data warehouse vs data lake

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Data Warehouse
&
Data Lake
with
Data Management
Strategy

An effective data management strategy is an
important component for staying competitive. Today,
the huge
volume of structured, semi-structured and
unstructured data is created and real-time analytics on
streaming
data is emerging as an important use case.
The challenge is to come up with a data architecture
that empowers users and enables wide-ranging use
of analytics across the enterprise. Data lakes and Data
warehouse are both core components in modern
data architecture.

Data Lakes vs Data Warehouse!
What Are the Differences?
Differences in Technology
 A data lake uses a flat architecture
to store a huge amount of raw data
in its native format until it is needed.
There is no fixed limit on account
size or file.
 The different data elements in data
lakes are assigned unique identifiers
and tagged with extended metadata
tags.
 On the other hand, a hierarchical
data warehouse stores data in files
or folders with a defined schema.
 The information in a data
warehouse is stored by subject in
order to assist management make
quick decisions.

Differences in Use
 Data Lakes are useful for
data scientists because they
allow experimentation on
massive data sets.
 The users of data lakes are
usually people who want to
do a thorough analysis of
data.
 A data warehouse, measures
and dimensions are conformed
to curable components which
are consistent, governed and
easier for an ever-scalable
audience to consume.
 80% of users of data
warehouses are business
users who need refined and
systematic data.

Differences in accessibility
and adaptability
A data lake, because it stores all
kinds of data in its raw form, is easily
available for access to any user.
Users are able to explore data in
novel ways.
A data warehouse takes a fairly long
period of time to set up. During its
development, a lot of time is dedicated
to analyzing the sources of data and
how it can be tuned to meet the needs
of a particular business.
Data Lake is a cheaper way to
store/manage data.
Data warehouse is a costlier way to
store/manage data

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Final Verdict
The data lake is a game-changer. It not only
saves IT a whole bunch of money, but it also
supports high-end analytics use cases.
Data warehouse, on the other hand, allows
for more strategic use of data.
Organizations typically look at data lakes as
additions to their existing data warehouse.
Data lakes will continue to evolve and play an ever-
increasingly important role in enterprise data
strategy. Enterprises must have an effective data
management architecture in place that includes data
lake.

data warehouse vs data lake

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