Data Warehouse vs. Data Lake vs. Data Lakehouse: A Guide for Non-Technical Users
Data now sits at the heart of every business across all industries. Knowing how to handle data ingestion, storage, analysis, and transformation has become more important than ever. When thinking about data storage solutions, it is essential for business leaders to understand the difference between a data warehouse, data lake, and a data lakehouse.
The concepts of data lakehouses, data warehouses, and data lakes can be intimidating for non-technical audiences. This concise guide simplifies the differences and provides the clarity leaders need to make informed decisions.
Why Data Storage Strategy Matters for Business Leaders
Before breaking down the differences between a data lake, data warehouse, and lakehouse, it’s important to understand why proper data storage matters in the first place.
How data is stored directly influences the quality of reporting, the accuracy of forecasting, and the confidence leaders have in their decisions. The next section highlights the main reasons why establishing the right data storage foundation is essential for better, more informed outcomes.
- Speed to insight: When data is accessible and delivered efficiently, leaders can make informed decisions more quickly without heavy dependence on technical teams. Faster access to insights improves organizational agility and responsiveness.
- Single source of truth: A well-designed data environment establishes a single, reliable source of truth across the organization. This alignment reduces confusion, eliminates conflicting reports, and minimizes data silos between departments.
- Self-service analytics: The right data foundation empowers teams to explore and analyze information independently, even without advanced technical skills. This reduces bottlenecks and allows IT and data teams to focus on higher-value initiatives.
- Cost control and scalability: An intentional data storage strategy ensures the architecture aligns with current business needs while allowing room to scale. This approach helps avoid unnecessary spending while supporting future growth.
- Risk management and compliance: Data storage and management systems play a critical role in security, privacy, and regulatory compliance. A strong data strategy ensures these requirements are met while adapting to evolving regulatory and business landscapes.
- AI and advanced analytics readiness: As AI and machine learning become increasingly integral to business operations, a modern data foundation is essential. Proper data storage enables organizations to leverage emerging technologies and remain competitive.
Understanding The Terms: Data Warehouse, Data Lake, Data Lakehouse
To better understand which option is most appropriate, it helps to start with a clear, high-level view of what a data warehouse, data lake, and data lakehouse are. Without getting overly technical, the following outlines what business leaders should know about each approach:
Data Warehouses
Data warehouses are designed to support curated, governed reporting and analytics. They primarily store structured data using standardized business definitions and consistent KPIs, enabling fast, reliable dashboards and reports. Data warehouses are commonly used to establish a trusted single source of truth for core business data such as ERP, CRM, and financial systems.
Best use case: ERP, CRM, and financial data → reporting and dashboards
Data Lakes
Data lakes provide flexible, low-cost storage for large volumes of raw data in many formats, including structured, semi-structured, and unstructured data. They are well-suited for data science, advanced analytics, and exploratory use cases. Without proper governance and management, data lakes can become difficult to maintain and may limit usability over time.
Best use case: IoT data, logs, files, and external data → exploration and experimentation
Data Lakehouses
Data lakehouses combine elements of both data warehouses and data lakes. They support multiple data types while applying modern table formats and governance controls to improve performance and reliability. Lakehouses are designed to reduce data duplication and support mixed workloads, enabling analytics and advanced use cases on a shared data foundation.
Best use case: Mixed workloads → reporting, analytics, and advanced analytics in one environment
Data Warehouse, Data Lake, Data Lakehouse Comparison Table

Decision Framework
There is no single data storage solution that fits every organization. The right approach depends on business priorities, data complexity, and long-term objectives. The following considerations can help guide the evaluation of data warehouse, data lake, and data lakehouse options:
- How mature are current data governance and ownership practices?
- Which teams and roles require access to data?
- How quickly must data be available for reporting or analysis?
- How frequently are data requirements expected to change?
- Is self-service reporting a priority across the organization?
- Should the focus be on near-term reporting needs or long-term scalability?
- How do current and projected budget constraints factor into the decision?
Common Pitfalls to Avoid
Boiling the ocean: Attempting to ingest and model all available data at once often leads to delays, complexity, and limited early value.
Lack of business definitions: Without clearly defined metrics and terminology, trust erodes as teams spend more time debating data than using it.
Ignoring data quality: Poor data quality results in unreliable insights and undermines confidence in reporting and decision-making.
Underestimating ownership and governance: Weak accountability and governance increase risk, particularly around security, compliance, and data misuse.
Selecting technology before defining use cases: When tools are chosen without clear business requirements, data environments become costly without delivering meaningful outcomes.
Recommended Approach for Most SMB and Mid-Market Organizations
While there is no universal answer when choosing between a data warehouse, data lake, or data lakehouse, most SMB and mid-market organizations benefit from starting with a warehouse-style, curated data layer. This approach provides a reliable foundation for core KPIs, standardized reporting, and consistent business definitions.
As data maturity increases, additional capabilities, such as data lake or lakehouse architectures, can be introduced to support unstructured data, advanced analytics, machine learning, or greater scale.
In most cases, the initial priority should be maximizing the value of structured data from financial systems, CRMs, and other core operational platforms. Establishing this foundation enables more confident decision-making, supports users across the organization (including non-technical teams), and creates a scalable path for future growth.
Get in Touch with Us!
Does your organization currently have a working data storage strategy? If not, reach out today to schedule a quick assessment and start the conversation.
Brewster Consulting Group is ready to help your business organize and optimize its data to support better reporting, analytics, and decision-making.
At Brewster Consulting Group, we recognize that managing data can be a daunting task for small and mid-sized enterprises. Allow us to assist you in harnessing the potential of operational intelligence! Reach out to one of our specialists today to refine your data strategy, optimize your processes, and establish solid governance. Ready to cultivate data analysis and propel scalable growth? Your journey begins right here!









