Data Stack is the
new data economies that draw people to participate because we offer a better
data experience. The data stack describes capabilities for delivering that data
to those who use it in multiple ways, through subscriptions or through APIs. In
other words, the data stack refers to a set of categories that describe the
different capabilities needed to transform data into more valuable forms.
Moore’s Law is linked in this new idea because of the more data, the more
value.
The other idea is data warehouse will become virtual, with
bits and pieces of the data spread across the landscape, owned by numerous and
sundry services. All of the different kinds of queries might be satisfied by
spreading the work across all of these presumably asymmetric processors. Also
there would be effective orchestration to manage the federation of the queries
to provide the kind of service that the applications require.
Machine learning (ML)
and statistical techniques are the key to transforming big data into actionable
knowledge.
MLbase is still a novel system harnessing the power of
machine learning for both end-users and ML researchers. MLbase provides
(1) A simple declarative way to specify ML tasks
(2) A novel optimizer to select and dynamically adapt the
choice of learning algorithm
(3) A set of high-level operators to enable ML researchers
to scalable implement a wide range of ML methods without deep systems knowledge
(4) A new run-time optimized for the data-access patterns of
these high-level operators.
Reason for all the ideas is money. Businesses want to
understand consumer’s behavior in order to make strategic decision. Many people
want to discover about Big Data which means the more data people can process,
the more value they might get. All of these reasons will make data warehouse
and business intelligence become better. In the other words, the world will
understand itself better.
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