

    This tutorial provides a deep and practical understanding of numeric data types in ClickHouse, how they behave internally, and how to use them correctly in analytical queries. Numeric types are heavily used in analytics for aggregations, metrics, financial data, and scientific computations, which makes understanding their behavior critical. Choosing the correct numeric type in ClickHouse…

  • Operators in ClickHouse

    —

    by

    Editorial Team
    in

    This tutorial explains the different types of operators in ClickHouse, how they work, and how to use them in real queries. Operators are fundamental to querying because they allow you to perform calculations, comparisons, logical decisions, and null handling directly inside SQL. We will cover: Each section includes queries and their textual table results so…

  • Joins and Unions in ClickHouse

    —

    by

    Editorial Team
    in

    This tutorial explains how Joins and Unions work in ClickHouse, how they combine data from multiple tables, and how to use them correctly in real scenarios. The goal is not only to show syntax, but to build a clear mental model of how ClickHouse processes relational queries. We will move step-by-step from concepts to syntax…

  • Views in ClickHouse: Regular, Parameterized, and Materialized Views

    —

    by

    Editorial Team
    in

    1. Introduction to Views in ClickHouse A view in ClickHouse is a database object that looks and behaves like a table when you query it, but its contents are derived from a SQL query rather than being stored as independent data like a normal table. A view allows you to encapsulate complex queries, business logic,…

  • Column-Level DDL Operations in ClickHouse

    —

    by

    Editorial Team
    in

    In ClickHouse, Data Definition Language does not only apply to creating or dropping entire tables and databases, but also to evolving the structure of existing tables over time, and this is an important capability because real-world analytical schemas rarely remain static as requirements change, new attributes are needed, or old fields become irrelevant. Column-level DDL…