Azure Unblogged - Event Hub on Azure Stack Hub

00:04 - In today’s episode, we’re going to speak, with Manoj Prasad, about Event Hubs on Azure Stack Hub. Stay tuned.

00:12 - [MUSIC] >> Hey, Manoj. How are you doing? >> I’m doing good, Thomas. How are you? >> Doing very well. Happy to have you on today’s Azure Unblogged.

00:30 - >> Pleasure is mine. >> Perfect. You are a PM working on Event Hubs on Azure Stack Hub.

00:38 - Can you please quickly introduce yourself a little bit on what you’re working for? >> Yeah, absolutely.

00:44 - My name is Manoj Prasad. I’m the PM for Event Hubs on Azure Stack Hub.

00:50 - I’ve been part of Azure Messaging team for four months now.

00:55 - It’s been great. As a product owner, it’s on me to drive the roadmap and take the features to the release, like GA and things like that.

01:07 - Those are the things that I manage for Event Hubs on Azure Stack.

01:12 - >> Okay. That is amazing. By the way, that sounds a really interesting job.

01:16 - >> Thank you. Absolutely. >> For the people who are watching this and are probably not so familiar with Event Hubs, can you give us a very short introduction of what Event Hubs is? >> Yeah, absolutely. Event Hubs, think about the streaming jobs.

01:34 - If you want to stream the data, and if you want to ingest those streaming data, Event Hubs is a perfect service, whether it be on Cloud or on-prem, like on Azure Stack Hub.

01:48 - Think about all the use cases, maybe you’re trying to ingest the streaming data, Event Hubs are perfectly capable of handling pretty high workloads.

01:59 - >> Okay. That is awesome. Yeah, I remember I’ve worked with Event Hubs in Azure itself.

02:06 - But now, again, as we are here for this Azure Unblogged, we’re talking about also bringing Event Hubs to Azure Stack Hub which basically allows customers now to run Event Hubs, that service we have, in their own data center or at their own actual locations.

02:24 - Can you explain to me a little bit of the use cases, why they would, for example, do that? >> Absolutely. The first thing is, let’s think about the oil gas industry where, think about those smart grids.

02:38 - Think about those smart grids, trying to stream the data.

02:44 - You can use those data to understand if any of those grids meets any maintenance, for example.

02:54 - I can get some insights into those data, and predict or prevent some batting that’s happening on those grids, for example.

03:04 - Maintenance use cases, you can pretty much stream those data through Event Hubs on Azure Stack Hub.

03:11 - Similarly, healthcare. Think about those hospitals where there’s a lot of devices trying to stream the data, and also it could be like small IoT sensors, for example, also trying to stream the data related to healthcare.

03:27 - You could as well use Event Hubs on Azure Stack Hub to ingest those streaming data, and you could also find some good insights from the data to understand, if any of those devices needs any maintenance or, sometimes, you could also use the data to get some insights on general profile, or maybe understand how the patients are doing, in general, or things like that related to healthcare, that’s going to be very beneficial if you use Event Hubs to ingest those data.

04:00 - Retail, for example, I’m offering something to the consumers.

04:05 - How do I get feedback from consumers, whether they’re happy with my offering? I’m doing some promotional events and pricing schemes, for example. How do I know that whether those are effective? How do I know that my consumers are always engaged? Event Hubs could be helpful to, again, ingest those data, and you could always want insights to get those feedback, that’s going to be a great use case on retail.

04:34 - In general application operations, think about those logs. Logs have a lot of things of valuable information.

04:42 - I could use the logs to do some monitoring, some troubleshooting, or pass the logs, and maybe find some patterns to see if there’s some issues, for example, things like that.

04:54 - You can always use Event Hubs to ingest those logs and, hence, do some analytics on those logs, as well. Now, financial.

05:04 - Think about those data related to those stocks, for example.

05:08 - You could use Event Hubs to ingest those streaming data.

05:13 - Also in credit card transactions, for example, anomaly detections, and things like that.

05:18 - If you want to find some anomalies, for example, in some credit card transactions, you could always ingest those data through Event Hubs and run some analytics or anomaly detections to find any fraudulent activity.

05:35 - That’s just an example of use case of financial.

05:39 - Security. For example, in your home, you have all the security devices trying to stream the data.

05:45 - You can always ingest them through Event Hubs and run some Threat Intelligence, run some analytics on that.

05:54 - Maybe, do you have any security holes, for example? Is there something you could do better? Things like that. In general, using the data to get some feedback, you could always use Event Hubs to ingest those data.

06:07 - Now, there’s many more use cases. These are just like simple, quite popular use cases I’m mentioning, but there’s many more things you could do with Event Hubs.

06:21 - >> Now, there are some pretty cool use cases.

06:25 - Absolutely. The healthcare makes, for me, a lot of sense, also financing.

06:30 - Obviously all of them make a lot of sense. But, basically, where you steam a lot of data, you don’t necessarily want to rely on the connectivity to the Cloud.

06:41 - For example, we don’t have enough bad or no connectivity to the Internet at all.

06:47 - You probably want to have a server which can handle that in your own location, not necessarily to, again, rely on this, and some things like as you just showed. In some use cases.

06:57 - It can be very critical, so you want to take that out of the equation.

07:03 - >> Absolutely. A lot of these use cases, they might not be needing them to be ingested into the Cloud, for example.

07:14 - Before it enters the Cloud, they might want to do some cleansing of the data, for example, Awesome enrichment, for example, that where Event Hubs on Azure Stack really comes into play.

07:25 - >> That’s awesome. That is super-interesting, and I’d love to hear more about that.

07:31 - >> Absolutely. >> Now what you express basically said you’re working on Events Hubs on Azure Stack.

07:39 - Can you give me a short overview about how that actually looks like in a technical sense? >> Absolutely.

07:47 - >> When you look at Azure Event Hubs, there is two things that’s important, right? One is the producer, the one that produces all this events and the other part is the consumer.

07:58 - It consumes all these events from Event Hubs.

08:01 - Now, producers, they could use any of the three protocols that are available, which is HTTPS or AMQP, or Kafka.

08:09 - They could use any of these protocols to produce the events.

08:13 - On the other hand, the receivers too can use any of these three protocols to consume the events as well.

08:20 - Now, when you have these consumers, it’s not like in a one or two, it’s many consumers, potentially there would be many consumers, and in order to have many consumers, the concurrency is pretty critical because one consumer trying to consume events and waiting for the other is not really practical, right? >> Yeah.

08:45 - >> The concurrency is critical, and in order to enable the concurrency of consuming the events, partitions are designed.

08:55 - [inaudible] Event Hub, all the events just get into this different partitions, which basically then consumers read from the different partitions, and that’s how they concurrently do that.

09:13 - One consumer consuming events should not impact the other consumer also trying to consume the same events which means there might be some kind of an isolation required, right? Each consumers might need their own private snapshot of events in the Event Hubs. How’s that enabled? That’s through this consumer groups.

09:32 - Consumer groups provides the consumers a private view of events in the Event Hubs, and in that way, that each consumers can independently view their data irrespective of what the other consumer is doing.

09:48 - From a high level, I would say these are all the big players that are involved with the Event Hubs on Azure Stack Hub.

09:55 - >> Okay, now that’s pretty interesting. Obviously, you just brought up a really large amount of points, like it obviously needs to be scalable, and it needs to work for consumers.

10:07 - Again, there’s a lot of engineering work which obviously go into this, right? >> Yeah, absolutely. That’s why the partition thing is pretty critical for a scalability aspect, and also the concurrency aspect, and consumer groups, so yeah, so pretty much.

10:28 - >> Yeah, now that is fantastic. Again I always love to know and really understand how things are working.

10:35 - The next thing I really want to talk about is, we already covered this a little bit with the use cases, but I want to understand a little bit deeper now on what can Events Hubs do for me and why would a customer need it. So I’m sure there are a couple of things you can explain.

10:53 - >> Absolutely. Great question. Before even diving deep into Event Hubs, I probably need to understand what are the key things that Event Hubs is bringing onto the table, and why do I need to use that? Why do I need to use it on Stack Hub? Let’s try to answer those questions.

11:14 - The first and foremost thing, what Event Hubs brings is processing large volumes of events per second.

11:22 - You can ingest huge volumes of events per second and also consume huge volumes of events per second.

11:31 - So processing really large volumes of events per second is the key feature of Event Hub.

11:38 - Now, with Event Hubs, you can also do processing, if you need real-time or also batch, if your use case demands.

11:48 - Now real-time. Generally, what you could do is, you could have Azure Stream Analytics job, for example, consuming the events real-time from Event Hub, and maybe do some kind of processing, like maybe do something of analytics on that real-time.

12:06 - Maybe through a Power BI, you could easily visualize it, the real-time, as well, so just a simple use case of how you can do a real-time solution.

12:16 - In terms of batch, Event Hub supports a feature called Capture, where if enabled, it’s going to store the events into storage, like a blog, for example.

12:30 - Now applications can start reading in, maybe in a cadence from the blog or in a batch to process the events and do some analytics on that, maybe.

12:42 - That’s why Event Hubs will allow both the cases, maybe a real-time or even batch processing.

12:49 - Now, the third point which we touched upon this previously where the concurrency, if multiple consumers need to concurrently consume the data, then you really need the partitions, and Event Hub is already supporting that partition logic.

13:09 - Kafka. Now the Kafka support is amazing. The Event Hub is going to expose an endpoint for Apache Kafka producer and consumer APIs, which means it’s actually going to support the pass on-prem model.

13:27 - Let me explain what the pass on-prem model is.

13:31 - Without this, for example, you might have to manage your own clusters and things like that.

13:38 - But with this support, Event Hub is already going to do that for you, meaning on your Azure Stack up with Kafka support, the Event Hub is going to manage all those clusters for you, and hence, you can actually truly get the pass on-prem experience with this Kafka support.

14:01 - >> This is pretty cool. This is really cool.

14:04 - Again, I don’t need to care of it. It is a service which is offered to me to actually take care of it.

14:11 - >> Absolutely. The other cool thing about Kafka is you can produce with one protocol and you can consume with the other.

14:22 - For example, you can produce with Kafka and then consume with the AMQP, or you could do vice versa where AMQP and Kafka, or you can do both Kafka, things like this.

14:33 - It’s going to support this multiprotocol which is pretty cool, I would say.

14:37 - >> I can also use it like I said to translate from one protocol to the other protocol if I need to, right? If the consumers leave the work with a different protocol, I could definitely leverage that? >> Yes, absolutely.

14:52 - Now, let’s answer the why question. Why do we need to use Event Hubs on Azure Stack Hub? Think about those use cases where I need to ingest the data to maybe Azure Cloud.

15:07 - But having said that, I need to do some kind of a processing where I need to do that on-prem, I need to do that locally on-prem, maybe do some kind of an enrichment, or maybe sometimes due to privacy concerns, I probably want to remove all, maybe, private PII, Identifiable Information, for example.

15:28 - You could do any of those cleansing or enrichment on-prem through Event Hubs.

15:35 - Basically, you can ingest those events through Event Hubs on Azure Stack Hub.

15:39 - Then have a consumer do this cleansing or enrichment, and then have those data ready to be ingested into the Cloud again through Event Hubs.

15:51 - The great news is, Event Hubs is supported both on Azure Cloud and Stack Hub, which means it’s going to support this perfect hybrid Cloud model, where you can use Event Hubs locally, do some processing, and then again use Event Hubs on the Cloud.

16:07 - >> Okay, that is also something pretty cool, and I hope we can talk about that in just a minute because that is really like this is just raw, real power.

16:14 - Let’s say I have a use case where I just need Azure, I could run Events Hubs, obviously, in Azure, then I have use cases where I only want to run it on-prem.

16:24 - But then the great thing now, you just told me, is I can even combine them.

16:29 - >> Absolutely >> I can say, well, let’s do some processing on-prem. As you said, you probably have data sovereignty challenges there, you have network connectivity challenges, so you want to do some [inaudible] off the critical parts on your Azure Stack Hub system.

16:44 - But then, you can take the data which can actually go to the Cloud, and then use Events Hubs in Azure.

16:49 - I’m really waiting for that one, that you’ll show me that one.

16:53 - >> Great, so stay tuned. I think we’ll go to double-click on this a little bit, the [inaudible] for details.

17:00 - The next use case is, for example, I want to ingest the data into my on-prem, and I do not want the data to leave my on-prem system.

17:12 - For example, I do not want that to be visible outside from my on-prem network, for example.

17:20 - Then in those disconnected solutions, for example, I do not want my data to be exposed to the Internet, for example, or even Azure, or some of the public Clouds.

17:33 - Then guess what? Event Hubs is also supportive for those disconnected solutions.

17:38 - In fact, we just Event Hubs for disconnected solutions just last month.

17:43 - With this, this scenario is also enabled, which is great.

17:48 - >> This is when I, for example, have scenarios where obviously I work with government or with companies which again have scenarios where they don’t have Internet connectivity, or, for example, the typical cruise ship scenario, which we all go a lot of times.

18:03 - But obviously, a lot of customers have that request that they also need to run it without having any connectivity.

18:10 - That is absolutely great to have that. Now, one thing I want to know, so obviously one is running in Azure, one is our Event Hubs in Azure, and then I have now Events Hubs on Azure Stack.

18:21 - Can I use the same tooling, or do I need to use different tools, or how is that working? >> Great question, so we support disparity where, for example, if you write any applications on Azure Cloud for Event Hubs rather, the producer-consumer applications, now, you could use the same in all Stack as well for Event Hubs.

18:46 - Any applications you’ve already developed on Event Hubs, on Azure Cloud, you can reuse the same because the SDKs, even the portal experience, we are going to take a look at that a little bit later, this PowerShell, the CLIs, the SDKs, the samples, all is pretty much the same.

19:04 - There’s a parity on both the platforms which is great.

19:07 - >> That is awesome. This is really what it is about.

19:10 - If I already have tools, if I’m familiar with the tools in Azure, I’m automatically familiar with the same thing on Stack.

19:17 - >> Absolutely. Yeah. >> I want to come back about that hybrid scenario.

19:24 - You really mentioned that I can actually take advantage of both.

19:29 - I can actually run again, some parts of my application on Events Hubs on Azure Stack Hub and do some processing there, and then take it further for other parts on Azure.

19:42 - Can you show me and talk a little bit about that? >> Yeah, so let’s dive deep into this.

19:48 - I’m going to show a sample architecture of how that hybrid solution could look like.

19:54 - Again, this is just a simple architecture diagram.

19:59 - By no means, this is complete, because there’s plenty of things you could do as well.

20:04 - Let’s navigate from left to right. Of course, you have all this multitude of data sources.

20:11 - Different types of data, for example, streaming data, it could be logs, it could be weather data, or different business applications.

20:17 - This box here, what you’re seeing is the Stack Hub, and this box is the Azure club.

20:23 - Let’s dive deep into each of these boxes. You have this data, and like I said, I’m not comfortable ingesting this data directly into the public Cloud, rather, I want to ingest it locally, and do some processing, and then have the processed data then go into the public Cloud, for example.

20:43 - In such cases, this is the workflow that we can follow, where all this data gets ingested into Event Hub, on Azure Stack.

20:51 - Now, we’ll have a consumer, basically consuming these events, and doing some processing.

20:58 - It could be enrichment. I’d like some data to enrich the ingested data, or, for privacy concerns, I want to remove some Personally Identifiable Information, for example.

21:14 - I could do any of those cleansing, enrichment here.

21:17 - I could optionally store it in the Blob, as well, if my use case demands that.

21:22 - But basically once I’m ready with the processed data, that’s when I’m just going to ingest this into Event Hubs on Azure Cloud.

21:33 - Think about this. This is actually doing two roles.

21:36 - It is a consumer for this event hub, but actually a producer to this Event Hub on Cloud.

21:41 - That is the cool thing that this consumer does.

21:45 - Now once it’s ingested into Azure Cloud through Event Hub on Azure, then depending on my use case, I could do any of these workflows.

21:56 - For example, if mine is a real-time scenario, I want to get some real insights, real time, from these events that were ingested, then you can run these events through an Azure Stream Analytics, where I could write some queries, and based on my use case, I could do some analytics.

22:19 - I could also have a Power BI dashboard to visualize these insights that Azure Stream Analytics is generating, for example.

22:28 - This is a real-time workflow, I would say. Now, on the other hand, for example, if I need to store these events, maybe in a SQL format, because, there’s already capture feature that’s enabled on Event Hub.

22:43 - If it’s enabled, then it’s, by default, going to store all these events into Blob, for example, some storage.

22:50 - That’s already available by default, but my scenario is that I need it to be in SQL format for example.

22:58 - Then, what we can do is, as soon as the events are captured from Event Hub, it’s going to generate these captured event, and we can leverage that event through Event Grid, and have Azure function, for example, get that event delivered, and once that event gets delivered, then Azure function can go read those events from the Blob, and maybe do that conversion into SQL, and maybe store it into a SQL Data Warehouse.

23:32 - So this could be a SQL scenario, I would say, and many more.

23:38 - This is just a couple of scenarios I’m highlighting.

23:42 - But, really, once the data is available, based on my use case, there’s many different workflows that’s possible.

23:48 - >> Yeah, especially with the last thing you showed me.

23:51 - Basically then from Event Hubs, I can actually trigger functions.

23:55 - From there I can really play around through really everything I actually need to do, and Event Hubs is just a great tool for this.

24:04 - I also like the part were you said, coming back on the first part [inaudible] were you said, well, we could also reduce some of the data to make sure that there’s no personal information in it.

24:17 - Some of these really have requests, or as you said, we can also enrich the data and add something to it, which I need too for my analysis in Azure, I want to run it there or for long term storage, so that is pretty cool.

24:31 - Now, I know that, obviously because, Edge is like Hub, is a small version of it, if you’re a part of Azure, you compare it to Azure, I’m sure there must be some limitation, even though we have great consistency with both of them.

24:49 - You said, I can use the same tooling, I can use the same codes, and all of that.

24:53 - But, are there some limitations or some features which are not available on one or the other.

25:01 - >> Yeah, so let’s look into this, comparison of our offering on Event Hubs on Azure Stack versus the Azure.

25:11 - The good news is Kafka support is available on both, so that’s a great news.

25:16 - The Capture feature which I was talking earlier, that’s available on Azure Event Hub, but currently it’s not available on Azure Stack.

25:24 - But the good news is, it’s already part of our roadmap.

25:27 - We are already trying to plug this gap, to say.

25:32 - Then the good news is the cluster creation is available on both.

25:37 - What this means is, on Azure Event Hub, we generally support three three tiers, a standard, a basic, and the dedicated.

25:48 - Now, the basic and standard are multi tenancy models, but the dedicated is a single tenancy model, where, the customers can create something called capacity units, which is basically clusters, that the customers can create those customers and basically, it’s single tenancy models, so they can he entire resource is for themselves to use.

26:11 - What we have done is we have supported this dedicated model on the Event Hub on Azure Stack, which means the cluster creation experience, from Azure Event Hub is exactly the same as Event Hubs on Azure Stack as well.

26:27 - The customers can, again, create CUs, and then have the entire cluster basically for themselves.

26:34 - Now, the operator admin and operator diagnostics, those are pretty Azure Stack Hub-related concepts.

26:43 - That’s not really applicable on Azure Event Hubs.

26:46 - But the Azure Monitor, which is really useful for diagnostic settings and, in general, diagnostics and troubleshooting, that support is available on both the platforms, be it on Azure Event Hub or the Event Hub on Azure Stack Hub.

27:04 - Pricing, Like I said, the dedicated model that we support on the Azure Event Hub, that’s the same model that we support on Event Hub.

27:12 - As a result, on Stack Hub, the pricing is more based on core count.

27:19 - How many courses you are using, you’re basically going to get charged based on that usage.

27:24 - >> Okay, that is awesome. You basically get the dedicated part.

27:28 - No basic or standard in that sense. You really get the high level part.

27:31 - But because it can obviously be used in disconnected scenarios, you will be using another pricing model.

27:39 - Obviously, it makes more sense, obviously why it will be with the operator experience, as you said, in Azure is not needed because Microsoft operates that part.

27:50 - Then it’s cool that we already have all these features, especially the integration with Azure Monitor, and then also that you already highlight here the roadmap, so this is pretty awesome.

28:02 - >> Again, thank you for showing this. Now, what I want to see, obviously, we want to see that and also the viewers of this video want to see it in action.

28:10 - Is it possible that you show us a quick demo about what you can do with Event Hubs on Azure Stack Hub.

28:19 - >> Absolutely. I’ll be glad to provide a demo.

28:23 - In fact, I’m going to provide the demo based on the hybrid Cloud model that we talked about.

28:30 - Before we dive deep into the demo, let’s quickly look at, from a high level, the architecture.

28:36 - So that the viewers understands what are the things that’s part of the demo and what’s the architecture? What are the pieces that are involved in the demo.

28:47 - Suppose let’s say, I’m a business owner, and I have some stores, may be in Redmond area, for example.

28:56 - Then my intent is to ingest all those business transactions, which has credit card details and things like that.

29:05 - My intent is, I would like to know among all my business transactions, how many of them have fraudulent credit card usage, for example? How many of them may have some anomalies that I would like to know, because it might be impacting my business if I have more of this fraudulent credit card transactions.

29:30 - So that’s my intent as a business owner that, number one, I want something to ingest and have the data ingested someplace, but also I need some anomaly detection to understand how that’s impacting my business.

29:46 - With this, what I have is, as a business owner, I do not want all the customer details, which has credit card transactions, directly in the Azure Cloud.

29:59 - What I would like to do is, all this data that’s coming from my different stores, I would like to ingest them on-prem on my Azure Stack Hub instance, for example, through Event Hubs.

30:13 - So I’m going to ingest all this credit card transactions and other transactions through Event Hub.

30:19 - Then I have this consumer basically trying to cleanse the data.

30:23 - I would like to remove any kind of a PII information from that, and maybe do some enrichment as well.

30:29 - I’m going to do that, and then basically ingest all this details or events into the Azure Event Hub.

30:39 - Once I get the events on the Cloud, I would like to use the Azure Stream Analytics and write a query to basically run real-time a query to understand how many credit card transactions are fraudulent? In my scenario, what I’ve done is, if I see any two transactions coming from the same credit card, which is in different locations and five seconds apart, then I’m flagging that, basically.

31:09 - To be practical, if I have one credit card transaction which is coming from Redmond and the other credit card transaction which is coming from Chicago, for example, and they are merely five seconds apart, same credit card, then something is wrong.

31:24 - That cannot be practical. It’s a simple logic that I’m using to flag it, but it could be much complicated than that.

31:35 - But this is just a demo to highlight the importance of this hybrid model, let’s say.

31:45 - What I’ve done is, I have this Python scripts to basically simulate these transactions.

31:55 - Then I have the Event Hub stood up on my instance of Azure Stack Hub.

32:01 - Then I have this Python script here, which is consuming this events, and then ingesting them into Event Hub.

32:09 - Then I have the Stream Analytics job running.

32:11 - Let’s dive straight into those. This is the portal.

32:20 - Like I said earlier, the portal experience is the same for when you create an Event Hub on Stack Hub or Event on Azure, the portal experience is going to be the same.

32:32 - This is the portal for my instance of Azure Stack Hub.

32:40 - This is the Event Hub on my Azure Cloud. So you can see portal. azure. com.

32:47 - This is the Event Hub instance on the Cloud versus this is on my Stack Hub.

32:53 - You can see that the portal experience, the creating the name space and creating an Event Hub, it’s pretty much the same.

33:02 - Then we have this Event Hub that’s already created.

33:06 - Now, just to save some time, I’ve already created this before, just to get going with the demo.

33:12 - I have this Stream Analytics job setup which is running right now and this is the query.

33:20 - As you can see, it’s a simple query. If there are any two transactions, if the credit card ID is the same, and the time difference is just like sometime between 0-5 seconds, and the most important thing, the location is not the same, then it’s basically taking account of how many of those transactions I’m seeing, so that’s what this query is trying to do.

33:51 - Let’s run these scripts and show you the output really quick.

33:56 - Again, this script is ingesting events into the Event Hub on Stack.

34:03 - This is the receiver. This is the Python script which is doing two things.

34:06 - It’s, number one, consuming the events from Azure Stack Hub, and it’s ingesting them into Event Hub on the Cloud.

34:16 - Final, this one is a simple one. It’s just consuming events and just printing it out, just to show that the transactions has made it all the way from Stack Hub to the Azure.

34:31 - Let’s run these scripts really quick. This one is simulating those credit card transactions, and it’s already sent to the Azure Stack.

34:44 - Now this is receiving it from Azure Stack Hub, and you can see it, it’s already starting to receive. You can see in the prints it might be difficult to see, but it’s basically saying, “I received this event, and I sent this event to the Event Hub. ” It received the event from Stack Hub and sending the event to the Event Hub on Azure.

35:05 - Now last, we’ll start receiving it from the Azure Event Hub from the Cloud.

35:13 - You can see all these transactions which we injected here to the Stack Hub, it has made it all the way to the Cloud.

35:25 - Because from this prints, we can see that it’s receiving these events from the Azure Event Hub.

35:30 - Now, let’s go back and run this query. Now basically what I did is simulate 12 such transactions, where the credit card ID were seen, and the location were different, and they were just five seconds apart.

35:50 - Now, the Stream Analytics’ job is detecting those 12 transactions, and it’s displaying that there were 12 such transactions which were fraudulent, which may be fraudulent.

36:01 - Let’s let’s put it that way, which maybe fraudulent.

36:04 - Now what I can do is I can take it forward from here.

36:08 - Maybe just have a Power BI display this information.

36:11 - It could also display many more which this query can generate, and have a Power BI dashboard up and running, which means I could keep seeing the dashboard periodically to understand how these fraudulent transactions are impacting my business, for example.

36:30 - >> That is pretty cool. This is exactly the example like it shows the power of having something running in your own datacenter or at your actual locations, and then combining it with running it in Azure to do some advanced analyses.

36:45 - As you said, with that data now, and also to show, like when you showed the different architectures before, it will be now also something we could either, as you said, visualize it in Power BI, or you could build a function basically to run this certain task, or send out an email or a message in Microsoft Teams, or any of that to make sure that we actually get notified about this.

37:09 - That is really cool. I really like that solution.

37:14 - >> That’s right. >> Perfect. This was awesome.

37:19 - Absolutely great to see Events Hubs on Azure Stack Hub.

37:24 - I’m sure now people have obviously questions.

37:27 - Where can they learn more about it? How do they get started? Where can I find more about this? So can you tell me where would people go? >> Absolutely. There’s plenty of documentation on Event Hubs, or even on Stack Hub, or Event Hubs on Azure Stack Hub.

37:47 - So plenty of documentation which talks about what Event Hubs is and what are the features, for example.

37:53 - Portal experience, there’s plenty of samples to show how I can get started using Event Hubs to start producing events, and consuming events, and things like that.

38:05 - There’s also many videos, actually. There’s plenty of videos, which actually goes in-depth.

38:10 - Remember that one slide we talked where I gave a high-level overview of that Event Hub, there’s actually videos which goes in-depth, explaining those consumer groups, and partitions, and Kafka, and things like that.

38:24 - There’s plenty of resources available. >> That is awesome. We will definitely link all these resources in the description below this video.

38:33 - With that, I really want to say thank you for your time today.

38:36 - That was really interesting. For all the viewers, thank you for watching. Hopefully, I’ll see you in the next one.

38:44 - [MUSIC].