Data Warehouse vs Data Lake

Data Lake

Data Warehouse vs Data Lake

Data Warehouse vs. Data Lake: What Is the Difference?

What Is a Data Lake? What Is a Data Warehouse?
A data lake is a large repository for storing raw data in the original format before a user or application processes it for analytics tasks. It is better suited for unstructured data than a data warehouse, which uses hierarchical tables and dimensions to store data. Data lakes have a flat storage architecture, usually object or file-based storage, giving users greater flexibility when storing, using, and managing data.

In the past, many organizations used the Hadoop Distributed File Systems to deploy a data lake using a distributed processing framework. The data resides across separate nodes in Hadoop clusters. However, the modern alternative to Hadoop is to build a data lake on a cloud-based object storage service like Amazon S3. Some organizations also use a NoSQL database such as MongoDB as the platform for their data lake.

A data warehouse is a database that stores structured data collected from different sources. Data warehouses are often part of an organization’s data management strategy, emphasizing capturing and collecting data from multiple sources. End-users such as data scientists and business analysts can access this data directly for analysis.

The most common type of data warehouse is a relational database hosted on an enterprise server such as a mainframe or a cloud platform. Organizations often select and consolidate data from different online transaction processing apps and other sources to provide actionable information for business intelligence (BI) tasks such as enterprise reports, ad hoc user queries, and decision support.

Data warehouses are also useful for online analytical processing technologies that organize information into data categories based on dimensions to support faster analytics processes.

Data Warehouse vs. Data Lake: Key Differences

Here are some main areas that differentiate between data lakes and data warehouses.


  • Data lakes have an architecture designed for cost-effective storage. They retain all the native formats regardless of the data’s structure and source. Data remains in its raw format until someone transforms it for use in an application. Data lakes store all data types, including currently used and unused data. It serves as an archive for data that may potentially be useful in the future.
  • Data warehouses are a more expensive option for storing large volumes of data. They store processed data extracted from various transactional systems that have already transformed and cleaned the data. Data warehouses usually only store data that serves a specific business purpose; they don’t contain data without a current use. They are also time-consuming because someone has to process all the data before storing it in a warehouse.


  • Data lakes have a schema applied by the query. While entering data objects into a data lake is easy, retrieving the data can be more complicated and time-consuming, given the need to write queries.
  • Data warehouses have a rigorous schema, so loading data into them is more complex. However, once in the data warehouse, the data is easy to retrieve.


  • Data lakes are suited for users that need to retain large amounts of data for deep analytics tasks. The data’s volume, complexity, and lack of structure often require an advanced analysis tool, typically accessible to data scientists, engineers, and analysts. For example, data specialists often have big data tools that allow them to process large, diverse data sets and perform various processing and analysis tasks.
  • Data warehouses are more suitable for operational users and non-specialists because the data is easier to understand and use. They contain already cleaned and transformed data often arranged to make it easy to locate data for specific business questions.


  • Data lakes are highly flexible because they support all data formats and are easily accessible to users. Teams can use innovative methods to find and query data to answer business questions. Data lakes are also more scalable, as there is no structure restricting data storage (as in a data warehouse). If an organization wants to repeat an action, it can easily automate the processes and apply a formal schema. Otherwise, exploring the data is low-cost because it doesn’t require adjusting the data structure.
  • Data warehouses are noticeably inflexible. Many users complain of the difficulty in changing them. Teams spend significant time in the up-front development process, trying to configure the right data warehouse structure. However, a well-designed data warehouse should adapt to changes. Making changes to the warehouse inevitably consumes developer time and resources, given the necessary workload to enable reporting and analysis and the complexity of loading data to the warehouse.

Types of Data

  • Data lakes use a schema-on-read approach and support all data types, including from conventional and other data sources. They can store any object regardless of its structure or source, only requiring transformation when used in a specific application.
  • Data warehouses use a schema-on-write approach and usually contain data collected from transactional systems, with attributes and quantitative metrics to describe objects. They rarely support other data sources like sensors, web server logs, social media activity, images, and text. These non-traditional data types are becoming increasingly important for various use cases, but they remain difficult and costly to store and consume in a data warehouse.


  • Data lakes often produce faster results because users can access data before the cleansing, transformation, and structuring process. The flip side of this fast access is that development teams and analysts may have to process the data themselves, often resulting in a time-consuming process overall. In other words, teams can access the data quickly, but the data is not always immediately useful. Users are responsible for exploring and transforming data, which can be a problem for business teams with little technical knowledge.
  • Data warehouses are slower in the initial phase because teams cannot simply dump raw data into them. However, they are often faster for teams and users that want to produce reports and perform business analysis tasks. The structured layout of a data warehouse is often easier for business teams to navigate, but this rigidity can also become an issue when developers want to adjust the structure.

Related content: Read our guide to Data Lakehouse.

Data Lake vs. Data Warehouse: Which to Choose?

Data consumer needs should always be the basis of deciding whether to use a data warehouse or a data lake.

For example, business users may be familiar with SQL and require an easy way to access specific data sets for queries and reports—a data warehouse is a suitable choice for these use cases. However, data warehouses are an overall more expensive solution than data lakes. Organizations that use a data warehouse must also consider the difficulty of changing the data properties or types.

On the other hand, some organizations need a way to store massive data sets with semi-structured or unstructured data—a data lake is an obvious choice for these use cases, especially if the data is not immediately necessary for a query. Data lakes offer cheaper storage, making them useful as archives for cold storage that might not have a use.

A data lake is useful for frequently changing data structures because it simplifies the schema.

In the real world, many organizations use both a data lake and a data warehouse to store different types of data for different use cases. Organizations can start by placing data in a data lake before processing and moving it to the data warehouse to make it available to business users.

Read more in our detailed guide: Data Warehouse vs Data Lake

Data Storage and Management with Cloudian

Regardless of which solution you choose you are likely to have data that is less frequently, if ever, accessed, consuming valuable space. Cloudian allows you to store this less used but no less valuable data at a reduced price on appliances that are scalable and integrate with existing NAS and cloud services.

Cloudian solutions are transparent to users and don’t affect user ability to access data. If migrated data is required, it is returned to the desired location automatically, eliminating loss of productivity caused by manual transfer or wait time. Your data is secured with integrated data protection either on-site or across sites managed from a central location, facilitating offline and disaster recovery.

Cloudian is not an all-inclusive solution, it is meant to complement your strategy and ensure you are fully protected as cost-efficiently as possible.

Learn how to lower your storage total cost of ownership with our TCO calculator.

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