Comprehensive Data Engineering with Hands-on Experience

About Course

The Comprehensive Data Engineering course is designed to equip learners with the knowledge and practical skills required to excel in the field of data engineering. Through a combination of theoretical concepts, hands-on exercises, and real-world projects, participants will gain a deep understanding of data engineering principles, tools, and techniques. By the end of this course, learners will be capable of building robust data pipelines, managing large-scale data systems, and applying best practices in data engineering.

Roles you can apply to as a Data Engineer

Data Engineer:
ETL Developer
Data Integration Specialist
Big Data Engineer
Data Warehouse Engineer
Data Platform Engineer
DataOps Engineer:
Data Quality Engineer

And many more!!!

Understand the role and importance of data engineering in modern data-driven organizations.
Gain proficiency in data modeling and designing efficient data architectures.
Learn various data storage and processing technologies, including databases, data lakes, and distributed systems.
Develop skills in building and managing scalable data pipelines.
Apply data transformation and integration techniques to clean and preprocess data.
Learn best practices for data quality, reliability, and data governance.
Acquire knowledge of real-time data processing and stream processing frameworks.
Explore data warehousing and business intelligence concepts.
Understand the fundamentals of cloud computing and its relevance to data engineering.
Work on hands-on projects to apply the learned concepts in real-world scenarios.

Course Content

Week 1: Introduction to Data Engineering

Understanding the role of data engineering in the data ecosystem

00:00
Overview of data engineering processes and workflows

00:00
Introduction to data engineering tools and technologies

00:00
Setting up the development environment

00:00

Week 2: Data Modeling and Design

Week 3: Data Storage and Processing Technologies

Week 4: Building Data Pipelines

Week 5: Data Transformation and Integration

Week 6: Data Quality and Reliability

Week 7: Real-time Data Processing

Week 8: Data Warehousing and Business Intelligence

Week 9: Cloud Computing and Data Engineering

Week 10-15: Hands-on Projects

Week 16: Course Recap and Final Project

Student Ratings & Reviews

No Review Yet

About Course

What Will You Learn?

Course Content

Week 1: Introduction to Data Engineering

Understanding the role of data engineering in the data ecosystem

Overview of data engineering processes and workflows

Introduction to data engineering tools and technologies

Setting up the development environment

Week 2: Data Modeling and Design

Understanding data models: relational, dimensional, and NoSQL

Entity-relationship diagrams and schema design

Normalization and denormalization techniques

Designing efficient data architectures for scalability and performance

Week 3: Data Storage and Processing Technologies

Relational databases: SQL fundamentals, indexing, and optimization

NoSQL databases: key-value stores, document databases, and columnar stores

Introduction to data lakes and data warehouses

Overview of distributed systems: Hadoop, Spark, and others

Week 4: Building Data Pipelines

Introduction to data ingestion and extraction techniques

Batch processing vs. real-time processing

Implementing data pipelines using ETL (Extract, Transform, Load) frameworks

Error handling, monitoring, and scheduling of data pipelines

Week 5: Data Transformation and Integration

Data cleaning and preprocessing techniques

Data integration methods: joins, unions, and merges

Introduction to data wrangling tools and techniques

Hands-on exercise: cleaning and transforming real-world datasets

Week 6: Data Quality and Reliability

Understanding data quality dimensions and metrics

Implementing data quality checks and validation

Data profiling and anomaly detection

Ensuring data reliability and fault tolerance in data pipelines

Week 7: Real-time Data Processing

Introduction to stream processing and event-driven architectures

Stream processing frameworks: Apache Kafka, Apache Flink, and others

Handling high-velocity data streams

Building real-time data pipelines for continuous processing

Week 8: Data Warehousing and Business Intelligence

Introduction to data warehousing concepts

Star and snowflake schemas

Implementing dimensional modeling

Overview of business intelligence tools and visualization techniques

Week 9: Cloud Computing and Data Engineering

Fundamentals of cloud computing: IaaS, PaaS, and SaaS

Cloud storage and database services

Building scalable data pipelines in the cloud

Securing and managing cloud-based data systems

Week 10-15: Hands-on Projects

Working on real-world data engineering projects

Applying learned concepts and techniques

Collaborating in a team environment

Implementing end-to-end data solutions

Week 16: Course Recap and Final Project

Review of key concepts and techniques

Q&A session and discussion

Final project presentation and evaluation

Student Ratings & Reviews