Discover the essence of a solid data infrastructure for sustainable data science. Learn about the data warehouse, data lake and data lakehouse, and how Researchable can support you.
In brief
- A solid data infrastructure and information management are the precondition for deploying data science structurally and sustainably.
- A data warehouse stores structured, cleaned historical data and excels at speed, clarity and reporting.
- A data lake keeps structured and unstructured data in its original form and offers flexibility, scalability and real-time capabilities.
- A data lakehouse combines the flexibility of a data lake with the structured capabilities of a data warehouse.
A solid data infrastructure and information management are essential to deploy data science structurally and sustainably. In this blog we cover three fundamental concepts of data management: the data warehouse, the data lake and the data lakehouse. We also show how Researchable supports organisations in setting up a solid data foundation.
Data warehouse
A data warehouse is a central place where large volumes of data from different departments are collected. The data is structured, organised and cleaned, and stored so that you can easily ask questions and run analyses on it. It concerns historical data, used to make decisions through reports and analyses.
- Clear data: all data is neatly organised and stored, which makes it easy to analyse.
- Fast answers: data warehouses are fast, so you get an immediate answer to your questions.
- Easy reporting: you can easily create reports and dashboards yourself.
Example in retail: a chain collects transaction data from all its stores (products, customers, purchase date and time, payment method) in a data warehouse. The company then analyses trends in buying behaviour, measures the effectiveness of campaigns and optimises its stock.
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Data lake
A data lake is a storage location for a wide range of data types, such as photos, videos, texts and sensor data. Unlike a data warehouse, a data lake stores both structured and unstructured data in its original form, without pre-processing. That makes it flexible and suitable for large volumes of data that traditional systems cannot handle.
- Flexibility: you can store all types of data, regardless of form or origin.
- Scalability: a data lake is easy to expand, so there is always room, even when the volume increases sharply.
- Real-time data: you can collect and analyse data in real time.
Example in healthcare: a healthcare organisation collects data from the electronic health record (EHR), medical equipment and sensors in a data lake. With data science it determines which treatments are effective and which risk factors are at play, in order to draw up personalised treatment plans and improve the quality of care.
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Data lakehouse
A data lakehouse combines the advantages of a data lake and a data warehouse: the flexibility of a lake and the structured, optimised capabilities of a warehouse. Where a data lake is flexible but can be complex to manage, and a data warehouse is structured but less flexible, a lakehouse aims to overcome both drawbacks.
Example in e-commerce: an online shop combines organised customer data with customer reviews and click behaviour. Through advanced analysis the shop sees which products are popular and which campaigns work, in order to make targeted recommendations, manage stock better and offer a more personal shopping experience.
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Researchable and data management
For a sustainable deployment of data science, a solid data infrastructure is important, whether it is a data lake for a research institution or a lakehouse architecture for an AI start-up. At Researchable we develop tailored solutions that fit the unique needs of your organisation. With expertise in data engineering, we help organisations make full use of their data, gain insights and drive innovation.
Frequently asked questions
What is a data warehouse?
A data warehouse is a central place where large volumes of data from different departments within a company are collected. The data is structured, organised and stored in a way that makes it easy to ask questions and run analyses.
What is the difference between a data warehouse and a data lake?
A data warehouse stores historical data that has been cleaned and organised, whereas a data lake accepts all types of data in its original form, regardless of structure and without pre-processing. A data warehouse excels at speed and reporting, a data lake at flexibility, scalability and real-time capabilities.
What is a data lakehouse?
A data lakehouse is a new approach to data storage that combines the advantages of a data lake and a data warehouse. You get the flexibility of a data lake together with the structured capabilities of a data warehouse.
Why is a solid data infrastructure important for data science?
A solid data infrastructure and information management are essential to deploy data science structurally and sustainably. Without a solid basis for storing and organising data, the foundation for reliable analyses is missing.
Which data do you store in a data lake?
In a data lake you store both structured and unstructured data in its original form, without the data needing to be processed first. Think of photos, videos and sensor data alongside classic structured data.
