AI and Machine Learning for networks

3 January 2023

Data engineering

Data lake vs. data warehouse—differences in data management

13 minutes reading

Data lake vs. data warehouse—differences in data management

The day-to-day activities of any organization bring in a lot of information. This data needs to be tracked, stored, and eventually analyzed to allow the business to learn and grow. The bigger the organization, the more data it has, so where a simple database used to be enough for data storage, increasingly often new solutions are required.

When it comes to managing big data, i.e. vast amounts of data from multiple data sources in multiple formats, two solutions have become very popular over the years: data warehouses and data lakes. To make an informed decision on which solution suits your own company better, it is necessary to understand what each solution entails and how the two are different from each other.

An image of a data engineer analyzing data. Text says: "Make your data truly valuable"

Data warehouse - brief overview

Before we provide a data warehouse definition, let’s take a look at what a database is first.

A database is a data repository that, as a rule, stores information from a single source. It can be information about one single department in an organization or the data generated by a single application. The data in a database is structured, which makes it easier for business analysts to search for and analyze specific information but requires processing the data according to a certain schema before it can be stored. In a way, a data warehouse is just an enormous database. The data in a data warehouse comes from disparate sources and is unified and optimized, which is perfect for data analysis and business intelligence purposes.

Originally, data warehouses required a lot of physical storage space for hosting, but these days cloud data warehouses are becoming the preferred option. Storing big data in the cloud guarantees scalability and lowers costs compared to “traditional” data warehouses.

Data lake at a glance 

A data lake, just like a data warehouse, is a repository for data from multiple sources. However, in a data lake you can store structured, semi-structured, and unstructured data, all in its raw format. Unlike in a data warehouse, data doesn’t need to be structured before it is stored and can stay in its native format. 

As a result, data in its raw form can be stored in a data lake faster, allowing storage of real-time data and making it easier to keep it up-to-date; plus no extra planning is required. Structure is applied to the information only when it needs to be accessed, which means special tools and skills are necessary to analyze the data and gain insights. 

Where most business users can get a report from a data warehouse, you might need a data scientist to do the same with a data lake. So, while you save money when storing data, you might spend more on keeping around specialists to make it useful. The results could be much more valuable though, due to being less expected. 

Data warehouse vs. data lake

To choose the best solution for your own data management needs, it is important to understand all the differences between a data warehouse and a data lake. These solutions are not interchangeable, so let’s see what exactly you get with each of them.

Data types

Data warehouses take structured or semi-structured data from traditional data sources like transactional systems. Data lakes can store unstructured data. So if you need to store raw data like text, images, sensor data from IoT devices or logs from a server, it is far easier to use a data lake for this purpose. With a data warehouse it is not completely impossible, but much more complicated. 

Data storage

Traditionally, the approach to accepting data is not the same for a data warehouse as it is with a data lake. A data warehouse defines the schema for the data beforehand, so only data that meets the pre-defined requirements and can potentially serve a specific purpose is accepted for storage. This is how the “schema on write” approach works. 

Data lakes use a different approach, named “schema on read”, which means all data is accepted, even if it is potentially not useful. A schema is only created for a specific use case at the time when a request for the data is made.

Different approaches also determine when most computing resources are engaged; for data warehouses it will be the moment when data is stored, for data lakes, when it is processed for a query. 


The way data warehouses and data lakes store data directly impacts the price of maintaining them. For data warehouses, the setup process is more expensive because you need to make a lot of decisions and carry out some pretty significant preparations before you can actually start using the storage. It is necessary to develop specific data models that will determine what kind of data will be accepted into the data warehouse and how it should be organized to facilitate quick reporting and analysis. However, since not all data is preserved, it reduces the costs of disk storage.

A data lake keeps all the data that goes into it, so it might seem it should be extremely expensive because its size can quickly be measured in petabytes. But since there is no need to process the data before it gets into the lake, the hardware required for storage is vastly different and much cheaper.

Data lake accessibility also contributes to saving on costs because you are not limited to certain pre-defined reports when exploring the raw data. A data warehouse is set up to provide quick reports and data analytics, but all the work that goes into organizing the storage process means if you want to start asking new questions, you have to wait for the developers to implement some design changes first. 


It takes a lot of work to develop a data warehouse, but when it is all set up, any user can generate a report with specific parameters out of a complex dataset in no time. For example, it works especially well for the members of the operations team, who are interested in key metrics or visualizing data. Unified and processed data from disparate sources makes it easier for business analysts to uncover trends and helps to facilitate BI processes, because it provides a complete picture.

All of this is possible when working with a data lake as well, but since the data there is not standardized beforehand, sorting through it becomes a complex task. Regular end users won’t be able to find answers without special tools and/or skills, since they will have to look through metadata rather than clearly structured tables. However, raw data that is not pre-processed allows data analysts to go above and beyond in search of business insights. Data in various formats from multiple sources is perfect for machine learning too.


Data warehouses are historically equivalent to relational databases, which allow quicker process queries but are more expensive to update with real-time data. This is not true for modern cloud data warehouses though. Traditional ETL (extract, transform, and load) pipelines are replaced with more modern ELT processes. 

Read more about ETL vs. ELT - what are they and when do we need them? 

Data warehouses hosted in the cloud become more agile and scalable, not to mention that special data management tools (like Google Big Query or Azure Synapse Analytics) save a lot of time and money.

The popularity of data lakes is directly related to the wider spread of cloud computing and big data technologies like the open-source Hadoop ecosystem, which is an easily adaptable and scalable distributed file system. If you are not feeling very secure about open-source solutions though, there are many other options for cloud data lake management from the biggest service providers, for instance, Google Cloud Storage or Azure Data Lake.

Read our article to learn more about data engineering tools.

Making the choice between data lakes vs. data warehouses

Despite having some key differences, data warehouses and data lakes are not mutually exclusive. Very often the same organization might need both solutions implemented simultaneously, but for different purposes. 

Still, using a data warehouse together with a data lake in one company can lead to unnecessary expense, potential issues with security, and duplication of data. So a couple of years ago, the data lakehouse was introduced. This new data management architecture offers both the advantages of structured data and lower costs for big data storage. 

A data lakehouse is a single data repository that allows storing any type of data, managing several data pipelines, and applying a schema to large datasets. It also doesn’t have the potential for the above-mentioned issues, since it doesn’t involve managing several solutions in parallel.

So when you need to determine which data management solution to adopt for your business, consider your own use cases. If you need to store unstructured data along with structured data, and your data scientists or machine learning specialists need to work with raw data, it is possible a data lake will work better for you. 

If your data is highly structured and complex, but you need to allow many business users from your organization to access it quickly to build reports and leverage BI activities, then a data warehouse might be a more appropriate choice. 

Huge enterprises with vast amounts of data from various sources might employ both solutions or look into implementing the cutting-edge lakehouse architecture. The variety of available solutions guarantees you will find something that will serve your particular business purposes best.

See how we help data-oriented companies with our data engineering services.


Maciej Manturewicz

Director of Engineering