There are many different fields related to data science, and you will come across different different terms in all of them. The most important of these are the data lake and the data warehouse. It is almost impossible to succeed in data science and management without learning the meanings of such terms. In this article, we will examine the difference between a data lake and a data warehouse.
A data lake is a space where your structured and unstructured data can be stored. It ensures the storage and processing of particularly large data.
According to the Wikipedia definition: a data warehouse is a repository where associated data is queried and analyzed. The data warehouse was created in order not to strain the database. A data warehouse performs the operations necessary to analyze relevant data in an easy, fast, and accurate form.
A data warehouse is an area that analyzes a specific data for a specific purpose. The data contained in the data warehouse can be configured in the database, as well as unstructured. When it comes to the difference between data lake and data warehouse, the most important difference is that the data warehouse is created for data analysis. It is of great importance in the strategic and operational decisions of enterprises.
What are the differences between a data lake and a data warehouse? 1. Data lakes cover all data. A lot of time is spent on the development of enterprises' data warehouse for work, to analyze the data and to use the processed data for the operation. In general, the areas where the most time is spent are what data will be used and stored. Waste of time is avoided to save space.
In the case of data lakes, they are the opposite. It is the data lake that protects and covers all data. It even protects data that is used today or will be used tomorrow or never used at all. 2. Data lakes support all data. Data in a data lake usually consists of quantifiable data. Data warehouse website logs cannot store data such as social media data, while data lakes store this data as well. Thus, the data lake stores all the data in its raw form and can change the data type when it is used.
Data lakes are at the side of all users. Data warehousing is important for users of creating up-to-date reports and performance metrics. The data lake, on the other hand, collects multiple data and assists all users in their operations.
Data lakes can adapt to change, while data warehouses cannot. Changing the data type in data warehouses is quite difficult, and this is the point that many users complain about. In the data lake, the data can be accessed and the type can be changed at any time.
Data lakes are faster than data warehouses. Data is easier to access because data lakes also hold raw data. Traditional data warehouses are lagging behind data lakes.
An enterprise data warehouse, on the other hand, is the warehouse in which enterprises hold various data, and the data contained in this warehouse can generate strategic analysis for decision-making in all units, including operational units.
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