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ETL vs ELT — Pilih Yang Tepat, Hemat Biaya! Halo, Data Enthusiasts! 👋 Kalau kamu lagi belajar atau kerja di dunia data engineering, pasti pernah denger istilah ETL dan ELT. Tapi tau nggak sih, salah pilih pendekatan bisa bikin biaya operasional membengkak? 💸 Di artikel terbaru ini, kita bahas tuntas: ✅ Kapan pakai ETL (Extract-Transform-Load) — cocok buat regulasi ketat & sistem on-premise ✅ Kapan pakai ELT (Extract-Load-Transform) — lebih cepat & fleksibel di cloud ✅ Perbandingan praktis dari sisi kecepatan, biaya, dan kepatuhan regulasi ✅ Pitfall yang sering bikin rugi & solusinya (partisi, dbt, data contract) Fakta menarik: Adopsi ELT meningkat 67% dalam 2 tahun terakhir seiring migrasi ke cloud! ☁️ 🎬 Nonton versi videonya di sini: ▶️ TikTok: https://vt.tiktok.com/ZSmKwt79Y/ ▶️ YouTube Shorts: https://youtube.com/shorts/T3basUGW_So?feature=share ▶️ Facebook: https://www.facebook.com/share/r/18ZNog3o66/ ▶️ Instagram Reels: https://www.instagram.com/reel/DVbjChsDzKn/?igsh=MWEwazBvdHp2cmY3MA== 🙏 Bantu kami berkembang ya! 👍 Like videonya 💬 Komen pendapat kamu — Tim ETL atau Tim ELT? 🔄 Share ke teman yang lagi belajar data 🔔 Subscribe & follow biar nggak ketinggalan materi berikutnya! 📢 MAU BELAJAR DATA ENGINEERING DARI NOL? Join komunitas Belajar12Jam sekarang — GRATIS! 🚀 👉 https://t.me/belajar12jam Di komunitas ini kamu bisa: 💡 Tanya jawab langsung soal data engineering 🔔 Dapat notifikasi materi & artikel terbaru 📦 Akses resources eksklusif 🤝 Networking bareng sesama learner & praktisi Jangan belajar sendirian — yuk gabung bareng ratusan learner lainnya! Klik sekarang 👉 https://t.me/belajar12jam

📊 ETL vs ELT — Choose the Right Approach, Save on Costs One of the most fundamental architectural decisions in data engineering is choosing between ETL and ELT. This choice directly impacts your operational costs, iteration speed, and team flexibility. Here's a quick breakdown: 🔸 ETL (Extract–Transform–Load) Data is cleaned and structured in a separate layer before entering the warehouse. Best for strict PII compliance and stable on-premise schemas. 🔹 ELT (Extract–Load–Transform) Raw data lands directly in the warehouse, then gets transformed using SQL tools like dbt. Leverages scalable cloud compute for speed and flexibility. Key takeaways: ⚡ Ingestion speed → ELT wins 🔒 Schema control → ETL wins 💰 Cloud cost → ELT is more economical 🛡️ Regulatory compliance → ETL is safer 3 pitfalls to avoid: 1. Full scans without filters → Use partitioning & clustering 2. Scattered transform logic → Centralize with dbt 3. Uncontrolled schema changes → Implement data contracts 📖 Full article: https://medium.com/p/2f1b0c2d625e 🎬 Watch the video version: ▶️ TikTok: https://www.tiktok.com/@belajar12jam.id/video/7613385029955276048 ▶️ Instagram: https://www.instagram.com/reel/DVdrGfAjwld/?igsh=MzJsYTNtaGx3MTN6 ▶️ YouTube: https://youtube.com/shorts/vaNDY4fUR9Q?feature=share ▶️ Facebook: https://www.facebook.com/share/r/18Hi8KFNFJ/ Which approach does your team use — ETL or ELT? I'd love to hear your experience in the comments! 👇 If you found this useful, please like, comment, and share with your network. And if you're learning data engineering, join our free community: 👉 https://t.me/belajar12jam #DataEngineering #ETL #ELT #dbt #CloudData #DataPipeline #BigData #Belajar12Jam

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