Data Warehouse vs. Data Lake vs. Data Lakehouse: What to Use in 2025

Updated December 04th, 2024

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The main difference between Data Warehouse, Data Lake, and Data Lakehouse is their approach to data storage and usage.

  • Data Warehouse: Optimized for structured data, Data Warehouses store information in a relational format, supporting complex queries and analytics. They are typically used for business intelligence and reporting.
  • Data Lake: Designed for unstructured, semi-structured, and structured data, Data Lakes store raw data in its native format. They support large-scale data processing and machine learning but require advanced tools for data querying.
  • Data Lakehouse: Combines features of both, offering a unified approach to data storage. It enables structured data analytics and unstructured data processing within the same system, supporting flexibility and performance for diverse workloads.

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Each system addresses specific data needs, from real-time analytics to batch processing. Understanding these differences helps businesses choose the right solution for scalability, cost, and governance.

With so many data storage systems available, it’s easy to get confused as to how they differ. Fear not! In this blog, we’re comparing three common data storage architectures – a data warehouse vs a data lake vs a data lakehouse – so you can choose the best option to meet your organization’s needs.

Table of contents #

  1. What is a data warehouse?
  2. What is a data lake?
  3. What is a data lakehouse?
  4. Data warehouse vs data lake
  5. Data warehouse vs data lakehouse
  6. Data warehouse vs data lake vs data lakehouse – at a glance
  7. How organizations making the most out of their data using Atlan
  8. Conclusion
  9. FAQs about Data Warehouse vs Data Lake vs Data Lakehouse
  10. Data Warehouse vs Data Lake vs Data Lakehouse: Related reads

What is a data warehouse? #

A data warehouse is a repository for large amounts of structured data from various data sources – structured being the operative word. Data usually falls under three categories: structured, unstructured, and semi-structured.

  • Structured data is quantitative and highly organized, such as names, birthdays, addresses, social security numbers, stock prices, and geolocation.
  • Unstructured data is qualitative. It has no clearly defined framework and is not easily searchable, such as online reviews, videos, photos, or audio files.
  • Semi-structured data combines elements of the other two. It has a loosely defined framework, such as emails that have addresses for sender/recipient, but a body that can contain anything.

Data that lives in a data warehouse is processed for validation, sorting, summarization, aggregation, analysis, reporting, or classification. A data warehouse is highly organized and is formatted for a specific purpose. It enables an organization to easily access and analyze relevant data in order to develop actionable insights.

Components of a data warehouse #


In order to provide these insights, a data warehouse consists of four core components: a central database, data integrations tools, metadata, and data access tools.

  • Central database: The backbone of a data warehouse, a central database houses data organized into tables that group together related objects.
  • Data integration tools: Data integration tools are used to pull data from various sources and transform it so it fits within the data warehouse. The traditional approach used here is called extract, transform, and load (ETL), though extract, load, and transform (ELT) techniques have also become popular.
  • Metadata: Metadata is data about your data and is used to create context and organization. For example, if a photo file is a data point, then its date, geolocation, and camera type is its metadata that helps to better contextualize and organize the file.
  • Data access tools: Data access tools, such as query tools, application development tools, data mining tools, and online analytical processing (OLAP) tools, give users the ability to interact with the data stored in their data warehouses.

Data Warehousing Global Market Report, 2024 suggests that the data warehousing market size has grown rapidly from $30.57 billion in 2023 to $34.52 billion in 2024 at a CAGR of 12.9%, attributing its growth to increase in enterprise data volume, increased complexity of data analysis, and business intelligence and reporting needs.

Data warehouses are a great solution for storing and obtaining insights from structured data. But we produce 2.5 quintillion data bytes per day, most of which is raw data that doesn’t fit so neatly inside a data warehouse.

So what is an organization to do when it comes to storing all this data? The answer – a data lake.

What is a data lake? #

Popularized in 2010, a data lake is a centralized repository for virtually all types of raw data. Structured, unstructured, and semi-structured data can all be quickly dumped into a data lake before it’s processed for validation, sorting, summarization, aggregation, analysis, reporting, or classification.

Components of a data lake #


A data lake architecture has five major components that can be remembered with the acronym ISASA – Ingest, Store, Analyze, Surface, Act.

  • Ingest: This refers to data migration, usually through APIs or batch processes.
  • Store: Data ingested from various sources is stored in a single repository, without silos.
  • Analyze: Users can then analyze the data to uncover relationships and even make forecasts.
  • Surface: To surface means to present findings of the analysis in easily discernible ways – usually in the form of a chart, graph, or actionable insight.
  • Act: With the data analyzed and surfaced, it can be acted upon to inform business decisions.

Data lakes are built on inexpensive object storage and provide organizations with simple, cost-effective, scalable storage. The problem with data lakes is that since they serve as repositories for virtually all types of data, they can very easily become disorganized and transform into a dreaded, inefficient data swamp where it’s hard to find or do anything useful.

The 2024 Data Lake Market Size, Share, and Forecast Report by Fortune Business Insights states that the data lake market was valued at $5.80 billion in 2022 and is expected to expand from $7.05 billion in 2023 to $34.07 billion by 2030, reflecting a compound annual growth rate (CAGR) of 25.3% over the forecast period (2023–2030).

What is a data lakehouse? #

A data lakehouse is a more recent data management architecture pioneered by Databricks that combines the flexibility, open format, and cost-effectiveness of data lakes with the accessibility, management, and advanced analytics support of data warehouses.

5 Composite layers of a data lakehouse #


There are typically five layers that make up a data lakehouse:

  1. Ingestion layer: Data is pulled from different sources and delivered to the storage layer.
  2. Storage layer: Various kinds of data (structured, semi-structured, and unstructured) are kept in a cost-effective object store, such as Amazon S3.
  3. Metadata layer: A unified catalog that provides metadata about all objects in the data lake and enables data indexing, quality enforcement, and ACID transactions, among other features. The metadata layer is the defining element of the data lakehouse.
  4. API layer: Metadata APIs allow users to understand what data is required for a particular use case and how to retrieve it.
  5. Consumption layer: The business tools and applications that leverage the data stored within the data lake for analytics, BI, and AI purposes.

As per a 2024 survey by MarketResearch.biz on the data lakehouse market size, the global data lakehouse market is expected to grow from USD 8.9 billion in 2023 to approximately USD 66.4 billion by 2033, with a projected compound annual growth rate (CAGR) of 22.9% between 2024 and 2033.

Also, read → Forrester Wave Data Lakehouses Report for Q2, 2024

Data warehouse vs data lake #

So what’s the difference between a data warehouse and a data lake? The two are more dissimilar than they are alike.

  • A data warehouse stores structured data that has been processed for a specific purpose. These systems are more organized than a data lake.
  • A data lake is a free-for-all, housing structured, unstructured, and semi-structured data. Data lakes can also store unprocessed data for some unknown, future use.

Let’s take a look at how these would be used in the real world, and how they could work together. Consider an airline. The company could neatly store passenger information in a data warehouse, where data is structured and includes items such as names, birthdays, addresses, origination airports, destination airports, and frequency of travel, among others. The company may also have a separate data warehouse where it stores financial data for each passenger.

The airline may also choose to have a data lake where unstructured data, like customer support emails, photographs from IDs, and social media content culled from various social channels, could live until further analysis is needed. Insights from this data, when coupled with the structured data insights, could empower the airline to create a unique customer offering that may have otherwise gone undetected as a means to attract new customers.

Data warehouse vs data lakehouse #

As we’ve discussed, data warehouses are rigid architectures that are well organized and provide fast discovery, query, and preparation of processed data. Worth noting is that the storage cost of a data warehouse can be quite expensive. This is why data retention is usually limited; historical data is generally removed to make way for new data.

A data lakehouse, however, is flexible (able to house all types of data) like a data lake, but it comes with the organization and management features of a data warehouse. They are more cost-effective than a data warehouse and enable both prescriptive analytics you’d find with a warehouse and predictive analytics usually performed in a data lake.

Using our previous airline example, the company might find that storing passenger info in a data lake is too messy and choose to house it in a data lakehouse instead. Under this architecture, the airline could store the data so that a passenger’s information, financial data (credit card numbers, ticket spend), customer support tickets, and social media profiles are all matched up to render a complete profile of any particular individual. It could then run predefined data analysis to find average ticket spend for passengers in Boston, or predictive analysis to forecast when college students from the midwest are likely to purchase tickets to Europe.

Data warehouse vs data lake vs data lakehouse – at a glance #

Data Warehouse Data Lake Data Lakehouse
Raw or processed data Processed Raw Raw and processed
Data structure Structured only Structured, semi-structured, unstructured Structured, semi-structured, unstructured
Organization Highly organized Little organization Highly organized
Data retention Low High High
Analytics Good for prescriptive analytics Good for predictive analytics Good for prescriptive and predictive analytics
Cost Costly Cost-effective Cost-effective

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

  1. Automatic cataloging of the entire technology, data, and AI ecosystem
  2. Enabling the data ecosystem AI and automation first
  3. Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #


  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

Conclusion #

As you have seen, when it comes to a data warehouse vs data lake vs data lakehouse, the key differentiator is organization. Think of it this way: A data lakehouse is a data lake with the organization of a data warehouse. Proper governance and management using metadata help organize the data so users can quickly and effectively perform analysis leading to actionable insights.

FAQs about Data Warehouse vs Data Lake vs Data Lakehouse #

1. What is the difference between a data warehouse, data lake, and data lakehouse? #


Data warehouses are optimized for structured data and complex queries, making them ideal for business intelligence. Data lakes, on the other hand, store unstructured, semi-structured, and structured data, offering flexibility and cost-effective storage. Data lakehouses combine the strengths of both, supporting real-time analytics and unified data management.

2. Which is better: data warehouse or data lake? #


The choice depends on your use case. Data warehouses excel in structured data analytics and reporting, while data lakes are better for big data and machine learning due to their ability to store diverse data formats.

3. What are the advantages of using a data lakehouse? #


Data lakehouses unify the storage and analytics of both structured and unstructured data. They provide real-time insights, scalability, and improved governance, bridging the gap between data lakes and warehouses.

4. How do data warehouses and data lakes handle structured and unstructured data? #


Data warehouses handle structured data using a predefined schema (schema-on-write). Data lakes, however, store data in its raw form, applying schema only when data is read (schema-on-read), making them more flexible for unstructured data.

5. What are the cost implications of data warehouses vs. data lakes? #


Data lakes generally offer lower storage costs due to their use of cheap, scalable storage. However, data warehouses can be more expensive due to their performance-optimized, high-speed query capabilities.

6. How do I migrate from a data warehouse to a data lakehouse? #


Migrating involves consolidating structured and unstructured data, updating governance policies, and leveraging tools that support both analytics and storage layers. Proper planning ensures seamless integration and performance optimization.

Photo by Taylor Vick on Unsplash

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