The main reason for writing this article is to project the difference between data lakes and data warehouses for helping you to know more about data management.
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The main reason for writing this article is to project the difference between data lakes
and data warehouses for helping you to know more about data management. Most
of the data and analytics practitioners will understand the term. Let us see the main
differences:
• Data Lakes Retain All Data
While developing the data warehouse there is a need to invest a good time to
analyze data sources and understand the business processes and profiling data.
You will get a highly structured data model, especially for reporting. In this process,
the major work is to identify the data to include and avoid. The main thing over here
is to make decisions about the type of data to add and to reject in the warehouse.
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• Data Lakes Assists All Data Types
Normally the data warehouses consist of data taken from the transactional systems
and are composed of quantitative metrics and they are defined by the attributes.
Sensor data, web server logs, social network activity, text, and images are avoided
and they are termed as Non-traditional data sources.
• Data Lakes Support All Users
Here you can find 80% or lots of users are working. They want to obtain reports and
check their performance metrics or slice in a spreadsheet daily. For these users, the
data warehouse is actually ideal and it is quite structured and easy to use and
understand and for answering these question it is built with some object.
4. • Data Lakes Adapt Easily to Modification
The important drawback of the data warehouse is its longer time consumptions for
changing them. While developing there is a lot of time invested and obtain the
warehouse' structure correctly. It is a familiar fact that a good warehouse will be
submissive to change but it will take a lot of time for the loading process and the
work was done to make analysis and report easy.
• Data Lakes Provide Rapid Insights
This difference has been got from the other four points and the reason is that data
lakes contain various data and data types as it enables users to fetch their results on
a rapid way when compared to the traditional data warehouse approach. Moreover,
this early access to data arrives at a price. The data warehouse development team
does the work and will not do work for some or other data sources needed for an
analysis. There are lots of structured views of the data in the data lake that actually
looks like what they have had earlier in the data warehouse.
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