Differences Between Data Lakes and Datawarehouse

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Differences Between Data Lakes and Datawarehouse
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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.

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• 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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