Data Lake vs Data Warehouse vs Data Mart: Key Differences and Best Use Cases

Data storage is a vital aspect of today’s digital landscape. Organizations require efficient ways to store, process, and analyze the data. Data storage plays an important role in modern business. Efficient storage systems are important for organizations to optimize their data strategies and harness insights for growth and success.

Data lakes, data warehouses, and data marts are three widely used storage solutions, each serving a distinct purpose. A data lake stores raw, unstructured, and structured data in its original form. A data warehouse organizes structured data for business intelligence. A data mart is a smaller, specialized subset of a data warehouse for specific business needs. This blog explores data lake vs data warehouse vs data mart – their characteristics, differences, and when to use each approach.

What Is a Data Lake?

A data lake is a distributed data repository in its native and raw format. A data lake uses object storage to store the data.‍ Data lakes help consolidate an organization’s data in a single, central location, where it can be saved in its raw form. The schema and meaning of the data are determined at the time of querying and not at the time of loading. Data lakes are affordable, have an open format, are highly durable, and have an agile environment.

Characteristics of a Data Lake

Certain characteristics of data lakes separate them from other types of big data storage. A few are listed below:

1. It can accommodate data regardless of the format or source of the data.
2. Transformation occurs only when data is retrieved for analysis, based on specific query requirements, which makes it a schema-on-read approach.
3. Data is stored in its original, unprocessed form.
4. Data lakes let you build AI initiatives on a vast and diverse data foundation, which is ideal for training AI and machine learning models.

Also, take a look at the differences between data fabric vs data lake to decide which is the right data structure for you.

What Is a Data Warehouse?

A data warehouse is a type of data management system that acts as a centralized repository of structured and curated data. The data is extracted, transformed, and loaded (ETL) from a wide range of sources such as application log files and transaction applications. A data warehouse supports Business Intelligence (BI) activities, especially analytics. In the case of a data warehouse, a schema needs to be imposed to store the data of an organization.  As data accumulates over time, a historical archive is created that benefits both business analysts and data scientists. 

Characteristics of a Data Warehouse

The characteristics of a data warehouse help in robust data analysis. Some of them are listed below:

1. Subject-oriented: A data warehouse is subject-oriented because it handles a specific theme to deliver information. It focuses on demonstrating and analyzing key business subjects like sales, distribution, marketing, customer data, etc. rather than operational processes.
2. Integrated: It combines data from multiple sources. Integration is done ensuring reliable naming conventions, format, and codes.
3. Time-variant: Data is stored with a time dimension, which allows users to analyze data over time. It is stored and updated on a weekly, monthly, or annual basis, depending on the requirements. 
4. Non-volatile: Once the data is stored in the data warehouse, it cannot be deleted when new data is added, which maintains the historical data. Only loading and accessing of the data is done in the data warehouse. 

You can also explore in detail the characteristics of data warehousing and business intelligence.

What Is a Data Mart?

A data mart is designed to focus on a single subject or line of business. It is more focused than a data warehouse and generally exists as a subset of an organization’s larger enterprise data warehouse. By using a data mart, users can access data and gain insights faster. Data marts have gained popularity as a centralized repository where relevant data is gathered and structured before reports, dashboards, and visualizations are created. This efficiency not only saves time but also helps reduce the costs.

Characteristics of a Data Mart

The following are some of the characteristics of a data mart:

1. End users have read-only access, which helps prevent accidental deleting or modifying of critical business data.
2. Data marts use a dimensional model and star schema and the data is typically queried using SQL commands.
3. The data is highly structured, having been managed by the enterprise data team to make it easy to understand and query.
4. It is designed around the unique needs of a particular line of business or use case.

Understand when to use a data mart vs. data warehouse for efficient data management and analytics.

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

The following table shows some of the key differences between a data lake, a data warehouse, and a data mart:

Feature	Data Lake	Data Warehouse	Data Mart
Definition	A storage system that holds vast amounts of structured and unstructured data in its raw form.	A data management system designed to support business intelligence and analytics for an entire organization.	A subset of a data warehouse focused on a single subject or business function.
Type of data	Structured, semi-structured, and unstructured.	Structured and curated data from multiple sources.	Structured data specific to a department or function.
Schema	Not predefined (schema-on-read)	Predefined before loading (schema-on-write)	Predefined before loading (schema-on-write)
Storage cost	Lower (scalable and uses inexpensive cloud storage)	Higher (optimized for performance and structured querying)	Moderate (smaller in size but structured)
Flexibility	High (can store any data type for future use)	Medium (designed for specific business intelligence use cases)	Low (focused on a particular department’s needs)
Users	Data scientists, developers, and data engineers working on AI, ML, and big data analytics.	Business analysts, data analysts, and decision-makers using BI tools.	Specific business users, department analysts, and managers.
Use case	Data science, real-time analytics, raw data storage.	Company-wide reporting and strategic decision-making.	Department-level reporting and analysis.

You can also explore the differences between data lake vs data warehouse vs data lakehouse.

The below three figures show the data warehouse, data lake, and data mart architecture:

Comparison Between Data Lake and Data Warehouse

Here are four key differences between a data lake and a data warehouse:

1. Storage: A data lake stores all types of raw data, including structured, semi-structured, and unstructured. In contrast, a data warehouse stores only structured, processed data that has been cleaned and transformed.
2. Schema: A data lake applies a schema only when the data is accessed, which is schema-on-read. On the other hand, a data warehouse has a predefined schema (schema-on-write), ensuring better performance and organization, but it requires upfront work.
3. Cost: Storing data in a data lake is generally more cost-effective because it leverages big data technologies, whereas a data warehouse is more expensive due to structured storage and processing requirements.
4. Users: Used by data scientists for AI, ML, and big data analytics. Data warehouses cater to business users and executives who require structured data for reporting and decision-making.

Also, take a look at the comparison between data lake vs data fabric.

Comparison Between Data Lake and Data Mart

Here are four key differences between a data lake and a data mart:

1. Storage: A data lake stores vast amounts of raw data from multiple sources. In contrast, a data mart is a smaller, specialized subset of a data warehouse that focuses on specific business functions.
2. Data structure: Data lakes contain structured, semi-structured, and unstructured data in raw form, without predefined organization. In contrast, data marts store only structured and processed data, optimized for quick access and analysis.
3. Schema: A data lake allows users to store any type of data without knowing its future use, offering high flexibility but requiring additional processing later. A data mart has a predefined schema, so, the query performance is faster, but the flexibility is limited.
4. Users: Data lakes are primarily used by data scientists, engineers, and analysts for advanced analytics and AI/ML projects. Data marts serve business users, managers, and department-level analysts who need quick insights for decision-making.

Comparison Between Data Warehouse and Data Mart

Here are four key differences between a data warehouse and a data mart:

1. Storage: A data warehouse stores large volumes of structured data from multiple sources across the entire organization. A data mart is a smaller, focused version, designed for specific business departments.
2. Data Volume: Data warehouses handle large datasets from various departments and business processes. Data marts, however, deal with smaller, department-specific datasets that are easier to manage and analyze.
3. Data Structure: Data warehouses store cleaned, structured, and integrated data from different sources, organized around key business subjects. Data marts also store structured data, but it is usually derived from the data warehouse and tailored for the needs of a specific function.
4. Users: Data warehouses are used by business analysts and decision-makers who need a holistic view of the organization. Data marts are used by department-specific users who need targeted insights for their specific area of business.

When to Use a Data Lake vs Data Warehouse vs Data Mart?

Large organizations often use a combination of data lakes, data warehouses, and data marts in their storage infrastructure. Usually, all the data is collected in a data lake, then it is distributed to data warehouses and data marts for various business needs. The choice of technology depends on several factors. Some of the factors are listed below:

• Type of data: In the case of relational and structured data such as customer records and business transactions, a data warehouse will be a better choice for storage. If the data is large in volume, it may benefit the organization to create data marts for specific business functions. For example, 
  • The supply chain team could use a data mart to monitor inventory levels and track supplier performance.
  • The human resources department might use another to analyze employee performance metrics and recruitment trends.

• Flexibility: Generally, data lakes provide greater flexibility at a lower cost. This is because data lakes allow different teams to work with the same raw data using various analytical tools and frameworks. The teams can save a significant amount of time as there is no need to predefine data structures, schema, and transformations.

• Cost and Storage Capacity: Data warehouses can efficiently process and store hundreds of petabytes (PB) of structured data. Data lakes provide a cost-effective solution for handling much larger volumes, particularly when dealing with unstructured data like images, videos, and sensor logs.

Conclusion

Choosing between a data lake, data warehouse, or data mart depends on an organization’s data strategy, business needs, and analytical goals. Data lakes provide flexibility and scalability for storing vast amounts of unprocessed data, making them ideal for AI, machine learning, and real-time analytics.

With their structured and curated approach, data warehouses are best suited for business intelligence and reporting. Data marts offer a streamlined, cost-effective solution for department-specific analysis. A well-balanced combination of these storage solutions can help organizations efficiently manage their data and drive informed decision-making.

To seamlessly integrate and manage your data across lakes, warehouses, and marts, you need a reliable data pipeline. Hevo automates data integration, ensuring real-time synchronization and accuracy. With its no-code platform, you can effortlessly move and transform data, making analytics-ready insights more accessible than ever. Sign up for a 14-day free trial today and explore Hevo’s unbeatable pricing to streamline your data strategy!

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