Big Data Architecture & Storage
Warehouse, lake or lakehouse - where data lives decides how fast it answers, how much it costs and who can ask.
layersThe Storage Stack
Every architecture tells the same story in layers. Sources - PMS, POS, web analytics, market feeds - flow first into a landing zone: raw, untouched, safely captured even when upstream formats change.
From there the flow divides. Cleaned and conformed data lands in a warehouse - structured, governed and query-fast, the single source of truth for KPIs. Raw data of any shape pools in a lake - cheap, flexible, schema applied later when the questions are known. The modern lakehouse blends both: warehouse discipline on top of lake economics.
Presentation and BI layers - dashboards, reports, models - read from whichever layer serves them. Get the stack right and new questions stay cheap; get it wrong and every report becomes an engineering project.
tuneArchitecture Choices
Data Warehouse
Curated and modeled - the single source of truth for KPIs and standard reporting.
Data Lake
Raw everything at low cost - built for exploration and data science on unknown questions.
Lakehouse
One platform serving both warehouse discipline and lake economics - the emerging default.
Cloud vs On-Premise
Elasticity and managed services versus control and residency - most hotels choose cloud.
Batch vs Streaming
Nightly loads versus continuous flows - match the cadence to the decision speed needed.
Hub-and-Spoke
A central platform feeding property tools, dashboards and models - one spine, many consumers.
balanceWarehouse vs Lake
The warehouse is optimized for known questions: clean, conformed tables, fast answers, controlled access - the finance-grade home for revenue reporting and standard KPIs. It costs more per byte, and its shape must be defined up front.
The lake is optimized for unknown questions: anything can be poured in at low cost, in any shape, and explored by analysts and data scientists - schema emerges when the question does. The price is slower answers and harder governance.
account_balanceWarehouse
Clean, consistent, fast answers to known questions - costs more per byte, shape defined up front.
water_dropLake
Raw, cheap, any shape for unknown questions - slower answers, governance harder.
The bottom line: architecture is a means, not an end - start from the decisions the hotel needs faster or deeper, then choose the smallest storage and processing footprint that serves them. Big data gravity pulls toward platforms nobody needed.
translatePenjelasan dalam Bahasa Indonesiaexpand_more
Arsitektur big data menentukan di mana data tinggal dan seberapa cepat, mahal, dan terbuka ia menjawab: data warehouse, data lake, atau lakehouse. Sumber seperti PMS, POS, dan feed pasar mengalir ke landing zone, lalu terbagi: warehouse yang bersih dan tergovernance untuk KPI, lake murah untuk data mentah bentuk apa pun. Pilihan cloud vs on-premise serta batch vs streaming melengkapi keputusan arsitektur setiap hotel.
- check_circleWarehouse: curated, modeled, single source of truth untuk KPI dan reporting standar.
- check_circleLake: raw everything berbiaya rendah untuk eksplorasi; skema diterapkan saat pertanyaan diketahui.
- check_circleLakehouse memadukan disiplin warehouse dan ekonomi lake; kian menjadi default modern.
- check_circleCloud menawarkan elastisitas dan managed services; on-premise kontrol dan residency; mayoritas hotel memilih cloud.
- check_circleBatch vs streaming dicocokkan dengan kecepatan keputusan yang dibutuhkan.
- check_circleMulai dari keputusan yang perlu lebih cepat, lalu pilih footprint terkecil; jangan terhisap gravitasi platform.
helpPertanyaan Umum (FAQ)
Apa perbedaan data warehouse, data lake, dan lakehouse?
Warehouse menyimpan data bersih dan terkonformasi untuk pertanyaan yang sudah dikenal: single source of truth KPI dengan jawaban cepat, namun lebih mahal per byte dan bentuknya didefinisikan di muka. Lake menampung data mentah bentuk apa pun dengan biaya rendah untuk pertanyaan yang belum diketahui, dengan governance lebih sulit. Lakehouse memadukan disiplin warehouse di atas ekonomi lake dan kini menjadi default modern.
Bagaimana memilih arsitektur big data hotel?
Mulai dari keputusan yang perlu dijawab lebih cepat atau lebih dalam, lalu pilih footprint penyimpanan dan pemrosesan terkecil yang melayaninya. Sebagian besar hotel memilih cloud karena elastisitas dan managed services, serta mencocokkan batch atau streaming dengan kecepatan keputusan. Gravitasi big data menarik ke platform besar yang tidak dibutuhkan siapa pun; hindari.
Cloud atau on-premise untuk data hotel?
Cloud menawarkan elastisitas dan layanan terkelola sehingga mayoritas hotel memilihnya. On-premise memberi kendali penuh dan kepastian residency data, relevan bila regulasi lokal mensyaratkan data tetap berada di negara tertentu. Keputusannya menyusul kebutuhan keputusan dan regulasi, bukan selera.