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Data Warehouse basics for absolute beginners in 30 mins

Data Warehouse basic concepts like architecture, dimensional modeling, fact vs dimension table, star vs snowflake schema
Rating: 4.4 out of 5 (496 ratings)
6,775 students
Created by Eshant Garg
English
English [Auto]
Data Warehouse Basic concepts
Dimensional modeling
Facts and Dimensional tables
Schema and Snowflake schema

Requirements

  • Basic Database Understanding

Description

Important Note:

Please note that this is NOT a full course but a single module of the full-length course, and intended to cover very basic fundamental concepts for absolute beginners so that they can speed up with Azure Synapse SQL Data Warehouse course.

This module is NOT GOOD for you if:

  • You are already experienced in this technology

  • You are looking for an intermediate or advance concepts

  • You are looking for practical examples or demo

This module is GOOD for you if:

  • You want to understand the basic fundamental concepts of this technology.

This is a free module to help others. If you are not in the intended audience, I request you to please feel free to unenroll.

Where I can find a full-length course?

Please look at the bonus lecture in the end.


What will students learn in this course?

  • Microsoft SQL Data Warehouse (Crash course to speed up with Cloud warehousing)

Level

  • Beginners

Intended Audience

  • Anyone who wants to start learning Data warehousing

Language

  • English

  • If you are not comfortable in English, please do not take the course, captions are not good enough to understand the course.

Target Students

  • Database and BI developers

  • Database Administrators

  • Data Analyst or similar profiles

Prerequisites

  • Basic T-SQL and Database concepts


Course In Detail

Data Warehouse Crash Course

  • In this module, you will learn, what is Data Warehouse, Why we need it and how it is different from the traditional transactional database.

  • We will learn the concept of dimensional modeling which is a database design method optimized for data warehouse solutions.

  • Then I will explain what we mean when we say facts and their corresponding fact tables. What are the dimensions and their corresponding dimension tables?

  • how are these special kinds of tables joined together to form a star schema or snowflake schema?

  • This section will establish the foundation before you start my course on Azure Synapse Analytics or formally known as Azure SQL Data Warehouse.


[Tags]

Microsoft SQL Server, Azure SQL Server, Azure SQL Data Warehouse, Data Factory, Data Lake, Azure Storage, Azure Synapse Analytics Service, PolyBase, Azure monitoring, Azure Security, Data Warehouse, SSIS

Who this course is for:

  • Database Developer
  • College Students
  • Data Analyst or similar profiles
  • Anyone interested to learn Basic Data Warehousing concept

Course content

1 section • 13 lectures • 34m total length

  • Is this course good for me? Set expectation
    00:31
  • Introduction
    01:30
  • What is Data Warehouse
    01:35
  • Why we need Data Warehouse?
    02:47
  • Ideal Data Warehouse Solution
    03:39
  • Responsibility of Data Warehouse designer
    02:04
  • SQL Server (OLTP) vs Data Warehouse (OLAP)
    02:26
  • Dimensional Modeling
    05:16
  • Facts and Fact Table
    06:10
  • Dimensions and Dimension table
    03:41
  • Star vs Snowflake
    03:03
  • Summary
    01:16
  • Bonus Lecture
    00:47

Instructor

Eshant Garg
Azure Data Architect || Author || Adviser
Eshant Garg
  • 4.6 Instructor Rating
  • 6,420 Reviews
  • 40,222 Students
  • 12 Courses

13 years of extensive professional experience with expertise in Microsoft Database and Business Intelligence Solutions, Advanced Analytics, Reporting and Azure cloud computing technologies

CERTIFICATIONS

- DP-200: Implementing an Azure Data Solution
- DP-201: Designing an Azure Data Solution
- Microsoft Certified Technology specialist (MCTS) – SQL Server Database development
- Information Technology Infrastructure Library (ITIL V3) Certified

SPECIALITY

Azure : Data Lake, Data Factory, Synapse Analytics (DW), PolyBase, Stream Analytics & Storage

Big Data Tech : HDInsgiht, Databricks, CosmosDB, Hadoop, Spark, PySpark, Hive, Sqoop

Database versions : Microsoft SQL Server 2005-2016, NoSQL

Languages : T-SQL, USQL, Python, MDX, DAX, PySpark

BI Tools : SSRS, SSIS, SSAS, Power BI


I have been messing around with data for more than a decade now, I love to work on Database internals, performance tuning, and optimization, Database design, modeling, and implementation especially using Microsoft SQL Server and Azure database and analytics technologies.

As a developer and architect, I have worked closely with customers, users, and colleagues to support business solutions across a variety of industries including healthcare, insurance, finance, and government ranging from small companies to fortune 500 companies. These experiences provided opportunities to see varied implementations, troubleshoot performance-related problems, give guidance on design, and provide consultation on optimization, availability, and recoverability.

I helped in End to End Implementations of many database projects, providing team leadership and project management, requirements gathering, specification documentation, functional/technical design, Data modeling & solution architecture.

Outside of the technical world, I love yoga and meditation. I am a student of ancient yogic text Bhagavad Gita and love to discuss and practice philosophical teachings.