- Cloud is a, it's a godsend for data scientists.
Primarily because you're able to take the,
or you take your data, take your information
and put it in the cloud,
put it in the central storage system.
It allows you to bypass the physical limitations
of the computers and the systems you're using
and it allows you to deploy the analytics
and storage capacities of advanced machines
that do not necessarily have to be your machine
or your company's machine.
Cloud allows you not just to store large amounts of data
on servers somewhere in California or in Nevada,
but it also allows you to deploy very advanced
computing algorithms and the ability to do
high performance computing
using machines that are not yours.
So, think of it as you have some information,
you can't store it, so you send it to storage space,
let's call it cloud, and the algorithms that you need to use
you don't have them with you, but then on the cloud
you have those algorithms available.
So, what you do, is you deploy those algorithms
on very large data sets and you're able to do it
even though your own systems, your own machines,
your own computing environments
were not allowing you to do so.
So, cloud is beautiful.
And, the other thing that cloud is beautiful for
is that it allows multiple entities
to work with same data at the same time.
So, you can be working with the same data
that your colleagues in, say, Germany,
and another team in India, and another team in Ghana,
they are collectively working
and they are able to do so because the information
and the algorithms and the tools and the answers
and the results, whatever they needed
is available at a central place.
Which we call cloud, so cloud is beautiful.
At the Big Data University which is an IBM initiative,
we have these courses people can take
and learn about data science,
but at the same time we provide this cloud based environment
for not only analytics,
but also for working with big and small data.
So one of the products that is integrated
with Big Data University is Data Scientist Workbench.
Data Scientist Workbench is an internet based solution,
you log in and the moment you log in,
you now have access to some
very advanced computing environment.
As simple as R and Rstudio and data
and algorithms to define the data set using OpenRefine,
but also the ability to work with very large data sets
using technologies like Spark.
So, the advantage of working with Data Scientist Workbench
is not only that you have the ability to work with
these advanced algorithms and two computing platforms,
but you also have the ability to work with
very large data sets because Spark
is integrated and it's all in the cloud,
you don't have to maintain it,
you don't have to download it,
you don't have to worry about updating it.
All is being done for you in the cloud
by the Data Scientist Workbench.