Introduction
Data Lakes and Warehouses are probably the two most widely used storage types when it comes to storing data on a permanent basis. In this article we are going to explore both, unfold their key differences and discuss their usage in the context of an organisation.
Data Warehouse and Data Lake in a nutshell
A Data Warehouse is used as a central storage for large amounts of structured data that might be coming from various sources. Such stores are very important to companies as they can be used to deliver insights from across the organisation to support decision making.
On the other hand, a Data Lake is a flexible storage that is used to store unstructured, semi-structured or structured raw data. The stored data is unprocessed and the structure is usually applied when it is retrieved. Note however that a Data Lake is not a replacement for a Data Warehouse.
Key differences
It is important to consider all related factors before choosing how to house the data in an organisation and whether you need to store data coming from a particular source into a Data Lake or a Data Warehouse. Typically, these considerations come down to the 4 topics discussed in this section.
Data Type and Processing
As we already discussed, Data Lakes can be used to store any form of data including unstructured and semi-structured while Data Warehouses are only capable of storing only structured data.
Since Data Warehouses can deal only with structured data this means they also require Extract-Transform-Load (ETL) processes that will transform the raw data into a target structure (schema-on-write) before storing them to the warehouse. In other words, data warehouses store historical data that has been pre-processed in order to fit a relational schema.
Data Lakes are much more flexible as they are capable of storing raw data, including metadata or schemas to be applied when extracting them. This is essentially the most fundamental difference between a Data Warehouse and a Data Lake.
Target User Group
Different users may require access to different storage types. Usually business or data analysts need to extract insights for reporting purposes and thus data warehouses are more suitable for them.
On the other hand, a Data Scientist may require to access unstructured data to detect patterns or build a deep learning model which means that a data lake is a perfect fit.
Ecosystem
Another important factor to take into account when choosing between data warehouses or lakes it the existing technology ecosystem of your organisation. Data Lakes have become quite popular due to the emerging use of Hadoop which is an open source software.
This means that if your organisation does not favour open source software then moving data into data lakes could be challenging.
Budget
The data management plan always takes into account the cost of the technologies and architectures we intend to use or build. Data Lakes are way less costly as the data is stored in its raw format in contrast to Data Warehouses that take up more storage size since they require data to be processed and ready-for-analysis.
Which to choose
Data Warehouses and Lakes are both used by organisations as centralised data stores that enable different users and organisation units to access and use data to extract insights and perform any sort of analysis. Usually an organisation will need both a Data Lake and a Warehouse to support all the required use-cases and end users.
A data lake is capable of housing all data of any form; from structured to unstructured. Additionally, it does not require any sort of pre-processing before storing the data as this can happen once it is stored in the data lake. Data Lakes are mostly useful to Data Scientists and Engineers that require access to even unstructured data that will help them build Artificial Intelligence or Machine Learning models. Data Lakes are also more cost efficient compared to Data Warehouses as they don’t require data to have any particular format such as a schema.
Now a data warehouse is only capable of storing structured data which are ready to be analysed by specific organisation units in order to unveil business insights. Therefore, ETL processes are usually required to be built around the Data Warehouse. ETL functionality enables data to be stored in the expected format and extracted or transformed so that users can perform particular tasks over them. For that reason, Data Warehouses are very powerful for business or operations analysts that require to have access to relational data with schema that will enable them to create reports and support decision making by discovering insights.
A Final Word
In this article, we discussed the key differences between Data Lakes and Warehouses. Note though that this is not an apple-to-apple comparison.Both support different use-cases and serve different users and usually organisations require both to operate efficiently.
Data Lakes are more flexible and schema-less stores that are capable of storing unstructured, semi-structured or structured data. They are usually useful to more technical users such as Data Scientists or Engineers. On the other hand, Data Warehouses can only accept relation data which in turn is more useful to less technical people who need access to ready-for-analysis data.
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