Data Warehousing
  • Data Warehousing
  • Readme
  • Fundamentals
    • Terms to Know
    • Jobs
    • Skills needed for DW developer
    • Application Tiers
    • Operational Database
    • What is a Data Warehouse
      • Typical Data Architecture
      • Problem Statement
      • Features of Data Warehouse
      • Need for Data Warehouse
      • Current State of the Art
    • Activities of Data Science
    • Types of Data
    • Data Storage Systems
    • Data Warehouse 1980 - Current
    • Data Warehouse vs Data Mart
    • Data Warehouse Architecture
      • Top-Down Approach
      • Bottom-Up Approach
    • Data Warehouse Characteristic
      • Subject Oriented
      • Integrated
      • Time Variant
      • Non Volatile
    • Tools
    • Cloud vs On-Premise
    • Steps to design a Data Warehouse
      • Gather Requirements
      • Environment
      • Data Modeling
      • Choosing ETL / ELT Solution
      • Online Analytic Processing
      • Front End
      • Query Optimization
    • Dataset Examples
    • Thoughts on some data
  • RDBMS
    • Data Model
      • Entity Relationship Model
      • Attributes
      • Keys
      • Transaction
      • ACID
    • Online vs Batch
    • DSL vs GPL
    • Connect to Elvis
    • SQL Concepts
      • Basic Select - 1
      • Basic Select - 2
      • UNION Operators
      • Wild Cards & Distinct
      • Group By & Having
      • Sub Queries
      • Derived Tables
      • Views
    • Practice using SQLBolt
  • Cloud
    • Overview
    • Types of Cloud Services
    • Challenges of Cloud Computing
    • AWS
      • AWS Global Infrastructure
      • EC2
      • S3
      • IAM
    • Terraform
  • Spark - Databricks
    • Storage Formats
    • File Formats
    • Medallion Architecture
    • Delta
  • Data Warehousing Concepts
    • Dimensional Modelling
      • Star Schema
      • Galaxy Schema
      • Snowflake Schema
      • Starflake Schema
      • Star vs Snowflake
      • GRAIN
      • Multi-Fact Star Schema
      • Vertabelo Tool
    • Dimension - Fact
    • Sample Excercise
    • Keys
      • Why Surrogate Keys are Important
    • More Examples
    • Master Data Management
    • Steps of Dimensional Modeling
    • Types of Dimensions
      • Date Dimension Table
      • Degenerate Dimension
      • Junk Dimension
      • Static Dimension
      • Conformed Dimensions
      • Slowly Changing Dimensions
        • SCD - Type 0
        • SCD - Type 1
        • SCD - Type 2
        • SCD - Type 3
        • SCD - Type 4
        • SCD - Type 6
        • SCD - Type 5 - Fun Fact
      • Role Playing Dimension
      • Conformed vs Role Playing
      • Shrunken Dimension
      • Swappable Dimension
      • Step Dimension
    • Types of Facts
      • Factless Fact Table
      • Transaction Fact
      • Periodic Fact
      • Accumulating Snapshot Fact Table
      • Transaction vs Periodic vs Accumulating
      • Additive, Semi-Additive, Non-Additive
      • Periodic Snapshot vs Additive
      • Conformed Fact
    • Sample Data Architecture Diagram
    • Data Pipeline Models
    • New DW Concepts
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  • Wild Cards
  • Distinct
  • Distinct and NULL
  • Distinct with Multiple Columns
  1. RDBMS
  2. SQL Concepts

Wild Cards & Distinct

Wild Cards

Used to replace one or more characters

% wild card to replace more than one character

_ wild card to replace one character.

-- Starting with character 'A'

select FirstName from Chinook.Customer where FirstName like 'A%';

Distinct

Distinct is used to get a unique set of records from a table.

-- select distinct column1 from table;

-- Table Alias
select FirstName from Chinook.Customer c order by FirstName;

-- Table Alias
select distinct FirstName from Chinook.Customer c;

Distinct and NULL

MySQL keeps one NULL value and eliminates the other because the DISTINCT clause treats all

NULL values as the same value.

Distinct with Multiple Columns

select FirstName,LastName from Chinook.Customer;
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Last updated 2 years ago