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From Fundamentals to Scalability

Modern Data Engineering:
From Pipelines to Analytics

Build and Scale Data Infrastructure - Master data pipelines, ETL processes, cloud data platforms (BigQuery, Snowflake), Spark, Airflow, and modern data engineering best practices

Overview Curriculum Projects FAQ

📚

What You'll Master

Python for data engineering, ETL pipelines, Apache Airflow, Spark, BigQuery, Snowflake, data lakes, and production data infrastructure

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12-Week Curriculum

Structured learning path from data fundamentals to production data engineering

12 Weeks 🚀

Capstone Projects

E-commerce Data Platform, Real-time Streaming Pipeline, Cloud Data Warehouse

3 Projects ❓

Frequently Asked Questions

Everything you need to know about the Modern Data Engineering bootcamp

FAQ

What You'll Master

Build and scale modern data infrastructure from pipelines to analytics

🐍 Python for Data Engineering

Master Python, pandas, NumPy for data processing, cleaning, and transformation

🔄 ETL/ELT Pipelines

Build production data pipelines using Apache Airflow for workflow orchestration

⚡ Big Data Processing

Process large-scale data with Apache Spark, PySpark, and distributed computing

🏗️ Data Lakes & Warehouses

Design and implement data lakes and cloud data warehouses

☁️ Cloud Data Platforms

Work with Google BigQuery, Snowflake, and cloud-native data services

📊 Real-time Processing

Build streaming pipelines with Kafka and Spark Streaming for real-time analytics

12 Weeks Intensive

3 Portfolio Projects

24/7 Support Available

100% Production-Ready

12-Week Intensive Curriculum

Structured learning path from foundations to production deployment

Phase 1: Foundations (Weeks 1-3)

Week 1: Python for Data Engineering

Python fundamentals for data processing
pandas for data manipulation and analysis
NumPy for numerical computing
Working with different data formats - CSV, JSON, Parquet
Data cleaning and preprocessing techniques

📝 Assignment: Build a data processing pipeline that reads data from multiple CSV files, cleans and transforms the data using pandas, handles missing values, and outputs to Parquet format. Implement data validation checks.

📊 Quiz: Python data structures, pandas operations, NumPy arrays, data formats, data cleaning techniques

Week 2: Database Systems & SQL

Relational database concepts and SQL fundamentals
PostgreSQL and MySQL - setup and operations
Advanced SQL - joins, subqueries, window functions
Database design and normalization
Working with databases in Python (psycopg2, SQLAlchemy)

📝 Assignment: Design and implement a database schema for an e-commerce data warehouse. Write complex SQL queries for analytics, implement data loading scripts using Python, and optimize query performance.

📊 Quiz: SQL fundamentals, database design, joins and subqueries, window functions, query optimization

Week 3: Data Analysis & Visualization

Exploratory data analysis (EDA) techniques
Data visualization with matplotlib and seaborn
Statistical analysis and data profiling
Handling time series data
Data quality assessment and profiling

📝 Assignment: Perform comprehensive EDA on a real-world dataset. Create visualizations, identify patterns and anomalies, generate data quality reports, and build interactive dashboards. Document insights and recommendations.

📊 Quiz: EDA techniques, visualization best practices, statistical analysis, time series handling, data quality metrics

Phase 2: ETL Pipelines & Data Processing (Weeks 4-6)

Week 4: ETL/ELT Fundamentals

ETL vs ELT architecture patterns
Extraction phase - data sources, extraction methods
Transformation phase - data cleaning, validation, enrichment
Load phase - loading strategies and optimization
Building Python ETL scripts

📝 Assignment: Build a complete ETL pipeline that extracts data from multiple sources (CSV, API, database), transforms and cleans the data, validates business rules, and loads into a data warehouse. Handle errors and implement logging.

📊 Quiz: ETL concepts, extraction methods, transformation techniques, loading strategies, error handling

Week 5: Apache Airflow for Workflow Orchestration

Apache Airflow architecture and concepts
Creating DAGs (Directed Acyclic Graphs)
Airflow operators - PythonOperator, BashOperator, SQLOperator
Task dependencies and scheduling
Variables, connections, and XComs

📝 Assignment: Create complex Airflow DAGs for a data pipeline. Implement task dependencies, error handling, retries, branching logic, and dynamic task generation. Set up monitoring and alerting.

📊 Quiz: Airflow architecture, DAG concepts, operators, task dependencies, scheduling, monitoring

Week 6: Advanced ETL Patterns & Data Quality

Incremental loading and change data capture (CDC)
Data quality checks and validation frameworks
Error handling and retry mechanisms
Data lineage and metadata management
Performance optimization techniques

📝 Assignment: Build a production-ready ETL pipeline with incremental loading, comprehensive data quality checks, error handling, data lineage tracking, and performance monitoring. Implement automated testing and documentation.

📊 Quiz: Incremental loading, CDC patterns, data quality frameworks, error handling, data lineage, optimization techniques

Phase 3: Big Data Processing & Data Lakes (Weeks 7-9)

Week 7: Apache Spark Fundamentals

Big data concepts and distributed computing
Apache Spark architecture and components
Spark RDDs (Resilient Distributed Datasets)
Spark DataFrames and Datasets
Spark SQL for structured data processing

📝 Assignment: Process large-scale datasets using Spark. Implement data transformations, aggregations, and joins on datasets with millions of records. Optimize Spark jobs for performance and compare RDD vs DataFrame APIs.

📊 Quiz: Spark architecture, RDD concepts, DataFrames, Spark SQL, distributed computing, performance optimization

Week 8: Advanced Spark & Data Lakes

Spark optimization techniques - partitioning, caching
Working with different data formats - Parquet, Avro, ORC
Data lake architecture and design patterns
Lakehouse architecture concepts
Streaming data processing with Spark Streaming

📝 Assignment: Build a data lake solution using cloud storage (GCS/S3). Implement Spark jobs to process data in the data lake, optimize for performance, handle different data formats, and set up data partitioning strategies.

📊 Quiz: Spark optimization, data formats, data lake architecture, lakehouse concepts, streaming processing

Week 9: Real-time Data Processing

Streaming data concepts and architectures
Apache Kafka fundamentals
Spark Structured Streaming
Event-driven architectures
Stream processing patterns and best practices

📝 Assignment: Build a real-time data processing pipeline using Kafka and Spark Structured Streaming. Process streaming events, implement windowing operations, handle late data, and write results to a data store. Set up monitoring and alerting.

📊 Quiz: Streaming concepts, Kafka architecture, Spark Structured Streaming, event-driven patterns, stream processing best practices

Phase 4: Cloud Data Platforms & Data Warehousing (Weeks 10-12)

Week 10: Google BigQuery

BigQuery architecture and fundamentals
Data loading strategies and best practices
BigQuery SQL and advanced queries
Partitioning and clustering for optimization
BigQuery ML for machine learning

📝 Assignment: Build a data warehouse in BigQuery. Load data from multiple sources, design partitioned and clustered tables, write complex analytical queries, optimize for cost and performance, and build ML models using BigQuery ML.

📊 Quiz: BigQuery architecture, data loading, SQL optimization, partitioning/clustering, BigQuery ML, cost optimization

Week 11: Snowflake & Data Warehousing

Snowflake architecture and concepts
Data warehousing fundamentals
Dimensional modeling - star schema, snowflake schema
ETL/ELT patterns for data warehouses
Data warehouse optimization techniques

📝 Assignment: Design and implement a data warehouse in Snowflake. Create dimensional models (star schema), build ETL pipelines to populate the warehouse, implement data quality checks, and create analytical views. Optimize for query performance.

📊 Quiz: Snowflake architecture, data warehousing concepts, dimensional modeling, ETL/ELT patterns, optimization techniques

Week 12: Production Data Engineering & Monitoring

Data pipeline monitoring and alerting
Data quality frameworks and testing
CI/CD for data pipelines
Data governance and compliance
Production best practices and troubleshooting

📝 Assignment: Deploy your complete data engineering solution to production. Set up comprehensive monitoring and alerting, implement data quality checks, create CI/CD pipelines, document data lineage, and establish governance practices. Perform load testing and create runbooks.

📊 Quiz: Monitoring strategies, data quality frameworks, CI/CD for data, data governance, production best practices, troubleshooting

Weeks 1-3: Programming 101 (Phase 1)

Week 1

Day 1: Variables, types, operators
Day 2: Control flow & boolean logic
Day 3: Loops & iteration patterns
Day 4: Functions/methods, scope & returns
Day 5: I/O & core collections (list/array/dict)

Assignment: CLI calculator (both languages). Quiz: 10 MCQs + 1 coding.

Week 2

Day 1: OOP basics (classes/objects)
Day 2: Encapsulation, inheritance, polymorphism
Day 3: Exceptions & error handling
Day 4: File I/O in Java & Python
Day 5: OOP workshop (build a mini library)

Assignment: Book Library class with save/load. Quiz: 10 MCQs + 1 coding.

Week 3

Day 1: Modular programming & packaging
Day 2: Complexity & Big-O
Day 3: Debugging & unit testing (JUnit/pytest)
Day 4: Git/GitHub basics; PR etiquette
Day 5: Mini-project: CLI To-Do app (CRUD)

Assignment: To-Do app with tests & README. Quiz: 10 MCQs + 1 coding.

Weeks 4-6: Data Structures (Phase 2)

Week 4

Day 1: Arrays & lists
Day 2: Linked lists (SLL/DLL)
Day 3: Stacks (LIFO) & use-cases
Day 4: Queues/Deque & variants
Day 5: Lab: implement DS in both languages

Assignment: Implement List/Stack/Queue APIs. Quiz: 10 MCQs + 1 coding.

Week 5

Day 1: Trees & recursion basics
Day 2: BST ops (insert/delete/search)
Day 3: Graphs & adjacency models
Day 4: Traversals: DFS/BFS
Day 5: Workshop: paths, levels, cycles

Assignment: BST + BFS on grid; README with Big-O. Quiz: 10 MCQs + 1 coding.

Week 6

Day 1: Hash tables (hashing, collisions)
Day 2: Heaps/PQs; heap sort
Day 3: Recursion deep-dive
Day 4: Memory mgmt, GC concepts
Day 5: DS practice set (mixed)

Assignment: LRU Cache (hashmap+DLL). Quiz: 10 MCQs + 1 coding.

Weeks 7-9: Problem Solving (Phase 3)

Focus on patterns and 30-40 easy/medium LeetCode-style questions with guided walkthroughs. Daily flow: short lecture goals → key takeaways → real-world analogy → hands-on exercise → stretch → review.

Week 7

Day 1: Problem analysis & constraints; pattern library intro
Day 2: Two-pointers; sorted arrays & string scans
Day 3: Sliding window (fixed & variable)
Day 4: Sorting fundamentals; stability & when to use what
Day 5: Review set (6-8 questions) + live walkthrough

Assignment: 10 questions (2× two-pointers, 4× sliding-window, 4× sorting). Quiz: 10 MCQs + 1 coding.

Week 8

Day 1: Recursion patterns & backtracking (subsets, permutations)
Day 2: Dynamic Programming I (memoization vs tabulation)
Day 3: DP II (knapsack, coin change, LIS ideas)
Day 4: Graph algorithms I (BFS shortest path, topo sort)
Day 5: Mock interview #1 (15-min DSA + 10-min feedback per student)

Assignment: 10 questions (3× recursion/backtracking, 5× DP, 2× graph BFS). Quiz: 10 MCQs + 1 coding.

Week 9

Day 1: Greedy techniques; exchange arguments & proofs of correctness (informal)
Day 2: Advanced graphs (Dijkstra intro; when BFS vs Dijkstra)
Day 3: Mixed set (hashing, heap, prefix sum)
Day 4: System-aware problem solving (I/O limits, memory caps)
Day 5: Mock interview #2 + feedback & personalized plan

Assignment: 10 questions (2× greedy, 3× heap, 3× hashing/prefix, 2× graph). Quiz: 10 MCQs + 1 coding.

Weeks 10-11: Databases (Phase 4)

Week 10

Day 1: Relational model, tables, PK/FK; ER → schema
Day 2: Normalization (1NF-3NF); denormalization trade-offs
Day 3: SELECT, WHERE, ORDER BY, LIMIT; CRUD basics
Day 4: Joins (INNER/LEFT/RIGHT/FULL), GROUP BY, HAVING
Day 5: Lab: design a Course–Student–Enrollment schema

Assignment: Create schema & seed data; 12 queries (mix of joins & groups). Quiz: 12 MCQs + 1 query.

Week 11

Day 1: Indexes & query plans; when indexes hurt/help
Day 2: Transactions, ACID; isolation levels & anomalies
Day 3: Stored routines & views (intro), pagination patterns
Day 4: NoSQL overview (key-value, document); when to choose which
Day 5: Mini-project: Analytics queries & simple dashboard export (CSV/JSON)

Assignment: Optimize queries (add/remove indexes), measure timings, document rationale. Quiz: 12 MCQs + 1 query.

Weeks 12-13: System Design (Phase 5)

Week 12

Day 1: Client-server, REST, HTTP verbs, idempotency; API design (resources, pagination)
Day 2: Statelessness, session vs token auth (concepts); rate limiting basics
Day 3: Caching (CDN, reverse proxy, app-level); cache invalidation strategies
Day 4: Load balancing (round-robin, least-conn); health checks; blue/green overview
Day 5: Design exercise: URL Shortener (read-heavy, cache, DB schema, API)

Assignment: Write a 2-page design doc + simple API spec (OpenAPI snippet encouraged). Quiz: 12 MCQs.

Week 13

Day 1: Databases at scale: replication vs sharding; read/write paths
Day 2: Consistency models; CAP & PACELC intuition; queues for decoupling
Day 3: Observability 101 (logs, metrics, traces) & SLOs; error budgets
Day 4: Design exercise: News Feed/Timeline (fan-out, denorm, caches)
Day 5: Mock system design interview + structured feedback rubric

Assignment: 3-page design doc with diagram and capacity estimates (QPS, storage). Quiz: 12 MCQs.

Week 14: Data Engineering Tools & Best Practices (Phase 6)

Week 14

Day 1: Data quality tools: Great Expectations, dbt for data testing and validation
Day 2: Monitoring and observability: Data pipeline monitoring, alerting, dashboards
Day 3: Data cataloging: Apache Atlas, DataHub for metadata management
Day 4: Performance optimization: Query optimization, partitioning strategies, caching
Day 5: Best practices: Data governance, security, compliance, documentation standards

Assignment: Implement data quality checks and monitoring for your capstone project. Quiz: 10 MCQs on data engineering tools and best practices.

Week 15: Capstone Project (Phase 7)

Build 2-3 complete end-to-end applications that combine API + Database + Frontend concepts with AI enhancement. Teams of 2-3; PR-based workflow on GitHub.

Project 1: Smart To-Do with Analytics (REST API, MySQL, dashboard)
Project 2: E-Commerce Lite (Catalog, cart, orders, inventory consistency)
Project 3: Minimal Chat Service (User/channel models, message APIs, pagination)

Deliverables: Design doc (3-5 pages), API spec (OpenAPI), DB schema (ER + DDL), runnable code, README, demo video (≤5 min).

🚀 Game-Changing Capstone Projects

Build production-grade AI applications that will make recruiters stop scrolling

      💼 These aren't toy projects! Each capstone demonstrates production-grade data engineering with real-world patterns that companies are actively hiring for.
    

🛒 Project 1: E-commerce Data Platform

Complete Data Engineering Solution

🎯 Why This Project Stands Out:

This comprehensive project demonstrates your ability to build production-ready data engineering solutions. You'll implement ETL pipelines, data lakes, and cloud data warehouses - exactly what companies are hiring for!

Apache Airflow Apache Spark PostgreSQL BigQuery Python

✨ Core Components You'll Build

📊 ETL Pipelines: Extract data from multiple sources (APIs, databases, files)
🔄 Data Transformation: Clean, validate, and transform e-commerce data
🏗️ Data Lake: Store raw and processed data in cloud storage (GCS/S3)
📈 Data Warehouse: Design star schema and load into BigQuery/Snowflake
📉 Analytics Dashboards: Build reporting and analytics layer
⚡ Orchestration: Schedule and monitor pipelines with Airflow

🎓 What You'll Master

Building production ETL/ELT pipelines
Data lake architecture and implementation
Data warehouse design (star schema, dimensional modeling)
Cloud data platform integration (BigQuery/Snowflake)
Data quality and monitoring

💼 Career Impact: E-commerce data platforms are the foundation of modern data engineering. This project showcases skills directly applicable to most data engineering roles!

⚡ Project 2: Real-time Streaming Data Pipeline

Process Streaming Data at Scale

🎯 Real-Time Data Processing:

Build a production-ready streaming data pipeline that processes events in real-time. This is what every modern data platform needs - real-time analytics, event processing, and stream processing capabilities!

Apache Kafka Spark Streaming Kafka Streams Redis Airflow

🔄 Pipeline Components

📨 Event Ingestion: Set up Kafka producers for real-time event streaming
⚡ Stream Processing: Process events with Spark Structured Streaming
🔄 Real-time Transformations: Window operations, aggregations, joins
💾 Data Storage: Store processed data in data lake and databases
📊 Real-time Dashboards: Build live analytics dashboards
🔔 Alerting: Implement real-time alerting for anomalies

🔥 Advanced Features

🔄 Event-Driven Architecture: Decouple producers and consumers
⚡ Low Latency Processing: Process events in milliseconds
🛡️ Fault Tolerance: Handle failures and ensure data consistency
📈 Scalability: Scale horizontally to handle millions of events
🔄 Exactly-Once Processing: Ensure no duplicate processing
📊 Monitoring: Real-time pipeline monitoring and metrics

🛠️ Use Cases You'll Implement

Real-time user activity tracking
Live inventory updates
Real-time fraud detection
Streaming analytics and aggregations

🌟 Industry Demand: Real-time data processing is critical for modern applications. Companies like Uber, Netflix, and Amazon rely heavily on streaming pipelines - and they're always hiring data engineers with these skills!

☁️ Project 3: Cloud Data Warehouse & Analytics

Enterprise Data Warehouse on Cloud

🎯 Enterprise Data Warehouse:

Design and implement a production-ready cloud data warehouse using BigQuery and Snowflake. This project demonstrates your ability to build scalable, optimized data warehouses that power business intelligence and analytics!

Google BigQuery Snowflake Dimensional Modeling ETL Pipelines Data Visualization

🏗️ Warehouse Architecture

📊 Dimensional Modeling

Design star schema and snowflake schema
Create fact tables and dimension tables
Implement slowly changing dimensions (SCDs)
Optimize for query performance

☁️ Cloud Data Platforms

BigQuery: Partitioning, clustering, optimization
Snowflake: Virtual warehouses, time travel
Data loading strategies and best practices
Cost optimization techniques

🔄 ETL Integration

Build ETL pipelines to populate warehouse
Implement incremental loading
Data quality checks and validation
Automated pipeline scheduling

📈 Analytics & Reporting

Build analytical queries and views
Create dashboards and reports
Implement data governance
Performance monitoring and optimization

🔬 Advanced Techniques You'll Master

📊 Query Optimization: Partitioning, clustering, query tuning
💰 Cost Management: Optimize storage and compute costs
🔄 Data Pipeline Integration: Connect ETL pipelines to warehouse
📈 Scalability: Design for petabyte-scale data
🛡️ Data Governance: Implement security and compliance

💎 Portfolio Differentiator: Cloud data warehouses are the backbone of modern analytics. Companies like Google, Snowflake, and major enterprises are constantly hiring data engineers with cloud data warehouse expertise!

🎓 Full Support for Every Project

📝

Weekly Code Reviews

Get expert feedback on your implementation

👥

Office Hours

1-on-1 guidance when you're stuck

🚀

Deployment Help

Launch your projects to production

📹

Demo Recording

Create impressive presentation videos

Register for Free Webinar

What You'll Master

🐍 Python for Data Engineering

🔄 ETL/ELT Pipelines

⚡ Big Data Processing

🏗️ Data Lakes & Warehouses

☁️ Cloud Data Platforms

📊 Real-time Processing

12-Week Intensive Curriculum

Phase 1: Foundations (Weeks 1-3)

Phase 2: ETL Pipelines & Data Processing (Weeks 4-6)

Phase 3: Big Data Processing & Data Lakes (Weeks 7-9)

Phase 4: Cloud Data Platforms & Data Warehousing (Weeks 10-12)

Weeks 1-3: Programming 101 (Phase 1)

Week 1

Week 2

Week 3

Weeks 4-6: Data Structures (Phase 2)

Week 4

Week 5

Week 6

Weeks 7-9: Problem Solving (Phase 3)

Week 7

Week 8

Week 9

Weeks 10-11: Databases (Phase 4)

Week 10

Week 11

Weeks 12-13: System Design (Phase 5)

Week 12

Week 13

Week 14: Data Engineering Tools & Best Practices (Phase 6)

Week 14

Week 15: Capstone Project (Phase 7)

🚀 Game-Changing Capstone Projects

🛒 Project 1: E-commerce Data Platform

✨ Core Components You'll Build

🎓 What You'll Master

⚡ Project 2: Real-time Streaming Data Pipeline

🔄 Pipeline Components

🔥 Advanced Features

🛠️ Use Cases You'll Implement

☁️ Project 3: Cloud Data Warehouse & Analytics

🏗️ Warehouse Architecture

🔬 Advanced Techniques You'll Master

🎓 Full Support for Every Project

Frequently Asked Questions

⚠️ The Job Market Harsh Reality

Ready to Master Modern Data Engineering?