Data Engineering Mastery Course

All of the content and courses at your own pace!

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Data Engineering Mastery Course

All of the content and courses at your own pace!

$1750 one time payment

• 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!

Platform Access Included:

Snowflake

PrerequisitesCurriculaHomeworkSchedule

• 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

• 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

Main Content

Dimensional Data Modeling in Iceberg 4.1 hrs

• Dimensional Data Modeling - Complex Data Types Arrays and Structs with Iceberg (Day 1 Lecture) - Pre-recorded from V3
• Dimensional Data Modeling - Complex Data Types Arrays and Structs with Iceberg (Day 1 Lab) - Pre-recorded from V3
• Dimensional Data Modeling - Slowly-changing Dimensions and Idempotent Queries in Iceberg (Day 2 Lecture) - Pre-recorded from V3
• Dimensional Data Modeling - Slowly-changing Dimensions and idempotent queries with Trino (Day 2 Lab) - Pre-recorded from V3
• Conceptual Data Modeling (Day 3 Lecture)
• Conceptual Data Modeling (Day 3 Lab)

Boot Camp Orientation1.8 hrs

• V3 Boot Camp Kick-Off
• V4 Boot Camp Kick-Off

V4 Capstone Project

Fact Data Modeling with Apache Iceberg 5.2 hrs

• Fact Data Modeling - Core Concepts, Deduplication Techniques, and Retention Considerations with Iceberg (Day 1 Lab) - Pre-recorded from V3
• Data Accessibility and Privacy (Day 4 Lecture)
• Data Accessibility and Privacy (Day 4 Lab)
• Fact Data Modeling - Core Elements in Data Modeling with Iceberg (Day 2 Lecture) - Pre-recorded from V3
• Fact Data Modeling - Core Concepts, Deduplication Techniques, and Retention Considerations with Iceberg (Day 1 Lecture) - Pre-recorded from V3
• Fact Data Modeling - Practical Guide to Formatting and Aggregating Data with Iceberg (Day 3 Lab) - Pre-recorded from V3
• Fact Data Modeling - Minimizing Shuffle and Reducing Facts with Iceberg (Day 3 Lecture) - Pre-recorded from V3
• Fact Data Modeling - Compact Tables for Efficient Data Representation with Iceberg (Day 2 Lab) - Pre-recorded from V3

Data Quality Patterns in Apache Airflow2.6 hrs

• Airflow Data Quality (Day 1 Lab)
• Airflow Data Quality (Day 2 Lecture)
• Airflow Data Quality (Day 2 Lab)
• Airflow Data Quality (Day 1 Lecture)

Unit and Integration Testing Spark Pipelines3.7 hrs

• Spark Fundamentals Spark Lab on REST API consumption (Day 1 Lab)
• Spark Fundamentals (Day 1 Lecture)
• Spark Data Quality (Day 3 Lab)
• Spark Data Quality (Day 3 Lecture)
• Advanced Spark (Day 2 Lecture)
• Advanced Spark (Day 2 Lab)

Streaming Pipelines with Kafka and Spark Streaming2.4 hrs

• Processing data with Apache Flink (Day 2 Lecture)
• Kafka Fundamentals, consumer APIs with Confluent (Day 1 Lecture)
• Kafka Fundamentals, consumer APIs with Confluent (Day 1 Lab)
• Processing data with Apache Flink (Day 2 Lab)

Analytical Patterns & Analysis with Trino 2.6 hrs

• Analytical Patterns (Day 2 Lecture)
• Advanced SQL and Applying Analytical patterns (Day 1 Lab)
• Analytical Patterns (Day 2 Lab)
• Advanced SQL and Applying Analytical patterns (Day 1 Lecture)

Large Language Models1.8 hrs

• LLMs - Using LLMs to provide business value Day 1 Lecture
• LLMs - Using LLMs to provide business value Day 1 Lab
• LLMs - Vector database for RAG applications Day 2 Lecture
• LLMs - Vector database for RAG applications Day 2 Lab

Data Impact Communication & Visualization 2.4 hrs

• Data Communication and Impact (Day 1 Lab)
• Data Visualization with tableau (Day 2 Lab)
• Data Visualization with tableau (Day 2 Lecture)
• Data Communication and Impact (Day 1 Lecture)

dbt2.9 hrs

• dbt day 1 lecture
• dbt day 1 lab
• dbt day 2 lecture
• dbt day 2 lab

Data Pipeline Maintenance (V4)1.6 hrs

• Data Pipelines Maintenance (Day 1 Lecture)
• Data Pipelines Maintenance (Day 1 Lab)

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