Data Engineering Bootcamp
Build production-grade data pipelines with Spark, Airflow, and modern data stack
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Data Engineering Bootcamp
Course overview
Learn to build robust data pipelines and infrastructure. Master Apache Spark, Airflow, Kafka, and cloud data services. Understand data modeling, ETL best practices, and data quality frameworks.
Duration
10 weeks
Format
Live + Offline
Problems
25+
Difficulty
Intermediate
What You'll Learn
Curriculum at a glance
| Week | Focus | Key topics | Problems |
|---|---|---|---|
| 1 | Data Engineering Fundamentals | Big Data Landscape & Tools, ETL vs ELT Paradigms, Batch vs Stream Processing | 3 |
| 2 | SQL & Data Modeling | Advanced SQL Techniques, Dimensional Modeling (Star & Snowflake), Data Warehousing Concepts | 3 |
| 3 | Apache Spark | Spark Architecture & RDDs, DataFrames & Spark SQL, Spark Streaming & Structured Streaming, Performance Tuning & Optimization | 4 |
| 4 | Apache Kafka & Streaming | Kafka Architecture & Producers, Kafka Consumers & Consumer Groups, Kafka Connect & Schema Registry | 3 |
| 5 | Apache Airflow | Airflow Architecture & DAGs, Operators & Task Dependencies, Best Practices & Monitoring | 3 |
| 6 | Data Quality & Testing | Data Validation with Great Expectations, Data Quality Metrics & SLAs, Testing Data Pipelines | 3 |
| 7 | Cloud Data Platforms | AWS Data Services Overview, Snowflake & Modern Data Stack, Infrastructure as Code for Data | 3 |
| 8 | Capstone Project | Project Planning & Requirements, Pipeline Implementation, Deployment & Presentation | 3 |
Course Curriculum
Total: 25+ problems
Note: Curriculum may vary based on cohort progress and student needs. All enrolled students get lifetime access to course materials and recordings.
Meet Your Instructor
AlgoEngineer Instructor
Course Instructor
Our instructors are experienced software engineers passionate about teaching in a practical, interview-focused manner.
Expertise:
Frequently Asked Questions
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