Powered by Apache Spark, Seahorse is a new
data analytics platform that defines a new
way to create data science pipelines.
Using Seahorse's visual interface, you can
solve complex problems in the areas of machine
learning and ETL (Extract, Transform, and
Load) without needing to code.
Seahorse provides tools to tackle real world,
Big Data, problems while letting you experience
a very gentle learning curve.
Seahorse takes care of many complicated concepts
and presents a simple, clean interface.
Seahorse emphasizes a visual approach to programming.
This results in your applications being extremely
readable: and in fact, the logic driving the
entire program is visible at first glance.
What's important to note, is that while
promoting a code-free working style, Seahorse
doesn't limit you to a predefined set of
actions.
Whenever you encounters a necessity to include
a non-standard action in your application,
for instance, something that's not covered
by Seahorse's palette of operations you're
still free to write your own transformations
in Python.
Seahorse offers a web-based interface that
presents a Spark application as a graph of
operations - a visual workflow so to speak.
A typical Seahorse session consists of three
alternating phases:
1.
Adding operations to the workflow,
2.
Executing the part of it that's already
been created
3.
And finally, exploring the results of the
execution.
This establishes an interactive process during
which you're able to track what happens
at each step.
Then, after the workflow has been constructed,
it can be exported and deployed as a typical
Spark application on production clusters.
This brings us to the end of this video
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