Rekap seluruh series Belajar Performance Test Engineer: checklist production readiness, jalur karir dari QA ke Performance Architect, sertifikasi yang relevan, sumber belajar lanjutan, dan refleksi akhir perjalanan 28 episode.

Selamat datang di episode terakhir series Belajar Performance Test Engineer! Dalam 27 episode sebelumnya, kita telah menempuh perjalanan panjang dari pre-requisites hingga performance architecture review. Episode ini adalah refleksi akhir: rekap seluruh yang telah dipelajari, checklist untuk memastikan kesiapan production, jalur karir ke depan, dan sumber belajar lanjutan.
Performance engineering bukan perjalanan yang berakhir di episode ini — ini adalah fondasi yang akan kalian bangun dan perbarui seiring karir. Tetapi dengan fondasi 28 episode ini, kalian sudah memiliki pengetahuan yang komprehensif untuk menjadi Performance Test Engineer yang efektif di organisasi mana pun.
| Episode | Topik | Poin Kunci |
|---|---|---|
| 0 | Pre-Requisites & Setup | Skill dasar, tools installation, lab setup |
| 1 | Peran & Karir | Tanggung jawab, konteks 2026, jalur karir |
| 2 | Konsep & Metodologi | Throughput, latency, percentile, SLO |
| Episode | Topik | Poin Kunci |
|---|---|---|
| 3 | Load Testing Fundamentals | Load profile, VUs, ramp-up, steady-state |
| 4 | Tools: k6 | Script structure, options, HTTP testing |
| 5 | Tools: JMeter & Locust | Perbandingan, kapan menggunakan masing-masing |
| 6 | Stress, Soak & Spike | Genre testing beyond load testing |
| 7 | API & Database Performance | Endpoint testing, query latency, connection pool |
| 8 | Frontend & Web Performance | Core Web Vitals, Lighthouse, optimization |
| 9 | Monitoring & Metrics | Grafana, Prometheus, distributed tracing |
| 10 | Analysis & Bottleneck Detection | Correlation, flame graphs, root cause analysis |
| 11 | Capacity Planning | Forecasting, right-sizing, cost optimization |
| Episode | Topik | Poin Kunci |
|---|---|---|
| 12 | Test Data & Scenarios | Realistic data, user journeys, weighted scenarios |
| 13 | Distributed Load Testing | Docker, k6 cloud, geolocation testing |
| 14 | Performance in CI/CD | Performance gates, regression detection |
| 15 | Caching & CDN Testing | Cache hit ratio, Redis, CDN optimization |
| 16 | Cloud & Serverless Performance | Cold start, autoscaling, container testing |
| 17 | Tuning & Optimization | Application, database, infrastructure tuning |
| Episode | Topik | Poin Kunci |
|---|---|---|
| 18 | Network & Latency Testing | Latency simulation, throttling, geographic |
| 19 | Security in Load Tests | Safe testing, DDoS-aware, credential security |
| 20 | Performance & Compliance SLO | SLO/SLA, error budgets, reporting |
| Episode | Topik | Poin Kunci |
|---|---|---|
| 21 | Performance Engineering (Shift-Left) | NFRs, performance-as-code, architecture review |
| 22 | Continuous Performance | Baseline, trend analysis, monitoring |
| 23 | AI Workload Performance | GPU profiling, LLM inference, model serving |
| 24 | Database Performance Engineering | Query profiling, indexing, connection pooling |
| 25 | Performance Architecture Review | Patterns, scalability, bottleneck identification |
| Episode | Topik | Poin Kunci |
|---|---|---|
| 26 | Ekosistem & Tren 2026 | Continuous perf, AI workloads, observability |
| 27 | Roadmap, Karir & Refleksi | Checklist, sertifikasi, sumber belajar |
Sebelum menyatakan diri siap sebagai Performance Test Engineer, pastikan kalian menguasai checklist ini:
□ Konsep performance: throughput, latency, percentile, concurrency
□ Load testing: profile, VUs, ramp-up, steady-state
□ Tools: k6 (primary), JMeter, Locust (secondary)
□ Monitoring: Grafana, Prometheus, distributed tracing
□ Analysis: correlation, flame graphs, root cause analysis
□ Database: query profiling, indexing, connection pooling□ Genre testing: stress, soak, spike, distributed
□ CI/CD integration: performance gates, regression detection
□ Capacity planning: forecasting, right-sizing, cost optimization
□ Architecture review: patterns, scalability, bottleneck identification
□ Cloud-native: Kubernetes, serverless, autoscaling
□ AI workloads: GPU profiling, LLM inference performance
□ Frontend performance: Core Web Vitals, Lighthouse
□ Caching: cache hit ratio, CDN testing, cache stampede□ Communication: menyampaikan hasil test ke stakeholder
□ Analysis: menghubungkan data dengan akar masalah
□ Documentation: SLO documents, test plans, reports
□ Collaboration: bekerja dengan developer, SRE, product
□ Continuous learning: mengikuti tren dan tools terbaru| Sertifikasi | Provider | Relevance |
|---|---|---|
| ISTQB Performance Testing | ISTQB | Fondasi testing performa |
| k6 Certified | Grafana Labs | Tool-specific expertise |
| AWS Performance Efficiency | AWS | Cloud performance |
| Google Cloud Performance | GCP | Cloud performance |
| Grafana Certified | Grafana Labs | Observability expertise |
Performance engineering adalah disiplin yang membutuhkan kombinasi technical depth dan breadth. Kalian perlu mendalami tools dan teknologi, tetapi juga memahami konteks bisnis dan user experience. Yang paling penting: performance engineering adalah journey, bukan destination — terus belajar, terus mengukur, terus mengoptimasi.
Terima kasih telah mengikuti series Belajar Performance Test Engineer selama 28 episode. Kalian sekarang memiliki fondasi yang kuat untuk memulai karir atau meningkatkan skill di bidang performance engineering. Selamat belajar dan semoga sukses!
Inti yang harus dibawa pulang dari seluruh series:
Performance testing bukan hanya tentang menjalankan load test — ini tentang memahami bagaimana sistem berperilaku, mengapa ia berperilaku demikian, dan bagaimana membuatnya lebih baik. Dengan fondasi 28 episode ini, kalian sudah siap untuk tantangan tersebut. Sampai jumpa di perjalanan karir kalian!