-: Welcome to the "Section Recap".
In this lesson, we're going to review everything
that we learned in the section.
Yes, it should be a review.
If you find that it's not, jump back in,
re-watch the lessons
to make sure that you understand the content.
Also, we are focusing on the DP-203.
So, there are several lessons especially in this section
that we didn't go as deep as you would need
to really understand the concepts,
but we went as deep as we needed to
to get you to pass the DP-203, which is the focus.
And finally, number 3,
again, if you don't know something review.
Jump back in, do the labs, review the lessons,
read through Microsoft docs,
make sure that you understand the material
before moving forward.
Alright, star schemas.
We talked about star schemas,
and if you remember, star schemas have the fact table.
That's the bit in the middle right there.
And that's full of all the countable items.
Star schemas have 1 dimension table level,
and those are the dimension tables.
And all of those tie into the fact table.
Also, we talked about it not being normalized,
which means that there's lots of copies of data.
So star schemas are known for not being normalized,
and they're used for simple queries.
So when we do star schemas, think simple queries
and not normalized, which means there's copies of data,
and 1 dimension table level.
When we talk about snowflake though,
that is going to have multiple dimension table levels,
as seen here.
And then, we talked a little bit about high cardinality,
and that was just little repetition.
So, it is very normalized.
So there's not going to be a lot of copies of data, there's
not going to be a lot of repetition, that's snowflake.
So this is going to be much better for complex queries,
that's where you would use a snowflake schema.
And snowflake schemas are going to take less storage space
because again, there's not tons of copies of the data.
We also talked about fact tables and fact table grains.
If you remember, fact tables contain numeric data.
So think profits, product sales,
registers, things like that.
Countable items.
Each row represents a single event.
So that could be a purchase,
or register receipt, something like that.
And those single events are going to give us data
that we can measure to give us insights.
We talked about fact table grains,
and the grain was just the level of detail
that the fact table contains.
We talked about how important it was
to adjust the grain appropriately,
so that you have a good fit for the queries you're running
and the data that exists in your tables.
Alright, we also talked about that relationship
between fact and dimension tables.
In fact, they do have a relationship
and that's defined by your keys.
We have primary keys and foreign keys.
So primary keys are the unique data column
used to define your relationships.
And then your primary keys,
that's going to link 2 different tables together.
We also talked about external tables.
External tables are just tables
that live outside of your database.
And it's helpful to be able to connect to those
when you need to access data,
but you don't want to copy the entire data set.
So we looked at some scripts to give us a fast ad hoc way
to access data outside the bounds of our databases.
And then, we talked about metastores.
And if you remember, metastores are just databases
that hold metadata about our data.
So think paths and formats.
And we talked about Databricks and Synapse Spark Pools,
creating a metadata database,
but 1 individual for each workspace.
And when you want to access multiple workspaces,
the metadata within that,
you can create a metastore and you can access
and connect all of that individual metadata
into 1 metastore that you can then work with.
And then we talked about the what, when, where, and why
of maintaining metadata.
And we talked about copy activities in Data Factory
and Azure Synapse Analytics. And being able to pull metadata
on customer-specified metadata language, disposition,
type, encoding, cache control,
and pulling that data in when we use
our copy data activity in Data Factory or Synapse.
Remember you need to use binary.
And remember, that we use it because we get continuity.
We get more information,
so we can kind of see the trail of what's happening.
So that's a very helpful thing as well.
And then finally, in summary,
so that takes us to the end of all of the lessons
that we've looked through in this section.
This was a much more conceptual section,
but still keep a focus on the services that you would use
for those concepts.
So make sure that you're thinking about that.
Also that database material
when we talk about snowflake and star schemas,
that should be largely a review.
If it's not, you might want to go back in
and just spend a little bit of time digging deeper
into snowflake and star schemas in general.
Then that would also hold true
for primary and secondary keys.
You don't need to know everything,
but you do need to know the basics.
Don't forget the labs.
You are going to need those for the exam
and for your career. So don't forget about the labs.
Lastly, if you're enjoying this course,
hey, do me a huge favor and smash that thumbs up button
as you go through the lessons.
It means the world to me,
I'm super excited we have finished this section.
We are getting very close,
we're in the last quarter of this course.
I'm getting excited for you as you near completion,
and get ready to take your exam,
and grab your certification.
Alright, with that, let's jump on to the next section.
I'll see you there.