Data Engineering

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Technology

Overview

Curriculum

Features

Overview

Welcome to the Data Engineering Fundamentals course! In today’s data-driven world, the ability to efficiently manage, process, and transform data is paramount. This course is designed to introduce you to the essential concepts, tools, and techniques of data engineering, empowering you to become a proficient data engineer capable of creating robust and scalable data pipelines.
Course Description:
Data Engineering Fundamentals is a comprehensive course that lays the foundation for understanding the pivotal role of data engineering in the realm of data management and analytics. Whether you’re a beginner in the field or looking to solidify your existing knowledge, this course equips you with the skills needed to design, build, and maintain data infrastructure effectively.

Curriculum

Introduction to Data Engineering
- Understanding the role of data engineering in the data lifecycle
- Differentiating between data engineering and data science
- Overview of data engineering tools and technologies
Data Modeling and Database Management
- Introduction to data modeling concepts (relational, NoSQL)
- Designing and creating relational databases (e.g., MySQL, PostgreSQL)
- Introduction to NoSQL databases (e.g., MongoDB, Cassandra)
Data Integration and ETL Processes
- Extract, Transform, Load (ETL) process fundamentals
- Exploring ETL tools (e.g., Apache NiFi, Apache Airflow)
- Hands-on: Building a simple ETL pipeline
Big Data and Distributed Computing
- Introduction to big data concepts and challenges
- Overview of distributed computing frameworks (e.g., Hadoop, Spark)
- Hands-on: Processing data using Apache Spark
Data Warehousing and Data Lakes
- Understanding data warehousing architecture
- Introduction to cloud-based data warehousing (e.g., Amazon Redshift, Google BigQuery)
- Creating and querying data warehouses
Streaming Data and Real-time Processing
- Exploring streaming data concepts
- Introduction to stream processing frameworks (e.g., Apache Kafka, Apache Flink)
- Building a simple real-time data pipeline
Data Quality and Governance
- Importance of data quality and data governance
- Implementing data validation and cleaning processes
- Ensuring data security and compliance
Scalable Infrastructure and Cloud Services
- Overview of cloud computing and its benefits
- Introduction to cloud services for data engineering (e.g., AWS, Azure, Google Cloud)
- Deploying data engineering solutions on the cloud
Workflow Orchestration and Automation
- Managing complex workflows using orchestration tools (e.g., Apache Airflow, Luigi)
- Creating automated data pipelines
- Hands-on: Designing and scheduling data workflows

Features

Course Start Date	09/26/2023
Estimated Duration	3/4 Weeks
Maximum Students	10
Levels	Advanced