What’s the Difference Between a Data Warehouse, Data Lake, and Data Mart?

What's the Difference Between a Data Warehouse, Data Lake, and Data Mart?

Data warehouses, data lakes, and data marts are different cloud storage solutions. A data warehouse stores data in a structured format. It is a central repository of preprocessed data for analytics and business intelligence. A data mart is a data warehouse that serves the needs of a specific business unit, like a company’s finance, marketing, or sales department. On the other hand, a data lake is a central repository for raw data and unstructured data. You can store data first and process it later on.

Similarities between data warehouses, data marts, and data lakes

Organizations today have access to ever-increasing volumes of data. However, they must sort, process, filter, and analyze the raw data to derive practical benefits. At the same time, they also have to follow rigid data protection and security practices for regulatory compliance. For example, here are practices organizations must follow:

  • Collect data from different sources like applications, vendors, Internet of Things (IoT) sensors, and other third parties.
  • Process data into a consistent, trustworthy, and useful format. For example, organizations could process data to make sure all dates in the system are in a common format or summarize daily reports.
  • Prepare the data by formatting XML files for machine learning software or generating reports for humans.

Organizations use various tools and solutions to achieve their data analytics outcomes. Data warehouses, marts, and lakes are all solutions that help with storing data.

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Benefits of a cloud-based data warehouse, data lake, and data mart

All three storage solutions help you increase your data's availability, reliability, and security. Here are examples of how you can use them:

  • Store your business data securely for analytics
  • Store unlimited data volume for as long as you need it
  • Break down silos with data integration from multiple business processes
  • Analyze historical data or legacy databases
  • Undertake real-time and batch data analysis

In addition, all three solutions are cost-efficient—you only pay for the storage space that you use. You can store all your data, analyze it for patterns and trends, and use the information to optimize your business operations.

Key differences: data warehouses vs. data marts

A data warehouse is a relational database that stores data from transactional systems and business function applications. All data in the warehouse is structured or pre-modeled into tables. The data structure and schema are designed to optimize for fast SQL queries. A data mart is a different marketing term for the same technology. It is also a relational database, but practical usage differs greatly from that of a data warehouse. Key points of difference are given below.

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Data sources

Data warehouses have multiple sources, both internal and external. You can extract data from anywhere, transform it into a structured format, and load it in your warehouse. Data marts have fewer data sources and tend to be smaller in size.


Data warehouses typically store data from multiple business units. They centrally integrate data from across the organization for comprehensive analytics. Data marts have a single-subject focus and are more decentralized in nature. They often filter and summarize information from another existing data warehouse.


Multiple users and projects require the data stored in data warehouses. Hence, warehouses often have a longer lifespan and are more complex in nature. Data marts, on the other hand, may be project-focused with limited use. Teams prefer creating data marts from the enterprise data warehouse and terminating them once the use case is finished.

Design approach

Data scientists use a top-down approach when designing a data warehouse. They plan the overall architecture first and solve challenges as they arise. However, with a data mart, the data engineer already knows details like values, data types, and external data sources. They can plan the implementation from the start and take a bottom-up approach to data mart design.


Characteristics Data Warehouse Data Mart

Centralized, multiple subject areas integrated together

Decentralized, specific subject area



A single community or department

Data source

Many sources

A single or a few sources, or a portion of data already collected in a data warehouse


Large, can be 100's of gigabytes to petabytes

Small, generally up to 10's of gigabytes




Data detail

Complete, detailed data

May hold summarized data


Learn more about Data Warehouses

Learn more about Data Marts

Key differences: data warehouses vs. data lakes

A data warehouse and a data lake are two related but fundamentally different technologies. While data warehouses store structured data, a lake is a centralized repository that allows you to store any data at any scale. A data lake offers more storage options, has more complexity, and has different use cases compared to a data warehouse. Key points of difference are given below.

Data sources

Both data lakes and warehouses can have unlimited data sources. However, data warehousing requires you to design your schema before you can save the data. You can only load structured data into the system. Conversely, data lakes have no such requirements. They can store unstructured and semi-structured data, such as web server logs, clickstreams, social media, and sensor data.


A data warehouse typically requires preprocessing before storage. Extract, Transform, Load (ETL) tools are used to clean, filter, and structure data sets beforehand. In contrast, data lakes hold any data. You have the flexibility to choose if you want to perform preprocessing or not. Organizations typically use Extract, Load, Transform (ELT) tools. They load the data in the lake first and transform it only when required.

Data quality

A data warehouse tends to be more reliable as you can perform processing beforehand. Several functions like de-duplication, sorting, summarizing, and verification can be done in advance to assure data accuracy. Duplicates or erroneous and unverified data may end up in a data lake if no checks are being done ahead of time.


A data warehouse is designed for the fastest query performance. Business users prefer data warehouses so they can generate reports more efficiently. In contrast, data lake architecture prioritizes storage volume and cost over performance. You get a much higher storage volume at a lower cost, and you can still access data at reasonable speeds.


Characteristics Data Warehouse Data Lake

Relational data from transactional systems, operational databases, and line of business applications

All data, including structured, semi-structured, and unstructured


Often designed prior to the data warehouse implementation but also can be written at the time of analysis

(schema-on-write or schema-on-read)

Written at the time of analysis (schema-on-read)


Fastest query results using local storage

Query results getting faster using low-cost storage and decoupling of compute and storage

Data quality

Highly curated data that serves as the central version of the truth

Any data that may or may not be curated (i.e. raw data)


Business analysts, data scientists, and data developers

Business analysts (using curated data), data scientists, data developers, data engineers, and data architects


Batch reporting, BI, and visualizations

Machine learning, exploratory analytics, data discovery, streaming, operational analytics, big data, and profiling

  Learn more about Data Warehouses Learn more about Data Lakes

When to use data lakes vs. data warehouses vs. data marts?

Most large organizations use a combination of data lakes, warehouses, and marts in their storage infrastructure. Typically, all data is ingested into a data lake then loaded into different warehouses and marts for assorted use cases. The technology decision depends on various factors as explained below. 


In general, data lakes offer more flexibility at a lower cost. Different teams can access the same data using their choice of analytic tools and frameworks. You can save time as there is no need to define data structures, schema, and transformations.

Data types

A data warehouse is better if you want to store relational data like customer and business process data. If you have a large volume of relational data, your team may consider creating some data marts for specific business needs. For example, the accounts department may create a data mart to maintain balance sheets and prepare customer account statements, while the marketing department may create another data mart for optimizing advertising campaigns.

Cost and volume

A data warehouse can efficiently handle hundreds of petabytes (PB) of data. Data lakes offer a comparatively lower cost for more volume, especially for large numbers of images and videos. However, not every organization may require that level of scale. 

How can AWS help with your data storage needs?

AWS provides the broadest selection of analytics services that fit all your data analytics needs. We enable industries and organizations of all sizes to reinvent their business with data. Here are examples of how you can use AWS:

  • Use Amazon Redshift for your data warehousing and data mart requirements. Get integrated insights by running real-time and predictive analytics on complex, scaled data across your operational databases, data lake, data warehouse, and thousands of third-party datasets. You can automatically create, train, and deploy machine learning models with ease.
  • Use AWS Lake Formation to build, manage, and secure a data lake within days. Quickly import data from all your data sources, then describe and manage them in a centralized data catalog.
  • Use Amazon S3 to build a custom data lake for big data analytics, artificial intelligence, machine learning, and high-performance computing applications.

Get started with data storage on AWS by creating a free account today.

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