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
Design a Change-Data-Capture Pipeline for an E-Commerce Reseller
Receive the MySQL schema (220 tables), 7 days of binlog samples, and the data team's freshness + correctness requirements. Design the CDC pipeline: Debezium for MySQL binlog cap…
- Change Data Capture
- Debezium
- Kafka
Big Data and Data-Intensive Systems - 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
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
Open coursework - AnalysisBeginnerNew
Sales Performance Analysis for a 40-Person SaaS Scale-Up
You will receive a dataset containing 500+ sales opportunities with fields like deal value, stage, source, close date, and account size. Your challenge is to design a data mart …
- Data Warehousing
- Etl
- Olap
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
- AnalysisBeginnerNew
Audit a Climate-Tech Sensor Dataset for Production Readiness
You receive 18 months of raw sensor readings from 1,200 sensors (about 800M rows), plus a sensor-metadata table (location, firmware version, deployment date). Profile the data f…
- Data Quality Audit
- Data Profiling
- Time Series Analysis
Applied Data Analysis and Practical Data Science
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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