Almost 90 per cent of the world's data was produced in the last two years alone, and data is expanding at an exponential rate. Data is dispersed in more formats and across more devices, applications, and cloud platforms. Organisations require the ability to keep track of the data they own, comprehend where it is kept, and manage access to it. 

To solve this problem, data lakes and data warehouses come in handy and are widely used to store big data. A data lake is a sizable collection of raw data, the use of which is currently unclear. On the other hand, a data warehouse is a pool of filtered and structured data that has previously been processed for a particular purpose. 

Deep dive into understanding differences 

The two forms of data storage are frequently mixed together, but they differ greatly from one another. In actuality, their primary function of data storage is the only significant resemblance between them. The first aspect in which they differ relates to the data structure. Data warehouses hold processed and refined data, whereas data lakes typically retain raw, unprocessed data. Data lakes, therefore, often need substantially more storage space than data warehouses. 

Additionally, unprocessed, raw data is pliable and suitable for machine learning. It may be easily evaluated for any purpose. However, the risk of all that unstructured data is that data lakes might occasionally turn into data swamps in the absence of adequate data quality and governance mechanisms. 

While data lakes are flooded with raw data, the data's purpose is unclear. The raw data is unstructured and flows to be stored or with a probable use in the future. On the other hand, the data warehouse has processed data - the raw data that has been put to use for a specific purpose. Hence, every piece of information in a data warehouse has been utilised within the company for a particular objective which will ultimately result in efficient use of storage capabilities. 

Additionally, with unprocessed data in the data lakes, it becomes a difficult task to gain optimum benefit. Moreover, the complexity requires the expertise of data scientists and complex tools to derive efficient business use from the data. While the processed data is good enough for business professionals to use in charts, spreadsheets, tables, and more.  

What suits best for you? 

Before going into the suitability aspect, the thing to consider first is the disadvantages that each form possesses. Considering data lakes, 

• First, although data lakes can be created on-site, cloud-based systems are where they perform best. To exploit data lakes, businesses that do not currently use cloud services may need to move their complete infrastructure there. 
• Second, data lakes involve cutting-edge technology that requires a highly-skilled workforce with the required knowledge. The companies need to upskill their own manpower or go for outside services. 
• Third, unlike the data warehouse, data lakes lack a query engine that makes finding what someone is looking for in the repository is simple. 

Similarly, many disadvantages exist with the data warehouses as well, and these include, 

• First, even though data warehouses are designed to streamline business processes, manually feeding raw data into the data warehouse may take a little longer - a cause of worry for enterprises. 
• Second, access to the data warehouse may be restricted due to the confidential nature of the data. And that may directly result in restricted data use. 
• Third, although the method isn't difficult, the cost may be a little higher than average to maintain and upgrade. 

With disadvantages in mind, the thing to consider is what is best for the firms to go with. To be precise, it depends on the nature of use and the field one is into. Say, for example, the usage of data warehouses in the healthcare sector dates back a long time; it has never been very effective. Healthcare already has unstructured data, and there is a need to have real-time insights, which makes the data warehouse of little use. Data lakes support structured and unstructured data, making them a better fit for healthcare organisations. 

When it comes to the financial sector, a data warehouse is frequently the optimal storage strategy because it may be organised for access by the entire firm rather than just a data scientist. As a result, data warehouses have played a major role in the advancements that big data has made possible for the financial services sector.