Examples of 'data lakes' in a sentence
Meaning of "data lakes"
data lakes: This phrase is used in the context of data storage and management to describe a large repository or storage system that holds vast amounts of raw data in its native format until needed for analysis or retrieval
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- plural of data lake
How to use "data lakes" in a sentence
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data lakes
The biggest advantage of data lakes is flexibility.
These repositories are commonly known as data lakes.
This is where the data lakes come in.
Data lakes can completely undermine this tactic.
They talk about data lakes.
Data lakes do not help answer this question.
One of the main attractions of data lakes is flexibility.
Why data lakes are the future of data storage.
Data warehouses are often called data lakes or data marts.
The arrival of data lakes reverses the previous reasoning.
This is one way in which data warehouses differ from data lakes.
Data lakes will need to demonstrate business value or die.
Organizations will move from data lakes to processing data platform.
Data lakes support storing data preserving its original format.
The major risk of data lakes is safety and access control.
See also
Data lakes are becoming more and more central to enterprise data strategies.
Creation of data lakes.
Data lakes could protect big data from all the perils of the public cloud.
EMC is now talking about data lakes.
Data warehouses and data lakes are an essential component of these systems.
AWS Lake Formation makes setting up data lakes eas.
Hadoop data lakes offer a new home for legacy data that still has analytical value.
But Russmann cautioned that data lakes are not for everyone.
Data Lakes are often used in the same context as Hadoop.
Delta Lake project joins the Linux Foundation as open data lakes standard.
However, data lakes can adapt to changes easily.
That 's not to say the idea behind data lakes is a bad one.
Data lakes are not on the horizon - they are here today.
Talend 's platform ensures that data lakes stay clean and accessible.
Data lakes are cheap and scalable repositories, used to store huge amounts of data.
An important question, what separates knowledge graphs from data lakes or data warehouses?
Because of this, data lakes typically require much larger storage capacity than data warehouses.
In fact, how to secure and govern data lakes is a huge topic for IT.
Data Lakes are a good example, but they are difficult for marketers to operate.
The differences between enterprise data warehouses ( EDW ) and data lakes are significant.
Data lakes and data wallets, Data lakes will enable new models of predictive analytics.
Amazon, Microsoft and Google offer data lakes in the cloud.
Data lakes and lakeshore data curation, Hadoop, NoSQL, relational databases.
Storage DIP - this type of DIP software is for data lakes on cloud storages.
Vendors are marketing Data Lakes as a panacea for Big Data projects, but that 's a fallacy . ”.
Agree, Forrester is right, data lakes will die.
Data lakes are particularly promising ( Exhibit 2 ).
Move over big data and welcome to data lakes ( where storage is king! ).
There are still instances of Hadoop -- there are still data lakes out there.
It 's in data lakes.
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