Welcome to Data Engineering Fundamentals! This comprehensive course is designed to immerse you in the core concepts, ecosystem, lifecycle, processes, and essential tools of data engineering.

The Data Engineering Ecosystem encompasses various components crucial for handling data efficiently. It involves managing data from diverse sources, utilizing data repositories like relational and non-relational databases, data warehouses, data marts, data lakes, and big data stores. These repositories play a pivotal role in storing and processing vast amounts of data.

Additionally, Data Integration Platforms merge this data into a unified view, ensuring secure and straightforward access for data consumers. Data consumers leverage Business Intelligence (BI), reporting, and analytical tools to extract valuable insights, driving better decision-making processes. Throughout this course, you will explore each of these integral components.

A typical Data Engineering Lifecycle involves architecting data platforms and designing data repositories and integration platforms. It includes constructing data pipelines, utilizing various programming languages, and employing BI and reporting tools. These pipelines are essential for gathering, importing, wrangling, cleaning, querying, and analyzing data efficiently. Ensuring systems and workflows operate at peak performance requires continuous monitoring and fine-tuning.

Through a series of hands-on labs, you will be guided to provision a database on Cloud, prepare and load data into the store, and perform fundamental operations on the data.

Data Engineering is recognized as one of the fastest-growing fields today, offering a plethora of career opportunities. This course discusses the various paths to becoming a data engineer, as well as advice from professionals in the field. Navigate through the day-to-day responsibilities of a data engineer and the skills and qualities that employers highly value in this dynamic field.

What you will learn:

After completing this course, you will be able to:

Describe what data engineering is and the responsibilities and skillsets of a Data Engineer.
Differentiate between the role of different data professionals, such as Data Engineers, Data Scientists, Data Analysts, Business Analysts, and Business Intelligence Analysts.
Differentiate between the different types of data structures, file formats, and sources of data.
Describe the different elements of a data engineering ecosystem, including data repositories, data pipelines, ETL and ELT processes, data integration platforms, and languages for data professionals.
Summarize the key stages of a data engineering life cycle, including data platform architecture, data store design, gathering data, wrangling data, querying and analyzing data, performance tuning, security, and compliance to governance regulations.
List the different career opportunities available in data engineering and the resources and learning paths for gaining entry into the field.
Provision a data store on cloud, prepare and load data into it, and access the data from the data store.

Course Syllabus

Module 1: What is Data Engineering

Modern Data Ecosystem
Key Players in the Data Ecosystem
Defining data engineering
Evolution of data engineering
Responsibilities and Skillsets of a Data Engineer
A day in the life of a Data Engineer

Module 2: Data Engineering Ecosystem

Overview of the Data Engineering Ecosystem
Types of Data
Understanding different types of File Formats
Sources of Data
Languages for Data Professionals
RDBMS
NoSQL
Data Warehouses, Data Marts, and Data Lakes
ETL, ELT, and Data Pipelines
Data Integration Platforms

Module 3: Data Engineering Lifecycle

Data Platform Architecture
Designing the Data Store
Importance of data security
How to Gather and Import Data
Data Wrangling
Tools for Data Wrangling
Querying and Analyzing data
Performance Tuning and Troubleshooting
Governance and Compliance

Module 4: Career Opportunities and Learning Paths

Career Opportunities in Data Engineering
Getting into data engineering
Data Engineering Learning Path
What do employers look for in a Data Engineer
The many paths to data engineering
Advice to aspiring data engineers

General Information

This course is self-paced.
This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.

Recommended Skills Prior to Taking this Course

Basic computer skills.
General familiarity of how computing works.

Data Engineering Fundamentals

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