Cost-Optimize a Large-Scale Spark Job for an Ad-Tech Platform
Overview
What this challenge is about.
Cost-Optimize a Large-Scale Spark Job for an Ad-Tech Platform. Advanced challenge in analysis. Analyzing real datasets and building models that drive decisio...
The Brief
What you'll do, and what you'll demonstrate.
Find and prove a 40 percent cost reduction on a 4TB nightly Spark job without breaking the 4-hour SLA.
This is not a data exercise. It is the work an analyst does when stakeholders need answers from messy data. That distinction matters to every hiring manager who has seen candidates describe statistical methods and none who have extracted insight from messy, real-world data.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Profile a real Spark job with the Spark UI and cloud-platform metrics
- Apply standard Spark optimizations (broadcast joins, partition tuning, instance mix)
- Build a defensible cost extrapolation from subset to full data
- Communicate cost trade-offs to finance + engineering stakeholders
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Cloud Computing for Data and ML
Master · Data Engineering
Strong alignment
This challenge maps to Cloud Computing for Data and ML at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Spark Optimization
Apply spark optimization to solve real industry problems and demonstrate production-level capability.
- Cloud Services
Apply cloud services to solve real industry problems and demonstrate production-level capability.
- Cost Engineering
Apply cost engineering to solve real industry problems and demonstrate production-level capability.
- Profiling
Apply profiling to solve real industry problems and demonstrate production-level capability.
- Etl Pipelines
Apply etl pipelines to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Engineer
Spark cost optimization on a real EMR workload is the kind of project a data engineer ships in the first quarter at any ad-tech or large-data company.
This challenge sharpens
- spark-optimization
- cost-engineering
- etl-pipelines
MLOps Engineer
Profiling and cost-optimizing large compute workloads is the same skillset MLOps engineers use to tame training-cluster bills.
This challenge sharpens
- profiling
- cloud-services
- benchmarking
AI Solutions Architect
Translating profiling + optimization into a finance-team-defensible recommendation is core AI solutions architect work.
This challenge sharpens
- cost-engineering
- cloud-services
- spark-optimization