Data Warehouse vs Data Lakes: What’s the Best Architecture for AI?

February 27, 2025

🕒 8 minutes

According to Gartner, 30% of enterprises will have implemented an AI development and testing strategy by the end of this year. However, it’s important to bear in mind that some of these applications need structured, real-time data, while others rely on massive amounts of raw, unstructured information.

So, in this context, data architectures are more important than ever, with two options standing out: data warehouse vs data lakes. But this also bring a series of questions for business:

What are they?

What are their differences?

What kind of data do they manage?

Which one is the best fit for your AI-driven business?

In this article, we’ll break down their roles, key differences, and how to choose the best option based on your organization’s needs.

Why Organizations Must Adopt a Data Infrastructure Strategy?

So, a strong data architecture strategy goes beyond AI adoption. In fact, they can significantly enhance an organization’s cost structure, operational efficiency, ROI, and innovation potential. By selecting the right data storage system (e.g., data lake or data warehouse), businesses can reduce unnecessary infrastructure costs, streamline data processing, and avoid redundancy.

On the other hand, a well-implemented strategy optimizes resource allocation, leading to faster decision-making and data-driven insights, which ultimately drive innovation. Moreover, it enhances data accessibility, enabling companies to leverage analytics for new revenue opportunities and competitive advantage, increasing your ROI.

But, to illustrate this a little better, let’s take a retail company as an example. By using a well-designed data infrastructure, these organizations can optimize their supply chain by integrating real-time inventory data from multiple locations. So, leveraging a data warehouse for structured sales data and a data lake for unstructured customer behavior insights, they can:

Improve demand forecasting.

Reduce stockouts.

Minimize excess inventory.

Achieving higher efficiency and cost savings.

What Are Data Warehouses and Data Lakes?

So, Data warehouses and data lakes are both Relational Database Management Systems (RDMS), but they serve different purposes. Data warehouses store structured, processed data optimized for analytical queries and BI. That’s why they rely heavily on ETL processes to ensure data is cleaned, transformed, and structured before being loaded into the warehouse for querying and analysis.

Data Warehouses and Lakes are two of the most used data architectures

In contrast, data lakes store raw, unstructured, and semi-structured data by using an ELT integration methodology that prioritizes loading data into a data storage system first before performing transformations. This way, they provide flexibility for AI and ML applications that require large, diverse datasets.

Data Warehouse vs Data Lakes: Key Differences

So, as we’ve been saying, these two RDMS have many differences. However, to save you some time, we summarize what sets these two AI data architectures apart in the following table:

Aspect	Data Warehouse	Data Lake
Function	Optimized for structured data analysis and BI.	Stores raw, unstructured, and structured data.
Architecture	Schema-on-write; data must be structured and processed before being stored, making data easily accessible for predefined queries and reporting.	Schema-on-read; data is stored as-is, and structure is applied only when needed, allowing greater flexibility but requiring additional processing.
Types of Data	Structured data only.	Structured and unstructured data (text, images, videos, IoT data, logs).
Processing	Uses the ETL process where data is cleaned and structured before being loaded into the warehouse, ensuring data integrity.	Uses ELT, where data is first stored in its raw format and transformed only when needed for specific use cases, providing more flexibility.
Use Cases	Best for business intelligence (BI), reporting, and historical data analysis where structured data is needed for decision-making.	Ideal for AI/ML model training, big data analytics, and real-time data processing.
Benefits	Enables organizations to create dashboards, reports, and KPIs.	Since it can store vast amounts of diverse data, it is useful for predictive modeling and pattern recognition.
Performance	Optimized for fast queries and high-performance analytics. Since data is pre-structured, query execution is quick and efficient.	Handles large-scale data but performance depends on indexing and processing tools.
Governance	High data governance and security.	Requires governance for quality control.

What is the difference between these AI data architectures and common databases?

So, these two AI data architectures and traditional databases are not like regular databases. This is because neither functions as just a “place” in digital environments—they are ongoing processes. Data warehouses involve continuous data management activities, including ETL processes, storage, and retrieval for analysis.

While data lakes focus more on raw storage and accessibility (often without predefined schemas), they still require data ingestion, organization, governance, and security measures. These factors make them dynamic systems rather than simple, static repositories.

Unlike a traditional storage location, a data warehouse is actively curated, ensuring that data is structured, optimized, and governed for business intelligence and analytics. This continuous refinement distinguishes it from a simple database or repository, as its goal is to transform raw data into actionable insights, making it an ongoing business function rather than just infrastructure.

The Role of Data Architectures in AI and ML

So, as we said before, while data itself is essential for AI and ML, data architectures play a crucial role in how effectively that data is stored, processed, and accessed. Unlike raw data, which to put it in a nutshell simply “exists”, data architectures provide structure, governance, and optimization, enabling AI systems to function efficiently.

So, the importance of data architectures (like data warehouses or data lakes) for AI & ML can be summarized as it follows:

Efficient Data Processing: AI models need large-scale data ingestion, transformation, and retrieval. A well-designed architecture ensures fast, scalable access to relevant data.

Data Quality: AI performance depends on high-quality, clean data. Architectures apply ETL/ELT pipelines, validation, and deduplication to improve accuracy.

Scalability & Storage Optimization: AI models require vast amounts of structured and unstructured data. Architectures like data lakes (raw storage) and data warehouses (structured querying) help manage data efficiently.

Security: AI-driven businesses handle sensitive data (e.g., financial, healthcare). Architectures enforce access controls, encryption, and regulatory compliance (GDPR, HIPAA).

Real-time vs. Batch Processing: Some AI applications need real-time data (e.g., fraud detection), while others rely on batch processing (e.g., AI model training). The right architecture balances for optimal performance.

Which is the best data architecture for AI and ML models?

For AI and ML systems, we can say that the role of data lakes is more pronounced. This is mostly because AI models (especially those used for ML) require vast datasets that may not fit neatly into the structured environment of a data warehouse. Data lakes provide the flexibility needed to store these varied data types, enabling the training of predictive models, deep learning applications, and other AI-driven insights.

This way, data lakes also excel in real-time data streaming, making them a superior choice for AI models that require continuously updated information. This is particularly relevant for LLMs that rely on retrieval-augmented generation (RAG) techniques to access fresh, unstructured data. For instance, chatbots that provide customer support may need real-time inventory updates to offer accurate product availability information.

The role of data lakes is more pronounced in AI models, but this doesn’t mean that data warehouses lack relevance

However, this doesn’t mean that data warehouses lack relevance. They are optimized for batch processing large amounts of structured historical data, making them highly effective for AI applications centered on BI, structured reporting, and historical trend analysis. For example, financial institutions could leverage data warehouses for fraud detection using historical transaction patterns.

So, ultimately, the choice between these data architectures is not exactly binary. On the contrary the most effective AI ecosystems strategically leverage both. While data warehouses ensure consistency and reliability for BI-driven AI applications, data lakes offer agility and scalability for ML-driven insights. But many organizations are adopting a hybrid solution: data lakehouse models.

What are Data Lakehouses?

So, while data warehouses offer speed and structure, data lakes provide scalability and flexibility. However, many businesses now use “data lakehouse” models. These are hybrid data architectures that combine the structured management of a data warehouse with the scalability and flexibility of a data lake.

Data lakehouses are hybrid data architectures that combine the structured management of a data warehouse with the scalability and flexibility of a data lake

This way, it allows businesses to store both raw and structured data while also enabling fast querying, governance, and analytics without requiring extensive data movement. Besides, data lakehouses support AI and ML workloads by allowing direct access to large datasets while maintaining schema enforcement, ACID transactions, and performance optimizations through a more efficient data management.

How Data Lakes and Data Lakehouses Support Model Training and RAG?

So, data lakes and lakehouses support model training and RAG processes. This mostly comes from their capability to store large, diverse datasets. In other words, the storage flexibility of both data architectures is essential for training ML models since they often require a variety of data types to learn complex patterns.

For example, in tasks like NLP or computer vision, models need unstructured data like text and images, as well as structured data (e.g. customer information or transaction records). Both data lakes and lakehouses allow for the integration of these diverse data sources without needing to pre-structure or preprocess them upfront, offering the needed flexibility for model development.

On the other hand, this flexibility and large storage capacity is what makes both data architectures essential in RAG techniques. These systems benefit from Data Lakes as they can store massive amounts of raw data, which can be queried in real-time. However, their slow retrieval times may be an issue for real-time applications.

But this is where data lakehouses set apart to make the difference. Since these data architectures support real-time data ingestion and processing (which is crucial for fast data retrieval) they can allow RAG systems to pull up relevant data quickly for augmented generation tasks.

Data Lake vs Data Warehouses vs Data Lakehouses

So, now it’s time to check what sets these three data architectures apart. While there are many differences between them, we summarized the main ones on the following table to save you some time:

Feature	Data Lake	Data Warehouse	Data Lakehouse
Data Type	Stores raw, unstructured, and structured data	Primarily structured data	Supports both raw (unstructured) and structured data
Processing	Schema-on-read (processed when queried)	Schema-on-write (processed before storage)	Combines schema-on-read and schema-on-write
Scalability	Highly scalable; handles petabytes of data efficiently	Limited scalability; optimized for structured queries	Scalable like a Data Lake but with structured data capabilities
Performance	Slower queries due to unstructured data	High performance for structured data analytics	Optimized query performance with structured and unstructured data
Use Cases	ML, big data analytics, real-time processing	BI, reporting, structured analytics	AI/ML workloads, real-time analytics, hybrid data processing
AI & ML Suitability	Ideal for AI/ML model training (raw data availability)	Limited ML support (requires structured data)	Optimized for AI/ML training and real-time analytics
Cost	Low-cost storage, but higher processing costs for analytics	High-cost storage and processing due to structured data optimization	Moderate cost, balancing storage efficiency and performance
Storage Costs	Uses cheap object storage (e.g., AWS S3, Azure Data Lake)	Expensive due to structured storage (e.g., Snowflake, Redshift)	Cost-efficient by combining object storage with structured indexing
Maintenance Complexity	Low storage costs but complex data governance and retrieval	High cost but easier management with predefined schema	Balanced approach with lower maintenance effort than a Data Lake
Best For	Organizations with massive raw data for AI/ML & big data	Companies focused on BI, reporting, and structured analytics	Enterprises needing both AI/ML capabilities and structured analytics

Is the rise of new data architectures redefining data value?

With the rise of AI in business workloads, the value of data within organizations has shifted dramatically. It’s no longer enough to simply collect strategic information for decision-making; the real value now lies in how that data is managed, processed, and applied.

What we’re seeing today is the evolution of a completely new data economy, where the volume and variety of data are constantly growing, and where quickly extracting actionable insights is key to success. As AI takes on more cognitive tasks—like decision-making, automation, and predictive analytics—its effectiveness will increasingly depend on the data architectures that support it.

However, this brings a new challenge: orchestrating both AI and human expertise, ensuring that data is not only available but optimized for ML, analytics, and informed decision-making. But don’t worry—Inclusion Cloud can help you lay a solid data foundation for a successful AI adoption.

Let’s connect and take the first step toward your digital transformation!