Data Lake vs Data Warehouse vs Data Mart - Difference Between Cloud Storage Solutions - AWS

The Growing Importance of Data in Business Decision-Making

Data warehouses and data lakes are two separate, yet fundamentally different technologies. Unlike structured data warehouses, data lakes can hold consolidated information at any size. Unlike data warehouses data lakes provide more storage options; the complexity of data storage is much more complex. The main differences are listed below.

Introduction to Data Lakes and Data Warehouses

In the realm of big data, data lakes and data warehouses are two crucial concepts that provide centralized repositories for storing data. These storage solutions are used to store, process, and analyze data, but they serve different purposes and cater to different business needs.

A data lake vs data warehouse is a vast pool of raw data, the purpose for which is not yet defined. It stores data from various sources in its raw form, unprocessed and unstructured. With the growing volume of big data from web server logs, sensor data, and other forms of raw data, data lakes are gaining immense popularity. They can store all types of data, be it structured, semi-structured, or unstructured data, making them highly versatile.

On the other hand, a data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose. Data warehouses are the backbone of business intelligence, providing structured and refined data necessary for business operations, and they allow business users to generate reports and perform online analytical processing. The data stored in data warehouses is typically processed and collected from various business data sources.

Key Differences Between Data Lakes and Data Warehouses

To comprehend the key differences between a data lake vs data warehouse, it is essential to look at various factors like data structure, data quality, data storage, and data users:

Type of Data: Data warehouses only store structured data, while data lakes can store both structured and unstructured data.

Data Quality and Processing: Data stored in data warehouses is cleansed, transformed, and categorized, ensuring high data quality and integrity. In contrast, data lakes store raw data in its original form. Users: Business analysts typically access data warehouses for their queries and reports, while data lakes are often accessed by data scientists and data engineers who require raw, unfiltered data for their advanced analytics and machine learning experiments.

Storage Space and Cost: Storing raw data in data lakes requires a massive amount of storage space, but it is typically cheaper than data warehousing options.

Understanding Data Warehouses

The concept of the data warehouse dates back to the 1980s and has evolved over time. Traditional data warehouses, or enterprise data warehouses, were on-premises systems built using relational database technologies. They served as centralized repositories where data from multiple sources was collected, processed, and stored for later use.

Data warehouses have a highly structured data storage system. The data structure within a data warehouse is designed to optimize query performance and ensure data integrity. This structure allows the enterprise data warehouse to support the current and historical data needed for trend analyses and reporting purposes.

Understanding Data Lakes

While data warehouses have been around for a while, the term data lake is relatively new to the big data landscape. Coined around 2010, it refers to a large storage repository and processing system that, in contrast to a data warehouse, holds a vast amount of raw data in its native format until it has processed data that's needed. These raw data lakes support the storage of unprocessed data, thus retaining all the details that might get lost when data is processed for storage in data warehouses.

Data lakes offer a more cost-effective solution for storing a colossal amount of data. They can also handle the speed, volume, and diversity of big data technologies, managing everything from structured to unstructured data, as well as machine data like web server logs and sensor data. The flexibility of a data lake architecture allows data engineers and data scientists to perform different types of predictive analytics – from dashboards and visualizations to big data analytics, real-time analytics, and machine learning to guide better decisions.

Data Lake Architecture

The architecture of a data lake consists of several key components:

Data Ingestion: Data lakes collect data from multiple sources, in various formats, at different speeds. The collected data could be structured, semi-structured, or unstructured.

Data Storage: Data lakes store raw data, help in maintaining the data engineer its native format until it is needed. This feature allows the storage of data as-is, without the need to understand what insights the data may reveal.

Data Processing: When required, data engineers or data scientists can explore the raw data in the lake, process it, and extract valuable insights.

Data Governance: Data lakes require robust governance strategies to ensure data quality and data integrity, given the diverse nature of data they hold.

Data Lake vs Data Warehouse: A Comparative Analysis

Use Case

The most effective way to understand the difference between a data lake vs a data warehouse is by considering their use-cases. For operational purposes, where structured data is used to generate reports, conduct current and historical data analysis, and gain insights on business operations, data warehouses are the ideal choice. However, for exploratory purposes, where unstructured or semi-structured data is analyzed for machine learning or predictive analytics, data lakes prove to be a superior choice.


Data lakes provide a higher degree of flexibility than data warehouses. With a data warehouse, to include new data sources or change the structure of the data, it often requires a considerable amount of work to redesign the data model and ETL processes. In contrast, data lakes can easily ingest new data sources without significant architectural changes.

Schema Design

Data warehouses use the schema-on-write, where data is organized and structured when it's written into the warehouse. However, data lakes use schema-on-read, where data remains in its raw form until someone wants to use it, providing more flexibility for data scientists and business analysts.


Data warehouses are highly optimized for quick data retrieval and are thus ideal for business users looking to perform OLAP (Online Analytical Processing) operations. In contrast, data lakes, due to their raw, unprocessed nature, require more time and effort to retrieve data but provide a deeper level of data analysis too.

Combining Data Lakes and Data Warehouses

Given the key differences between data lakes and data warehouses, many enterprises are realizing the value of using both in their big data strategies. By using an existing data warehouse alongside a data lake, organizations can benefit from the best of both worlds. Raw data can be stored cost-effectively in a data lake and used for detailed analytics and machine learning. At the same time, business-critical structured data can be stored in a data warehouse for more immediate business intelligence needs. This allows companies to operate with agility while maintaining critical business operations.

Role of Azure Data Lake in Data Storage

Azure Data Lake, Microsoft's data lake offering, takes this hybrid approach a step further. It integrates multiple big data analytics capabilities into one cohesive solution. It provides vast storage and analytic capabilities, allowing businesses to store all their data, structured and unstructured, in a centralized repository while giving them the ability to run diverse analytic workloads on that data.

Azure Data Lake allows you to analyze large amounts of data using familiar tools like Azure Data Lake Analytics, HDInsight (Apache Hadoop, Apache Spark), and Azure Machine Learning. With its schema-on-read capabilities, Azure Data Lake makes it easy to process and analyze data from multiple sources without worrying about the complexity of the data structure.

The Rise of Data Lakehouses

One of the newest trends in big data management is the emergence of the data lakehouse. This architectural paradigm merges the best aspects of data lakes and data warehouses into a unified platform. It retains the low-cost storage of data lakes for raw and unprocessed data while incorporating the performance, data quality, and business semantics layers of a data lakehouse and warehousing. This new structure enables businesses to access data for a variety of analytics workloads, including data science, machine learning, and business intelligence.

Data lakehouses provide the ability to handle all types of data – structured, semi-structured, and unstructured – at any scale. They also support both batch and real-time processing, empowering businesses with more agile data management capabilities.

Data Lakehouses vs Data Warehouses

While data lakehouses incorporate many features of data warehouses, they differ in a few key aspects:

Data Types and Structure: Unlike data warehouses, which primarily deal with structured data, data lakehouses can handle a diverse range of data types – structured, semi-structured, and unstructured. They also retain data in its raw form, like data lakes, allowing for more versatile data exploration and analysis.

Storage and Computation: While data warehouses use a proprietary storage format, data lakehouses use open formats, thereby reducing storage costs. They also separate storage and computation, allowing companies to scale each independently based on their needs.

Governance: Data lakehouses incorporate stronger governance capabilities than data lakes, ensuring higher data quality and integrity.

Understanding Data Marts

Data marts are yet another integral component of the data management ecosystem. These are subsets of data warehouses that provide a layer of data abstraction for specific business units or functions. Data marts are designed to cater to a specific business need or department, such as finance or sales, and typically contain refined data relevant to these domains. They can enhance business intelligence efforts by providing business users with a tailored view of their data, making it easier for them to analyze and generate reports.

Data Marts vs Data Warehouses and Data Lakes

The concept of data marts is often compared with data warehouses and data lakes. However, each serves a distinct role in managing enterprise data.

Data mart Scope: Unlike data warehouses that store a wide range of enterprise data and data lakes that store raw data from various sources, data marts are more focused. They store and manage a subset of data specific to a particular department or business function.

User Group: Data marts are primarily used by business analysts and business users for generating reports and conducting analysis specific to their department. In contrast, data warehouses are used by business users across an organization, while data lakes are primarily used by the data scientists business analysts and data engineers.

Data Type and Structure: Data marts, like data warehouses, primarily deal with structured data. However, data warehouses and data marts use a relational database, while relational data can lakes store unstructured and semi-structured data along with structured data.

Best Practices for Designing Data Lakes

Implementing a data lake solution can be a complex undertaking. It requires careful planning and design to ensure it meets the organization's needs. Here are some best practices for designing data lakes:

Establish Clear Objectives: Understand the business problems you want to solve with your data lake and establish clear objectives. This will guide the design and implementation process.

Maintain Data Quality: Ensure that the data ingested into the data lake is of high quality. Data quality tools can help maintain the integrity of the data.

Implement Robust Security: Implement strong security measures to protect sensitive data. This includes access controls, data encryption, and regular security audits.

Optimize for Performance: Design the data lake to optimize query performance. This includes proper data partitioning and indexing.

Plan for Scalability: Data lakes must be able to scale as data volume grows. Consider the future data needs of the organization when designing the data lake.

By understanding the distinct roles and capabilities of data warehouses, data lakes, and data marts, businesses can make informed decisions about which data storage solutions are best suited to their needs. These technologies, used individually or in combination, can empower organizations to derive valuable insights from their data, leading to improved decision-making and business outcomes.


Question1: What's the distinction between a data lake and a data warehouse?

Data that store data lakes generally store unprocessed raw data, usually including multimedia and data logs. On the other hand, data warehouses primarily store structured and processed data, typically composed of text and numbers.

Question2: Is data lake replacing the traditional data warehouse?

Data lakes are not substitutes for data warehouses; they are complementary technologies serving varying needs with some overlap. Many data-driven companies utilize both data lakes and data warehouses for comprehensive data storage systems.

Question3: Can Snowflake be considered as a data lake?

Snowflake can function as a hybrid of both Data Warehouse and Data Lake. It allows the creation and management of data lakes in internal stages, offering a flexible database storage solution.

Question4: What sets apart a database from a data lake?

Contrary to traditional databases that store only structured data, data lakes permit storage of data files in their original form. This new model provides maximum flexibility in the types of data that can be stored, and facilitates easy transport of data between storage sites.

Question5: How do data lake and data warehouse resemble each other?

Data Lake has some similarities to data warehouses; they both serve as storage repositories. They offer cloud-based storage and can house structured data. Both keep historical data and current data on the same storage site.

Question6: What is the difference between data visualization and data lake?

Data Lakes typically store data in unstructured formats. Business users might be interested in analyzing the data, but traditional BI tools, built for relational data, often don't support analyzing data stored in an unstructured format.

Question7: What are the roles of a data lake and a data warehouse in an organization?

Data warehousing can be a source of truth within a company as it stores historical company data that's been cleansed and organized. In contrast, data lakes usually gather data from numerous different sources.

Question8: Is Snowflake a data warehouse, a data lake, or both?

Snowflake functions as a hybrid of both a data warehouse and a data lake. It offers the benefits of both, providing optimal data storage solutions for enterprises with a keen focus on data security.

Question9: Will cloud computing continue to grow in the future? Why or why not?

Many businesses use cloud storage platforms such as Google Cloud Storage or Amazon S3, or distributed file systems like Apache Hadoop distributed file systems (HDFS) as their data lakes.

Question10: What's the role of a data warehouse in OLAP or OLTP?

Data warehouses serve as examples of OLAP (Online Analytical Processing) systems, which support the analysis of information. In contrast, OLTP (Online Transaction Processing) systems manage short online transactions in various environments.