Data is king, and organisations that can effectively manage and analyse their data will unlock significant competitive advantages. Effective data analysis allows businesses to better understand their performance drivers, reduce running costs, more effectively target consumers, and optimise marketing efforts.
A vital step in extracting the most value from your organisation’s data is storing it in a repository that supports the right kind of data uses, ensuring users can access data in the formats they need. Data lakes, data warehouses and data lakehouses are storage facilities used to house data for recall, reporting and analysis. But each does so in a different way, offering varying benefits to users and businesses.
To make sure you understand the key differences between data lakes, data warehouses and data lakehouses, here we explain what each is and how they differ. Then you can make an informed judgement about which data solution is right for your business.
What is a data warehouse?
Data warehouses are centralised data storage facilities used to collect data from a variety of sources. They house data and allow it to be readily recalled, manipulated and analysed.
With data warehouses, data is usually pre-processed and stored in predefined formats established by the warehouse’s architecture. Think of a data warehouse as offering shelf space for data, but with those spaces being a specific dimension and only capable of accepting data of a given format or schema.
Whilst it may take time to properly load data to a data warehouse – as the data will have to be properly processed and prepared – once the data is stored, it can be quickly recalled and analysed. This is because it is unified and highly structured.
What is a data lake?
Whilst data lakes are also centralised repositories for business data, they use a different storage approach to data warehouses.
With data lakes, data doesn’t have to be stored in a predefined format or schema. It can typically be uploaded in the format it already exists in. This means data lakes are versatile storage solutions that users can quickly and easily upload data to without being concerned with any data processing.
What is a data lakehouse?
Data lakehouses are a hybrid storage solution that combines the best features of a data warehouse and a data lake.
With data lakehouses, data can be stored in both unstructured/ad-hoc forms and also in predefined schemas that require it to be processed before uploading. This flexibility means that data lakehouses can be used to store a variety of data types. They can support a range of uses too, from managers generating standardised reports to data scientists running the latest machine learning approaches using custom Python scripts.
What are the key differences between data warehouses and data lakes and data lakehouses?
| Data Lake | Data Warehouse | Data Lakehouse | |
| Data Storage | Data can be uploaded in a variety of formats, including in unstructured and raw formats | Data is structured in predefined formats, and must be cleaned and processed before it is uploaded | Data can be uploaded in both unstructured and structured formats |
| Data Architecture | Data is stored using object-based storage, like Amazon S3, Azure Blob Storage, or HDFS | Data is stored using a relational database management system (RDBMS) | Data is stored using both object-based and relational database storage |
| Scalability | Data lakes are usually highly scalable with new capacity easy to add | Data warehouses usually have a defined capacity and may require additional software and/or hardware to up-scale | Data lakehouses offer similar scalability benefits to data lakes |
| Data Schema | The schema is defined after the data is loaded to the lake when the data is used | The schema is defined before the data is loaded to the warehouse | Data lakehouses allow data to be imported with or without a defined schema |
| Processing | Data is processed after it’s been accessed from the data lake, and processed to the user’s requirements | Data is processed and structured before uploading to the data warehouse, for quick and simple access by users | Data can be processed before uploading, or processed when accessed, depending upon the needs of the user |
| Users | Data is typically accessed by users who are experienced in a wide range of data formats, and data manipulation and conversion, like data scientists and software engineers | Data is typically accessed by users looking to run existing analysis and reporting tools, like managers, accountants, and business analysts | Data lakehouses support a variety of users, from data scientists and software engineers to accounts and managers |
| Analysis | Data lakes support a wide range of analyses, including ad-hoc data visualisation, machine learning, and big data analytics | Data warehouses support the fast production of pre-defined analyses, like business reporting and data analytics | Unstructured data can support advanced analysis, like machine learning, whilst structured data allows standardised analysis, reporting, and visualisation |
| Cost | Comparable data lakes tend to be less expensive than data warehouses due to lower storage costs. Plus lower operational costs as lakes tend to be less time-consuming to manage | Data warehouses are usually more expensive than data lakes, as they require more management and storage costs | Data lakehouses typically sit between lakes and warehouses in terms of cost, with more management and operational costs involved than pure data lakes |
Is a data warehouse, data lake, or data lakehouse the right choice for my business?
Each data storage solution is suitable for different use cases and access by different kinds of end user:
Data lakes are the right solution if your organisation:
- Needs to store large volumes of data in a variety of formats
- Needs the flexibility to perform a wide range of different types of data analysis
- Has experienced users, like data scientists and software engineers, who require the versatility offered by raw and unstructured data
- Requires an easily scalable storage solution
Data warehouses are the right solution if your organisation:
- Needs to store data in a predefined schema
- Needs the speed and ease that comes from accessing data in defined formats
- Has users that require simple and standardised data reporting and visualisation, like business intelligence analysts, accountants, and general users
- Has the resources to manage or pay for the outsourcing of the management, of a structured data repository
Data lakehouses are the right solution if your organisation:
- Needs to store data in both ad-hoc and predefined formats
- Needs to both perform advanced data analysis and standardised business information analysis and reporting
- Has a variety of users, both dedicated data analysis experts, like data scientists, and data non-specialists, including general business users
- Requires a data storage solution that can be easily scaled
With the benefits of standardisation and automation on offer, data warehouses are more often the best data repository for most organisations. Data lakes are typically used by dedicated data scientists for advanced forms of analysis, like machine learning.
However, many organisations use both data lakes and a data warehouses to cover their data storage and analysis needs. Data lakehouses are now becoming an increasingly popular data storage solution too.
Find your ideal data storage solution with DeeperThanBlue
At DeeperThanBlue, we can work with you to understand your organisation’s unique data storage and use requirements, to help ensure you maximise the potential of your data.
Get in contact with us to find out how we can help transform your business.