Data lakes and data lakehouses are storage solutions built to support real-time analytics and business intelligence for smart decision-making. Here, we’ll compare data lakehouses vs. data warehouses vs. data lakes to determine which framework is the best for C-suites.
Can you run your organization without data? The answer is no. Data is the core of any establishment and can provide in-depth and hidden insights to make better and more effective decisions. With so much data being generated daily, businesses struggle to store and utilise it without exceeding their budget. Moreover, the traditional method of department silos is no longer useful in modern businesses. Manual processes are time-consuming, costly, and effort-intensive. With the right technology, you can not only store large datasets but also derive actionable insights in real-time.
That’s why data warehouses, data lakes, and data lakehouses have become popular in recent times. According to Mordor Intelligence, the data lake market is expected to be $18.68 billion in 2025, with a CAGR (compound annual growth rate) of 22.62% to reach $51.78 billion by 2030. While North America is the largest, the Asia Pacific region is the fastest-growing in the global market. Microsoft, Amazon, Oracle, Teradata, and Capgemini are the major players, offering robust data lake solutions. Cloud-based data lakes occupied 65% of the market share in 2024, indicating the high demand for cloud services.
But what exactly are they? As a CTO or a CEO, how do you choose between them? Does it matter what storage solution you implement in your business? What is the best choice for your organization to seamlessly manage data and analytics? What do data engineering services companies recommend?
In this blog, we’ll find answers to these questions and more. Let’s first understand the similarities and differences between data lakehouses vs. data warehouses vs. data lakes.
Data Lakehouses vs. Data Warehouses vs. Data Lakes
Modern data storage solutions are robust and fast-evolving, designed to be scaled as required. However, they can be expensive if you don’t choose the right option based on your business requirements (current and future).
Data Warehouse
Data warehouses support structured datasets and deliver accurate analytical insights and business intelligence reports. They can be hosted on-premises and on cloud platforms with different security settings. However, data warehouses are not great at handling unstructured data or running AI-powered analytics on raw data.
Data Lake
A data lake is a storage center for all types of raw data—structured, unstructured, and semi-structured. It can support big data analytics and AI tools to derive insights from raw data. However, without proper layering and data architecture, a data lake can turn into a data swamp, which consumes too many resources and gives weak insights. It is preferably hosted on cloud platforms due to the extensive size and capacity demands.
Data Lakehouse
The data lakehouse definition clarifies that this is a combination of a data warehouse and a data lake intended to give better results for everyone. It is a cloud-based modern data architecture that blends the strengths of data warehouses and data lakes. It is scalable, flexible, reliable, and delivers great performance. From unlimited storage to accurate insights, data lakehouses sound like they are the best choice for every business.
However, when choosing between data lakehouses vs. data warehouses vs. data lakes, you should consider various other factors and plan for the long term. Additionally, the strategy to implement data engineering services will also influence your decision. Hence, many organizations partner with service providers to audit existing systems and create a comprehensive blueprint to integrate, customize, and maintain the right data storage solution.
Data Lake vs. Lakehouse Architecture: What Should the C-Suites Choose?
Be it a startup, a growing business, or an established enterprise, every organization has to evolve periodically, revamp its internal operations, and adopt new technologies to survive in competitive markets. In today’s world, this deals with digital transformation and implementing data-driven models to make intelligent decisions in real-time. Naturally, IT directors, CTOs, and CEOs have to ensure that their teams have access to the right technological solutions to achieve business objectives and keep customers happy. For that, it is important to know the similarities and differences between data lakehouses vs. data warehouses vs. data lakes.
Design and Architecture
Data lakes support raw data in all formats and is hence structured to have a flat architecture. While this allows for great flexibility, it doesn’t really help in optimizing the query performance. Due to this, each query could consume a lot of resources and end up being an expensive project. A data lakehouse builds on a data lake by adding a metadata and governance layer before it is integrated with AI, BI, and analytical tools. The difference in Data lake vs. lakehouse architecture is this extra layer, which ensures the central repository is flexible but can also support query performance optimization to reduce resource consumption.
Schema
Schema is the process of structuring data for analytical insights. It determines how the data is read and processed to answer the query or provide an outcome. Data lakes use schema-on-read, in which the relevant data is structured only when it has to be analyzed. While this ensures a quick data ingestion, it increases the time taken to process each query. Data lake services help in optimizing this to an extent, but the issue can persist in large enterprises, resulting in increased expenses. A data lakehouse uses both schema-on-read and schema-on-write for flexibility as well as for structuring the data. By following a mixed approach, the processing time can be optimized, and the insights derived will also be of better quality.
Data Type
As we discussed earlier, data lake vs. warehouse and lakehouse work with different data types, making it crucial for organizations to understand their data before selecting a solution. Data lakes are highly flexible as they support unstructured, semi-structured, and structured data. That means videos, images, text, tables, charts, logs, audio, etc., can be stored in their existing formats. Lakehouses also offer this flexibility and enhance it by adding a metadata layer to provide some structure to the setup. So, the same platform has structured table formats that can handle raw and refined data. You can hire data lakehouse consulting services to set up the architecture and optimize it.
Compliance
When deciding between data lake, warehouse, and lakehouse, ACID (Atomicity, Consistency, Isolation, and Durability) compliance is another factor to consider. This is vital to keep the data systems reliable and consistent. Data lakes, by default, don’t have ACID transaction support. This can result in complications when your teams run several operations simultaneously. A data lakehouse can fix the issue using transaction logs, atomic operations, and table-level locks. With these features, employees across the organization can access data simultaneously and derive reliable insights for decision-making. Moreover, the extra layer in the data lakehouse deals with metadata and governance, thus ensuring data compliance and security.
Cost and Maintenance
The cost comparison between a lakehouse vs. data warehouse is noteworthy as it is the cost difference between a data lake and a lakehouse. In both instances, the data lakehouse costs less for deployment and long-term maintenance. There is no need for complex ETL pipelines or high processing times. That’s why a data lakehouse is considered the best of both worlds. Additionally, lakehouses can be seamlessly hosted on cloud platforms and hybrid architectures, making them useful in diverse environments.
Conclusion
We can conclude that the best data system for a business depends on various factors. However, data lakehouses have their advantages and can be built upon existing data lakes or connected with data warehouses to create a robust architecture to derive real-time insights.
C-suites should thoroughly discuss their requirements with data analytics consulting services providers to select the right solution and future-proof the systems for better decision-making, improved customer experience, and higher ROI.
More in Data Analytics Services Providers
Data analytics services deal with collecting, cleaning, storing, and analyzing datasets to derive meaningful, accurate, and relevant insights. These are used to make decisions at different levels in the organization. The insights are shared with employees through data-driven dashboards customized for their day-to-day responsibilities. From mid-level managers to C-suites, everyone benefits from analytical insights and helps the enterprise gain a competitive edge.
Read the following links for more information.
- How to Scale Your Startup With Managed Data Analytics?
- Data Lake vs Data Warehouse: Which is best for you?
- Choosing the Best Data Lake Companies in 2026
FAQs
How do I decide whether a data warehouse, data lake, or lakehouse best fits my 2026 analytics roadmap?
Deciding between data lakehouses vs. data warehouses vs. data lakes for your 2026 analytics roadmap depends on your business needs, data types, in-house talent, budget, and other factors. For example, a startup with structured data can opt for a data warehouse to ensure consistent and high-quality insights. Talk to our certified experts at DataToBiz to know which solution is the best fits your needs in 2026 and beyond.
What business outcomes should I expect from moving to a lakehouse architecture compared to a warehouse or lake?
Due to the differences between data lakehouses vs. data warehouses vs. data lakes, you have to be prepared for some changes in outcomes when moving from one architecture to another. Typically, by moving to data lakehouses, you can expect better collaboration between teams, greater access to data, and actionable insights for decision-making. At DataToBiz, we help businesses to seamlessly migrate and revamp their data architectures and generate higher ROI.
Can I transition from my existing data warehouse to a lakehouse without disrupting ongoing reporting?
Yes, you can transition from an existing data warehouse to a lakehouse without disrupting ongoing reporting. This is done using the Databricks Lakehouse architecture, designed to allow such transitions without causing delays at work. DataToBiz has a team of expert data engineers with the required expertise to handle such complex projects and build a unified data ecosystem in your enterprise.
How do the costs of maintaining warehouses, lakes, and lakehouses differ for enterprise-scale workloads?
The costs of maintaining data lakehouses vs. data warehouses vs. data lakes differ significantly. For enterprise-scale workloads, data warehouses are the most expensive option. Data lakes can be cheaper for storage, but consume more resources for processing. Data lakehouses provide a nice balance between long-term processing costs and storage costs. This also depends on your existing processes and requirements. Schedule a meeting with DataToBiz to get a detailed quote.
Which architecture is better for AI, live analytics, and automation initiatives?
No single data architecture is ‘right’ or ‘ideal’ for AI, live analytics, and automation. A hybrid approach with a combination of a data lake and a data lakehouse or a data warehouse and a data lakehouse gives better results. At DataToBiz, our team will audit your systems and requirements to build the best hybrid data architecture with robust and optimized processes.
What governance risks should I consider before choosing between a lake, warehouse, or lakehouse?
When choosing between data lakehouses vs. data warehouses vs. data lakes, consider the following risks:
- Data governance frameworks
- Cost and scalability
- Security measures
- Data quality
- Compliance
- Datasets’ complexity, etc.
Our expert team at DataToBiz has helped many organizations minimize governance risks when adopting data architectures.
Fact checked by –
Akansha Rani ~ Content Management Executive
