Data Engineering Boot Camp V2 Combined Track
The second live boot camp offered by Zach Wilson in Summer 2023.
Start Date
Jul 17, 2023
End Date
Aug 25, 2023
Status
Not Enrolled
This program is not accepting enrollments right now!
Program Structure
1
Dimensional Data Modeling in Postgres
6 Lessons•5h 22m
1 Assignment
Lessons
Dimensional Data Modeling - Graph Data Modeling with Postgres (Day 3 Lab)
60m
Dimensional Data Modeling - Complex Data Types Arrays and Structs with Postgres (Day 1 Lecture)
53m
Dimensional Data Modeling - Graph Dimensional Modeling with Postgres (Day 3 Lecture)
45m
Dimensional Data Modeling - Complex Data Types Arrays and Structs with Postgres (Day 1 Lab)
59m
Dimensional Data Modeling - Slowly-changing Dimensions and idempotent queries with Postgres (Day 2 Lab)
63m
Dimensional Data Modeling - Slowly-changing Dimensions and idempotent queries with Postgres (Day 2 Lecture)
40m
Assignments
2
Dimensional Data Modeling with Apache Iceberg
6 Lessons•5h 50m
Lessons
Dimensional Data Modeling - Complex Data Types Arrays and Structs with Iceberg (Day 1 Lab)
55m
Dimensional Data Modeling - Complex Data Types Arrays and Structs with Iceberg (Day 1 Lecture)
61m
Dimensional Data Modeling - Slowly-changing Dimensions and idempotent queries with Trino (Day 2 Lab)
62m
Dimensional Data Modeling - Slowly-changing Dimensions and Idempotent Queries in Iceberg (Day 2 Lecture)
52m
Dimensional Data Modeling - Graph Data Modeling with Iceberg (Day 3 Lab)
62m
Dimensional Data Modeling - Graph Data Modeling with Iceberg (Day 3 Lecture)
56m
3
Fact Data Modeling in Postgres
6 Lessons•5h 22m
1 Assignment
Lessons
Fact Data Modeling - Navigating Challenges: Denormalization and Large Volume Data Processing (Day 3 Lecture)
57m
Fact Data Modeling - Exploring Datelist Structures for User Growth Analysis (Day 2 Lab)
67m
Fact Data Modeling - Navigating Dimensions and Graph Modeling (Day 1 Lab)
53m
Fact Data Modeling - Mastering Denormalization Timing and Processing Large Volume Data (Day 3 Lab)
48m
Fact Data Modeling - Additive vs Non-Additive Dimensions and Beyond (Day 1 Lecture)
58m
Fact Data Modeling - Distinguishing Facts from Dimensions and Leveraging Reduced Facts for Insightful Analysis (Day 2 Lecture)
36m
Assignments
4
Capstone Project
2 Assignments
5
Fact Data Modeling with Apache Iceberg
6 Lessons•5h 55m
Lessons
Fact Data Modeling - Practical Insights into Data Modeling and Analysis with Iceberg (Day 1 Lab)
69m
Fact Data Modeling - Core Concepts, Deduplication Techniques, and Retention Considerations with Iceberg (Day 1 Lecture)
59m
Fact Data Modeling - Compact Tables for Efficient Data Representation with Iceberg (Day 2 Lab)
53m
Fact Data Modeling - Core Elements in Data Modeling with Iceberg (Day 2 Lecture)
50m
Fact Data Modeling - Practical Guide to Formatting and Aggregating Data with Iceberg (Day 3 Lab)
58m
Fact Data Modeling - Minimizing Shuffle and Reducing Facts with Iceberg (Day 3 Lecture)
63m
6
Pipeline Spec Building and dbt fundamentals
4 Lessons•2h 41m
Lessons
Analytics Data Quality - Exploring Data Modeling and Quality Checks (Day 1 Lab)
44m
Analytics Data Quality - Strategies and Insights from Zach's Airbnb Experience (Day 1 Lecture)
52m
Analytics Data Quality - Mastering DBT Projects with Bruno: Troubleshooting, Profiles, Snapshots, and Testing (Day 2 Lab)
1m
Analytics Data Quality - Mastering DBT Projects with Bruno: Practical Overview and Hands-On Demonstrations (Day 2 Lecture)
62m
7
Unit Testing Spark Pipelines and Write-Audit-Publish
4 Lessons•3h 26m
1 Assignment
Lessons
Infrastructure Data Quality - Mastering Spark and PySpark Testing: Comprehensive Overview and Practical Guidance (Day 1 Lab)
Infrastructure Data Quality - Elevating Data Quality in Analytics Engineering: Importance, Challenges, and Leadership Perspectives (Day 1 Lecture)
60m
Infrastructure Data Quality - Implementing Data Quality Measures with Astronomer and Airflow: Hands-On Lab with Marc Lamberti (Day 2 Lab)
72m
Infrastructure Data Quality - Elevating Infrastructure Data Quality: Insights and Best Practices with Marc Lamberti (Day 2 Lecture)
74m
Assignments
8
Analytical Patterns & Analysis with Trino
4 Lessons•3h 39m
Lessons
Applying Advanced SQL for Analytical Insights - Mastering Growth Accounting: Hands-On Journey through User Growth and Retention Analysis (Day 1 Lab)
51m
Applying Advanced SQL for Analytical Insights - Exploring SQL, Scaling Projects, and Aggregation Analysis (Day 1 Lecture)
63m
Applying Advanced SQL for Analytical Insights - Aggregations and Cardinality Reduction in Bootcamp Web Events (Day 2 Lab)
59m
Applying Advanced SQL for Analytical Insights - Recursive CTEs, Window Functions, and Practical Insights (Day 2 Lecture)
45m
9
Streaming Pipelines with Apache Flink
4 Lessons•2h 45m
1 Assignment
Lessons
Flink Streaming - Setting Up Streaming Pipelines and Integrating Kafka with Postgres (Day 1 Lab)
62m
Flink Streaming - Real-Time Data Processing Essentials: Lambda vs Kappa Architectures, UDFs, and Windowing Techniques (Day 2 Lecture)
47m
Flink Streaming - Analyzing TechCreator.io Popularity with Flink: Tumbling Windows for Real-Time Traffic Insights (Day 2 Lab)
Flink Streaming - Fundamentals of Real-Time Data Processing (Day 1 Lecture)
55m
Assignments
10
Data Pipeline Maintenance
3 Lessons•3h 15m
1 Assignment
Lessons
Data Pipeline Maintenance - Challenges, Ownership Models, and Team Structures (Day 1 Lecture)
10m
Data Pipeline Maintenance - Simulated Scenarios and Runbook Creation for Effective On-Call Procedures (Day 1 Lab)
88m
Advanced Data Pipeline Maintenance - Signals of Technical Debt, Data Migration Models, and On-Call Best Practices (Day 2 Lecture)
97m
Assignments
11
Product Sense, KPIs and Experimentation
3 Lessons•4h 12m
1 Assignment
Lessons
Applying KPIs and Experimentation - Understanding User Behavior and Driving Business Growth (Day 1 Lecture)
107m
Applying KPIs and Experimentation - Leading vs. Lagging Metrics and the Power of Funnels in Data Analysis (Day 2 Lecture)
96m
Applying KPIs and Experimentation - Setting Up Web Experiments in Statsig (Day 1 Lab)
49m
Assignments
12
Batch Pipelines with Apache Spark
4 Lessons•3h 55m
1 Assignment
Lessons
Spark Batch Processing - Caching, DataFrame, Dataset, SparkSQL, and Bucketing in Iceberg (Day 2 Lab)
62m
Spark Batch Processing - Comparing with Hive and MapReduce, Key Components, and Performance Optimization (Day 1 Lecture)
73m
Spark Batch Processing - Data Partitioning, Performance Optimization, and Iceberg Tables (Day 1 Lab)
45m
Spark Batch Processing - Caching, UDFs, DataFrames, Datasets, SparkSQL, and Parquet (Day 2 Lecture)
53m
Assignments
13
Data Impact Communication & Visualization
4 Lessons•2h 26m
Lessons
Data Impact Communication & Visualization - Creating Executive Summary and Exploratory Dashboards with events.csv Dataset (Day 2 Lab)
54m
Data Impact Communication & Visualization - Preparing for Complex Visualizations (Day 1 Lab)
20m
Data Impact Communication & Visualization - Preparing for Complex Visualizations (Day 1 Lab)
43m
Data Impact Communication & Visualization - Performant Dashboard Design and Effective Visualization Combinations (Day 2 Lecture)
28m
14
Write-Audit-Publish pattern and CI/CD (recorded)
4 Lessons•2h 50m
Lessons
Infrastructure Data Quality - Real-World Validation with PySpark and PyTest (Day 1 Lab)
1m
Infrastructure Data Quality - Enhancing Data Quality and Infrastructure Efficiency in Data Engineering (Day 2 Lecture)
56m
Infrastructure Data Quality - Preemptive Data Quality Assurance: Integrating Software Engineering Best Practices (Day 1 Lecture)
62m
Infrastructure Data Quality - Setting Up CI/CD with GitHub Actions for Data Quality Assurance (Day 2 Lab)
49m
15
Write-Audit-Publish and Pipeline Spec Building
4 Lessons•3h 20m
1 Assignment
Lessons
Analytics Data Quality - Mitigating Poor Data Quality: Causes, Contracts, and Row Count Thresholds (Day 2 Lecture)
40m
Analytics Data Quality - Overcoming Data Quality Challenges: Identifying Poor Data Sources and Setting Row Count Checks (Day 2 Lab)
50m
Analytics Data Quality - Building Trust in Data: Validation Techniques and Quality Checks (Day 1 Lecture)
64m
Analytics Data Quality - Hands-On Exercises and Validating Real-World Datasets (Day 1 Lab)
44m
Assignments
16
Analytical Patterns & Analysis with Postgres
4 Lessons•3h 29m
1 Assignment
Lessons
Applying Advanced SQL for Analytical Insights - Repeatable Analyses and State Change Tracking (Day 1 Lecture)
61m
Applying Advanced SQL for Analytical Insights - Bridging SQL Features and Data Modeling Insights (Day 2 Lecture)
49m
Applying Advanced SQL for Analytical Insights - Growth Accounting, Survivorship Analysis, and Smoothing Trends (Day 1 Lab)
45m
Applying Advanced SQL for Analytical Insights - Window Functions, GROUPING SETS, CUBE, ROLLUP, and Funnel Analytics (Day 2 Lab)
52m
Assignments