- [Instructor] Next up, we're going to talk
a little bit about big data databases, and specifically,
two different categories of those databases,
NoSQL databases, and NewSQL databases as well.
Now, of course big data is massive, and as such,
requires massive amounts of storage.
Typically, that storage is going to be spread across
massive data centers world wide, and of course,
those data centers are networked together.
And, the concepts of NoSQL and NewSQL databases
were specifically designed to deal with that type of data,
where it's, just, so large,
that it has to be stored worldwide.
And those are challenges, and, sizes,
that relational databases really were not designed for,
in the first place.
So, these new databases had to be created to handle,
not only the massive amounts of data
that are available to us in big data, but also,
the speed with which that data gets created, as well.
So, NoSQL itself originally meant what its name implies,
that is not using sequel to manipulate the
data structured quarried language,
like we demonstrated earlier, in this lesson.
But, nowadays, there's actually more and more sequel
being used with big data, so,
it's now said to mean
"Not Only SQL."
and in fact, later on in this lesson,
when we get to the Spark application framework,
one of the things that we'll use there
is a concept called Spark SQL, to quarry distributed data
that resides on many nodes in a cluster.
Now, NoSQL databases are specifically meant for
a combination of unstructured, and/or semi-structured data.
So unstructured data are things like photos and videos
that we're all posting on our social media accounts,
various concepts with natural language,
such as the text we type in emails,
text messages with one another,
social media posts on Twitter, and Facebook,
and other sites as well.
And semi-structured data is often
JSON or XML documents
that wrap unstructured data with additional information,
usually called metadata.
So, for instance, back when we were looking
at tweets in Twitter, we saw that with respect to a tweet
that was either 140 characters maximum,
or 280 characters maximum,
what we actually got back from the Twitter APIs
was a much larger piece of data,
in the form of a JSON object,
that had tons and tons of additional metadata in it as well.
So, often, semi-structured data is stuff like
the tweets that we were getting back from Twitter.
Now, as we work our way through the next couple of videos,
we're going to talk about four major types
of NoSQL databases, key-values stores, document data stores,
columnar databases, which are kind of similar
to SQL databases and relational databases.
And, also a concept called graph databases as well.
And one of the things that you'll find
as you start diving in to these different database types,
is that many of the database vendors out there
in the big-data space, of course provide
free tiers or free trials, that enable you to
work with their tools, get used to using them,
and determine if they're the right ones
for your applications.
And, in fact, when we get to our NoSQL example,
we'll use the MongoDB database,
and we'll specifically take advantage of their free version
of what they call an Atlas Cluster.