Data Lakehouse: Cost-Optimized AI Architecture for Business

04/09/2026

Don't Confuse Data Lakes with Data Warehouses

Many operations directors I meet still hold an outdated belief: to analyze data, you must build a Data Lake, then ETL it into a Data Warehouse, and finally feed it into AI. They consider this the standard. In reality, this approach causes businesses to waste about one-third of their budget on operating redundant data transformation layers. Data sits idle in the lake, often becoming corrupted during transformation before it even reaches its destination.

Practical experience shows this model is already obsolete, even in Vietnam. When you need a real-time inventory report for a supermarket chain, waiting for an overnight ETL pipeline is a waste of downtime. The solution lies in changing your mindset from the start: adopt the Data Lakehouse. This is not a marketing buzzword; it is a necessity for AI data architecture to run smoothly without bottlenecks.

Choosing Between Flexibility and Operational Costs

The first fork in the road you face is: Do you want raw data or cleaned data? In the old model, you had to choose one. If you chose a Data Lake, you had raw data but found it hard to analyze. If you chose a Data Warehouse, analysis was excellent, but storage costs were high, and cleaning took time.

Data Lakehouse solves this problem by allowing you to store raw data at low cost while still enabling direct querying like a Data Warehouse. Imagine managing a chain of instant noodle or beer factories. Sensor data from production lines is massive. If you force this data into a traditional Data Warehouse, storage costs could double compared to actual needs. However, if you leave it on a standard Data Lake, data engineers might spend weeks cleaning it before running predictive maintenance models.

By combining both, you pay only for cheap storage capacity while still running complex SQL queries directly on that data. This is the trade-off: you sacrifice some initial technical complexity in exchange for long-term flexibility.

Infrastructure Decisions: Build or Buy?

There is no one-size-fits-all answer to this question. For large conglomerates like TTN or Masan, building a custom Data Lakehouse infrastructure can provide absolute control over sensitive data and optimize costs when data scales to petabytes. However, for small and medium-sized enterprises, or branches in Thailand and the Philippines expanding rapidly, building an in-house operations team is a heavy personnel burden.

Most clients I have advised recently choose a hybrid model. They use cloud platforms for compute processing when needed but store data on cost-optimized solutions. AIVISION often recommends this model for companies in the lubricant and food industries due to the high volatility of market data. You do not need to pay for a server cluster running 24/7 if you only need to run reports on weekends.

The trade-off here is dependency on service providers. You must ensure your AI data architecture is not locked into a single vendor (vendor lock-in), especially if you plan to expand into markets like Mexico or other ASEAN countries.

Data Quality Issues: When to Clean?

Companies often think a Data Lakehouse is a trash bin. That is a mistake. If you input dirty data, the AI model will learn incorrectly. But if you clean all data before ingestion, you revert to the old, high-cost model.

The critical decision is: Clean on-demand or clean on a schedule? For Agentic AI or Chatbot projects, data must be clean immediately to ensure accurate responses. However, for long-term trend analysis, you can accept slightly rawer data to gain a quick overview.

I once saw a project at a large meat processing plant where they attempted to clean 100% of sensor data before ingestion. The result was a three-day delay compared to real-time. After switching to a Data Lakehouse and applying streaming cleaning rules, they reduced latency to just a few minutes. That is the difference between reacting to incidents and preventing them.

Real Costs and the Talent Challenge

Do not be misled by numbers on paper. The cost of a Data Lakehouse is not just in software or the cloud; it lies in personnel. You need people who understand both big data storage and SQL analytics. In Vietnam, this talent pool is still scarce.

When deploying for partners like Meat Deli or Gene Solutions, we often have to retrain existing teams. This training cost can account for about 20% of the total project, but it is far cheaper than hiring new staff and waiting for them to onboard. If you do not have a solid internal technical team, consider partnering with a consultancy like AIVISION to design a roadmap, avoiding infrastructure investments that no one knows how to leverage.

Enterprise data infrastructure will be cheaper if you know how to cut out intermediate layers. Each intermediate layer is a potential point of failure and an unnecessary operational cost.

Frequently Asked Questions About Data Lakehouse

Will Data Lakehouse completely replace Data Warehouses?

Not entirely. For extremely strict financial reporting systems requiring absolute accuracy and rigorous compliance, Data Warehouses still have a place. Data Lakehouse is best suited for AI problems, big data analytics, and unstructured data processing.

Do small businesses need to build this infrastructure immediately?

If your data is only a few gigabytes and runs on Excel, then no. However, if you plan to deploy AI Chatbots, Computer Vision, or Agentic AI, preparing a Data Lakehouse foundation from the start will save you billions of dong in future upgrades.

How long does it take to migrate from an old Data Lake to a Data Lakehouse?

The timeline depends on the complexity of your existing data. Typically, the process takes between 3 to 6 months. Importantly, you do not need to migrate all old data at once; you can run systems in parallel during a transition phase.

AIVISION helps enterprises turn AI into working systems. Explore our enterprise AI solutions, read more on the AIVISION blog, or talk to our team about your own use case.