Here are seven key differences between data lakes vs data warehouses:

  1. Data lakes are schema-on-read; data warehouses are schema-on-write access.
  2. Data lakes can store all data types; data warehouses store structured data.
  3. Data lakes have decoupled storage and compute; on-premise data warehouses have tightly coupled storage and compute.
  4. Data lakes are good for general ad hoc queries; data warehouses return immediately usable results.
  5. Data lakes store large sets of data for a long time; data warehouse storage is expensive, so data retention is much shorter.
  6. Data lakes use extract, load, transform as needed (ELT); data warehouses use extract, transform and then load (ETL).
  7. Data lakes are flexible and easy to change; data warehouses are highly structured and can be difficult to change and scale.

A lot of terms get thrown around in the big data space that every business should understand. Many of these terms are easily confused with each other. This is the case with data lakes vs data warehouses. What are some of the most important differences between them, and how can your business use them most effectively for data analytics and data management? Read on to learn the differences between data lakes and data warehouses. 

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Data Lakes vs Data Warehouses

A data warehouse is a repository in which businesses store structured, integrated data. This data is then used for BI (business intelligence) to support important business decisions. While a data lake is also a data repository, it is used to store data from various sources in both structured and unstructured forms.

Many mistakenly think that data lakes vs data warehouses are identical. And they do have a few things in common:

  • Repositories for data storage
  • Can be cloud-based or on-premises
  • Incredible data processing capabilities

Here are some differences between data lakes vs data warehouses in more detail:

Schema-on-Read vs Schema-on-Write Access

A schema is a set of definitions, creating a formal language regulated by the DBMS (the Database Management System) of a particular database. It brings some level of organization and structure to data by ensuring that descriptions, tables, IDs, etc. use a common language that can be easily understood and searched on the web or in a database by most users.

Data lakes save the work of applying schema for when the data is immediately necessary. In other words, as a user is viewing the data, they can apply the schema. Experts call this process schema-on-read. This process is extremely useful for businesses that need to add multiple new data sources on a regular basis. Rather than having to define a schema up front for each, which is very time-consuming, users can define the schema as the data is needed.

This is as opposed to most data warehouses. Users instead apply schema-on-write, which requires time and effort at the beginning of the process of viewing data instead of at the end. Users define the schema immediately before loading the data into the warehouse. Schema-on-write may prevent the use of certain data that cannot be conformed to the schema, and it is best suited to cases where a business has to process a great deal of repetitive data.

This leads directly to the second difference between data lakes vs data warehouses.

Still confused about data lakes vs data warehouses? can help. Contact a team member to learn more about how to move data to a data lake or warehouse of your choice with a seven-day demo. Email with questions!

All Data Types vs Structured Data

People call data lakes as such because they receive data in all different unstructured as well as structured forms from various different sources. Unlike a warehouse, which would tend to have organized packages, it is more similar to a lake, which might receive water from various sources and might, therefore, be at various levels of organization or cleanliness.

Because users access data on a schema-on-read basis, it is unstructured when it enters the data lake. The data may have plenty of text but little or no worthwhile information. Because of this, many users may have a hard time understanding the data before it has been structured. Data warehouses deal only with structured data and exclude most data that does not answer direct questions or deal with specific reports. This means that CEOs, marketing teams, business intelligence professionals, or data analysts can all view and utilize the organized data.

Decoupled vs Tightly Coupled Storage and Compute

Data lakes tend to feature decoupled storage and commute. Data warehouses based in the cloud may also include this important feature. Decoupled storage and computing allows both to scale independently of one another. This is important because in data lakes there may be a considerable amount of data stored that is never processed. Therefore, increasing the compute would often be unnecessary and costly. 

On-premise data warehouses use tightly coupled storage and compute. As one scales up, the other must also scale up. This increases costs since increasing storage is generally much cheaper than scaling both storage and compute at the same time. However, it can also mean faster functionality, which is essential in many situations, especially for transactional systems.

General vs Immediately Usable Data

Because data lakes include all kinds of unstructured data, the provided results are general and often not immediately applicable to business processes. The result is that data scientists and other data experts have to spend a great deal of time sorting through the data lake to find worthwhile information. This general data can be used for analytical experimentation, aiding predictive analytics.

The results from data warehouses are immediately usable and easier to understand. Through reporting dashboards and other methods of viewing organized and sorted data, users can easily analyze results and rapidly use them to make important business decisions.

Long vs Short Data Retention Time

Users can store their data in data lakes for an extended amount of time and companies can refer to it again and again. They will archive some data, but they will not generally delete it as they would from a data warehouse. It may be retained for a short time to 10 years, depending on the legal requirements for the retention of a specific type of data. This may be especially important in research-based or scientific industries that may need to refer to the same data for various different purposes or over long periods of time.

Companies usually only store data in data warehouses for very limited periods of time, at which point users can either transfer it to another repository such as a data lake or destroy it. 


Data lakes use ELT, (extract, load, transfer) whereas data warehouses use ETL (extract, transfer, load). ELT and ETL are both important data processes, but the order of the process changes several things.

ETL brings raw data from source to staging to destination. Data tends to be processed in batches.

ELT instead goes straight from the source to the destination, often in a continuous, near real-time or real-time stream. The destination is where the user then applies the transformation.

Because the transformation involves applying certain security measures and encryption where required, ETL tends to be a more secure method of managing data. This means that data will generally be more secure in a data warehouse than a data lake, which may be essential for such sensitive industries as healthcare. However, ELT offers the kind of near-real-time view of business processes that supports the highest agility.

Data Governance

Because data lakes use ELT, these data management systems load data into a third-party platform (for example, Athena) before transforming that data into a compliant format for analytics. That can raise questions about the validity of data when adhering to data governance principles such as GDPR and HIPAA. Data warehouses, on the other hand, transform data before loading it to an external system, which can help organizations improve compliance. 

Easy vs Difficult to Change and Scale

Data lakes are more agile and flexible than data warehouses because they are less structured. Developers and data scientists can alter or reconfigure them with greater ease. When data sources and volumes are constantly changing, this may be essential.

Data warehousing providese highly structured repositories for data, making them considerably less easy to change. They may require a lot of time and effort to substantially re-structure. This also means that they are ideal for performing repetitive processes and building data pipelines.

Many well-known data software providers offer excellent and cutting-edge technology for data lakes vs data warehouses.

Still deciding between data lakes vs data warehouses? lets you connect to both of these data management systems so you can centralize your data and run it through business intelligence tools for deeper data insights. Start your demo!  

Read on to learn about popular data lakes vs data warehouses.

Popular Data Lakes


Amazon Athena works together with Amazon S3 as an ideal data lake solution. Athena provides the ability to run queries and analyze data from data lakes on a serverless basis. Users can start querying immediately using standard SQL, without ETL.

Built on Presto, Athena performs well and is reasonably fast, even when dealing with large datasets. It uses machine learning algorithms to simplify normally extensive tasks, making it an excellent option for data-based businesses.

Microsoft Azure Data Lake

Microsoft developed a data lake solution, built on Azure Blob Storage. The cloud data lake is highly scalable and features massive storage capabilities. Azure includes advanced security measures, one of which is tracking possible vulnerabilities. Additionally, they offer unusual help to developers through deep integration with Visual Studio and Eclipse. This enables developers to use their accustomed tools while working with Azure.


Popular Data Warehouses


Amazon Redshift is a comprehensive data warehouse solution. Over 10,000 different customers use it, including such companies as Lyft, Yelp, and the pharmaceutical giant Pfizer, among many others. 

Amazon Redshift offers materialized views that users can maintain incrementally. It comes with advanced machine learning algorithms and the ability to run a nearly unlimited number of queries concurrently. By running automated backups and offering native spatial data processing, Redshift is capable of surpassing most other similar solutions and offering businesses a secure data warehouse.


PostgreSQL is better known in many circles as simply Postgres. Postgres is a relational database management system (RDBMS) offered as an open-source solution. It also functions as a low-cost data warehouse solution. The creators focused on helping developers build applications and aiding businesses in protecting their data.

Postgres has a unique feature that permits developers to write code in different coding languages without having to recompile a database.

Data Lakes vs Data Warehouses: How Helps is a data integration solution that lets you move data from various sources to multiple data lakes vs data warehouses. The platform integrates data using various methods such as ETL, ELT, ReverseETL, CDC, and API management. So you can move data to a new destination, transform data to the correct format for analytics, manage APIs, track changes to data in databases, and not have to deal with complicated code. The result? You can improve data integration without the stress. has out-of-the-box connectors for some of the most popular data lakes vs data warehouses, helping you transform your data integration strategy. Plus, you can benefit from live chat support, excellent documentation, and a unique pricing model that charges you for the number of integrations you use and not the amount of data you consume. enables businesses to perform data integration securely and quickly. Schedule a demo to experience the platform for yourself and move data to data lakes vs data warehouses