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Data Lake vs. Data Warehouse – Key Differentiators

Posted on July 12, 2018 at 6:37 pm

The digital world is moving on to data lakes and they are being given preference over data warehouses. Having said that, it should be noted that the data lake and data warehouse may exhibit similarities but they are fundamentally different in terms of how data is stored.

A data lake can be defined as a massive tank or repository of raw and unstructured data while a data warehouse, more often than not, stores meaningful and structured data that’s used for making business management decisions. For data warehouse to work, you first find the specific use of data and purpose it accordingly before storing. On the other hand, in Data Lake you can simply dump all the data and only organize and structure it at the time of retrieval.

There are some fundamental and strategical differences between both technologies including:

Processing Power

Before information can be stacked into an information distribution center or data warehouse, it should first be given some shape and structure. This process of organizing and structuring data is known as schema on write.

On the other hand, a data lake stores all types of data in its crude shape. When one needs to utilize the stored data, they, at that point, organize and structure the required data. This is called schema on reading.

Storage

One of the primary highlights of big data advancements is the cost-effectiveness of storing large amounts of digital data. When you are looking to simply store data in its raw form, it tends to be much more economical as compared to when you are bound by the prerequisite of shaping and structuring it. This happens because data storage technology usually depends on open source software where businesses don’t have to pay licensing fee and they get community support for free as well. Moreover, open source software is often designed to be run on commodity hardware enabling businesses to cut down hardware costs as well.

Data warehouse storage can get very expensive real quick, particularly if the volume of information is huge. Contrastingly, an information lake is intended for minimal cost storage.

Data Type

On the data warehouse, you have another bottleneck to worry about. It doesn’t store data that hasn’t been purposed and structured adequately. This means when you are looking to simply store raw data you are out of luck because you’ll either have to put in the effort of unnecessarily structuring data or investing in a solution that can store data as is.

In comparison, Data Lake doesn’t have any such issues. It doesn’t care if your data is structured, half structured or entirely unstructured. You can use it to store data regardless.

Flexibility

From a technical standpoint, data warehouses have a fixed structure and configuration which can be changed but not without putting in a lot of time and effort. In contrast, Data Lake is quite agile and can be structured or restructured in a multitude of ways.

This gives developers and data personnel the ability to simply access the data and configure it as necessary.

Security

This is one front where data warehouses offer a more mature solution since they have been around for quite a while. That doesn’t mean data lakes are insecure but, being a new technology the security solutions have not had enough time to evolve and enhance as much as those of its counterparts.