Apache Spark
If you like applying Apache Spark, every challenge here gives you a chance to practice it on a real industry brief.
- 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…
- Spark
- Distributed Systems
- Performance Profiling
Machine Learning at Scale - AnalysisIntermediateNew
Halve a Daily Spark Bill Without Breaking the SLA
Work only from the materials in this file. Read the representative PySpark module (PULSE-JOB) and the cluster configuration (PULSE-CLUSTER) to understand how the nightly job is …
- Spark
- Cost Optimization
- Etl Pipelines
Open coursework - 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 - 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
- Spark
Machine Learning at Scale Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- AnalysisIntermediateNew
Mine Basket History to Justify Grocery Shelf-Adjacency Changes
Work only from the materials provided. Use the basket transactions extract to mine frequent itemsets with FP-growth, tuning the minimum support level separately for the food, ho…
- Frequent Itemset Mining
- Fp Growth
- Spark
Open coursework - 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
- Spark
Data Engineering and Big Data Systems - 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.
Industry teams behind a decade of practitioner briefs
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Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.