RST Software
Editorial Team
Ross Krawczyk
Reviewed by a tech expert

Data Lake vs Data Warehouse vs Data Mart: difference between cloud storage solutions

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With data generation growing rapidly across industries, organizations today face both a blessing and a challenge of effectively collecting, storing, and leveraging massive volumes of information to drive innovation and strategic decision-making. Like spinning straw into gold, businesses must transform raw data into data-driven insights. Your cloud data storage architecture forms the crucial foundation to manage the data deluge and turn it into business value.

In this article, we will explain the differences between data lakes, data warehouses, and data marts – three popular cloud storage solutions. We will explore key factors to consider when selecting the right approach, including data types, business needs, users, and analytics goals.

Understanding the basics

Data, the lifeblood of modern enterprises, finds its value not just in its existence, but in how it is stored, accessed, and utilized. Let's take a primer into the core of the data lake vs data warehouse vs data mart riddle.

What is a data lake?

A data lake acts as an expandable repository for storing raw, unprocessed data in its original format. Like a real lake, it collects incoming streams of data from across the organization, as well as allows for inexpensive, practically unlimited storage of structured data (e.g., relational databases), semi-structured data (e.g., CSV files), and unstructured data (e.g., social media posts).

The key benefit? You can store data first, then structure it later as needed. This “schema-on-read” approach provides immense flexibility. When you need to analyze the data, you apply structure. Each element stored in a data lake can be assigned a set of extended metadata tags. When a business question arises, the data lake can be queried for relevant data holding a unique identifier, and only that particular data can then be analyzed to answer the business question.

What is a data warehouse?

In contrast, a data warehouse ingests, integrates, structures, and stores data specifically for querying, reporting, and analysis. It acts as a central repository, providing business users with a “single source of truth”. Imported data undergoes what is called the ETL (extract, transform, load) process to clean and model it prior to storage, known as “schema-on-write”.

This upfront investment in curating and structuring data enables fast, efficient analytics. The schema optimizes query performance. Picture a data warehouse as your neighborhood supermarket – everything is neatly shelved, sorted, and prepped for use. It stands as a trusted resource for business analysts seeking dependable, orderly, and uniform data to drive smart decisions.

Difference between a database and a data warehouse

Databases and data warehouses are both important repositories of data, but they serve different purposes and have different characteristics.

Databases are designed to store and manage current operational data for a specific application or set of applications. They are typically optimized for high-volume transaction processing (OLTP), which means they can quickly insert, update, and delete data. Databases are also designed to ensure data integrity and consistency, which is important for maintaining accurate records.

Data warehouses, on the other hand, are designed to store historical data from multiple sources for the purpose of analysis. They are typically optimized for online analytical processing (OLAP), which means they can quickly aggregate and analyze large amounts of data. Data warehouses are also designed to be subject-oriented, which means that they are organized around specific business topics or areas of interest.

Database Data warehouse
Purpose Store and manage current operational data Store historical data for analysis
Optimization OLTP (online transaction processing) OLAP (online analytical processing)
Data type Current data Historical data
Data source Single application or set of applications Multiple sources
Data structure Normalized Denormalized
Access Real-time Batch processing
Usage Supporting day-to-day operations Making informed business decisions

Examples of how databases and data warehouses can be used


  • An e-commerce website might use a database to store customer information, product details, and order history.
  • A bank might use a database to store customer account information and transaction history.
  • An airline might use a database to store passenger information, flight schedules, and reservation data.

Data warehouses:

  • An e-commerce company might use a data warehouse to analyze sales trends and identify customer segments.
  • A hospital might use a data warehouse to track patient outcomes and identify areas for improvement.
  • A financial institution might use a data warehouse to analyze market trends and make investment decisions.

What is a data mart?

If a data warehouse represents the entire grocery store, a data mart is the veggie aisle – a subset tailored for specific users. Data marts are analytical repositories supporting focused business needs such as sales, marketing, or finance. They contain cleansed data from one or more sources structured, and enable decentralized, departmental analytics.

For example, the marketing team could have their own data mart with customer purchase history for campaign targeting.

Data lake vs data warehouse: key differences

Though similar in objective, data lake and warehouse differ considerably in approach and application. When weighing a data lake vs data warehouse, consider a breakdown of their key variables to check how they align with your strategy.

Criteria Data lake Data warehouse
Data types All types – structured, semi-structured, unstructured Structured, modeled data
Data structure Schema-on-read (structure applied when data is read/analyzed) Schema-on-write (data structured prior to storage)
Data quality Raw, uncurated data, inconsistent quality Cleansed, curated data with high consistent quality
Purpose Low-cost storage reservoir for future use Integrated data hub for analytics and business intelligence
Users Data engineers, data scientists Business analysts, non-technical users
Analytics use cases Machine learning, exploratory analytics, data discovery SQL queries, business intelligence, dashboards
Latency High latency for analytics queries Optimized for fast query performance
Cost Lower storage cost but requires engineering to prepare data More expensive but faster time to insight
Skill required Data wrangling, programming SQL querying, dashboarding
Schema flexibility Highly flexible schema Rigid schema
Data volumes Very high, limitless data volumes Lower volumes than data lake, 100s TB to PB
Data velocity High ingestion volumes and velocity Lower data velocity
Security More exposed surface due to diverse data More consistent security with controlled access
Compliance Harder to meet compliance requirements Easier for compliance via data governance
Cloud services AWS S3, Google Cloud Storage AWS Redshift, Snowflake, BigQuery

Data type

Data lakes offer immense flexibility to cost-effectively store all kinds of data, whether structured, semi-structured or unstructured. For example, a media company could dump raw advertisement data like clicks or impressions, audio transcripts, video files and more into a single data lake.

In contrast, data warehouses require structured relational data modeled into columns and rows. For instance, a retailer's warehouse may hold transaction records, customer info, order history and other structured data.

If you need a cheap repository to hold raw logs, sensor data, social posts and other unstructured data from across your business, a data lake can consolidate it all. But for centralized storage of core business entities like customers, products, financial data etc., a data warehouse aligned to your analysis needs may be preferable.

Data structure

A key differentiator for data lake vs data warehouse comparison lies in the data structure itself:

  • Data lake – schema-on-read, meaning structure is applied when data is read and analyzed. This provides flexibility to store data first, impose structure later.
  • Data warehouse – schema-on-write, meaning data is structured and modeled before storage during ETL processes.

This upfront investment in curating data enables fast query performance but reduces flexibility. Data lakes allow more agility for modern data types like Internet of Things (IoT) data. But warehouses provide out-of-the-box analytics with standardized, governed data.

Data quality

Given their different treatment of incoming sources, data warehouse vs data lake differ in quality of stored material:

  • Data lake – stores raw data “as is” without governance or curation. This enables cheap, “unlimited” data storage but results in inconsistent quality.
  • Data warehouse – data is cleansed, standardized and processed during ingestion. Quality and reliability are higher but warehouses are more expensive due to processing overhead.

If your analytics teams demands trusted information, the upfront investment of a warehouse pays dividends through clean governed data. But if your teams need flexibility with new data sources, a data lake can affordably store it now, clean – later.


The distinct philosophies behind data lakes and data warehouses drive different use cases. For instance, data science teams can tinker with data lakes to discover new algorithms before re-engineering data flows to productionalize models in a data warehouse. Or product teams can archive old application data cheaply. Generally, data lake serves as a low-cost, limitless data dump to support experimental analytics like machine learning, big data and real-time analytics, catering to data scientists who need a playground for data discovery. Acts more as “cold” storage until data is refined.

Oppositely, data stored in a warehouse is “hot”, readily available for business insights. Data warehouse is an integrated data hub enabling reporting, dashboards and analytics. Tailored for business professionals to perform analysis on historical data for strategic decision-making.


The difference in data formats suits different analytics professionals:

  • Data lake is mostly used by data engineers and data scientists comfortable wrangling and exploring unstructured data.
  • Data warehouse is more of a business analysts’ domain, those seeking to generate clean, trustworthy reports.


The breadth of analytics use cases also differs in the data lake vs data warehouse comparison. Data lake facilitates advanced, exploratory analytics including machine learning, predictive analytics, data discovery, and real-time analytics requiring huge data volumes, varieties and velocities.

Data warehouse, designed for structured querying, is perfectly suited for SQL-based business intelligence, dashboards and reporting, delivering strategic insights.

For instance, data scientists can conduct sentiment analysis on large corpuses of raw social media data stored in a data lake. Business leaders can track KPIs through a data warehouse generating daily executive reports.


Lastly, cost considerations come into play.

  • Data lake – is less expensive to implement but requires engineering and data wrangling before business value is realized.
  • Data warehouse – more expensive upfront due to the infrastructure and processing but with faster time-to-insight.

In summary, data lakes provide a low-cost repository for limitless volumes of raw data while data warehouses enable direct business insights on integrated, trustworthy data. Often, both play symbiotic roles with cheap raw data in lakes feeding into refined warehouses. Now let's compare another popular option: data marts.

Data mart vs data warehouse: key differences

The divergence between data mart vs data warehouse often lies in the scope and accessibility of data. For departmental analytics, data marts shine compared to an entire warehouse.

Criteria Data mart Data warehouse
Scope Narrow, focused on specific business unit or function Broad, enterprise-wide
Data sources Typically one source of data Multiple sources
Data size Up to 10s of GB 100s TB to PB
Data details Summarized, aggregated data Granular, detailed data
Design approach Start from required dataset Start from overall enterprise data model
Users Specific department Organization-wide
Access Distributed deployment Centralized access from one repository
Agility Faster to adapt to new needs Slower to change, more governance
Time to value Rapid insights for targeted users Slower to implement for company-wide use
Cost Lower cost due to smaller size Higher cost for large-scale enterprise data
Security Potentially less secure with decentralized access More consistent security with unified controls
Customization Highly customized for department's needs Standardized for cross-functional use

Data scope

To make a long story short, warehouses centralize company-wide data and serve multiple subjects; while marts are department-specific and provide local analytics. As a result, your company can have separate sales team data mart, marketing team data mart, and so on.

Data source

When contrasting a data mart vs data warehouse, the distinction in their data sources becomes clear:

  • Data warehouse – consolidates many internal and external sources across the organization.
  • Data mart – typically sourced from one or a few specific data sources, refine specific data domains.

Data size

Data marts collect information from specific segments like CRM systems or financial records, resulting in a more compact footprint often measured in tens of gigabytes.

Data warehouses boast vastly larger capacities, with storage ranging from hundreds of terabytes to petabytes, underscoring their capability to house an expansive ocean of an organization's data including e.g. ERP, web traffic, social media, purchased third-party data, and more.

Data detail

Warehouses have entire data universes. Marts tailor relevant info. In the comparison of data mart vs data warehouse regarding the specifics of data detail, warehouses include both summarized and granular, transaction-level data for a wide variety of uses across the enterprise. Data marts, conversely, focus on summarized, aggregated data that pertains to a specific department's operations.

Design approach

Data marts address specific needs. They are created with a specialized, user-centered design, focusing on the immediate analytical needs of individual business units. This bottom-up approach allows for quick deployment and immediate results within a specific context.

Data warehouses, on the other hand, are architected from a top-down perspective to ensure comprehensive data integration. They are structured to integrate a wide array of data from across the entire organization, ensuring that all business areas can be analyzed in conjunction with one another.

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

The choice between data lake vs data warehouse vs data mart boils down to the organizational needs, the scale of data operations, and the level of analysis required. With the rapid pace of data growth, organizations often leverage a combination of storage platforms:

  • Data lakes provide low-cost, limitless storage for raw data in its original format. Great for aggregating massive, varied data before structuring.
  • Data warehouses enable widespread analytics and business intelligence for structured data. Ideal for performance-intensive reporting.
  • Data martsgive focused analytics on relevant, refined data for specific teams. Allow decentralized, self-service insights.

The right solution depends on your business needs, data types, analytics users, and budget. Aim for maximum flexibility to adapt as data piles grow larger and larger.

How can RST help with your data storage needs?

Whether your priorities are limitless storage, accelerated insights, or decentralized analytics, RST has the experience to architect the right solution.

Contact us, and let's turn your data into your most valuable asset.

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