Data Lake vs Data Warehouse: Where and How to Store Your Data

It’s needless to say how much modern businesses depend on the data which allows them to form accurate decisions and build solid development strategies. But in order for that data to be of maximum value, companies need to take proper care of its storage and organization. This is where the battle of data lake vs data warehouse comes into play – but how do you know which one will be the best for your business?

In this article, we will look at data lakes, data warehouses, and even data marts and will give a comprehensive overview of each data storage solution.

Data Lake vs Data Warehouse: Where and How to Store Your Data

What is a data lake?

A data lake is exactly what the name implies – it is a repository that stores structured, unstructured, and semi-structured data. In other words, data lake stores data in its raw format and it collects data from the most various sources (i.e. social media, CRM systems, etc.). Think of it as a huge pool where the data floats freely and is completely unlabelled and unstructured. 

Data lake works by the ELT (Extract – Load – Transform) approach which means, the data is first extracted from a source, then loaded into the storage, and only then is processed upon the request. The data is also updated in real-time and every data element is assigned a unique identifier and tagged with a set of metatags. This is needed for easy data lake querying and easy data search.

The main things to remember about a data lake are:

  • Flat architecture is used to store the data;
  • Data is kept for all time, meaning, data lake stores not only the data for immediate use but also data for future use and historical data;
  • The schema is defined after the data is stored;
  • All data and data types can be stored in a data lake.

The pros and cons of a data lake

If you are not convinced that a data lake can bring you certain benefits, let’s have a look at some of its biggest pros. And obviously, we will look at the cons as well because you need to know both the good and the bad of this data storage type.

The main pros of a data lake:

  • Unlimited scalability and effortless horizontal scaling;
  • High flexibility that allows you to create various environments (i.e. microservices or heterogeneous environments);
  • Great integration with machine learning systems and Internet of Things;
  • Support for complex algorithms (such as deep learning).

The main cons of a data lake:

  • Complex to search through and sort the data;
  • A high chance of turning into a data swamp if not managed properly;
  • Security risks due to possible data access control issues and potential leaks of sensitive data;
  • Possible issues with data integrity due to the lack of transaction control.

The most common use cases of data lakes

For now, you might be thinking that a data lake is a rather messy way to store data. However, it is a real gem for data scientists and data engineers as raw data can be fed to machine learning models for making accurate future predictions. Here are the most popular data lake use cases that you may not be aware of:

  • Smart cities: since IoT is the heart and soul of any smart city, it integrates perfectly with a data lake. A data lake, in turn, allows real-time data updating and collection as well as storage of massive data amounts that are constantly generated by IoT devices.
  • Healthcare: this industry can benefit a lot from using data lakes since they provide access to real-time insights and store unstructured data that is collected from wearables.
  • Marketing: since data lakes store a vast amount of data related to customers and their behavior and preferences, marketers can benefit a lot from such data by using it to create hyper-personalized campaigns.
  • Transportation: because data lakes integrate perfectly with machine learning, this allows companies within the transportation industry to implement predictive maintenance and reduce costs significantly.

Actually, a data lake can benefit an organization within any industry – but what if you need your data neat and structured? 


It’s time to talk about data warehousing.

What is a data warehouse?

A data warehouse is a repository that stores structured relational data. Before being loaded into a data warehouse, the data is cleansed and categorized. So, unlike a data lake, a data warehouse works by the Extract-Transform-Load approach (ETL). 

It is important to note that the data warehouse is extremely integrated which means the data is always processed in the same way. As well, contrary to the data lake, the data in the data warehouse is not updated in real-time but on the schedule instead.

What other important things should you know about a data warehouse? They are:

  • The data is extremely well-structured and easy to understand;
  • This storage solution is also very scalable (same as a data lake);
  • Data warehouse integrates greatly with Business Intelligence (BI) tools to provide valuable business insights.

The pros and cons of a data warehouse

Data warehouse is one of the most efficient and popular ways of data storage due to the simplicity of searching through data and great opportunities for data analysis. So does this repository type have any cons? Let’s have a look.

The main pros of a data warehouse:

  • Thorough analysis of relational data that and hence, improved business intelligence;
  • A very high level of data quality and consistency due to the ETL approach;
  • Comparison of historical data to new data and hence, an option to gain better insights into any changes;
  • No need for data preparation when you need to use it since the data in a DW is already processed;
  • Data serving as a single source of truth for the organization.

The main cons of a data lake:

  • Implementation of any changes may be complex;
  • Might take too much time to retrieve and process the data to be stored in a data warehouse;
  • High maintenance costs;
  • There may be issues with the compatibility of a data warehouse with your existing systems.

The most common use cases of data warehouses

The biggest advantage of a data warehouse is the generation of detailed reports and consistent high-quality data. Hence, this repository type is most often used by business analysts and operational users in general because the data is so well-structured and easy to understand. As for the industries and specific examples of using data warehouses, here are the main use cases.

  • Finances and banking: due to efficient structuring and well-organized storage of processed data, the financial industry benefits a lot from deploying data warehouses;
  • Marketing and PR: data warehouse offers marketers a single source of truth and access to standardized data. In this way, marketers can rely on this data to create efficient campaigns ad won’t have to worry about the data being irrelevant or inconsistent.
  • Integration with legacy systems: yes, you’ve heard it right. Data warehouses typically connect with legacy systems in an effortless manner and thus, allow you to retrieve the needed data and present it in a format that would be compatible with new systems.

Hold on, there is one more thing, though.

But what about data marts?

Before we move forward and compare data lake vs data warehouse, there is one more thing that we need to clarify. When it comes to discussing data warehousing, you may hear the term data mart. And since there are so many data-related terms floating around, let’s take some time and find out how data mart relates to data warehouse so we avoid any confusion.

A data mart is a subset of a data warehouse that is focused on a specific line of business. In other words, it is a simple form of a data warehouse that is used by a specific department and focuses on a particular subject. For example, marketing teams often use data marts since they need to quickly access standardized customer data and don’t have time to sift through all the data stored in a data warehouse.

Data marts are usually built from an existing data warehouse (if you have one) or they can be built from any other data source that you use. And yes, the process of building and setting up a data mart can be quite complex. On the other hand, a data mart rewards you with highly focused insights on the needed subject and provides quick and easy access to the data.

The more you know!

Data lake vs data warehouse: key differences

And now, before deciding which type of data repository will be the best for your business, let’s quickly recap the main differences between a data lake and a data warehouse.

Data warehouseData lake
Data typeStructured data that is extracted from transactional systems and is cleansed and processedData in a raw, unstructured format that is collected from multiple sources
UsersOperational users and business analystsData scientists, data engineers
Data integration processETL: Extract-Transform-Load. Thus, the data is ready for analysis.ELT: Extract-Load-Transform. The data is structured only when needed.
Main tasksProvides insights to pre-defined questions, data visualization, data analytics, Business IntelligenceMachine learning, IoT, Big Data analytics, predictive analytics
SchemaDefined before the data is storedDefined after the data is stored
Storage costsHighRelatively low

Which data storage type is more suitable for you?

Both data lakes and data warehouses are extremely valuable for organizations and more and more companies started actually deploying both solutions. But if you need to choose between data lake vs data warehouse, here are a few questions that you need to answer and that might help you decide.

Do you already have a set-up structure and do you use an SQL database?

An SQL database is a relational database and since data warehouse stores relational data, it will obviously be your choice. As well, if you already have a CRM, an ERP, or an HRM (or a similar system) in use, the data warehouse will integrate seamlessly with it.

Is your data unified or not?

If your organization works with well-structured and unified data, it’s obvious that you will need a data warehouse. But if you collect data from diverse sources and in various formats (and you don’t plan to thoroughly structure it), you can choose data lake as the preferred storage option.

Will your budget allow you to efficiently scale your data?

Storage costs may be a bit high in the case of data warehousing and they might grow as data will be increasing in volume. Data lakes offer low storage costs due to the flat architecture so you might want to consider this option. Hence, when choosing between data warehouse vs data lake, the budget will play a big role in the decision.

What kind of processes run in your company?

If you specialize in machine learning, data science, and IoT, a data lake will be perfect for you. But if you work with pre-determined data and need clear structure and organization, then you better opt for a data warehouse.

Summing up

It’s obvious that your business needs a data storage solution but when deciding between data lake vs data warehouse, choose depending on the type of data that you work with and what you want to get from it. There is no right or wrong option and both data lakes and data warehouses can bring immense benefits to a business. It may even turn out that you need to use both – but before making the final decision, ensure that you’ve analyzed your business and its processes and that the selected option will be 100% worth the investment. 

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