Data lake vs. data warehouse: explained in a nutshell

from | 21 June 2024 | Basics

Did you know that companies generate around 2.5 quintillion bytes of data every day? Every customer interaction, every sensor reading and every mention on social media provides valuable insights. But with so much information flowing in, how do you use this data to make strategic decisions? 

This is where data storage solutions such as data lakes and data warehouses come into play. Understanding the differences between these two systems is crucial for data experts who want to use data effectively for decision-making and business intelligence.

What is a data lake?

A Data Lake is a large-scale one, central repository in which raw, unstructured and structured data can be can be stored in their native format. It serves as a storage pool for different types of data and enables scalability and flexibility when processing large volumes of data. Data lakes are extremely customisable. They can accommodate various data sources and formats, including text files, images, audio and video data as well as sensor data. 

Features of a data lake

  • ScalabilityData lakes are designed to process large volumes of data and can be easily scaled up or down to accommodate growth.
  • Cost-efficient storageSince data lakes store raw data without extensive pre-processing, they can be a cost-effective option for storing large amounts of information.
  • Storage of raw dataThe Data are usually saved in their original format, which allows them to be explored and analysed later without being restricted by predefined structures.

Advantages of a data lake

Data lakes offer a unique approach to data storage that emphasises flexibility and scalability for large amounts of information. This open approach offers companies several important advantages:

  • Cost-efficient scalabilityData lakes offer a scalable and economical way to store large amounts of data. They are ideal for companies that are experiencing rapid data growth.
  • Future-proof flexibilityData lakes allow you to store any type of data, regardless of its current purpose. This adaptability ensures that your data storage can evolve with your business needs.
  • Fast data transferData Lakes can quickly ingest data from multiple sources, minimising delays between data collection and data processing. Data analysis minimise.
An emblematic data lake. A data lake can best be imagined as an oversized hard drive.

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What is a data warehouse?

A Data Warehouse is a data management system that was created to Business intelligence activities and analyses. This is a curated collection of historical data, carefully organised and optimised for querying and reporting. Data warehouses usually contain data that has already been processed, cleansed and converted to ensure consistency and quality. This structured approach enables faster and more efficient analyses than data lakes.

Features of a data warehouse

  • Topic-orientatedData warehouses are organised according to specific business areas, e.g. sales, marketing or finance. This thematic organisation makes it easier for users to find and analyse relevant data.
  • Integrated dataData from different sources is transformed and integrated into a standardised format within the data warehouse. This eliminates data silos and ensures that users work with accurate and reliable information.
  • Time variableData warehouses usually store historical data so that users can track trends and patterns over time. This is crucial for tasks such as sales forecasting, customer behaviour analysis and performance measurement.

Advantages of a data warehouse

Data warehouses are characterised by the fact that they offer a structured and optimised environment for targeted analyses. This structured approach brings several valuable advantages: 

  • Improved data qualityEnforce data warehouses Data cleansing processes and Data conversion processes and thus ensure the accuracy and consistency of the data used for the analysis.
  • Improved data managementData warehouses generally have stricter requirements for Data governance controls. This ensures the Data securityprotects sensitive information and facilitates compliance with data protection regulations.
  • Simplified reporting and visualisation: The structured nature of data warehouses facilitates the creation of reports and Data visualisations. This allows business users to quickly recognise trends, identify patterns and share data-driven insights with stakeholders.
Data warehouse, a large warehouse filled with numerous boxes

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Differences between data lakes and data warehouses

Both data lakes and data warehouses are valuable tools for data management and analysis, but they fulfil different requirements. Below you will find a breakdown of the differences to help you understand which solution is right for your organisation:

FeatureData LakeData Warehouse
Data typeUnstructured, semi-structured and structured dataStructured data
Processingprocesses raw and unprocessed dataprocesses cleansed and transformed data
SchemeSchema-on-read (flexible and evolving schema)Schema-on-write (predefined and rigid schema)
AccessOpen access for various use cases and analysis toolsControlled access and optimised for BI tools and SQL queries
Flexibilityoffers flexibility in data exploration and analysisoffers less flexibility, but ensures data consistency
CostsLower storage costs due to compression and lack of structuring Higher processing and storage costs
ScalabilityHorizontally scalable, but higher processing and storage costsVertically scalable, but requires more planning and management
MobilityHigh agility due to schema flexibility and the ability to process different data typesLess agility, as the focus is on structured data and predefined schemas
Query performancePossibly slower query performance due to schema-on-readoffers faster query performance due to the predefined schema
Data managementLimited governance capabilities due to the storage of raw dataStrong governance options with structured data
End usermainly used by data scientists, engineers and analysts for advanced analyses and machine learningSuitable for business users, analysts and decision-makers for business intelligence and reporting
ApplicationResearch into new trends, advanced analyses, data collection and future requirements Reporting, historical analysis, trend analysis, answering specific questions, decision-making
Differences between data lakes and data warehouses

Combination of data lake and data warehouse

While data lakes and data warehouses serve different purposes in the data ecosystem, they share common goals when it comes to storing, managing and analysing data. Both systems aim to provide a centralised, accessible location for data storage that enables data sharing, collaboration and informed decision-making.

The combination of data lakes and data warehouses offers a comprehensive approach to data management that enables companies to utilise the strengths of both storage systems. By integrating data lakes and data warehouses, companies can:

  • Save and process different data typesThe combination of Data Lake and Data Warehouse enables companies to store and process different types of data, from unstructured raw data to processed, structured data, and thus obtain a comprehensive overview of their data stocks.
  • Optimise data storage and processing costsThe combination of the cost efficiency of data lakes with the performance and reliability of data warehouses ensures optimised costs for data storage and processing.
  • Facilitating real-time insights and historical analysesOrganisations gain real-time insights and historical data analysis capabilities to get a holistic view of their data.
  • Enable advanced analyses and business intelligenceIntegration of data lakes and data warehouses allows companies to support internal analyses, machine learning and business intelligence, ensuring a smooth transition from data exploration to reporting and decision-making.
Business Intelligence illustration with laptop in a café - in the foreground a coffee cup and in the centre a data visualisation application on the laptop monitor - the logo of Alexander Thamm GmbH in the upper right corner.

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A solid data strategy as the foundation of good data management

The decision between a data lake and a data warehouse, or possibly even a combined approach, depends on your specific data strategy and analytical goals. By understanding the strengths and weaknesses of each system, you can make an informed decision that will enable you to realise the full potential of your data and drive your business forward.



Pat has been responsible for Web Analysis & Web Publishing at Alexander Thamm GmbH since the end of 2021 and oversees a large part of our online presence. In doing so, he beats his way through every Google or Wordpress update and is happy to give the team tips on how to make your articles or own websites even more comprehensible for the reader as well as the search engines.

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