Data Lakehouse vs Data Warehouse: Which Fits Your Business in 2025?

Sep 8, 2025 • 25 min read

Businesses face a storage architecture decision that could make or break their data strategy.

With 2.5 quintillion bytes of data generated dailyand projections showing this will surge to 463 exabytes by 2025, choosing the wrong approach carries serious consequences. The pressure intensifies when 81% of IT leaders report their C-suite has mandated no additional spending or even reductions in cloud costs.

Data warehouses and data lakes have dominated the conversation around optimal data storage for analytics needs for years, serving as the go-to architectures for big data. Then data lakehouses emerged in 2020, shaking up established thinking. Data warehouses excel at organizing structured data. Data lakes handle large volumes of structured, semi-structured, and unstructured data. Data lakehouses merge features from both approaches, bringing flexibility that traditional warehouse architectures often lack.

What does this mean for your business? The choice between these architectures will shape how effectively your organization extracts value from data. From data structure and processing capabilities to cost efficiency and governance features, each option serves different needs.

This guide breaks down the key differences between data warehouses, data lakes, and the hybrid data lakehouse approach. You'll discover which solution aligns with your specific business requirements as we head into 2025 and beyond.

Key Takeaways

Understanding the fundamental differences between data warehouses, data lakes, and lakehouses is crucial for making the right architectural choice that aligns with your business needs and budget constraints in 2025.

Data warehouses excel at structured data and BI reporting with strong governance, ACID compliance, and fast query performance for traditional business intelligence needs.
Data lakehouses combine the best of both worlds by offering warehouse-like management features with lake-like flexibility and cost-effectiveness for diverse workloads.
Cost efficiency favors object storage solutions as lakehouses and data lakes typically cost less than proprietary warehouse storage, especially at scale.
ML and AI workloads perform better on lakehouses due to native integration with frameworks like TensorFlow and PyTorch, plus support for all data types.
Your choice depends on specific use cases - choose warehouses for pure BI/reporting, lakehouses for hybrid BI+ML needs, or consider both for comprehensive coverage.

The decision ultimately comes down to balancing your current structured data requirements against future AI/ML ambitions, with many organizations finding success in hybrid approaches that leverage both architectures for different use cases.

Understanding the Core Architectures

Each data storage approach tackles specific challenges with distinct capabilities and trade-offs. Let's examine how these systems work and what makes them different.

What is a Data Warehouse?

A data warehouse connects and harmonizes large amounts of data from various sources, acting as a single source of truth for an organization. William Inmon defined four key attributes that characterize these systems, subject-oriented, integrated, nonvolatile, and time-variant, making them particularly effective for business intelligence activities.

The architecture follows a schema-on-write approach, which means the data structure must be defined before storage. This includes a relational database, ETL (extraction, transformation, loading) processes, and analytical capabilities. Modern data warehouses have expanded beyond their traditional scope, now handling both structured data and increasingly unstructured content like videos and image files.

What is a Data Lake?

Data lakes take a fundamentally different approach. They store all types of data—structured, semi-structured, and unstructured—in raw, unprocessed form. Rather than requiring upfront structure, data lakes use a schema-on-read approach, interpreting data only when someone accesses it.

Cloud infrastructure powers most data lakes due to the massive volumes they handle and their need for scalability. Amazon S3, Azure Blob Storage, and Google Cloud Storage provide the foundation through inexpensive object storage. The concept emerged over the last decade as organizations faced exponential growth in data volume and variety, seeking cost-effective solutions for storing massive datasets.

What is a Data Lakehouse?

Here's where things get interesting. A data lakehouse represents a hybrid architecture that merges the benefits of both data warehouses and data lakes. It delivers the management features and performance you'd expect from a data warehouse while preserving a data lake's flexibility and cost-effectiveness.

The key innovation lies in implementing warehouse-like data structures directly on the low-cost storage that data lakes use. This approach eliminates the need for isolated systems serving different workloads. Data scientists and ML engineers can now build models from the same data that powers BI reports.

Data Lake vs Data Warehouse vs Data Lakehouse: Key Differences

The core differences between these architectures span several critical dimensions:

Data Structure: Warehouses primarily store structured data, lakes store any data type, and lakehouses accommodate both.
Schema Approach: Warehouses use schema-on-write, lakes use schema-on-read, and lakehouses can employ both.
Processing Models: Warehouses typically use ETL workflows, whereas lakes often employ ELT (Extract, Load, Transform), and lakehouses support both approaches.
Use Cases: Warehouses excel at business intelligence, lakes at data science/machine learning, and lakehouses attempt to serve both needs.
Cost Efficiency: Data lakes and lakehouses typically offer more cost-effective storage than traditional warehouses.
ACID Compliance: Warehouses and lakehouses support ACID transactions, while traditional data lakes do not.

Warehouses and lakehouses also provide superior governance and data quality controls, making them better suited for enterprise analytics where consistency matters most.

Data Structure and Processing Models

How you structure and process data determines everything about your system's capabilities. The underlying approaches separate these three architectures in ways that directly affect how organizations interact with their data assets and extract business value.

Schema-on-Write vs Schema-on-Read

Data warehouses implement a schema-on-write approach, requiring all data to follow a predefined structure before storage. This method ensures consistency and optimizes query performance, but it demands upfront schema design work. Data lakes take the opposite route with schema-on-read, postponing structure application until data retrieval. This flexibility allows storing data in its raw form without transformation, making data lakes particularly effective for big data analytics.

Data lakehouses uniquely support both paradigms—enabling raw data ingestion for flexibility (schema-on-read) alongside structured datasets for traditional analytics (schema-on-write). This dual capability means different workloads can coexist without requiring separate systems.

Support for Structured, Semi-Structured, and Unstructured Data

The data type support reveals significant architectural differences. Data warehouses primarily handle structured and some semi-structured data, making them well-suited for traditional business processes like financial reporting. Data lakes store unlimited amounts of both structured and unstructured data without restrictions.

Data lakehouses bridge this divide by accommodating all data types—structured data (sales transactions), semi-structured data (JSON files, sensor readings), and unstructured data (IoT logs, images, audio). This comprehensive support eliminates the need for separate storage environments.

ETL vs ELT Workflows

Processing workflows create another fundamental distinction. Data warehouses traditionally employ Extract, Transform, Load (ETL) processes, where data undergoes transformation before loading. ETL requires analytics involvement from the start to define target data types and structures.

Data lakes and lakehouses often utilize Extract, Load, Transform (ELT), loading raw data immediately and transforming it later as needed. ELT provides greater adaptability for evolving data requirements while leveraging the processing power of modern cloud platforms.

ACID Compliance and Transactional Integrity

ACID compliance - Atomicity, Consistency, Isolation, and Durability, ensures reliable data management for mission-critical applications. Traditional data warehouses support ACID transactions, maintaining data consistency across operations. Standard data lakes, particularly those built on NoSQL databases, typically lack ACID compliance, making them unsuitable for financial transactions.

Data lakehouses have addressed this limitation by implementing ACID support specifically for Delta-formatted tables. Through technologies like two-phase commit protocols and the Raft consensus algorithm, lakehouses ensure transactional integrity while maintaining flexibility—a crucial advancement for organizations requiring both analytics capabilities and reliable data processing.

Performance, Scalability, and Cost Efficiency

Performance and cost factors ultimately determine whether businesses choose data lakehouses or data warehouses. The operational differences between these architectures directly influence which solution fits your specific requirements.

Query Speed and Latency: Batch vs Real-Time

Data warehouses deliver fast, consistent performance for structured data through batch processing and transactional workloads. Data lakehouses support both real-time and batch processing, making them suitable for big data analytics and machine learning applications.

Traditional batch processing can take hours or even days to complete time-sensitive operations. Modern data lakehouse architectures enable near real-time processing, allowing businesses to analyze data as it arrives. This capability proves critical for fraud detection, recommendation systems, and IoT applications.

Organizations implementing real-time data streaming report significant benefits: 95% see improvements in customer experience, 92% in security and risk management, and 90% in product innovation.

Scalability: Vertical vs Horizontal Scaling

The scaling approach fundamentally differs between architectures. Data warehouses typically employ vertical scaling (scaling up), increasing the resources of existing machines. This offers simplified resource management but creates potential single points of failure.

Data lakehouses generally utilize horizontal scaling (scaling out), adding more machines to distribute workloads. This method delivers:

Enhanced load distribution across multiple servers
On-demand scalability to meet changing business needs
Improved fault tolerance and system resilience

Horizontal scaling provides greater long-term flexibility, particularly for businesses experiencing rapid growth.

Storage Costs: Proprietary vs Object Storage

Storage expenses create substantial differences between architectures. Data warehouses often rely on proprietary storage formats, which can increase costs as data volumes grow. Data lakehouses use cost-effective cloud object storage like Amazon S3 or Azure Blob Storage.

Object storage typically costs less than block storage for high-volume needs. Public cloud providers charge approximately $0.02 per GB for standard-tier storage, with costs decreasing at higher volumes.

Compute Resource Optimization

Data lakehouses offer several advantages for optimizing compute resources and operational costs:

Serverless capabilities that automatically scale resources based on workload demands
Advanced query engines like Photon, which can significantly reduce processing costs
Separation of storage and compute, allowing independent scaling of each component

Choosing appropriate instance types—memory-optimized for ML workloads, compute-optimized for streaming—can substantially improve the price-performance ratio.

Use Cases and Business Fit in 2025

The architecture you choose ultimately depends on what your business actually needs to accomplish. As we head into 2025, each solution serves distinct purposes, though they often work better together than alone.

Best for Business Intelligence and Reporting

Data warehouses still reign supreme for organizations focused on business intelligence, reporting, and analytics built around structured data. Their predefined schemas deliver the high data quality that decision-makers depend on. When you're dealing with financial reporting or regulatory compliance, warehouses provide the accurate, auditable records that SOX, HIPAA, and GDPR requirements demand.

The real strength lies in how well warehouses work with familiar tools that business analysts already know - BI platforms, SQL clients, and analytics applications. Take Walgreens, for example. They migrated inventory management data to a cloud data warehouse, letting supply chain analysts build visualizations in Microsoft Power BI. The result? Previous-day reports became available at the start of the business day start instead of hours later.

Ideal for Machine Learning and AI Workloads

Data lakehouses have become the go-to choice for companies ready to move beyond basic BI into artificial intelligence capabilities. The architecture was built specifically for developing, testing, and scaling AI/ML workloads. Many lakehouse providers include machine learning libraries, tools, and analytics that make AI development less complicated.

What makes lakehouses particularly valuable is their support for the complete ML lifecycle. Data scientists can explore raw data, engineer features, train models, and deploy them all within the same environment. The schema-on-read flexibility handles diverse datasets perfectly, especially when you're experimenting with unstructured data sources. Data scientists and ML engineers can work with the same data that powers your BI reports.

Real-Time Analytics and IoT Applications

IoT generates an incredible mix of data types - structured sensor readings alongside unstructured images, text, and voice data. Data lakehouses match IoT requirements perfectly, handling real-time or near-real-time data streams when quick decisions matter. Applications that need streaming data to show changes as they happen find lakehouse architectures handle this naturally.

Smart city projects illustrate this well. IoT sensors initially collect traffic and environmental data for basic reporting during the BI phase. As cities get more sophisticated, AI integration allows dynamic traffic signal optimization, congestion prediction, and better urban planning through analysis of multiple sensor data sources.

Hybrid Use Cases: Combining BI and ML

Many organizations find they need multiple data solutions working together. Healthcare provides a perfect example - you might use a data lake for unstructured patient research data, a data warehouse for operational reporting, a data hub for controlled data distribution, and a data lakehouse for advanced analytics that combine insights from all these sources.

Data mesh is gaining traction as an approach for creating data-driven value, with self-serve data platforms that help people discover data and build data products. This lets BI analysts and data scientists collaborate effectively. Analysts define established methods and KPIs while data scientists develop new approaches through AI/ML that eventually become standard business metrics.

Governance, Security, and Tooling Ecosystem

Your data architecture choice needs strong governance and security foundations. These elements directly impact how effectively your organization can use data while staying compliant and integrating with existing systems.

Data Governance and Metadata Management

Effective data governance connects data requirements with business strategy while promoting data democratization through accuracy, consistency, and trustworthiness. Data warehouses provide centralized governance with predefined schemas. Data lakehouses take a different approach, requiring fine-grained access controls like AWS Lake Formation or Unity Catalog.

Both architectures handle metadata management well—tracking data lineage, enabling impact analysis, and supporting regulatory compliance. This approach helps organizations protect against cybercriminal activities, insider threats, and human error.

Security and Compliance Features

Security approaches differ between architectures, though all modern solutions provide encryption (both at rest and in transit), role-based access control, and compliance frameworks. Data lakehouses typically implement advanced features like workspace-level permissions, cluster ACLs, and single-use clusters.

Both architectures meet regulatory requirements, including GDPR, HIPAA, and CCPA. Snowflake allows setting regions for data storage to comply with regulatory guidelines. Databricks provides workload security with code repository management and built-in secret management.

Integration with BI Tools (Power BI, Tableau)

Business intelligence tool integration works well across both architectures. Data warehouses traditionally offer smoother connections to business intelligence platforms. Data lakehouses have caught up, providing rich ecosystems that work seamlessly with existing tools to deliver real-time insights.

Through Partner Connect, data lakehouses can establish connections with visualization tools without moving data into separate warehouses. This eliminates the data movement overhead that previously slowed down analytics workflows.

ML and AI Tool Compatibility (TensorFlow, PyTorch)

Data lakehouses excel at ML tool integration. They connect directly with frameworks like TensorFlow and PyTorch, enabling model training on large datasets without data export. Data warehouses offer limited ML support and often require moving data to external systems.

Databricks provides pre-installed distributed machine learning libraries and MLflow for tracking experiments, registering models, and deployment. This native integration streamlines the entire machine learning workflow.

Vendor Ecosystem: Databricks vs Snowflake

Databricks (lakehouse) and Snowflake (warehouse) represent leading vendors with different philosophies. Databricks offers a more open ecosystem running in users' cloud VPC, allowing custom library installation and low-level cluster access.

Snowflake focuses on robust governance through column-level security, object tagging, and automatic sensitive data classification. Both platforms support visualizations and BI dashboards, though Databricks claims significant cost advantages for ETL workloads.

Comparison Table

To help you evaluate these architectures side by side, here's a breakdown of how data warehouses, data lakes, and data lakehouses stack up across key factors:

Data Structure Support	Primarily structured data, some semi-structured	All types (structured, semi-structured, unstructured)	All types (structured, semi-structured, unstructured)
Schema Approach	Schema-on-write	Schema-on-read	Supports both schema-on-write and schema-on-read
Processing Model	ETL (Extract, Transform, Load)	ELT (Extract, Load, Transform)	Supports both ETL and ELT
ACID Compliance	Yes	No	Yes (for Delta formatted tables)
Primary Use Cases	Business intelligence, reporting	Data science, raw data storage	Combined BI, ML/AI, real-time analytics
Scaling Method	Vertical scaling	Horizontal scaling	Horizontal scaling
Storage Type	Proprietary storage formats	Object storage (e.g., S3, Azure Blob)	Object storage (e.g., S3, Azure Blob)
Query Performance	Fast for structured data	Varies based on implementation	Supports both batch and real-time processing
Cost Efficiency	Higher storage costs	Cost-effective	Cost-effective
Data Governance	Strong built-in controls	Limited	Strong governance features
ML/AI Tool Integration	Limited	Good	Native integration with ML frameworks
Real-time Processing	Limited	Yes	Yes

This comparison reveals why data lakehouses have gained traction. They combine the strengths of both traditional approaches while addressing many of their individual limitations. The choice still depends on your specific requirements, budget constraints, and technical capabilities.

Conclusion

Your data architecture decision will shape how effectively your organization competes in 2025's data-driven marketplace. Traditional data warehouses continue to excel at structured data management, delivering robust business intelligence and reporting capabilities with reliable ACID compliance. Data lakes offer flexible storage solutions for diverse data types through cost-effective object storage.

Data lakehouses represent the hybrid approach that bridges both worlds. They deliver warehouse-like management features alongside lake-like flexibility and cost-effectiveness. Organizations balancing traditional analytics with advanced AI/ML workloads find this combination particularly compelling.

The choice comes down to your specific business requirements. Does your primary focus remain structured data analysis, reporting, and compliance? A data warehouse serves these needs well. Do you need both traditional BI capabilities and advanced AI applications while working with diverse data types? A data lakehouse offers more advantages.

Your storage architecture will determine how effectively you extract value from rapidly growing data volumes. Cloud-based scalability now exists across both solutions. Data lakehouses typically provide more cost-effective horizontal scaling compared to the vertical scaling approach common in warehouses.

Security and governance capabilities have reached comparable levels across architectures. The implementations differ - data warehouses provide centralized governance with predefined schemas, while lakehouses implement fine-grained access controls that preserve flexibility.

Many organizations discover value through complementary approaches rather than binary choices. Healthcare providers use warehouses for operational reporting and lakehouses for advanced analytics. Financial institutions deploy both architectures to address different use cases. Retail businesses follow similar patterns.

Which architecture aligns best with your organization's data strategy, technical capabilities, and business objectives? That question matters more than determining which solution is inherently superior. Your data architecture must support current requirements while positioning your business for future analytical opportunities in 2025's increasingly data-dependent landscape.

Frequently Asked Questions (FAQ)

What is the main difference between a data warehouse and a data lakehouse?

A data warehouse primarily handles structured data and excels at business intelligence and reporting, while a data lakehouse combines features of both data warehouses and data lakes, supporting all data types and enabling both traditional analytics and advanced AI/ML workloads.

How do data lakehouses handle real-time analytics compared to traditional data warehouses?

Data lakehouses are better equipped for real-time analytics, supporting both batch and real-time processing. This makes them ideal for IoT applications and near-real-time data streams, whereas traditional data warehouses are more focused on batch processing of structured data.

Are data lakehouses more cost-effective than data warehouses?

Generally, yes. Data lakehouses typically use cost-effective cloud object storage (like Amazon S3 or Azure Blob Storage) and offer more flexible scaling options, potentially resulting in lower overall costs compared to the proprietary storage formats often used by data warehouses.

Which solution is better for machine learning and AI workloads?

Data lakehouses are generally better suited for machine learning and AI workloads. They provide native integration with popular ML frameworks like TensorFlow and PyTorch, and support all data types (structured, semi-structured, and unstructured), which is crucial for diverse ML/AI applications.

Can organizations use both data warehouses and data lakehouses?

Yes, many organizations adopt a hybrid approach, using both data warehouses and data lakehouses to address different use cases. For example, they might use a data warehouse for operational reporting and compliance, while leveraging a data lakehouse for advanced analytics and AI development.