dbt (Data Build Tool) : dbt for Data Engineers

dbt (Data Build Tool) : dbt for Data Engineers, available at $64.99, has an average rating of 4.35, with 52 lectures, based on 36 reviews, and has 228 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Engineer Professionals who want to learn modern data stack transformation tools or University Students/Fresh Graduates looking for a career in the field of Analytical Engineer It is particularly useful for Data Engineer Professionals who want to learn modern data stack transformation tools or University Students/Fresh Graduates looking for a career in the field of Analytical Engineer.

Enroll now: dbt (Data Build Tool) : dbt for Data Engineers

Summary

Title: dbt (Data Build Tool) : dbt for Data Engineers

Price: $64.99

Average Rating: 4.35

Number of Lectures: 52

Number of Published Lectures: 49

Number of Curriculum Items: 52

Number of Published Curriculum Objects: 49

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 warehouse
dbt in simple words is an open-source command line tool that helps analysts and engineers transform data in their warehouse more effectively and more efficiently
dbt is a modern data stack tool. Modern data stack tools are used to analyse data and uncover new insights and improve efficiency

What makes dbt moremore secure,fast and easier to maintain is the ability to do all the calculation at the database level rather than memory level

Data 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 Setup
Data Warehousing Concepts
dbt Models and it’s deployment to database
Materialization and its different types
Slowly Changing Dimension
Snapshot in dbt
dbt(data build tool) Sources and Seeds
Macros and Packages
Basic Overview of Jinja Templating language and it’s usage in dbt models
Macro and dbt Testing
dbt(data build tool) documentation and Job deployment

After this Course

Once 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!

Course Curriculum

Chapter 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 & Connection With Snowflake

Chapter 2: dbt Connection With Google BigQuery

Lecture 1: Google BigQuery Account Creation

Lecture 2: dbt Core Connection with BigQuery

Chapter 3: dbt(data build tool) : Data Warehouse Fundamentals

Lecture 1: Data Warehouse Concepts

Lecture 2: Benefits and Limitation of Data Warehouse

Lecture 3: ETL in Data Warehouse

Lecture 4: ETL vs ELT

Lecture 5: Overview of OLTP

Chapter 4: dbt(data build tool) Models

Lecture 1: Create your First Models

Lecture 2: Creating a Sample Tables for Models

Lecture 3: Use of Ref Function in dbt Model

Lecture 4: Creating a Python Model in dbt

Lecture 5: Creating a Second Python Model in dbt

Chapter 5: dbt(data build tool): Sources and Seeds

Lecture 1: Sources in dbt(data build tool)

Lecture 2: Source Freshness in dbt

Lecture 3: Seeds in dbt(data build tool)

Chapter 6: Materialization in dbt(data build tool)

Lecture 1: Materialization in dbt

Lecture 2: Ephemeral Model

Lecture 3: Incremental Model Part-01

Lecture 4: Incremental Model Part-02

Lecture 5: Incremental Model Part-03

Lecture 6: delete+Insert Model

Chapter 7: Snapshots in dbt(data build tool)

Lecture 1: Slowly Changing Dimensions Concepts

Lecture 2: Timestamp Strategy SCD TYPE 2

Lecture 3: CHECK STRATEGY SCD TYPE 2

Chapter 8: Jinja Basic Fundamentals

Lecture 1: Jinja Introduction

Lecture 2: Jinja Basics – Control Structures

Lecture 3: Use of Jinja in dbt model Part-01

Lecture 4: Use of Jinja in dbt Part-02

Chapter 9: dbt(Data build tool): Macros and Hooks

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

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

Lecture 3: dbt Hooks

Chapter 10: dbt documentation & Job Deployment

Lecture 1: dbt documentation

Lecture 2: dbt Job Deployment

Chapter 11: dbt Testing

Lecture 1: dbt testing

Lecture 2: Singular Testing

Lecture 3: Generic Testing Part-01

Lecture 4: Severity Test in dbt

Lecture 5: dbt Store failure and limit Config

Lecture 6: Custom Generic Test

Lecture 7: dbt_utils

Lecture 8: dbt_expectations

Lecture 9: Audit_helper

Instructors

Saad Qureshi
Computer Scientist || Freelancer || Programmer

Rating Distribution

1 stars: 1 votes
2 stars: 2 votes
3 stars: 5 votes
4 stars: 15 votes
5 stars: 13 votes

Frequently Asked Questions

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