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.
- CodeIntermediateNew
Bridge a Healthcare Messaging System from RabbitMQ to Kafka
Working from the provided RabbitMQ topology specification and the 30-day message-volume dataset, map each of the 24 queues to a Kafka topic and choose a sound partition key for …
- Kafka
- Rabbitmq
- Message Broker Migration
Open coursework - DesignSeniorNew
Designing a Data Warehouse for a Renewable Energy Firm
You are given sample data from three sources: energy production logs, weather data, and equipment maintenance records. Your task is to: (1) design a star schema with fact and di…
- Data Warehousing
- Star Schema
- Etl
Database Systems - DesignIntermediateNew
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
Open coursework - CodeIntermediateNew
Build a Real-Time Streaming Pipeline for Card-Fraud Scoring
Using the provided transaction-event sample, the cardholder feature snapshot, the reference scoring service, and the risk team's experiment specification, build a stream-process…
- Stream Processing
- Kafka
- Flink
Open coursework Develop in-demand professional skills.
Each challenge names the skills it strengthens. Over time, your profile fills with the competences a hiring manager would actually look for.
Why Ewance
- DesignIntermediateNew
Design Schema Evolution for a Multi-Tenant Event Platform
Design a schema-evolution model covering: schema-registry topology (per-tenant subjects vs shared), compatibility modes per topic class (strict vs forward-only vs none), tenant-…
- Schema Evolution
- Kafka
- Schema Registry
Event-Driven Architecture
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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