Dbt(Data Build Tool): Dbt For Analytical Engineers

Comprehensive dbt (Data Build Tool) course covering basic to advanced level dbt concepts

What you'll learn

dbt(data build tool) Cloud and Core Configuration to Snowflake

dbt Models and their deployment

Materialization and its different types

Basic Data Warehouse Concepts

Slowly Changing Dimensions and Snapshot in dbt

dbt Sources and Seeds

Jinja basic fundamentals

Macros and Packages

Testing of Different Models

Basic Overview of Jinja Templating language and it's usage in dbt models

dbt documentation and Job deployment

Requirements

Very basic knowledge of SQL and database are required

No High Level programming knowledge/Background is required

All you need a Computer machine; windows, Mac, and Linux users are all welcome

Description

What is dbt(data build tool)?dbt is not an ETL tool that you use in you warehouse to extract data from multiple heterogeneous sources and then transform it and then finally load the data in the data warehousedbt  in simple words is an open-source command line tool that helps analysts and engineers transform data in their warehouse more effectively and more efficientlydbt is a modern data stack tool. Modern data stack tools are used to analyse data and uncover new insights and improve efficiencyWhat makes dbt more more secure,fast  and easier to maintain is the ability to do all the calculation at the database level rather than memory levelData engineers work in different ways to collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret.Their main goal is to make data available and accessible for the organisation so that timely and effective decisions are taken for the business.Why to Learn dbt(data buildTool):dbt(data build tool) is becoming the most popular tool in Data Warehouse industry.Many big companies like IBM,Jet Blue,Dyson, Capgemini etc are using this tool,around the world in 2023 over 3000 companies have started using this tool in their Data Warehouse department.Career Perspective:If you want to pursue a career in the field of Data Warehouse as a Data Engineer,Data Analyst or Data Scientist then you must learn this modern data stack tool.The pre-requisite of this course is basic SQL,no advance knowledge of SQL is required,neither any programming concepts are needed.Important topics:Introduction to dbt(data build tool)dbt(data build tool) Cloud and Core Configuration and SetupData Warehousing Conceptsdbt Models and their deployment to databaseMaterialization and its different typesSlowly Changing DimensionSnapshot in dbtdbt(data build tool) Sources and SeedsMacros and PackagesBasic Overview of Jinja Templating language and it's usage in dbt modelsMacro and dbt Testingdbt(data build tool) documentation and Job deploymentAfter this CourseOnce you're done with the course,you will have maximum knowledge of this tool,plus you will get to see hands-On examples of using this tool.Moreover after attaining all the practical knowledge you can apply these concept in different field as mentioned above.Cheers..!!Having a Great Learning!

Overview

Section 1: dbt(data build tool) Connections

Lecture 1 dbt Introduction

Lecture 2 Create a GitHub Repository

Lecture 3 Free Trial Account

Lecture 4 Data loading From AWS S3 Bucket

Lecture 5 Python Installation

Lecture 6 dbt Cloud connection with Snowflake and Github

Lecture 7 dbt Core Installation

Section 2: dbt(data build tool) : Data Warehouse Fundamentals

Lecture 8 Data Warehouse Concepts

Lecture 9 Benefits and Limitation of Data Warehouse

Lecture 10 ETL in Data Warehouse

Lecture 11 ETL vs ELT

Lecture 12 Overview of OLTP

Section 3: dbt(data build tool) Models, Sources & Seeds

Lecture 13 Create your First Models

Lecture 14 Creating a Sample Tables for Models

Lecture 15 Use of Ref Function in dbt Model

Lecture 16 Creating a Python Model in dbt

Lecture 17 Creating a Second Python Model in dbt

Lecture 18 Sources in dbt(data build tool)

Lecture 19 Source Freshness in dbt

Lecture 20 Seeds in dbt(data build tool)

Section 4: Materialization in dbt(data build tool)

Lecture 21 Ephemeral Model

Lecture 22 Incremental Model Part-01

Lecture 23 Incremental Model Part-02

Lecture 24 Incremental Model Part-03

Lecture 25 delete+Insert Model

Section 5: Snapshots in dbt(data build tool)

Lecture 26 Slowly Changing Dimensions Concepts

Lecture 27 Timestamp Strategy SCD TYPE 2

Lecture 28 CHECK STRATEGY SCD TYPE 2

Section 6: Jinja Basic Fundamentals

Lecture 29 Jinja Introduction

Lecture 30 Jinja Basics - Control Structures

Lecture 31 Use of Jinja in dbt model Part-01

Lecture 32 Use of Jinja in dbt Part-02

Section 7: dbt(Data build tool): Macros and Hooks

Lecture 33 Macro in dbt(data build tool) Part-1

Lecture 34 Macros in dbt(data build tool) Part-2

Lecture 35 dbt Hooks

Lecture 36 dbt documentation

Section 8: dbt Testing

Lecture 37 dbt testing

Lecture 38 Singular Testing

Lecture 39 Generic Testing Part-01

Lecture 40 Severity Test in dbt

Lecture 41 dbt Store failure and limit Config

Lecture 42 Custom Generic Test

Lecture 43 dbt_utils

Lecture 44 dbt_expectations

Lecture 45 Audit_helper

Data Engineer Professionals who want to learn modern data stack transformation tools,University Students/Fresh Graduates looking for a career in the field of Analytical Engineer