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🏆 Data Engineering Arena

Master Data Engineering through realistic business challenges inspired by production systems.

Practice SQL and PySpark by solving business scenarios that mirror the types of problems Data Engineers face in production environments.

From data cleaning and validation to joins, aggregations, and customer analytics, each challenge builds practical skills step by step.

10
Challenges
155
Points
1
Capstone

Level 1 — Data Engineering Fundamentals​

Build the foundation every Data Engineer needs before working on enterprise-scale data platforms.

Skills Covered​

  • Customer Deduplication
  • Revenue Aggregation
  • NULL Handling
  • Invalid Data Detection
  • Date Standardization
  • Duplicate Removal
  • Data Cleansing
  • Data Standardization
  • Business Rule Validation
  • Customer Data Sanitization
  • INNER JOIN
  • LEFT JOIN
  • GROUP BY
  • ORDER BY
  • LIMIT
  • Revenue Analytics

Progress​

✅ 15 Challenges

✅ 295 Points

✅ 1 Capstone Challenge

Start Level 1 →

Challenge Journey​

Level 1 introduces progressively more realistic business scenarios.

Challenge RangeFocus Area
ISA-001 → ISA-005Data Cleaning & Standardization
ISA-006 → ISA-010Data Quality & Production Data Preparation
ISA-011 → ISA-015SQL Joins, Aggregations & Business Analytics

What You'll Learn​

Throughout Level 1 you'll gain hands-on experience with:

  • Data Cleaning
  • Data Quality Validation
  • NULL Handling
  • Deduplication
  • String Functions
  • Date Functions
  • Business Rule Enforcement
  • INNER JOIN
  • LEFT JOIN
  • GROUP BY
  • Aggregations
  • ORDER BY
  • LIMIT
  • Customer Analytics
  • Revenue Reporting
  • Production Data Preparation

What's Coming Next​

🚧 Level 2 — Intermediate SQL & PySpark​

Learn more advanced transformation techniques commonly used in production pipelines.

Topics include:

  • RIGHT JOIN
  • FULL OUTER JOIN
  • UNION
  • UNION ALL
  • CASE WHEN
  • Multi-table Joins
  • Complex Aggregations
  • Data Integration

🚧 Level 3 — Window Functions & Analytics​

Master analytical SQL and PySpark used in enterprise reporting.

Topics include:

  • ROW_NUMBER()
  • RANK()
  • DENSE_RANK()
  • LEAD()
  • LAG()
  • Running Totals
  • Moving Averages
  • Partitioned Analytics

🚧 Level 4 — Production Data Engineering​

Build production-ready ETL pipelines and warehouse solutions.

Topics include:

  • Incremental Loads
  • Change Data Capture (CDC)
  • Slowly Changing Dimensions (SCD)
  • Fact & Dimension Modeling
  • Data Warehouse Design
  • Pipeline Optimization
  • Data Quality Frameworks
  • Production ETL Patterns

Learn Data Engineering by solving realistic business problems one challenge at a time.