>> Most of the world's largest enterprises
are still using mainframes today.
Learn how you can use data virtualization to
access that data from places like Power BI.
This week on Data Exposed.
[MUSIC] Hi, I'm Anna Hoffman,
and welcome to this episode of Data Exposed.
Today I'm joined by two special members from
the Azure core specialized workloads team, Bhuvi and Jim.
Thanks so much for coming on the show today.
I think we'll get right into it because we
have a lot of interesting things to talk
about related to exposing mainframe data to Azure.
And I'm not really sure what this even means,
so I'd love to just kick it off and
ask what types of scenarios are you seeing
customers have where they might want to
do data virtualization from their mainframes?
>> Sure. Thanks for having me here,
Anna, and again, to answer your question,
it's a very interesting question
itself because a lot of organizations,
even now, are having most of
their golden copies of the data on the mainframe.
So as per stats,
44 out of the 50 leading banks stopped and insurance,
largest retailers that you can think of,
unbelievable or not, but
they're still having their mainframe data.
And what becomes challenging is to expose that data outside of
the mainframe and integrate with
the other data types that they have outside of that.
It might be on Cloud,
it might be through
their mobile apps or other deep learning or AI or other use cases
that they might want to expose
their mainframe data and combine it,
integrate it in a more holistic fashion.
So that is where this becomes
a very strong product or service itself to offer.
>> Got it. Cool. Can you tell
us a little bit more about how it works?
>> Yeah, sure. Let me move you on a little bit
about how do you want to
go around it and then how it exactly works.
Like I said, there is a variety of data that's out there,
and sometimes it becomes a little bit more
time-consuming and costly to
access that data from the outside world.
And what we want is quick data, not stale data,
not data with latency and we want deeper analysis of the data.
So to make that happen,
let me quickly take you to the architecture.
Like I said, there can be
many clients that would be looking for that data,
and they want that data to come out of
the mainframe or combine it with the mainframe data.
To do that, let's say a request comes on the mainframe.
Now, the power of this solution is,
which is what I'm going to talk about is
virtualizing the data on the mainframe.
To virtualize the data on the mainframe,
imagine a data layer that sits on
top of the data that's already on your mainframe,
but it leverages a lot of data optimization
and through different type of drivers that we can
expose for you to get access to that data.
So if you walk with me all the way to the right,
you can see different data sources
that reside on to the mainframe.
Now, if you wanted to directly go from
the client side onto those data sources,
you'll have to go to the actual data, but in this case,
what we are doing is
that data that sits on the mainframe is virtualized,
and through virtual views,
you can get access to that data.
To optimize it even further,
for example, if you have big queries,
you can have that data fetched in
parallel or in serialized manner through buffers.
So imagine you have a query which
would have essentially taken you hours,
but using this solution,
the time can be significantly reduced.
With all of that processing happening,
the access to that data becomes very easy.
>> Got you. Super cool.
Makes a lot of sense why I would want to do this.
One question I have for you is,
on the screen there you have a zIIP type of server or engine.
What is that and how does that help
in saving costs for data virtualization?
>> Sure. So zIIP stands for
z-systems integrated information processor.
It's a mainframe specialty engine.
There are two types of engines that are on the mainframe:
specialty engine and gender processor engines.
Now, what happens is that
when you are talking to general processor,
it's more costly,
so that every time you make a request to the general processor,
there's more cost involved.
For zIIP engines,
they are more cost-effective,
better performance, and then at the same time,
they can give you results to big queries,
they can even run Java or Linux.
And then what happens using
this particular solution running on the zIIP engines,
what happens is that if
the workloads are efficient enough and qualified to run
on zIIP then these requests that are coming from
the client will not be routed to the general processor engines,
they will be routed to the zIIP engines,
and that is how you can reduce a lot of cost.
Also, the software that we are talking about,
it might be a software coming from IBM,
it can be software coming from Rocket,
and in both of these solution,
their solution or their software itself runs on zIIP engine,
majority, almost 99 percent of that.
>> Got you. So you're set up to get
the best cost savings using this method.
When it comes to this mainframe data,
how's it typically classified?
>> There are many categories,
but at the broad level,
we can call them as structured data,
semi-structured data or operational data.
So as you see on the right,
the structured data can be DB2,
Adabas natural, IDMS, IMS.
Semi-structured is more around MQ, VSAM,
or sequential data sets,
but operationally is all about data logging.
So you can imagine things like system logs,
SMF records, or tape data.
>> Got it. Cool. Now that we understand why we want this,
what the approach is,
I heard there was a great demo.
Jim, you want to take us through that?
>> Sure, yeah.
What we have here is actually using Power BI.
And one thing I also wanted to point out was this is
not copying the data over from the mainframe.
When you're accessing it via virtual host,
you are accessing the data via a virtual host,
or the data is virtually exposed via
the virtual host back to the mainframe store.
So when you're talking about VSAM data or they're
flat file data sources
that to the mainframe look like data sources.
When you write back to those data sources via virtual database,
is it actually writes back to those.
In this case, I'm actually showing connecting
to mainframe data from Power BI.
So this data right here is Power BI's read-only,
but when I click into different items,
it actually updates using the regular Power BI functionality,
but this data physically resides on the mainframe.
In this case, it's actually VSAM data,
but it could be DB2 data,
it could be IMS data,
all the other data sources that were listed in there.
So this is a really simple example
of using database virtualization,
which is housed on the mainframe,
exposing the data and using it,
in this case Power BI,
but it could be any other Azure source as well.
>> Got it. Cool. That was a super great demo, concise.
I like the simplicity of it.
I always love to see Power BI I am
not a great person at building dashboards.
But as we close out,
I'd love to get you-all's thoughts on any tips or tricks for folks
who are either just getting started with
this or pretty far down the road with this,
what advice do you have to them?
>> I think it's just experimenting with the tool themselves.
There's a lot of information
available from rocket and also from IBM.
IBM has a red book on database virtualization.
They call it different things, rocket data virtualization.
So there's tons of information
to go out there and experiment with.
Anything you can think of, Bhuvi?
>> Yeah. And one thing that I would like to add on is try to
understand what is the best use case for the organization.
So if it is keeping the data on the mainframe,
this is going to work very well.
You might want to consider taking data out of
the mainframe or keeping some data here, some data there.
So as you mentioned,
you have to start studying about
different solutions that are available.
And then the power of this solution is the data doesn't move,
the data is in place,
and on top of that,
you are virtualizing the data.
>> Awesome. Cool. Well, thanks so much for coming on the show.
Personally I learned a lot about how to do
data virtualization from mainframes. To our viewers.
I'm sure you learned a lot as well.
If you like this episode, go ahead, give it a like,
leave a comment and let us know
what you think or what you learned.
Check the description for some information on how to learn more,
and we hope to see you next time on Data Exposed. [MUSIC]