Computer Science
Data Engineering & Pipelines Challenges
Data Engineering & Pipelines challenges put you inside the work of moving data reliably from source to insight. You'll develop skills in ETL Fundamentals, Data Pipeline Design, and Data Wrangling, and you'll write SQL for Analytics and dbt Models while building Airflow DAGs that orchestrate the flow.
From there you'll handle the harder edges — Kafka event streaming, Streaming-first design, Lakehouse architecture, and Data observability — working with Apache Spark and Snowflake or BigQuery query optimization the way data teams actually do. Each challenge you solve earns a verified credential you can share with recruiters.
- DesignIntermediateNew
Build a Feature Store for a Fintech Fraud Team
You will design a feature-store layer covering 12 representative fraud features (account-level, merchant-level, transaction-level), with both batch (Spark) and online (low-laten…
- Feature Stores
- Data Pipelines
- Apache Spark
Machine Learning at Scale - CodeIntermediateNew
Migrate a Legacy Warehouse to a Lakehouse for an Edtech AI Platform
You receive a Postgres dump of around 50 GB and the current dbt models that produce the student-attempts mart. Land the raw data in object storage (S3 or GCS) as Parquet partiti…
- Lakehouse Architecture
- Delta Lake
- Apache Spark
Data Engineering and Big Data Systems - DesignIntermediateNew
Migrate a 200TB Data Lake from Parquet to Iceberg
Receive an inventory of the 200TB hot tier (around 1,200 tables, around 38 PB of historical data referenced), the current Spark + Trino read patterns, and 6 months of schema-cha…
- Iceberg
- Parquet
- Data Lake
Big Data and Data-Intensive Systems - AnalysisIntermediateNew
Frequent-Itemset Mining on a Grocery Retailer's Basket History
Load 18 months of basket-level transaction data (Parquet, around 92 GB) into a Spark cluster. Run FP-growth at support thresholds tuned per category (food vs household vs fresh)…
- Frequent Itemset Mining
- Fp Growth
- Apache Spark
Data Mining and Information Retrieval Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- AnalysisIntermediateNew
Cost-Profile a Spark Job at Scale and Cut the Bill in Half
Receive the PySpark job (around 1,800 lines), 5 nights of Spark UI + EMR metrics, and the EMR cluster config. Profile to find the top 3 cost drivers (likely candidates: skewed j…
- Apache Spark
- Finops & Cost Optimization
- Etl Pipelines
Big Data and Data-Intensive Systems - CodeIntermediateNew
Scale Feature Pipelines for a Hyperscaler Search-Ranking Team
You receive a synthetic-but-realistic 80 GB sample of the ranking events plus the existing Spark pipeline (PySpark) and a Spark UI snapshot from a recent production run. Profile…
- Apache Spark
- Distributed Systems Design
- Performance Profiling
Machine Learning at Scale - DesignBeginnerNew
Scaling a Sydney D2C Cosmetics Startup's Data Pipeline
You are tasked with designing a cloud-based data pipeline for GlowUp. The pipeline must ingest real-time user events (page views, purchases, returns) from web and mobile apps, p…
- Cloud Computing
- Apache Spark
- Nosql
Big Data and Cloud Technologies
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Related skill families
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