This article discusses key differences between Data Lake and Data Warehouse.
If we have to put it in simple words, there’s a huge difference between “Data lake” and “ Data warehouse”. Data lake refers to the concept of storing the data in its raw form. Also, the data is unstructured, so Data lake is capable of storing large amounts of data in less costs compared to the Data warehouse.
On the other hand, Data warehouse uses hardware components to store huge amounts of structured and modified data. Therefore, it requires devices and physical space as well. Moving further, we are discussing the key differences between these two concepts to end your confusion, if any.
Before we move to the data of key differences, let’s define data lake vs data warehouse concepts separately. Data lake is a storage system that uses open-source software to keep the data in its raw format safe. The data can be structured or unstructured - data lake is capable of storing it within the cheaper cost compared to the data warehouse concept.
The data warehouse storage system is designed to resolve BI activities. Following the concept of extraction, loading, and transformation (ELT), the data warehouse system used to save huge amounts of data. The data warehouse system is used due to its analytical capabilities and the freedom of control it provides over the data.
In simple words, the answer to the question of what is a data warehouse can be that it’s a data storage system used to keep the structured data safe. It’s analytical and mining capabilities make it one of the most popular methods of storing a large amount of data.
Moving further, we are picking a few points to explain major differences between both concepts.
The data that is stored in Data lake is different from the one stored in the data warehouse. In other words, Data lake stores the raw form of data. It can be structured, unstructured, mobile app data, website data, and digital files, among others.
On the other hand, Data warehouse keeps a huge amount of data safe that includes historical data from many years in the past, derived data that has been transformed through mathematical operations, and Meta data that is used to sort the data saved for easy retrieval. The data is gathered and managed with WMS systems.
While storing the data in data lake stores, the schema for the data is defined after storing the data. However, for the data warehouse, the schema is done at the prior of initiating the data storage process.
Data warehouse uses the ETL (Extract Transform Load) technique to store the data. First raw data is modified and sorted before initial loading of the data in the storage system. However, the Data lake follows a concept opposite to that. ELT (Extract Load Transform) is used to make sure that the data is first loaded into the store so it can be retrieved and modified later as per the need.
The Data lake storage system is less costly compared to Data warehouse for obvious reasons. The data lake is useful to store data in a less costly manner. The data stored in data lake is less in size and is more flexible. However, it is mainly suitable to store data from sources like websites, mobile applications, gaming apps, and more.
On the other hand, the data warehouse is used to store a large amount of data. Take Enterprise Data Warehouse (EDW) as an example. The cost of the data warehouse might vary depending on the options you choose. You can either sign up for a hardware storage system or a cloud storage system. The hardware storage system will require a space where you can keep the data warehouse storage devices safe.
In terms of the security, data warehouses are more reliable as they have been existing for quite some time. The data warehouse storage system is used to store sensitive data such as financial information, passwords, and more. However, the Data lake storage system is new and based on the internet. Therefore, the technology used for its security is still evolving and applying updated protocols according to the situations and trends.
To conclude, we can revise the major points to reflect the difference between data lake and data warehouse in the end. If we have to put it in short, the data lake is comparatively less costly but more suitable for small organizations, best developers, or entrepreneurs as the data volume they need to store might be less. However, large enterprises, especially involved in server based products or services can find data warehouses more useful. It will directly impact the cost as well.
Aparna is a growth specialist with handsful knowledge in business development. She values marketing as key a driver for sales, keeping up with the latest in the Mobile App industry. Her getting things done attitude makes her a magnet for the trickiest of tasks. In free times, which are few and far between, you can catch up with her at a game of Fussball.
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