Data storage is a big deal. Considering how important big data collection is to the success of a business, it’s mandatory for businesses to invest in data storage. Data lakes and data warehouses are both extensively used for big data storage, but they are very different, from the structure and processing to who uses them and why. In this article, we’ll focus on Data Lake Vs Data Warehouse — the differences between the two types of data storage to help you decide how to manage your data better.  

Before directly jumping to Data Lake Vs Data Warehouse, let’s discuss them one by one.

What is a Data Warehouse?

A Data Warehouse is a large repository of organizational data accumulated from a wide range of operational and external data sources. The data is structured, filtered, and already processed for a specific purpose. Data warehouses periodically pull processed data from various internal applications and external partner systems for advanced querying and analytics. 

Medium and large-size businesses use data warehouse basics to share data and content across department-specific databases. The purpose of a data warehouse can be to store information about products, orders, customers, inventory, employees, etc. 

End-users of a data warehouse are entrepreneurs and business users.

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What is a Data Lake?

A data lake definition explains it as a highly scalable data storage area to store a large amount of raw data in its original format until it is required for use. A data lake can store all types of data with no fixed limitation on account size or file and with no specific purpose defined yet. The data comes from disparate sources and can be structured, semi-structured, or even unstructured. Data-lake data can be queried as needed. 

Businesses that need to collect and store a vast volume of data — without needing to process or analyze all of it immediately — use the data lake concept for quick storage without transformation.     

End-users of data lakes are data scientists and engineers.   

Now , let’s understand the types of Data Lake Vs Data Warehouse 

Types of Data Lake Vs Data Warehouse

Let’s first discuss the types of Data Lake.

Types of Data Lake can be:

Structured – containing structured data from relational databases, i.e., rows and columns

Unstructured – containing unstructured data from emails, documents, PDFs 

Semi-structured – containing semi-structured data like CSV, logs, XML, JSON

Binary – containing images, audio, video

Top 6 Difference Between Data Lake and Data Warehouse 

  • Data Structure 

A data warehouse can only store data that has been processed and refined. Data lakes, on the other hand, store raw data that has not been processed for a purpose yet. Therefore, data lakes require a much larger storage capacity than data warehouses; the data is flexible, quickly analyzed, and perfect for machine learning.   

  • Processing

A data warehouse uses a schema-on-write approach to processed data to give it shape and structure. A data lake uses schema-on-read on raw data to process it. 

  • Cost

Storing in a data warehouse can be costly, particularly if there is a large volume of data. A data lake is a cheaper option designed for low-cost data storage. This explains why data lake is preferred by many companies.  

  • Purpose

Data warehouses only hold processed data that has been used for a specific purpose. One of the benefits of a data warehouse is that storage space is not wasted on data that may not be used. Data lake stores raw data that can sometimes have a specific future use and sometimes just for hoarding. Hence, data is less organized and filtered in the data lake. 

  • Users

Data warehouses are used mostly by IT or business professionals who are familiar with the topic represented in the processed data used. The unstructured data in data lakes usually require data scientists or engineers for organizing data lakes before putting the data to use. 

  • Accessibility

Data warehouses are structured by design, making them difficult to access and manipulate. In contrast, data lakes have few limitations and are easy to access and change. Data can be updated quickly. This counts as one of the key data lake benefits.

There Are Three Main Types of Data Warehouses

Enterprise Data Warehouse (EDW) 

This type of data warehouse acts as the main database that aids in decision-support services within the enterprise. EDW offers access to cross-organizational information, an integrated approach to data representation, and can run complex queries. 

Operational Data Store (ODS)

ODS refreshes in real-time and is used to run routine tasks, including storage of employee records. Data stored here can be scrubbed, and redundancy checked and resolved. It can also be used to integrate contrasting data from various sources so that business operations, analysis, and reporting can run smoothly. 

Data Mart

A data mart is a subset of the data warehouse as it stores data for a particular department, region, or unit of a business. Data mart helps increase user responses and reduces the volume of data for analysis. Data from here is stored in the ODS from time to time. The ODS then sends it to the EDW, where it is stored and used.   

Data Warehouse Technologies Vs Data Lake Technologies

Data Warehouse technologies are aligned with relational databases because they excel at high-speed queries against highly structured data. Relational databases are continually evolving to make data warehouses faster, more scalable, and more reliable.  

Big data technologies like Hadoop Distributed File System (HDFS) are used to boost the impact of Data lakes on analytics. HDFS shows easy adaptability and scalability for vast volumes of data of any type of structure. Plus, Hadoop supports data warehouse scenarios by applying structured views to raw data. This flexibility makes Hadoop an excellent choice for providing data and insights to every tier of business users.

Many companies like Amazon (Amazon S3), Microsoft (Azure Data Lake), and Google (Google Cloud Storage) are offering on-the-Cloud managed services for storage technology in Data Lake management.  

Those were the types of Data Lake Vs Data Warehouse. Moving forward, let’s discuss the tools differences between Data Lake Vs Data Warehouse.

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Data Lake Tools

Top-rated data lake tools are:

  • Azure Data Lake Storage – creates single, unified data storage space. The tool offers advanced security facilities, accurate data authentication, and limited access to specific roles. Ideal for large scale queries  
  • AWS Lake Formation – provides a very simple solution to set up a data lake. Seamless integration with AWS-based analytics and machine learning services. The tool creates a meticulous, searchable data catalog with an audit log in place for identifying data access history. 
  • Qubole – this data lake solution stores data in an open format that can be accessed through open standards. Key features include the provision of ad hoc analytics reports, combining data pipelines to offer unified insight in real-time.
  • Infor Data Lake – collects data from different sources and ingests into a structure that immediately begins to derive value from it. Data stored here will never turn into a swamp due to intelligent cataloging. 
  • Intelligent Data Lake – this tool helps customers to gain maximum value from Hadoop-based Data Lake. The underlying Hadoop system ensures users don’t need much coding for running large-scale data queries. 

Due to all these differences, organizations often need both data lakes to harness big data while still needing data warehouses for use in analytics.  

Data Warehouse Tools

One of the key factors in Data Lake vs Data Warehouse is the choice of tools and software. 

Here are some of the best data warehouse tools that are fast, easily scalable, and available on a pay-per-use basis. 

  • Amazon Redshift – a cloud data warehousing tool that is excellent for high-speed data analytics. This data warehouse example can execute numerous concurrent queries without any operational overhead. 
  • Microsoft Azure – it is a node-based platform that allows massive parallel processing, which helps extract and visualize business insights much quickly.
  • Google BigQuery – this data warehousing tool can be integrated with Cloud ML and TensorFlow to build powerful AI models. 
  • Snowflake – it allows the analysis of data from various structured and unstructured sources. It consists of a shared architecture, which separates storage from processing power. As a result, users can scale CPU resources according to user activities.  
  • Micro Focus Vertica – this SQL data warehouse is available in the cloud on platforms including AWS and Azure. It offers built-in analytics capability for machine learning, pattern matching, and time series.   
  • Amazon DynamoDB – the scalable DynamoDB can scale querying capacity up to 10 or 20 trillion requests in a day over petabytes of data. 

That was all about Data Lake vs Data Warehouse

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