10 things you need to know about Spark
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Transcript of 10 things you need to know about Spark
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Suited for real-time applications—such as the
Internet of Things—where much or most of the
data analysis will be performed on cached, live
data, rather than stored, historical data.
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.
Includes runtime engines that are optimized
for in-memory processing, streaming analytics,
graph analysis and machine learning.
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Leverages existing
programming languages
such as Python,
Scala or SQL and
provides seamless
access to enterprise
data with familiar tools.
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Boosts data scientist
productivity through
in-memory performance,
easier APIs, support for
any programming
language and more
workflows.
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Evolves user investments in advanced analytics, machine
learning platforms and big data platforms such as Hadoop.
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Parallelizes big data analytics models across distributed
in-memory clusters, combining SQL, streaming and graph
analytics within the same application.
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Initially developed at University of California Berkeley’s
AMPLab starting in 2009 and deepened through efforts
of an expanding open-source community and industry.
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Open-sourced in 2013 by the Apache
Software Foundation to top-level status.
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Continues to gain
active members,
with the Apache
Spark community
now boasting over
465 contributors.
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Adoption by a growing range of organizations as the future
of their big data analytics environment for new challenges
requiring in-memory, machine learning, stream computing
and graph analysis.
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Hungry for more information on Spark?
Get started learning more about Spark today at
BigDataUniversity.com