Data Pipelines
If you like applying Data Pipelines, every challenge here gives you a chance to practice it on a real industry brief.
- CodeAdvancedNew
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 - DesignAdvancedNew
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 - DesignAdvancedNew
Stand Up a Feature Store for a Series-B Fintech
Pick one priority feature group (recommend the 25 transaction-history features used by the fraud model). Define the offline source-of-truth (likely Snowflake or BigQuery), the o…
- Feature Store
- Feature Engineering
- Airflow
ML Engineering and Production ML - DesignAdvancedNew
Design an Event-Driven Reporting Pipeline for an Enterprise BI Team
Map the 9 source systems by extraction approach: CDC where possible (Debezium for SAP HANA + MES databases), event hooks where the source supports them (Salesforce streaming API…
- Change Data Capture
- Kafka
- Debezium
Enterprise and Business Software Engineering 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
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.
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