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>> Hi, my name is Amit Banerjee.
I'm a Group Program Manager with the SQL Server product group.
My day job is to take care of
the SQL Server database engine that runs your beloved SQL Server.
So in today's episode,
we will actually talk about how
your container licensing that we
talked about in the previous episode can be
applied to SQL Server
big data clusters that we introduced in SQL Server 2019.
So you have a concept called big data cluster
that actually allows you to join your relational database,
that's your SQL Server Instance.
You can take any database and join it
with any data that's sitting inside
HDFS or any other external data
source that is sitting outside of the big data cluster.
There are a few scenarios that we enable with
the SQL Server big data cluster in 2019.
The first one is a data virtualization,
the second one is a scenario where if
your data needs to be cached inside SQL Server,
you can create caches of data sitting inside your storage
and your data pools and you can create data marts on top of them,
and the last one is you can actually run
AI and ML experiments on top of it using things like Spark,
or Python, or Java,
or R. These are all capabilities that we actually
had in SQL Server 2017 and below.
The Java one and the Spark are new.
So let's see what a SQL Server architecture
for big data cluster looks like.
So you have something called the master,
which is a SQL Server Enterprise Edition
and Standard Edition instance,
and then you have all the other components in it.
So these are essentially a bunch of
containers that are running either compute pools,
so I'll call this compute pool,
there's a data pool,
and then there is storage pool.
So all of this is what we refer to as the cluster nodes.
Now what the cluster nodes do for you is it allows
you to actually expand horizontally.
If you're running out of compute capacity,
you can extend this horizontally,
the data pools and the storage pools.
Now, if you have
a SQL Server 2019 or 2017 Instance
which is not a big data cluster,
you can actually take the database and you can,
I'm going to use BDC as an abbreviation here,
you can actually take the database and restore it into the master.
So the BDC master can actually
host a SQL Server database in there,
and it is not just a 2019 database,
you can host it from 2008,
you can host it from 2012, or higher.
A backup restore just works.
The other piece that you could do is you can actually
use data virtualization to read from it.
So if you have external data sources like another RDBMS or if you
have a source like
another HDFS source sitting in your enterprise environment,
you can actually pull data from
these and you can actually read from that.
So this gives you a bunch of
different business scenarios that you can
enable with using SQL Server 2019 big data cluster.
Now the question comes in, how do you actually license for these?
So let's say the master instance in
my scenario is using an Enterprise Edition of SQL Server
and let's say that has about 10 Enterprise Edition cores,
and then your big data cluster nodes
have the compute pool, the data pool,
and the storage pool, and let's say all of them
have 10 cores each just to keep the calculation simple.
So now in this example,
you have 30 cores of big data cluster in there.
So you would need to license for 10 cores of
Enterprise Edition and 30 cores of big data clusters in there.
These are priced differently.
Each big data cluster node is licensed by cores and
each big data cluster core is licensed at $200 per year.
What that does for you is it allows you to scale out
your big data cluster environment
or your master environment separately.
Now what happens when you do
something like a data cache and you need to expand?
So in this scenario, you can actually,
for the first time, expand horizontally.
So your big data cluster nodes could go from 10 to 20 to 30 to 40,
and it could keep increasing as you
need and as your needs for your enterprise actually grow.
So in this scenario,
your 30 cores could become 40 without affecting
the number of cores that are required in the master to run them.
As long as you have compute capacity
to run all of these scenarios,
you should be able to
license only for what's required for the master
and what's required for
the big data cluster cores
inside the compute, data and storage pools.
Now the interesting piece comes in is
what happens when you actually need more?
Do you need to go buy? The simple answer is yes.
But if you have, let's say, Software Assurance,
which is a licensing benefit that you can
enjoy with enterprise agreements with Microsoft.
What that allows you to do is for each Enterprise Edition core,
you actually get an entitlement of eight big data cores,
and if you have a standard edition core in the master,
you get one big data core.
So for each of these cores that you get,
you can actually get
more licenses or more entitlement to run this environment.
So if I took this scenario here,
and let's say I started with 10 cores in my master and
30 cores in my BDC and I have software assurance,
I am essentially not running this with any cost.
This is actually an entitlement because these 10 cores now give
me 80 cores of big data cluster for free.
So till I actually exceed 80 cores,
I don't need to actually license.
So in PoC scenario,
if I had to actually take
this entire environment and draw what I drew below here,
you would actually go from 10,
20 until you reach 80.
There is actually no cost.
You're running this for absolutely free even in production.
Then when you went to 90,
100 you would actually
start needing to license 10 additional cores
here and 20 additional cores here for
big data at $200 per core per year,
and the master continues to stay.
Now for example, if your database has
started getting bigger and you had to
move from 10 to let's say you now need to go to 20.
Now if I take that architecture here,
now if you had 20 EE cores,
you would end up getting another 100 cores.
So now, you would
get 80 into two.
Previously we're getting 80 cores
because you had 10 in the master.
Now you have another 80,
so now that gets you a 160 big data cores.
So essentially in this entire architecture,
what ends up happening is if your workload increases for
the big data as long as you're not
exceeding your entitlement and you have Software Assurance,
you can keep increasing and you only pay the difference.
If your master workload,
if this workload keeps increasing,
you would need to keep increasing your master.
But what that does as a side benefit is,
it gives you more ability to scale
horizontally for your big data in the future when you need to.
So running PoC is a lot easier,
moving the POC workload into
production without incurring an additional cost for
your environment becomes even more
easier with SQL Server 2019 and Software Assurance.
If you need to learn more about these capabilities,
we have videos in this series earlier,
where we have talked about SQL Server 2019 and
the capabilities and the architecture of
the big data cluster. Thank you for joining this one.
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