Ultimate Data Engineering, SnowFlake & SQL and Ad Terraform

Name: Ultimate Data Engineering, SnowFlake & SQL and Ad Terraform
Rating: 4.8 (36 reviews)

Ultimate Data Engineering, Snowflake, Python, SQL, Spark from beginner to Advanced with End to End Projects

Highest Rated

Created byLakshmana Korada

Last updated 6/2026

English

What you'll learn

Master Python for Data Engineering
Develop Advanced SQL Expertise
Build Scalable Big Data Pipelines with Apache Spark
Design End-to-End Data Engineering Solutions

Course content

7 sections • 80 lectures • 31h 44m total length

Introduction5:53
Explore advanced Terraform concepts through a step-by-step course with 14 core topics and 40+ demos, covering providers, resources, variables, data sources, state, workspaces, modules, provisioners, graph, and sentinel.

Python Introduction10:41
Discover the fundamental blocks in Terraform—the Terraform block, provider block, and resource block—and explore how versioning, backends, required providers, and provisioners operate within root and child modules.
What is Python in Data Engineering20:00
Learn how to automatically create and manage AWS Route 53 records for Kubernetes ingress and services on EKS using external DNS, Terraform, and Helm.
Project Structuring25:47
Implement read-only access to an eks cluster using kubernetes cluster role and binding, and grant full access to the dev namespace with kubernetes role and binding, via terraform iam groups.
Project Structuring Part - 222:20
Explore kubernetes ingress with annotations and class name, review ingress spec and target groups, and deploy a sample app behind a node port service via an ALB DNS URL.
Data Structures25:28
Data Structures Part - 222:38
Raw API Response in Python26:14
CSV Read and Write in Python27:12
Json in Python27:42
Function Flow in Data Engineering25:19
Non-idempotent in Python26:28
Pandas vs Spark vs SQL in Data Engineering27:32
Pandas Generate27:32
Pandas Clean22:47
Exception Flow in Python24:01
Exception Files in Python25:58
Log Levels in Python26:46
Python - Snowflake Flow26:28
Snowflake Demo25:19
REST Basics in Python26:46
Class Structure26:14
Data Pipeline in Data Engineering25:12
Load Data in Data Engineering23:38
Pipeline Config in Python29:37
Multithreading vs Multiprocessing34:33

SQL Introduction5:54
Learn how the AWS load balancer controller enables a single application load balancer across multiple namespaces using ingress groups and cross-namespace ingresses across app one, app two, and app three.
Basics of SQL14:49
Learn to provision an ECS cluster with a dev bastion and an EBS CSI add-on using Terraform, including backend state, DynamoDB table, remote state, IAM policies, and outputs.
Database Objects in SQL20:06
Create Tables Queries23:06
Transitions & ACID Properties25:01
Select Command Deep Dive23:13
Order by, Limit and Fetch Clauses25:01
String, Numeric and Date Functions25:01
Subqueries (Inline, Correlated, Derived)24:24
Window Functions27:30

PySpark Introduction5:24
Explore how Terraform modules package and reuse resources with root and child modules, including local and remote (public/private) registries, and practice building and publishing a local module.
Spark Architecture14:56
Explore ingress groups to split multiple app rules into separate ingress resources and merge them into a single application load balancer, with defined priorities and group annotations.
Cluster Manager (The Resource Authority)17:02
Automate Terraform driven ingress path routing for app1 and app2 with their node port services, while defaulting unmatched traffic to app3. Learn ingress rules, ALB integration, and manifest ordering.
Conceptual Diagram18:31
Configure Terraform with S3 remote state and DynamoDB state locking, using AWS and Kubernetes providers to deploy an ingress manifest for an EKS cluster.
Workers in Spart Master22:19
Learn how to configure Kubernetes horizontal pod autoscaler with a metrics server, setting a 50% cpu target to auto scale deployment replicas from 1 to 10 in eks.
Why Lazy Evaluation Exists24:18
Implement name-based virtual host routing with ingress using host arguments for app1 and app2. External DNS registers hosts in Route 53, routing to app1, app2, or default app3 via Terraform.
Spark Setup Using Docker25:41
Running Application in Spark24:28
Reading CSV, JSON, Parquet23:13
SQL and Dataframe25:55
StructType and DDL Schema Definitions23:44
Data frame Transformations21:40
Predicate Pushdown Opportunity23:23
Joins in Spart - Broadcast vs Shuffle Join24:49
Windows Functions - Analytical Power with Hidden Shuffles25:48
Common Skew Mitigation Strategies27:04
Data Writing & Storage Layer22:28
Tables, Metastore & Spark SQL22:23
Advanced SQL in Data Engineering5:20

Requirements

You need to be willing to learn Data Engineering

Description

Unlock the power of modern cloud data platforms and become a job-ready Data Engineer with this comprehensive Snowflake-focused course. Designed for beginners and professionals alike, this course takes you from the fundamentals of data engineering to advanced Snowflake concepts used by leading organizations worldwide.

You will begin by learning the core principles of data engineering, including data warehousing, ETL/ELT processes, data modeling, and cloud-based analytics architectures. From there, you'll dive deep into Snowflake, one of the industry's most powerful and rapidly growing cloud data platforms.

The course covers Snowflake architecture, virtual warehouses, databases, schemas, tables, stages, file formats, data loading, data transformation, query optimization, security, data sharing, and performance tuning. You will gain hands-on experience working with structured and semi-structured data, including JSON, Parquet, and Avro formats.

As you progress, you'll build real-world data pipelines, automate workflows, implement data governance strategies, and learn best practices for managing enterprise-scale data environments. The course also introduces advanced topics such as Snowpark, Streams and Tasks, Dynamic Tables, Time Travel, Data Cloning, Secure Data Sharing, and Snowflake performance optimization.

Through practical projects and industry-relevant case studies, you will develop the skills required to design, build, and maintain modern data platforms in cloud environments. By the end of the course, you will be confident in leveraging Snowflake to solve real-world data engineering challenges and accelerate your career in the data ecosystem. Whether you are an aspiring Data Engineer, Data Analyst, Cloud Engineer, BI Developer, or Database Professional, this course provides a complete roadmap to mastering Data Engineering with Snowflake from beginner to advanced level.

Who this course is for:

It is for anyone who wants to master Data Engineering from beginner to Advanced

Ultimate Data Engineering, SnowFlake & SQL and Ad Terraform

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 6min

Python for Data Engineering25 lectures • 10hr 32min

SQL for Data Engineering10 lectures • 3hr 34min

PySpark for Data Engineering19 lectures • 6hr 38min

Snowflake for Data Engineering6 lectures • 3hr 56min

Power BI For Data Engineering7 lectures • 2hr 5min

End-to-End Banking Data Engineering Project _ Snowflake + DBT + Airflow12 lectures • 4hr 54min

Requirements

Description

Who this course is for: