Data Lake vs Data Warehouse vs Data Mart

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Data integration, cloud storage, and unified data repositories - these trends are driving all business tasks from product design to sales.  And for businesses that want to optimize data-related tasks with technology, there are several data repository options.

In searching for the right cloud-based data repository, you might come across three terms: data lake, data warehouse, and data mart. These terms are often used interchangeably as they are all storage platforms for data sourced from multiple pipelines.

But each data storage platform is unique in design and purpose.

What exactly are these differences, and how does it affect their utilization? Understanding this will help you determine the right data storage platform that aligns with your business requirements.

This article will tell you about data warehouse vs. data lake vs. data mart, their similarities and differences, and what to use them for.

What is a Data Lake

A data lake is a platform that stores data from various pipelines. The data doesn’t undergo any processing and is stored as it is, whether structured or unstructured. 

A data lake is simply a storage platform to aggregate all the data in its original form. It isn’t designed for a specific purpose and acts as a large, single-point repository from which other platforms can collect data.

Characteristics of a data lake

  • It collects and stores data in the raw format. The data doesn’t require any processing, structuring, or modification.
  • It is a massive data storage unit for structured, unstructured, and semi-structured data.
  • It supports multiple data source types with full and incremental retrieval capabilities.
  • Data loading in a data lake doesn’t follow a particular schema.
  • A data lake is easily manageable and provides complete metadata such as source, format, and all stored data types permissions.
  • It acts as the storage platform for the entire data lifecycle management by storing raw data and later storing the immediate results of data processing.
  • It is highly scalable as it expands data storage according to business data volume.

What is a Data Warehouse

A data warehouse is a central storage platform that specifically stores structured data. Unlike a data lake, a data warehouse is built to support business intelligence processes such as analytics, forecasting, and machine learning. These processes require highly organized and structured data.

The data stored in a data warehouse is prepared to be analytics-ready. Companies can use the data warehouse directly as a source for business intelligence tasks without optimizing or processing it further.

Characteristics of a data warehouse

  • It stores processed and organized data for a specific purpose, usually analytics or BI tasks.
  • It is built for a topic, such as sales, administration, or inventory management, and usually provides information on a single theme.
  • The data is integrated from various pipelines to match a particular schema.
  • The data must undergo the extract, transform, load (ETL) process before being loaded in the data warehouse.
  • The data warehouse allows the data to be analyzed based on time-variant metrics by storing data in specific intervals such as monthly or weekly.
  • Historical data is secure and isn’t erased even when new data is added, analyzed, or modified. 
  • Data warehouses have a fast response time that helps process large amounts of analytical queries.

What is a Data Mart

A data mart is a more focused version of a data warehouse that contains information about a specific business function. The data in the data mart is integrated to suit the unique requirements of a business unit. It serves a single team or department and has limited visibility. 

For example, a data mart may contain sales-related information for the audit unit.

While the unit could access the same data from a warehouse, a data mart optimizes the process by providing only relevant data. 

Characteristics of a data mart

  • It stores data related to a particular topic or business function.
  • It is a subset of the larger data warehouse.
  • Data mart users have read-only access to information relevant to their unit.
  • Data mart typically stores high-quality data that is cleansed and optimized for a business function.
  • It integrates and filters data from various sources to aggregate information for specific queries.
  • It provides quick access and a faster query response time due to a smaller data pool.
  • It is highly secure and restricts data access by creating subject-wise segregation.
  • Users don’t need advanced technical knowledge to query the data in data marts.

A quick comparison of data mart vs. data warehouse vs. data lake 

A data lake acts as the parent data storage unit that contains all types of data. A data warehouse is a subset of the data lake that stores only organized and structured data. A data mart is a subset of the data warehouse with only data around a certain topic or business function.

But how can you effectively implement these data storage solutions for your business? To assess this, you need to understand how each platform differs in terms of design, utility, and application.

Data Lake vs. Data Warehouse vs. Data Mart: Key Differences

Data lakes, data warehouses, and data marts are all cloud data storage solutions. But they have distinct differences and applications that affect how they are used in a business.

For example, a data warehouse is a large aggregator for processed and structured data. But a data lake is usually more cost-effective as it stores raw data that doesn’t require processing. Similarly, a data mart is more efficient if you require data only for a particular business process. 

To assess each platform's application, you must understand the key similarities and differences between the three data storage solutions.

Similarities between data lake vs. data mart vs. data warehouse

All three data storage platforms can be used as a centralized data repository for organizations to store large volumes of data. They have more similarities:

  • The platforms integrate data from multiple pipelines and store it in a single repository.
  • They create a reliable and trustworthy data source for business analytics.
  • They preserve historical data while allowing the modification and loading of new data.
  • They can be queried using SQL or other languages to extract data for specific purposes.
  • All three platforms provide access to metadata of the stored information.
  • They ensure compatibility with data regulations and protect sensitive data through encryption, authentication, etc.
  • They are scalable and can be expanded in terms of storage and capabilities depending on business processes and data volume.

While the three platforms appear similar, you can understand more about their differences by comparing two platforms at a time.

For example, a data lake and warehouse store large data volumes encompassing all business information. But a data warehouse restricts the storage to only organized data.

Similarly, a data warehouse and a data mart are meant for business intelligence processes. But a data mart is more specific when compared to the warehouse.

We have provided a detailed comparison of the three platforms below to differentiate between them.

Key Differences: Data Mart vs. Data Warehouse

A data mart is typically described as a subset of the data warehouse. While the data warehouse stores structured data, a data mart serves a particular business unit. Here are some more differences between the two platforms:

  • Size: A data warehouse stores large data volumes at a scale of one terabyte or more. A data mart contains more specific information, which usually doesn’t exceed 100 gigabytes.
  • Purpose: A data warehouse caters to all business units by storing data from multiple pipelines. A specific department uses a data mart with limited data sources, usually just a warehouse.
  • Access: Multiple users and departments can access a data warehouse containing a wider range of data. In comparison, only specific users can access a data mart, making it a more secure data storage platform for sensitive information.
  • Design: A data warehouse focuses on integrating and processing data from multiple sources. On the other hand, a data mart has a solution-oriented design as the data stored is already processed.
  • Schema: A data warehouse employs a star or snowflake schema to accommodate all data types. A data mart has a more simplified schema since it deals with a specific subset of data.
Aspect Data Warehouse Data Mart
Focus Enterprise-wide Specific function, unit, or process
Size Large - ranges from 10 to 100s of terabytes Small, usually between 10 to 100 gigabytes
Source Broad range of data pipelines containing all types of business data Selected data pipelines or a single data warehouse
Users Multiple users across teams or departments Users from a specific business unit
Design Approach Top-down moving from data integration to solution Bottom-up moving from solution to sourcing integrated data
Schema Covering multiple data dimensions such as star schema or snowflake schema May use dimensional data models but typically contains simplified data structures
Processing Longer processing time due to large data volumes Real-time query processing and reduced latency

Key differences: Data Lake vs. Data Warehouse

Data warehouses and data lakes are both large data repositories. But they store different data types and are used for distinct purposes. Here are the key differences between the two platforms:

  • Processing: Data warehouses store processed, organized, and structured data. A data lake stores data in its raw form, whether structured, semistructured, or unstructured.
  • Loading: Data goes through the ETL process before being loaded into a data warehouse. Data lakes don’t follow a specific methodology for data loading.
  • Quality: The data warehouse ensures quality as the data undergo cleansing, verification, and structuring before getting stored in the warehouse. However, data in a data lake may be unreliable as it isn’t processed before loading.
  • Usage: Data warehouses are used for business analytics, whereas data lakes are simply large storage platforms for all kinds of business data.
  • Security: Data warehouses follow governance and security standards. A data lake may not have strict regulations around data governance.
Aspect Data Warehouse Data Lake
Usage Business intelligence and analytics Large-scale data storage
Data Type Clean and organized data Structured, semi-structured, and unstructured data
Schema Schema-on-write, the structure is decided before loading the data Schema-on-read, data structure is specified during query
Data Quality High-quality data with no duplication and cleansing issues Inconsistent data quality due to lack of processing
Cost More expensive due to complex methodologies and maintenance Low-cost with minimal processing and maintenance requirements
Latency Low latency as the data is already processed High latency as data requires additional processing

When to use data lakes, data warehouses, and data marts?

While all three data storage platforms serve different purposes, effectively combining them helps optimize your data processing capabilities. Depending on your business goals, budget, and requirements, you can carefully choose the data platform(s) that work for you.

Consider the following factors before choosing a data storage solution:

  • Purpose: Do you have specific departments to handle data requirements? A data mart works best to store data for a particular business unit. However, a data warehouse is more optimal to analyze entire business data.
  • Cost v/s requirement: Data warehouses can handle large-scale data processing but are more expensive. If you have small-scale requirements, a data mart will be more economical.
  • Data volume: If you want to store large data volumes with or without processing, data lakes are an excellent, cost-effective option.

You should also consider the flexibility, maintenance, and compatibility with your existing tech stack. 

The database vs. data warehouse vs. data mart vs. data lake dilemma has riddled many data-driven organizations. However, choosing the right solution depending on your requirements can be fundamental for your business growth.

Frequently Asked Questions (FAQs)

  1. Is a data mart the same as a data warehouse?

A: A data mart and a data warehouse have similar properties. They store structured data and are used for business intelligence processes. However, a data mart serves a specific unit and stores information only on a particular topic. A data warehouse is broader and contains data related to all business tasks.

  1. Can a data lake store process data?

A: Yes, a data lake can store any information, including processed data. However, its use is limited in business intelligence tasks as data in a data lake is usually raw and unreliable.

  1. When should we choose a data lake over a data warehouse vs. a data mart?

A: A data lake is more useful for companies that use raw data for analysis and machine learning. If the data processing isn’t your main goal, you should choose a data lake.

  1. Which platform is more cost-effective, data mart vs. data lake vs. data warehouse?

A: A data lake is usually less expensive, followed by a data mart and then a data warehouse. But the cost-effectiveness of implementing such storage platforms varies depending on business requirements and goals.

Written by
Soham Dutta


Data Lake vs Data Warehouse vs Data Mart