PNDA.io
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Garnier, Jeremie
In this presentation, we will showcase the whole scope of PNDA, from baremetalprovisioning to application deployment. Also, we will describe the current usecase on NFV & IoT.
 Just. We are in Brazil, so thanks for coming. So I will just present Panda, which is a platform for network data analytics, but keeping just data analytics and also platform. So just few words. So what I like really is innovation, so it's doing the right stuff at the right time, but also DevOps because at the moment tech is very good. There's no issue with tech, we just issue with people. So that's why I like DevOps. So just to give you a few context about the project, you will see that virtualization, automation and the orchestration has made real-time service possible. There's a lot of open source, big data technologies at the moment. I will speak later on what they are. And there's a lot of big change in the network space. That's why Panda is already network oriented, but I will show you some new scales which is not fully networked. So what we've seen so far and what I explained Panda at all is that we've seen a sealed analytics pipelines. And the issue is that once you implement a data pipeline and by using dedicated technology, you are putting constraints on your pipeline. So it is not really optimal and you are duplicating efforts. So that's what we define Panda and why we want to achieve a really good big data platform. So we do not invent a lot of stuff. We just present you some key aspects of the platform. But basically, this is a Linux Foundation open source project. Why Panda? So we really want to make all the big data space really easy for you to use. So I will give you a live demo about deploying some Spark streaming applications. And so what we want to achieve is we need to remove the complexity of working with big data technologies. And if you want to have a quick look, so there's a lot of technologies on the big data space and you've got some open source distribution of Adoop like Ortonworks, which is fully open source, but there is also Cludera distribution, MAPAR. But you can also go the other side with Planck, which is not open source at all. So we are right in the middle. So just to illustrate one thing, if you want to just keep one takeaway of this presentation, Panda is about the platform. So do not spend any time on building a platform for doing big data stuff. Just be focused on your use case and on the application. Because you will waste a lot of time of fine tuning your Adoop cluster, making analysis, etc. So we do not care about the platform. So here is just a short view about what is Panda. So the platform is just in the middle. So on the left-hand side, you've got how to integrate with the platform. So we just use Kafka, a well-known database technology, which is the better one at the moment, in order to be able to push data to Panda. So it's quite easy to use. We are providing as part of the project all the producer, the sample code, and the plugin to connect to the platform. So you can use LogStash. We've got the plugin for LogStash. We've got a plugin for OpenDelight. And we've got all sample code in PHP, Python, and Java. So it is just about using the Kafka API with the Agrocap situation. And then once you've got your data in the platform, you can either consume them in real-time directly from Kafka, or you can build a near-time application based on Spark streaming, or Spark batch processing, etc. So what we provide at the moment, so we are on the release 3.4 as part of this release, so we are providing all the installation to build your platform on top of OpenStack, or on Bar Metal. So this is based on each template. So in less than one hour, you can have your platform ready to use. It's also possible to swap the platform on Amazon. As I say, we've got plugins for LogStash, OpenVMP, and a lot more. And so as mentioned, so you must be focused on the right side, because this is where the value is. Once you've got all the data pushed to a cluster, then you need to build the app that really extracts the value of the data. And so we are already providing some simple applications such as full-planetics, and not more, but maybe I want to spend more time on all the text that are inside Thunder. So we are bringing all together using either each template or CloudFormation template for Amazon. We are using Solstack in order to orchestrate all these stacks. And so one benefit of Thunder is for every data you push inside the platform, this is automatically stored, well stored in HDFS. So do not care about the data, we'll manage it. So we are using Goblin in order to store that. And then after that, you can deploy any application on top of Thunder. And what we've built so far is with simple this process. We provide some simple code in order to help you build streaming applications, for example. And deploying it to Thunder is quite easy. We just build a component, you post your application, it is automatically deployed on the platform. You do not care about how to configure your arm, etc. Everything is managed by the platform, as I said. And so just a few words about the console, after I will do the demo. So we provide a console in order to make available all the relevant information and everything you need for using Thunder. So this is a kind of dashboard that shows you that all the components are fully working. And it gives you also all the statistics and all the links you need to know. So what we provide also is that, so you've got a set of software available. But we provide also all the operational UI to manage everything within the system. So as for example for Kafka, if you want to manage it, we are using a Kafka manager. So in order to create topics, set the repetition factor or set the partition, etc. So everything is available through the UI for the other part. So you can access to the Closet Arm Manager. If you want to start, because you are putting a lot of data within Thunder, if you want to start some investigation, we include Jupyter. Jupyter is a notebook in order to start working in an iterative mode with the data. So in order to play with all the data sets and start building something. And after, so you've got the links for Grafana, OpenTSDB, Impala, and all the stuff. So rather than spending time on the slide, I will show you a live demo if this is back. So everything is in GitHub. So if you go to github.com.pl under project, you will find 25 repository, which consists of, so you have everything from the heat template to the sample application. So for the example, I use heat template. So yesterday, I bootstrap a platform. So you've got, okay, so you can see that. So you've got a simple CLI with some parameters in order to bootstrap a cluster. So you've got a lot of steps. So in 40 minutes, I've got a ready to go big data platform. So just to show you, so on the horizon UI, so here what to collect. So when we build a platform on top of OpenStack, so we build a dedicated environment for Thunder. So it consists of a bastion, a dedicated network, and a set of VMs. So everything is automatically created. So as I said, you do not care about the platform, we manage that. So this is the real Thunder console. So just to give you some insight. So on the metrics part, what we've built, so we've built a platform testing component, which will add all the metrics for all the components in order to ensure that they are working and expose all the information you need. So you've got all the information for HGPase, for example. You've got all the information for Iparra, Uzi, et cetera, et cetera. And you've got also all the stats for all the workers available in this cluster. So this is quite useful. Just to show you, so here for the demo, I've just created a topic. And so you will see that there is some data coming over it. And as I mentioned, so here's the U-Service, which is part of CDH. This is the way to browse the HGFS. So everything is stored in a data set. So any data push to Kafka goes over HGFS through Goblin, and it's sorted by source. So we've got a little, we do not put any constraints on the data pipeline, as I said. So we just put an everyone's calculation in order to specify a little platform schema, which is just about defining a source in order to know where the data is coming from. A timestamp, a host, and then the raw data. So the raw data can contain whatever you want. This is where you will put all your event data for the metrics. So here's an example, so I've got a collect this source, and so it is sorted also by dates. So just to give you some information about the demo, so I just have a little bit on script that will push data, so random data to a Kafka topic. So this is the avod.kiso.metrics. So just to show you that with just 10 lines of Python, you can push data to Kafka, to Panda. So to Kafka, you just need to use the raw schema and then put your raw data within the message. So just to show you that the raw schema is quite simple. It says timestamp source or SIP and raw data. So I will push data just after. Just let me show you that. So at the moment, so I've got my working cluster. What I will do is I will deploy a streaming application. So I already compiled the sample code available on GitHub, which saw the Spark streaming application with just read information from Kafka, and we'll push it to open TSDB, which is a time-server database. So I just use the package, so there's a few configurations to set. I called the application for the demo. Good name. It's creating. Okay. So the application is available on top of the cluster. I can run it and just to show you that now I can produce data to the cluster. If I go to open TSDB. Okay, so I've got my data that are coming over. You can see that I've got some points. So it's just to show you that this is a real-time application. Basically, it was easy to produce data to Panda to deploy in the application, which just get the data from Kafka and push it to open TSDB. So open TSDB is a well-known time-server database. So most of the time, all the data are based on timestamp, especially on the network side. So that's why we use to do this kind of demo. So just to show you that it's quite easy to use and simple. So I'll show you that this was back upside in case we lost the network. So there is bare metal and open stack deployment based on heat template. You can also deploy on AWS if you want. So we've seen the console, all the platform testing. So the data ingestion is quite easy to do just with a simple Python code. So we've got also ODEI plugin and log stack. So log stack is just a matter of configuration. On the data storage part, so I show you that with Govling, everything is stored in the HGFS, but you can use also HBase as part of the platform. The application management is made simple. You just deploy package and it is automatically deployed within the adobe cluster. And then after you've got all the time series in Pala interface available. We use also in Pala because this is an easy connected to Tableau, which is a well-known BI tool. And so just behind that, so we've got at the moment the Clodior distribution of adobe. So you and then you've got all the management UI behind that. So now I just run through the platform. So you see that this is everything is available. So we apply it first on NFV in order to get more analytics on top of the network. So the first use case built on top of that were the service analysis and the service impact analysis. And we've done also with MOOC software. So we integrate MOOC software in order to provide some fault tolerance on top of NFV infrastructure. We also apply Ponder for IoT and smart cities, for example. This is what we've done so far in Paris. So this is good because this was a data driven approach. And so we put a lot of sensor within Place Delanation, which is a big place where we've got a lot of traffic. And the city wants to get invents from the work that they will open. They will close all the roads that come to the place in order to ensure that there is less pollution, etc. So we put a quality sensor. We get data from the device available in the place through the wireless connection. We are able also to interact with video analytics solution in order to identify the flows. So once you've got the infrastructure, you can build applications on top of that. So we are able to identify the different flows happening on the vehicle flows for each road. So then in case of the closed one, we identify the impact. So we are able also to correlate the noise with the vehicle. We are able also to identify the density of the pedestrian per section. And also we are in France. We can do more analytics regarding the noise and correlate that with strikes because we are used to strikes in France. So as you see, there's a lot of spikes here. And this was... So there's some spikes and we can identify that this was when they are striking. So a lot of spikes. Also, as you see, we talked a lot about Open Daylight and BGP. So we have done some work on top of that. Maybe what I can do is I can show you a short video about the work done on... So this is... So what we've done so far, so we've put all the prefix within Panda. So as Jill demonstrated just later on, just so extracting everything from Open Daylight and putting that into Panda. So we've got all the data. So this is currently, I would say, in development, but we will soon demonstrate it on the Cisco Live Berlin. So you will see that all the BGP events are stored automatically in HDDFS. And then after that, so you can run... So BGP application on top of Panda. So this is a video. This is good because this is working faster than a live demo. So we can launch multiple instances of an application and then after... So we've done some data exploration using Jupyter. So this is a way to show you how the Jupyter works. So you can do some iteration on top of all the data set and start doing some visualization. And here you can have a network topology graph based on all those BGP data. And after, you are free to spend more time on the application and visualization, which is a key part of all the big data space. And so you can look at all the prefix. You can sort them, their IS. And after just for the fun, after you are free to do nice visualization on top of all those big data. So this is... But keep in mind also that Panda is about big data. So you need to have a huge data set or a big stream of data coming to the platform. Otherwise, this is not about big data. So this is just a nice example. Okay, so we are building an ecosystem around Panda in order to... So we are a small team at the moment on the core development. But we are working with a lot of SP companies in order to build this ecosystem and also with third party. So just to mention that so we are working with the Linux Foundation. So we are working with MOOC, especially for the full analysis in the network with Topol, with strings. We are working also with Open Datasoft around all the smart cities topic with opening all the data. We also have NGNR, ontology, etc. Just to give you a few words about what's coming for the next three years. So we plan to do some stuff, especially the big stuff is the data security, which is always good to consider once working with the data. We will do open source ADOOP at the moment. We are leveraging through the distribution, but we plan to work on the ADOOP upstream in order to have a fully open source platform. And also on the app side, which is key, we will have the BGPD panetics, which will be showcased at the Syscoli Berlin. We also have black hole detection, etc. So you still wonder why you plan to have an open source platform and you've got everything you need for playing with big data. And if you still wonder what is Panda, go to the website on panda.io, we've put a guide. So I would say read the fucking manual, but this is really... Yeah, we put efforts on documentation. But now you have a complete guide which explains all the technologies we try to give you as much insight as possible. So you have to understand all the technologies, how to work with Panda, how to integrate, how to produce, how to work on the data instrumentation, how to use all the sample code in order to build your application on top of Panda, etc. And so that now I've got five minutes for our question. Yes. Sorry? Yes. So we can discuss that, but we plan to... Oh, okay. So the question is... I was thinking that it was two minutes. Okay, so the question is what about VPP? So we are currently... we will integrate VPP, but this is in the other list. We can discuss that this part, but I'm not focusing on this part at the moment, but... Yes. So the question relates to reporting using Kafka today, well that's required to be VBS. So using Kafka or... Ah, it's not about V... Okay, this is stream set, right? Yes, it's the same case, really, so I'm going to be some of the last... I have to do a break from the last one. Okay, so it's about... So the question is about... So using a stream set instead of Kafka. So for Angular, at least, so if you go to the Angular website, you will see that they're using stream set and we are also putting Panda on the architecture. So I've got a diagram, we can discuss that. So we... stream set do not replace Kafka, but we can be compatible with this kind of solution. Yes, sure. Any other question? And I do not have stickers, so... Okay, so thanks a lot of time. Thank you. So here, so we are... So from stream set we put... So it could be like some kind of infuse stuff coming in or whatever. Yeah, yeah, yeah. I know that more giants have... I was having the AT&T guys already because they actually made the meeting here. But in some conversation around that, I just wondered whether it was making that plan and so on. Yeah, so... Currently, the issues that... so we are only five in the team. So we are focused on the core development. We are supporting only two customers, we cannot talk more. So we've got focused on Verizon and Angular that will also... There is also a... AT&T. Okay, everybody, this is Billy and I just want to get popular and talk so we are going to need everybody who comes to MTC to move in towards the middle. So we can definitely be putting up a group. Also, if you go into that core, there are six of both sides of the team. You can come across here and I'll take you to the other side. Okay. Okay.
 Just.  We are in Brazil, so thanks for coming.  So I will just present Panda, which is a platform for network data analytics,  but keeping just data analytics and also platform.  So just few words.  So what I like really is innovation, so it's doing the right stuff at the right time,  but also DevOps because at the moment tech is very good.  There's no issue with tech, we just issue with people.  So that's why I like DevOps.  So just to give you a few context about the project,  you will see that virtualization, automation and the orchestration  has made real-time service possible.  There's a lot of open source, big data technologies at the moment.  I will speak later on what they are.  And there's a lot of big change in the network space.  That's why Panda is already network oriented,  but I will show you some new scales which is not fully networked.  So what we've seen so far and what I explained Panda at all  is that we've seen a sealed analytics pipelines.  And the issue is that once you implement a data pipeline  and by using dedicated technology,  you are putting constraints on your pipeline.  So it is not really optimal and you are duplicating efforts.  So that's what we define Panda and why we want to achieve a really good big data platform.  So we do not invent a lot of stuff.  We just present you some key aspects of the platform.  But basically, this is a Linux Foundation open source project.  Why Panda?  So we really want to make all the big data space really easy for you to use.  So I will give you a live demo about deploying some Spark streaming applications.  And so what we want to achieve is we need to remove the complexity of working with big data technologies.  And if you want to have a quick look,  so there's a lot of technologies on the big data space  and you've got some open source distribution of Adoop like Ortonworks,  which is fully open source, but there is also Cludera distribution, MAPAR.  But you can also go the other side with Planck, which is not open source at all.  So we are right in the middle.  So just to illustrate one thing, if you want to just keep one takeaway of this presentation,  Panda is about the platform.  So do not spend any time on building a platform for doing big data stuff.  Just be focused on your use case and on the application.  Because you will waste a lot of time of fine tuning your Adoop cluster,  making analysis, etc. So we do not care about the platform.  So here is just a short view about what is Panda.  So the platform is just in the middle.  So on the left-hand side, you've got how to integrate with the platform.  So we just use Kafka, a well-known database technology,  which is the better one at the moment,  in order to be able to push data to Panda.  So it's quite easy to use.  We are providing as part of the project all the producer, the sample code,  and the plugin to connect to the platform.  So you can use LogStash.  We've got the plugin for LogStash.  We've got a plugin for OpenDelight.  And we've got all sample code in PHP, Python, and Java.  So it is just about using the Kafka API with the Agrocap situation.  And then once you've got your data in the platform,  you can either consume them in real-time directly from Kafka,  or you can build a near-time application based on Spark streaming,  or Spark batch processing, etc.  So what we provide at the moment, so we are on the release 3.4 as part of this release,  so we are providing all the installation to build your platform on top of OpenStack,  or on Bar Metal.  So this is based on each template.  So in less than one hour, you can have your platform ready to use.  It's also possible to swap the platform on Amazon.  As I say, we've got plugins for LogStash, OpenVMP, and a lot more.  And so as mentioned, so you must be focused on the right side,  because this is where the value is.  Once you've got all the data pushed to a cluster,  then you need to build the app that really extracts the value of the data.  And so we are already providing some simple applications such as full-planetics,  and not more, but maybe I want to spend more time on all the text that are inside Thunder.  So we are bringing all together using either each template or CloudFormation template for Amazon.  We are using Solstack in order to orchestrate all these stacks.  And so one benefit of Thunder is for every data you push inside the platform,  this is automatically stored, well stored in HDFS.  So do not care about the data, we'll manage it.  So we are using Goblin in order to store that.  And then after that, you can deploy any application on top of Thunder.  And what we've built so far is with simple this process.  We provide some simple code in order to help you build streaming applications, for example.  And deploying it to Thunder is quite easy.  We just build a component, you post your application,  it is automatically deployed on the platform.  You do not care about how to configure your arm, etc.  Everything is managed by the platform, as I said.  And so just a few words about the console, after I will do the demo.  So we provide a console in order to make available all the relevant information  and everything you need for using Thunder.  So this is a kind of dashboard that shows you that all the components are fully working.  And it gives you also all the statistics and all the links you need to know.  So what we provide also is that, so you've got a set of software available.  But we provide also all the operational UI to manage everything within the system.  So as for example for Kafka, if you want to manage it, we are using a Kafka manager.  So in order to create topics, set the repetition factor or set the partition, etc.  So everything is available through the UI for the other part.  So you can access to the Closet Arm Manager.  If you want to start, because you are putting a lot of data within Thunder,  if you want to start some investigation, we include Jupyter.  Jupyter is a notebook in order to start working in an iterative mode with the data.  So in order to play with all the data sets and start building something.  And after, so you've got the links for Grafana, OpenTSDB, Impala, and all the stuff.  So rather than spending time on the slide, I will show you a live demo if this is back.  So everything is in GitHub.  So if you go to github.com.pl under project, you will find 25 repository,  which consists of, so you have everything from the heat template to the sample application.  So for the example, I use heat template.  So yesterday, I bootstrap a platform.  So you've got, okay, so you can see that.  So you've got a simple CLI with some parameters in order to bootstrap a cluster.  So you've got a lot of steps.  So in 40 minutes, I've got a ready to go big data platform.  So just to show you, so on the horizon UI, so here what to collect.  So when we build a platform on top of OpenStack, so we build a dedicated environment for Thunder.  So it consists of a bastion, a dedicated network, and a set of VMs.  So everything is automatically created.  So as I said, you do not care about the platform, we manage that.  So this is the real Thunder console.  So just to give you some insight.  So on the metrics part, what we've built, so we've built a platform testing component,  which will add all the metrics for all the components in order to ensure that they are working  and expose all the information you need.  So you've got all the information for HGPase, for example.  You've got all the information for Iparra, Uzi, et cetera, et cetera.  And you've got also all the stats for all the workers available in this cluster.  So this is quite useful.  Just to show you, so here for the demo, I've just created a topic.  And so you will see that there is some data coming over it.  And as I mentioned, so here's the U-Service, which is part of CDH.  This is the way to browse the HGFS.  So everything is stored in a data set.  So any data push to Kafka goes over HGFS through Goblin, and it's sorted by source.  So we've got a little, we do not put any constraints on the data pipeline, as I said.  So we just put an everyone's calculation in order to specify a little platform schema,  which is just about defining a source in order to know where the data is coming from.  A timestamp, a host, and then the raw data.  So the raw data can contain whatever you want.  This is where you will put all your event data for the metrics.  So here's an example, so I've got a collect this source, and so it is sorted also by dates.  So just to give you some information about the demo,  so I just have a little bit on script that will push data, so random data to a Kafka topic.  So this is the avod.kiso.metrics.  So just to show you that with just 10 lines of Python, you can push data to Kafka, to Panda.  So to Kafka, you just need to use the raw schema and then put your raw data within the message.  So just to show you that the raw schema is quite simple.  It says timestamp source or SIP and raw data.  So I will push data just after.  Just let me show you that.  So at the moment, so I've got my working cluster.  What I will do is I will deploy a streaming application.  So I already compiled the sample code available on GitHub,  which saw the Spark streaming application with just read information from Kafka,  and we'll push it to open TSDB, which is a time-server database.  So I just use the package, so there's a few configurations to set.  I called the application for the demo.  Good name.  It's creating.  Okay.  So the application is available on top of the cluster.  I can run it and just to show you that now I can produce data to the cluster.  If I go to open TSDB.  Okay, so I've got my data that are coming over.  You can see that I've got some points.  So it's just to show you that this is a real-time application.  Basically, it was easy to produce data to Panda to deploy in the application,  which just get the data from Kafka and push it to open TSDB.  So open TSDB is a well-known time-server database.  So most of the time, all the data are based on timestamp,  especially on the network side.  So that's why we use to do this kind of demo.  So just to show you that it's quite easy to use and simple.  So I'll show you that this was back upside in case we lost the network.  So there is bare metal and open stack deployment based on heat template.  You can also deploy on AWS if you want.  So we've seen the console, all the platform testing.  So the data ingestion is quite easy to do just with a simple Python code.  So we've got also ODEI plugin and log stack.  So log stack is just a matter of configuration.  On the data storage part, so I show you that with Govling,  everything is stored in the HGFS,  but you can use also HBase as part of the platform.  The application management is made simple.  You just deploy package and it is automatically deployed within the adobe cluster.  And then after you've got all the time series in Pala interface available.  We use also in Pala because this is an easy connected to Tableau,  which is a well-known BI tool.  And so just behind that, so we've got at the moment the Clodior distribution of adobe.  So you and then you've got all the management UI behind that.  So now I just run through the platform.  So you see that this is everything is available.  So we apply it first on NFV in order to get more analytics on top of the network.  So the first use case built on top of that were the service analysis and the service impact analysis.  And we've done also with MOOC software.  So we integrate MOOC software in order to provide some fault tolerance on top of NFV infrastructure.  We also apply Ponder for IoT and smart cities, for example.  This is what we've done so far in Paris.  So this is good because this was a data driven approach.  And so we put a lot of sensor within Place Delanation, which is a big place where we've got a lot of traffic.  And the city wants to get invents from the work that they will open.  They will close all the roads that come to the place in order to ensure that there is less pollution, etc.  So we put a quality sensor.  We get data from the device available in the place through the wireless connection.  We are able also to interact with video analytics solution in order to identify the flows.  So once you've got the infrastructure, you can build applications on top of that.  So we are able to identify the different flows happening on the vehicle flows for each road.  So then in case of the closed one, we identify the impact.  So we are able also to correlate the noise with the vehicle.  We are able also to identify the density of the pedestrian per section.  And also we are in France.  We can do more analytics regarding the noise and correlate that with strikes because we are used to strikes in France.  So as you see, there's a lot of spikes here.  And this was...  So there's some spikes and we can identify that this was when they are striking.  So a lot of spikes.  Also, as you see, we talked a lot about Open Daylight and BGP.  So we have done some work on top of that.  Maybe what I can do is I can show you a short video about the work done on...  So this is...  So what we've done so far, so we've put all the prefix within Panda.  So as Jill demonstrated just later on, just so extracting everything from Open Daylight and putting that into Panda.  So we've got all the data.  So this is currently, I would say, in development, but we will soon demonstrate it on the Cisco Live Berlin.  So you will see that all the BGP events are stored automatically in HDDFS.  And then after that, so you can run...  So BGP application on top of Panda.  So this is a video. This is good because this is working faster than a live demo.  So we can launch multiple instances of an application and then after...  So we've done some data exploration using Jupyter.  So this is a way to show you how the Jupyter works.  So you can do some iteration on top of all the data set and start doing some visualization.  And here you can have a network topology graph based on all those BGP data.  And after, you are free to spend more time on the application and visualization,  which is a key part of all the big data space.  And so you can look at all the prefix.  You can sort them, their IS.  And after just for the fun, after you are free to do nice visualization on top of all those big data.  So this is...  But keep in mind also that Panda is about big data.  So you need to have a huge data set or a big stream of data coming to the platform.  Otherwise, this is not about big data.  So this is just a nice example.  Okay, so we are building an ecosystem around Panda in order to...  So we are a small team at the moment on the core development.  But we are working with a lot of SP companies in order to build this ecosystem and also with third party.  So just to mention that so we are working with the Linux Foundation.  So we are working with MOOC, especially for the full analysis in the network with Topol, with strings.  We are working also with Open Datasoft around all the smart cities topic with opening all the data.  We also have NGNR, ontology, etc.  Just to give you a few words about what's coming for the next three years.  So we plan to do some stuff, especially the big stuff is the data security,  which is always good to consider once working with the data.  We will do open source ADOOP at the moment.  We are leveraging through the distribution, but we plan to work on the ADOOP upstream in order to have a fully open source platform.  And also on the app side, which is key, we will have the BGPD panetics, which will be showcased at the Syscoli Berlin.  We also have black hole detection, etc.  So you still wonder why you plan to have an open source platform and you've got everything you need for playing with big data.  And if you still wonder what is Panda, go to the website on panda.io, we've put a guide.  So I would say read the fucking manual, but this is really...  Yeah, we put efforts on documentation.  But now you have a complete guide which explains all the technologies we try to give you as much insight as possible.  So you have to understand all the technologies, how to work with Panda, how to integrate, how to produce,  how to work on the data instrumentation, how to use all the sample code in order to build your application on top of Panda, etc.  And so that now I've got five minutes for our question.  Yes.  Sorry?  Yes.  So we can discuss that, but we plan to...  Oh, okay. So the question is...  I was thinking that it was two minutes.  Okay, so the question is what about VPP?  So we are currently... we will integrate VPP, but this is in the other list.  We can discuss that this part, but I'm not focusing on this part at the moment, but...  Yes.  So the question relates to reporting using Kafka today, well that's required to be VBS.  So using Kafka or...  Ah, it's not about V...  Okay, this is stream set, right?  Yes, it's the same case, really, so I'm going to be some of the last...  I have to do a break from the last one.  Okay, so it's about...  So the question is about...  So using a stream set instead of Kafka.  So for Angular, at least, so if you go to the Angular website, you will see that they're using stream set and we are also putting Panda on the architecture.  So I've got a diagram, we can discuss that.  So we... stream set do not replace Kafka, but we can be compatible with this kind of solution.  Yes, sure.  Any other question?  And I do not have stickers, so...  Okay, so thanks a lot of time.  Thank you.  So here, so we are...  So from stream set we put...  So it could be like some kind of infuse stuff coming in or whatever.  Yeah, yeah, yeah.  I know that more giants have...  I was having the AT&T guys already because they actually made the meeting here.  But in some conversation around that, I just wondered whether it was making that plan and so on.  Yeah, so...  Currently, the issues that... so we are only five in the team.  So we are focused on the core development.  We are supporting only two customers, we cannot talk more.  So we've got focused on Verizon and Angular that will also...  There is also a... AT&T.  Okay, everybody, this is Billy and I just want to get popular and talk so we are going to need everybody who comes to MTC to move in towards the middle.  So we can definitely be putting up a group.  Also, if you go into that core, there are six of both sides of the team.  You can come across here and I'll take you to the other side.  Okay.  Okay.
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