Data Engineering Mastery Course
All of the content and courses at your own pace!
Status
Not Enrolled
This program is not accepting enrollments right now!
What You'll Learn
- 1 year of access
- 100+ Hours of Content
- Free Cloud Tooling
- AWS, Astronomer, Spark, Trino
- Manage data lake tables with Iceberg
- Manage real-time data with Kafka
- Build an awesome portfolio!
Prerequisites
- Proficiency in Python and SQL, at least 6 months of experience in both
- Basic understanding of Docker, Flink, and Kafka
- Basic understanding of SQL Window Functions
Program Structure
1
Dimensional Data Modeling in Iceberg
6 Lessons•4h 5m
1 Assignment
Lessons
Dimensional Data Modeling - Complex Data Types Arrays and Structs with Iceberg (Day 1 Lecture) - Pre-recorded from V3
48m
Dimensional Data Modeling - Complex Data Types Arrays and Structs with Iceberg (Day 1 Lab) - Pre-recorded from V3
30m
Dimensional Data Modeling - Slowly-changing Dimensions and Idempotent Queries in Iceberg (Day 2 Lecture) - Pre-recorded from V3
46m
Dimensional Data Modeling - Slowly-changing Dimensions and idempotent queries with Trino (Day 2 Lab) - Pre-recorded from V3
37m
Conceptual Data Modeling (Day 3 Lecture)
47m
Conceptual Data Modeling (Day 3 Lab)
35m
Assignments
2
Boot Camp Orientation
2 Lessons•1h 45m
3
V4 Capstone Project
1 Assignment
Assignments
4
Fact Data Modeling with Apache Iceberg
8 Lessons•5h 11m
1 Assignment
Lessons
Fact Data Modeling - Core Concepts, Deduplication Techniques, and Retention Considerations with Iceberg (Day 1 Lecture) - Pre-recorded from V3
46m
Data Accessibility and Privacy (Day 4 Lecture)
37m
Fact Data Modeling - Minimizing Shuffle and Reducing Facts with Iceberg (Day 3 Lecture) - Pre-recorded from V3
35m
Data Accessibility and Privacy (Day 4 Lab)
39m
Fact Data Modeling - Compact Tables for Efficient Data Representation with Iceberg (Day 2 Lab) - Pre-recorded from V3
39m
Fact Data Modeling - Practical Guide to Formatting and Aggregating Data with Iceberg (Day 3 Lab) - Pre-recorded from V3
38m
Fact Data Modeling - Core Elements in Data Modeling with Iceberg (Day 2 Lecture) - Pre-recorded from V3
38m
Fact Data Modeling - Core Concepts, Deduplication Techniques, and Retention Considerations with Iceberg (Day 1 Lab) - Pre-recorded from V3
37m
Assignments
5
Data Quality Patterns in Apache Airflow
4 Lessons•2h 35m
1 Assignment
6
Unit and Integration Testing Spark Pipelines
6 Lessons•3h 43m
7
Streaming Pipelines with Kafka and Spark Streaming
4 Lessons•2h 25m
1 Assignment
8
Analytical Patterns & Analysis with Trino
4 Lessons•2h 38m
1 Assignment
9
Large Language Models
4 Lessons•1h 49m
10
Data Impact Communication & Visualization
4 Lessons•2h 25m
11
dbt
4 Lessons•2h 56m
12
Data Pipeline Maintenance (V4)
2 Lessons•1h 38m