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Low Code ETL With DBT - Basic To Advance

By Sahil In Grow Data Skills

Self Paced
English
Completion Certificate

Demo Class Video

1 Year

Validity (From the date of Enrollment)

30 Hours

Duration

12

Sessions

2

Projects

INR 2200

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This course includes

Content Duration30 Hours
Total Video Sessions12
Total Industry Projects2
Quality Assignments & quizzes after each module
Interview preparation guide
Dedicated placement assistance
Resume & Linkedin profile making
Doubt support on private discord community
Certificate of completion

Course Content

Data Architecture Overview
Warehousing Fundamentals
Modern Data Stack
DBT Overview: Introduction, Market Share, Competitors, and Architecture Overview

Installation & Setup of the tools & environment (dbt, python, snowflake)
Interface Walkthrough of dbt
dbt concepts - Models, project setup, and materializations
Creating the dbt data models
SQL queries for dbt - DQL Commands
Creating Low Code SQL ETL Pipeline using dbt

dbt advance concepts - snapshots, hooks
dbt data loading techniques - full load vs incremental load
dbt templating using jinja
Creating Row Level Security in dbt
Orchestrating dbt pipelines
CI/CD of dbt pipelines with Github
dbt Best practices

dbt version control
dbt governance of data models
dbt testing of data models
dbt documentation

Project 1: An e-commerce company has its raw, unclean data from source systems present in snowflake warehouse. Their goal is to setup a end to end data pipeline that includes data extraction, data cleansing, and the creation of a golden layer for storing the transformed data.

Approach:
- 📍 Setup Snowflake + Data Loading
- 📍 Setup dbt
- 📍 Creating Low Code SQL dbt Pipelines
- 📍 Creating Transformed Models (Facts and datamarts)
- 📍 Testing the dbt pipeline
- 📍 Deploying the model using Airflow

Project 2: A leading edtech platform, is setting up an initiative to modernize their ELT pipelines, data transformation & management practices. They want to setup a pipeline that should enable them to do the data transformation while following best practices of Software engineering like CI/CD, Documentation, testing the pipelines & finally deploying the pipeline.

Approach:
- 📍 Setup dbt
- 📍 Creating the dbt Pipelines
- 📍 Setting up the data transformation pipelines to clean the data
- 📍 Creating the documentation of the datamodel using yaml files
- 📍 Unit testing the deployed pipeline using dbt generic & custom tests
- 📍 Setting up CI/CD pipeline with Github

Attention Seeking Resume Preparation and Interview Strategies
Strategies To Crack Tech Interviews
Linkedin Profile Making
How To Expand Your Professional Network On Linkedin
How To Use Various Job Portals
How To Approach For Referrals

Course Schedule

Mode of the Course:

Recorded

Course Duration:

30 Hours

Total Sessions:

Total Projects:

Validity:

1 Year (From the date of enrollment)

Prerequisite:

Passion And Willingness To Learn

⚠️ Important Notice :

The video may not work on Linux due to DRM restrictions. It is only accessible on Chrome when using Windows or macOS.

Workaround: To access the video on Linux, you can create a Windows virtual machine (VM) and watch the video through the VM. Alternatively, you can use our Android or iOS application to view the video on your mobile device.

Instructor

Sahil Choudhary

Sahil is a Google Certified Data Analyst and a Senior Data Analyst at Sigmoid Analytics. He has worked with startups and Fortune 500 companies, helping them make scalable data-driven decisions. He mentored over 350 students in analytics domain and developed third party products and has written multiple whitepapers for BI applications.