Data Lake vs Data Warehouse: How They Differ

When teams start collecting more data than they can immediately use, one question comes up: Where does it all belong?

Whether you’re part of a data team building models or an ops leader trying to unify reporting, the way you store data impacts how you access, analyze, and act on it. Two of the most common storage options—data lakes and data warehouses—sound similar, but they’re built for different needs and ways of working.

Choosing the right option isn’t just a technical call. It’s a strategic one that affects how people engage with data, from engineers and analysts to decision-makers.

This guide will walk you through the core differences between data lakes and warehouses, and how to choose a setup that supports your team’s goals today and as you scale.

What is a data lake?

A data lake is a flexible storage system that allows teams to store all their data in its raw form at any scale. That includes structured data (like tables), semi-structured data (like JSON or XML), and unstructured data (like images, videos, or sensor logs). Unlike traditional systems that require data to be cleaned and structured first, a data lake lets you store everything up front and decide what to do with it later.

This flexibility makes data lakes especially useful for technical teams like data scientists, engineers, and developers who need broad access to large, diverse data sets. With a data lake, they can run exploratory analysis, train machine learning models, or power AI applications without being boxed into a pre-defined structure.

Benefits of data lakes

Because data lakes accept data in any format, they’re a natural fit for modern, modular data architecture. That flexibility supports a wide range of tools and use cases, including:

• Machine learning and AI. Data lakes preserve raw historical data, which is essential for model training and testing.
• Open data access. Multiple teams can pull what they need without waiting on structured pipelines.
• Cost-effective storage. Especially in cloud environments, lakes scale to petabytes without requiring rigid infrastructure.
• ELT workflows. Teams can store first, then apply data transformation logic downstream when the use case is clear.

Tradeoffs to consider

Without strong data management practices, data lakes can become overwhelming to use and maintain. Key tradeoffs include:

• Risk of a “data swamp”. Uncatalogued, inconsistent, or redundant data can pile up quickly if governance and thoughtful data transformation are lacking.
• Limited accessibility for non-technical teams. Raw data typically requires advanced skills to analyze and interpret.
• Slower performance for reporting. Unlike warehouses, lakes aren’t optimized for high-speed SQL queries.
• Ongoing transformation needs. Since data isn’t standardized on entry, teams must apply business logic later.

When teams use a data lake

Data lakes are especially useful when teams want to store large volumes of diverse data without immediately knowing how it’ll be used. Common scenarios include:

• Experimenting with large-scale machine learning models.
• Storing unstructured data like images or IoT sensor feeds.
• Archiving data that may be needed later but not right away.
• Supporting ELT (extract, load, transform) workflows where transformation happens downstream.

What is a data warehouse?

A data warehouse is a structured data storage system designed to support reporting, dashboards, and business intelligence. Unlike a data lake, which stores raw, unprocessed data, a warehouse organizes information into clean, consistent formats that are ready for analysis.

This structure makes it easier for different teams to access reliable data without writing complex queries or transforming the data themselves. Whether you’re tracking important performance indicators in a dashboard or running quarterly sales reports, a well-managed data warehouse helps ensure everyone’s working from the same version of the truth.

Data warehouse benefits

Teams rely on warehouses to fuel dashboards, build forecasts, and answer questions across the business. Here’s why they remain a go-to for reporting and decision support:

• Data is structured up front. Warehouses use schema-on-write, which means data is transformed before it’s stored, giving analysts and decision-makers a consistent foundation to work from.
• They’re tuned for performance. With fast queries and indexing, data warehouses make it easy to analyze large volumes of structured data without lag.
• Governance is built in. Most platforms include permissions, lineage tracking, and auditability, making it easier to enforce data governance best practices.
• They support business-wide reporting. From finance to marketing, teams can access a shared view of trusted data using BI dashboards, SQL tools, or scheduled reports.

Tradeoffs to consider

A data warehouse’s structure comes with challenges, especially for teams that need agility, unstructured data access, or real-time insights. Here are a few things to keep in mind:

• Less flexibility with data types. Warehouses typically work best with structured data like tables and records, and may struggle with unstructured sources like image files or clickstream logs.
• Upfront modeling required. You’ll need to define your schema early on, which may slow things down when use cases are still evolving.
• Higher technical overhead. Engineering teams may need to get involved to onboard new data sources or troubleshoot performance.
• Scaling can get expensive. Depending on usage patterns, computing and storage costs may increase quickly as the team scales reporting or query complexity.

When teams use a data warehouse

Teams reach for data warehouses when they need consistency, structure, and fast access to business-ready data. You’ll often see them used for:

• Creating dashboards for executives and department heads.
• Comparing performance over time (like quarterly pipeline or revenue).
• Aligning KPIs across distributed teams.
• Supporting finance, ops, and analytics with trusted numbers.

Data lake vs data warehouse: Key differences

Now that you know what data lakes and data warehouses are on their own, the next step is understanding how they compare and when each one makes sense for your team. The right choice depends on your goals, your users, and how your data will be used.

Data type: Variety vs structure

Data lakes handle everything—tables, JSON, logs, images, video—so they work well when your inputs are diverse or still evolving. Data warehouses focus on structured, tabular data. If your team is training models on raw files or blending semi-structured feeds, a lake is more accommodating; if you’re standardizing operational metrics for dashboards, a warehouse is the cleaner path.

Schema: Flexibility vs consistency

A core difference between the two is when structure is applied. Data lakes use a schema-on-read approach, applying structure only when data is accessed. It gives data engineers and scientists the freedom to explore and experiment. 

Data warehouses, by contrast, enforce schema-on-write, ensuring data is modeled and validated before storage. That upfront work provides consistency and reliability for reporting. If your team values flexibility and speed of ingestion, a lake is a good fit; if accuracy and repeatability are your priorities, a warehouse is the better choice.

Processing style: ELT vs ETL

Data lakes often follow an ELT model—data is extracted, loaded, and transformed later when needed. This processing style speeds up ingestion and preserves data in its raw state. Warehouses typically follow ETL, where data is cleaned and transformed before storage. That means cleaner, analytics-ready data at the cost of more upfront processing. Teams running iterative analyses may prefer ELT; those producing standard reports may prefer ETL.

Accessibility: Technical vs non-technical teams

Because data lakes often contain raw, unprocessed data, they’re best suited for technical teams comfortable with code or data science tools. Data warehouses are designed for broader access, allowing analysts, marketers, and executives to interact with structured data through dashboards or drag-and-drop interfaces. For teams that need company-wide visibility, a warehouse lowers the barrier to entry.

Performance: Exploration vs efficiency

Data lakes prioritize scale and exploration, not query performance. They’re ideal for AI, ML, and big data processing but can feel slower for dashboards or reporting. Warehouses, meanwhile, are tuned for high-speed queries and real-time business intelligence. Teams delivering dashboards or daily operational reports will see better performance in a warehouse, while those focused on experimentation will benefit more from a lake.

Scalability and cost

Data lakes are highly scalable and typically more affordable for storing large volumes of raw data, especially when teams need to store information long-term. Warehouses also scale, but the cost rises with data processing and computing usage. Many teams use a lake for storage and a warehouse for analytics to balance scale and speed.

Governance and security

Data warehouses typically include built-in access controls, audit logs, and permissions, making governance straightforward and efficient. Data lakes require more effort to achieve the same level of security and compliance, but they can be just as safe with proper tooling. Following data governance best practices ensures that both environments remain compliant and trustworthy.

Here’s a table summarizing the key differences between data lakes and data warehouses:

‍

Understanding the differences between data lakes and data warehouses isn’t about choosing sides—it’s about choosing what works best for your data, your workflows, and your people. Teams working in AI, automation, or AI data analytics often benefit from the raw scale of a data lake. Teams delivering dashboards to stakeholders may need the speed and consistency of a warehouse.

With modern data platforms, you don’t have to choose just one. You can connect to both, blend the best of each, and build a strategy that gives everyone—from data scientists to executives—what they need.

Other considerations: data lakehouses and data marts

In addition to data lakes and data warehouses, many teams use complementary architectures that offer more specialized or blended capabilities. Two of the most common are data lakehouses and data marts.

Data lakehouse

A data lakehouse combines the openness of a data lake with the structure and performance of a warehouse. It allows teams to manage both AI and BI workloads in one place, enabling them to run machine learning models and build business intelligence dashboards from the same environment. Modern lakehouse frameworks give teams a unified foundation for governed, cross-functional analytics.

Data mart

A data mart, by contrast, is a smaller, department-focused subset of a warehouse. It’s optimized for speed and relevance, giving teams quick access to curated data sets tailored to their needs. However, over time, too many isolated marts can lead to data silos if they’re not connected through shared governance or augmented analytics practices.

How to choose the right data storage solution

Choosing between a data lake, a warehouse, or a hybrid approach isn’t about preference; it’s about aligning your team’s goals with the right architecture. The best fit depends on what kind of data you work with, how you use it, and who needs access to it. It should be reusable and adaptable, serving multiple teams and use cases.

When deciding what’s right for you, consider:

• Data types. What formats are you working with: structured, semi-structured, or unstructured? Your data type will determine whether flexibility or structure matters more.
• Processing style. Do you need to analyze data in real time, or can batch updates meet your needs?
• AI and machine learning use cases. If your team focuses on experimentation and model training, flexible systems that support AI data analytics are more suited.
• Reporting and dashboards. For consistent, business-ready insights, structured data in a warehouse often works best.
• Stakeholders. Consider who will use the data (technical teams, analysts, or business teams) and what level of accessibility they need.
• Infrastructure. Cloud-based platforms and cloud analytics tools simplify integration and reduce maintenance, especially in hybrid environments.

Building a modern data storage strategy

A modern data strategy starts with clarity and understanding where data originates, how it moves, and how it creates value. Teams should plan for scalability and governance from the start while keeping accessibility and collaboration at the center.

Hybrid architectures that combine data lakes and warehouses have become the backbone of modern ecosystems. Next-generation data architecture featuring modular and reusable systems helps teams adapt more quickly while reducing duplication and maintaining consistency as data needs evolve.

Domo helps put these strategies into practice—bridging lakes, warehouses, and every source in between to give teams a single, trusted view of their data and delivering actionable insights.

See how Domo helps teams modernize their data strategy. Get in touch with us today.