Data lake vs. data warehouse - they have similar functions but differ in certain factors, structure, data origin, etc. Here is a closer look.
Data Lake vs. Data Warehouse is a debate we often encounter when discussing vast quantities of data. Large amounts of data go beyond traditional database capacities, forming the space for better tools and systems.
Even though both repositories have similar base functions in the data lake vs. data warehouse question, they vary in certain attributes, structure, data origin, types of data stored, and data access protocols. Here is a closer look.
Data Lake vs. Data Warehouse
Enterprises choose a data lake vs. data warehouse solution when they deal with vast quantities of data from different sources - data that needs to be analyzed, sorted and stored as soon as possible. To understand the comparison better, let us break down the terms.
A data lake is a huge repository that can store raw data in its native form. A significant advantage of the data lake is that data of varying structures can easily be stored. Each element of such stored data has a unique identifier along with metadata to ensure easy access. These days, many vendors offer data lakes in the cloud as well.
On the other hand, data warehouses are repositories used to store data from business applications for predetermined tasks. Before storage of the collected/generated data, data warehouses apply a predefined schema that helps sort and organize the information. Since data in data warehouses are already processed so, enterprises find it easier for high-level analysis.
The principal difference between both the systems is that While data lakes get information from a varied range of sources, data warehouses get them from operational systems. Furthermore, since the data within data lakes is unstructured, it isn't a good fit for analytically oriented businesses; they are primarily for data scientists and other similar experts.
What Is The Right Choice?
Data lake vs. a data warehouse - what is the right choice? Well, the answer to that question rests on how you plan to use the data. A data lake is ideal for organizations with a high volume of data from multiple sources. Such data is easy to store and flexible but complicated to navigate.
In data warehouses, the data use has already been processed and is ready for analysis. Therefore, they suit companies with less capacity and knowledge to handle vast data.
In some cases, a combination of data lake and data warehouse could make better sense.
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In the data lake vs. data warehouse debate, organizations should focus on the solution that suits their business requirements and facilitate steady growth, even if it is a combined strategy. As a reputed IaaS provider, we ensure that our clients get the best storage services for their data. Our storage services include object storage, block storage, and file storage, using an open source platform, removing vendor-lock-ins. At VEXXHOST, we provide cloud solutions for a multitude of clients worldwide. We provide OpenStack-based clouds, including public clouds and dedicated and highly secure private cloud environments, ensuring utmost security and agility.
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