A data lake prioritizes flexibility to store various data; a data warehouse optimizes structured information for BI and reporting; a lakehouse It combines flexible storage with governance, transactions, and advanced analytics. The right choice depends on the type of data, workloads, latency, governance, and TCO; not on which architecture is newer.
Data lake
Central repository designed to store large volumes of data structured, semi-structured and unstructured, Often in its original format. It usually uses an approach of schema-on-read: The structure applies when data is consumed, not necessarily when it is stored. AWS and Google Cloud describe this model as especially suitable for exploration, data science, machine learning, and heterogeneous sources.
Data warehouse
Optimized platform for storing and consulting information structured, refined and modeled. It is oriented towards SQL, business intelligence, dashboards, historical analysis, and high-throughput queries. The schema and quality rules are usually defined before the data reaches the consumption layer.
Lakehouse
Architecture that seeks to preserve the flexibility and scalability of a data lake, but incorporating functions traditionally associated with a warehouse: managed tables, transactional control, schemas, governance and performance for SQL, without necessarily separating the data used by BI, engineering, data science and AI.
Comparative table: data lake vs data warehouse vs lakehouse
| Criterion | Data lake | Data warehouse | Lakehouse |
| Type of data | Structured, semi-structured and unstructured | Mainly structured and curated | Structured, semi-structured and unstructured |
| Model of the scheme | Schema-on-read dominates | Schema-on-write / controlled schema prevails | It combines flexibility with enforcement and scheme evolution |
| Main fortress | Flexible intake and storage on a scale | SQL, reporting, BI, and corporate models | BI, data engineering, ML and AI on a common basis |
| Main users | Data engineers, data scientists | Analysts, BI, finance, business areas | Data engineers, data scientists, analysts, and AI teams |
| Data not processed | Yes | It is generally not their primary purpose. | Yes |
| Business SQL | It depends on the additional layers | Central fortress | Yes, using compatible boards and motors |
| Machine learning / AI | Very suitable | Possible, but not always optimal for direct access | Designed to coexist with ML, AI, and BI |
| ACID transactions | They are not inherent to a traditional lake | Yes | Yes, when using transactional formats/layers like Delta |
| Government | It must be designed explicitly | Maduro in structured scenarios | Common government for different types of workloads |
| Storage cost | Generally low with object storage | It can be increased with specialized storage and computing | Look for separating storage and computing and reducing copies |
| Typical risk | Becoming a “data swamp” | Silos and duplication versus data science/AI | Complexity if implemented without government or architecture |
| Best scenario | Ingesta, historical, logs, files, images, exploration | Financial statements, reporting, KPIs, corporate BI | Organizations where BI, data, and AI use shared information |
Microsoft explicitly states that a lakehouse allows for working with structured and unstructured data, while its warehouse is optimized for relational analytics and high-performance SQL. It also recognizes that both can coexist: for example, a lakehouse for processing and engineering and a warehouse for highly curated information intended for reporting.
Which business problem does each architecture actually solve?
The wrong question is: “Which technology is better?”
The correct question is: “What should the company do with that data and how much does it cost to get it?”
A company does not generate EBITDA by having a lakehouse. It generates it when that architecture reduces decision time, avoids manual reconciliations, reduces errors, improves a forecast, identifies anomalies, enables automation, or allows an AI model to be put into production.
In the Data Intelligence model of MAIA by Scanda, the objective is not precisely to deploy an isolated technology, but to enable Reliable and fast decisions through governed data, Integrating capabilities such as MDM, Data Quality, ETL/ELT, data lakes, data warehouses, predictive models, and dashboards.
When to choose a data lake?
A data lake makes sense when the first problem is capturing and preserving various data before knowing all the uses they will have.
Choose a data lake when:
- You receive large volumes of JSON, logs, IoT, images, documents, video, telemetry or files in addition to relational information.
- You need to keep historical data or in original format.
- Data science needs to experiment with information that does not yet have a definitive business model.
- There are multiple sources and changing their structure before storing them would generate too much friction.
- You need to decouple compute storage from compute.
- You want to build a landing layer for later engineering, ML, or AI processes.
AWS defines a data lake precisely as a repository capable of containing structured, semi-structured, and unstructured data without having to define its schema before storing it.
The risk: storing does not equal governing
Storage can be inexpensive; the disorder does not.
A lake without a catalog, ownership, classification, retention policies, access controls, quality, and traceability can end up accumulating multiple files that no one knows if they are current, reliable, or authorized.
The cost appears later:
Cost of poorly governed data lake = storage + wasted computing + preparation time + duplicates + reconciliations + risk of improper access.
Therefore, “we have all the data in the lake” is not synonymous with “we have the data ready for AI”.
When to choose a data warehouse?
The data warehouse remains an especially efficient architecture when the problem is clear and most users need it. consult structured and consistent information through SQL.
Choose a data warehouse when:
- The main objective is financial or executive reporting.
- Your sources are predominantly transactional and structured.
- There are stable corporate definitions for sales, costs, customers, inventory, or profitability.
- You need star models or snowflakes.
- BI requires predictable, controlled, and concurrent queries.
- Finance needs that the same metric has an unambiguous definition.
- The use of images, free text, ML or semi-structured data is secondary.
AWS describes the warehouse as a central repository for information from transactional systems and other sources, designed specifically to allow BI, SQL, and other analytical applications to query large volumes efficiently.
Microsoft, for its part, recommends warehouse when the scenario requires structured relational data, business analytics, advanced SQL, ACID transactions, and high performance for reporting.
Their main financial risk: keeping too many copies
The problem arises when the warehouse ceases to be the only analytical platform.
For example:
ERP → ETL → Data Lake → ETL → Warehouse → export → ML platform.
Each arrow represents:
- Pipelines.
- Monitoring.
- Computing.
- Reattempts.
- Checks.
- Copies.
- Personal.
- Possible out-of-date.
The original paper on lakehouse architecture precisely identifies this duplication between lake and warehouse as a source of greater complexity, inconsistencies, and data staleness.
When to choose a lakehouse?
The lakehouse makes sense when the same data must serve multiple workloadsBI, engineering, data science, AI, streaming, and advanced analytics.
It doesn’t mean “putting a data lake and calling it a lakehouse”. It requires layers of management that convert the stored objects into reliable and transacted data.
Among them:
- Catalog and metadata.
- Scheme control.
- ACID transactions.
- Versioned.
- Lineage.
- Security and policies.
- Table optimization.
- Separation between storage and computing.
- Access from SQL, Spark, ML, and AI engines.
The concept was formalized in 2021 by Armbrust, Ghodsi, Xin, and Zaharia as an architecture based on open access formats, with first-class support for machine learning and data science and features traditionally associated with the warehouse.
In Delta-based implementations, for example, the tables can incorporate ACID properties—atomicity, consistency, isolation, and durability—over object storage.
¿How to decide between data lake, data warehouse, and lakehouse?
Step 1. Start with the use cases, not the technology
It documents the decisions the organization wants to improve over the next 12 to 24 months.
Examples:
| Need | Architecture that usually has an advantage |
| Monthly consolidated financial statement | Data warehouse |
| Corporate sales dashboard | Data warehouse / lakehouse |
| Store IoT and plant telemetry | Data lake / lakehouse |
| Analyzing production images | Data lake / lakehouse |
| Forecast using machine learning | Lakehouse |
| Generative AI on corporate information | Lakehouse + government and semantic layer |
| Preserving historical records for uses that have not yet been defined | Data lake |
| BI + ML on a common version of the data | Lakehouse |
The important word is “usually”. The architecture is validated against the real environment; there is no table capable of replacing discovery.
Step 2. Categorize the data you actually have
Calculate what proportion corresponds to:
- Relational information.
- JSON, XML or other semi-structured formats.
- Documents.
- Images and video.
- Events or telemetry.
- Data in streaming.
- Historical data.
- Sensitive or regulated information.
If 95% of the demand is SQL on structured financial information, building a complex platform for computer vision “just in case” is technology looking for a problem.
But if BI represents only a part of an ecosystem that also needs RAG, predictive models, telemetry, documents, and agents, separating each load into its own silo can increase the TCO.
Step 3. Identify how many times you are copying the same data
This is one of the indicators that reveals architectural debt most quickly.
Question:
How many different copies of sales, inventories, customers, or production data do BI, data science, AI, and reporting require?
Each duplication can create:
- Latency.
- Version differences.
- Storage costs.
- Moving costs.
- Additional pipelines.
- Additional safety surface.
Modern platforms seek to reduce that problem. Microsoft Fabric, for example, uses OneLake as common storage and allows different workloads to consume shared data; its documentation even highlights access mechanisms. zero-copy via shortcuts.
That doesn’t mean that “a single copy” is always possible, but it does mean that Each additional copy must have an economic or technical reason..
Step 4. Separate storage, processing, and consumption
They should not be evaluated as a single item.
A real data TCO should consider:
Annual TCO = storage + compute + ingestion + transformation + orchestration + governance + observability + security + licenses + personnel + transfers + backups/DR.
A platform with cheap storage can be expensive if each query scans huge amounts of information.
A platform with efficient SQL can be expensive if it requires duplicating all the data for machine learning.
The CFO needs the full cost of the route source → reliable data → decision, Not just the price per terabyte.
Step 5. Measure the cost of latency
Architecture also affects how long it takes for the data to become a decision.
Example:
If sales closes on Friday, the pipeline ends on Sunday and the report arrives on Tuesday, technically there is BI. Operationally, there are four days of latency.
For MAIA by Scanda, “time from a given data to a decision” It is a particularly useful metric: when the information appears after the decision has already been made, the architectural problem becomes a business cost. It is recommended to review how many versions of the same figure exist and what percentage of sources are integrated into the master catalog.
Step 6. Decide how much government the business needs
The greater the regulatory exposure or sensitivity of the data, the greater their importance becomes:
- Data lineage.
- RBAC.
- Catalog.
- Classification.
- Access policies.
- Versioned.
- Auditing.
- Retention.
- Quality rules.
- Ownership.
- Evidence of changes.
AI increases this requirement because data is no longer used solely to produce reports: it begins to feed models, assistants, and agents.
Architecture should no longer respond solely to “Can I check this data?”, But rather:
“Who can use it, for which model, under which version, with what authorization and with what evidence?”
The hybrid architecture that many companies really need
Choosing between the three alternatives does not always mean eliminating the other two.
Microsoft explicitly documents patterns in which layers bronze and silver They are implemented as a lakehouse and the layer gold like a warehouse. The lakehouse conserves and transforms diverse information; the warehouse provides a highly curated surface for SQL and BI.
An entrepreneurial pattern can be seen as follows:
Sources → Bronze → Silver → Gold → consumption
Bronze
Information as it arrives.
- Logs.
- CSV.
- JSON.
- ERP.
- CRM.
- IoT.
- Archives.
- Historical data.
Silver
Clean and governed information.
- Duplication.
- Normalization.
- Quality.
- MDM.
- Integration.
- Common rules.
- Reconciled identities.
Gold
Information prepared for a decision or product.
- Finance.
- Sales.
- Margin.
- Inventory.
- Forecast.
- ML models.
- Semantic layer.
- AI features.
Microsoft precisely defines the Bronze, Silver, and Gold layers as a progression of raw, enriched, and ultimately curated information for consumption.
The financial impact: where you gain or lose ROI
Data architecture affects EBITDA indirectly but concretely.
| Architectural problem | Economic impact |
| Duplicate data | Greater storage, processing, integration, and support |
| Redundant pipelines | More engineering hours and higher failure risk |
| Inconsistent information | Business and financial decisions with different figures |
| Slow reporting | Decisions taken after the window of opportunity |
| Data without quality | Reprocesses, conciliation and unreliable models |
| Lack of government | Greater operational and compliance exposure |
| Closed platform | Switching costs and technological dependence |
| Data not prepared for AI | Pilots who don't climb and a frozen budget |
| Overly large architecture | Technological CAPEX/OPEX without proportional result |
Therefore, the business case should not be formulated as:
“Migrating to a lakehouse architecture will cost X.”
It should be formulated as:
“Today we spend X on integrating, copying, reconciling, and preparing information; it takes Y hours/days from the moment a piece of data appears until it can be used, and we have Z business processes that cannot use it for AI. The proposed architecture must improve these baseline capabilities.”
That change in the question is important: Architecture ceases to be an infrastructure purchase and becomes an investment against an economic baseline.
Does a lakehouse always replace the data warehouse?
No.
It is one of the most common mistakes in modernization.
If a company has predominantly structured data, stable reporting, a mature semantic model, and virtually no demand for ML documents or unstructured data, a warehouse can still be the simplest alternative.
Microsoft even explicitly differentiates between both options within the same platform: warehouse for structured relational analytics and lakehouse for engineering, heterogeneous data, Spark, data science, and more flexible workloads.
Modern architecture does not mean maximum architecture.
It means the least complexity capable of supporting current and planned use cases without creating new technical debt.
What should a CIO review before approving the architecture?
Before deciding, the organization should be able to answer:
- What business decisions should the platform enable?
- What percentage of our data is structured, semi-structured, and unstructured?
- How many different copies do we have of the critical datasets?
- What workloads do SQL, BI, ML, generative AI, or real-time processing need?
- How long does it take for a data point to be generated and ready to be used today?
- What are the annual costs of integration, quality, reconciliation, and data operation?
- Can we track who used what data and under what authorization?
- Which components are open and which create dependency with a manufacturer?
- Can we separate storage and compute to scale economically?
- Will the architecture allow the next AI cases to use governed data, or will we have to build another platform?
The last questions usually weigh more than “which manufacturer earns the benchmark”.
Predictive conclusion
The business debate will gradually stop being data lake vs data warehouse. The trend is towards architectures where multiple engines work on shared and governed data, with open formats and less need to move information between platforms. Fabric already combines lakehouse and warehouse on OneLake, while lakehouse platforms like Databricks offer BI, engineering, ML, and AI on a common layer.
That doesn't eliminate the data warehouse: change your role.
The warehouse will remain valuable as a highly curated consumer layer. The data lake will remain useful as a flexible space for diverse information. What loses economic sense is maintaining multiple repositories, pipelines, and copies when they all exist solely to compensate for limitations of the previous architecture.
For the next data and AI projects, the advantage will not lie in having more information stored, but in reducing four distances:
data → reliable data → decision → action.
That’s where architecture starts to produce ROI.
Your data doesn't need another platform before it needs a purpose.
Choosing between a data lake, a data warehouse, or a lakehouse should begin with a business question: What decision, process, or AI case does the company need to enable and what is preventing it from doing so today?
At MAIA by Scanda, Data Intelligence starts from that baseline to identify what data exists, how it is integrated, what quality it has, what architecture each case needs, and where there is enough value to justify the investment. MAIA’s approach is explicit: build the business case with real customer information and demonstrate value before scaling the budget.
Schedule a diagnostic session with MAIA by Scanda and let's build a data and AI roadmap based on your use cases, your current architecture, and your expected return.
Frequently asked questions
Which is better: data lake, data warehouse, or lakehouse?
There is no universally superior architecture. A data warehouse is efficient for BI and structured data; a data lake for storing and exploring diverse information; and a lakehouse when BI, data engineering, ML, and AI need to work on a common basis. The choice should consider workloads, governance, latency, and TCO, not just storage.
Do I need a lakehouse to implement artificial intelligence?
Not necessarily, but you do need accessible, reliable, governed, and sufficiently up-to-date data. A lakehouse can make it easier for the same datasets to serve SQL, machine learning, and AI without maintaining multiple copies. If the data already meets those requirements in another architecture, migrating just to adopt the label is sufficient. lakehouse It can destroy ROI.
Does a lakehouse reduce costs compared to a data warehouse?
It can reduce data duplication, movement between platforms, and some pipelines, but it does not guarantee lower TCO. The result depends on compute consumption, governance, storage, licensing, operation, and talent. The correct comparison should calculate the full cost from ingestion to consumption, not just the price of the storage.
Español 
English