Why this matters
As a Data Platform Engineer, you enable analytics and AI without letting cloud bills spiral. Real tasks you will face include:
- Right-sizing clusters so nightly ETL finishes on time without overpaying.
- Designing storage lifecycle rules that move cold data to cheaper tiers automatically.
- Choosing formats and partitioning to reduce scanned bytes and runtime.
- Mixing on-demand and spot/preemptible capacity safely for batch workloads.
- Setting budgets, alerts, and cost attribution so teams own their costs.
Mini task: 60-second audit prompt
Open your last month's compute and storage usage report (or imagine a typical workload) and note three biggest cost drivers. Write one hypothesis for each driver on how to reduce it by 20% without missing SLAs.
Concept explained simply
Cost is the product of three levers:
- Volume: how much you store or compute.
- Duration: how long resources run or data persists.
- Unit price: how much per vCPU-hour, GB-month, or TB scanned.
Mental model: Picture a three-slider control. You can cut cost by:
- Reducing volume (compress, prune, sample, partition).
- Reducing duration (auto-stop, autoscale, schedule jobs into shorter bursts).
- Lowering unit price (right-size instances, use cheaper tiers, use spot/preemptible for fault-tolerant tasks).
Rule of thumb cheatsheet
- If a job can tolerate restart: consider spot/preemptible for 30–70% of capacity.
- If data is rarely read: tier it; aim for < 10% hot.
- If query scans > 10x the rows returned: fix partitioning or filters.
- Idle clusters > 15 minutes: auto-stop or scale to zero.
Key levers you can pull
Compute
- Right-sizing: choose CPU/RAM to match workload; measure CPU, memory, and I/O headroom.
- Autoscaling: scale based on queue depth, CPU, or throughput; set min for SLA, max for bursts.
- Workload shaping: run batch at off-peak hours; consolidate small jobs.
- Spot/Preemptible: blend with on-demand; checkpoint progress; use retries.
- Job optimization: vectorized engines, reduce shuffles, cache hot datasets, push down filters.
Storage
- Lifecycle policies: auto-move from hot to warm to archive after access thresholds.
- Right storage class: match access patterns and durability to tier.
- Data layout: columnar formats (e.g., Parquet/ORC), partitioning, file sizing (avoid too many tiny files).
- Retention: delete data past policy; aggregate or sample if full detail is unnecessary.
- Egress awareness: keep compute near data; cache cross-region reads when possible.
Worked examples
Example 1: Right-size and autoscale a nightly ETL
Assume on-demand node cost is $0.50/hour (illustrative). Current setup: 10 nodes running 24/7.
- Current monthly: 10 nodes × $0.50 × 24 × 30 ≈ $3,600.
- Observed: ETL needs 10 nodes for 6 hours/night; other time 3 nodes suffice.
- Autoscaled monthly: (10×$0.50×6×30) + (3×$0.50×18×30) ≈ $900 + $810 = $1,710.
- Savings ≈ $1,890/month (~52%). SLA unchanged.
Checklist: set min=3, max=12, scale on queue depth, and auto-stop after 15 minutes idle.
Example 2: Blend on-demand with spot for batch
Assume on-demand vCPU-hour = $0.04; spot discount 60% (i.e., $0.016).
- Need 50,000 vCPU-hours/month.
- All on-demand: 50,000 × $0.04 = $2,000.
- Blend 60% spot, 40% on-demand: (30,000×$0.016) + (20,000×$0.04) = $480 + $800 = $1,280.
- Savings ≈ $720 (36%). Add checkpointing and retries.
Example 3: Storage tiering with lifecycle
Assume unit prices (illustrative): hot $0.023/GB-month, warm $0.012, archive $0.004.
- Total 100 TB: 10 TB hot, 70 TB warm, 20 TB archive.
- Monthly: (10,000×0.023) + (70,000×0.012) + (20,000×0.004) = $230 + $840 + $80 = $1,150.
- If all were hot: 100,000×0.023 = $2,300. Savings ≈ $1,150 (50%).
Lifecycle rule: >30 days no access → warm; >180 days no access → archive.
Example 4: Columnar format and partition pruning
Assume query engine cost $5/TB scanned (illustrative). Current: 1 TB/day scanned from CSV. Convert to Parquet with 70% size reduction and partition by date, reducing scan to 0.2 TB/day.
- Before: 1 TB/day × $5 × 30 ≈ $150/month.
- After: 0.2 TB/day × $5 × 30 ≈ $30/month.
- Savings: $120/month (80%) plus faster jobs.
Step-by-step playbook
- Tag and attribute costs: enforce tags per project, environment, and owner for all resources.
- Baseline: export cost and usage. Identify top 5 services and workloads by spend and by waste (idle time, failed jobs, cold data in hot tier).
- Quick wins (2 weeks): enable autoscaling and auto-stop; set lifecycle policies; convert largest raw datasets to columnar; fix most wasteful queries.
- Medium-term (1–2 months): refactor batch to support spot with checkpointing; right-size instance families; consolidate small files.
- Governance: budgets and alerts; guardrails (quotas, max cluster sizes); periodic reviews.
Heuristics to decide fast
- Target cluster utilization 60–80% during peaks.
- Files 128–512 MB each for columnar storage to balance metadata and parallelism.
- Keep hot tier <= 10–20% of total bytes unless analytics proves otherwise.
Exercises
These mirror the tasks below in the Exercises section. Do them here, then record your answers.
- Exercise 1 (Right-size + autoscale): Given a 24/7 8-node cluster and observed demand peaks for 5 hours/day, propose min/max nodes, expected monthly cost before/after (assume $0.60/node-hour), and risk mitigations.
- Exercise 2 (Tiering + lifecycle): Given 60 TB data (6 TB hot, 42 TB warm, 12 TB cold), estimate monthly storage with illustrative prices hot $0.025/GB, warm $0.012, archive $0.004. Propose lifecycle transitions.
- Checklist:
- Included SLA and performance notes
- Showed before/after cost with assumptions
- Included operational risks and mitigations
- Defined measurable success criteria
How to self-check your answers
- Are unit prices and hours clearly stated as assumptions?
- Do you justify min/max autoscaling with observed peaks?
- Did you consider retrieval penalties for archive when proposing lifecycle?
- Are risks (preemptions, cold-starts) addressed?
Common mistakes and how to avoid them
- Focusing only on unit price: ignoring that poor design increases volume scanned/processed.
- Over-partitioning: too many small files; fix by compaction and reasonable partition keys.
- Putting everything in the cheapest tier: surprise retrieval costs and latency; know access patterns.
- Ignoring egress: running compute far from data; co-locate workloads.
- No guardrails: lack of budgets/alerts leads to runaway spend.
Self-audit in 5 minutes
- Top 3 datasets by bytes in hot tier: can any move to warm/archive?
- Top 3 jobs by cost: can any use spot or be time-shifted?
- Any cluster with > 20% idle time this week?
Practical projects
- Implement lifecycle policies for a staging bucket/lake: simulate with folders for hot/warm/cold and a schedule to move files based on last access time.
- Convert a 100 GB sample dataset from CSV to Parquet, choose partitions, and measure query scan reduction.
- Build a small autoscaling policy using job queue depth to scale a worker pool between 1 and 10 instances; log scale events.
- Create a cost dashboard: daily spend, top projects, idle hours estimate, hot vs cold bytes.
Who this is for
- Data Platform Engineers, Data Engineers, and Analytics Engineers who manage cloud data workloads.
Prerequisites
- Basic understanding of compute (instances/containers) and storage (object/columnar formats).
- Comfort with reading simple cost and usage reports.
Learning path
- Measure and tag: ensure cost attribution is reliable.
- Quick wins: autoscale, auto-stop, lifecycle policies, columnar conversion.
- Deeper optimization: workload refactors for spot, query pruning, compaction.
- Governance and monitoring: budgets, alerts, weekly reviews.
Next steps
- Complete the exercises and mini challenge.
- Build one practical project end-to-end.
- Take the quick test to validate your understanding.
Mini challenge
Pick one production-like workload. Draft a one-page optimization plan with:
- Baseline cost and KPIs
- Three changes and expected savings
- Risks, rollout plan, and rollback trigger
Revisit after one week to compare expected vs actual.
Note on progress
The quick test is available to everyone for free. If you are logged in, your results and progress will be saved automatically.
Quick Test
When ready, take the quick test to check your understanding. Aim for at least 70%.