Azure Databricks Tutorial | Data transformations at scale
hey There! Tthis is Adam again and in this
video I'm going to be talking about,Azure Databricks. Dne of the leading
technologies for big data processing,it's fast its scalable and it's easy to
use in this video I'm gonna show you why,is that
so stay tuned,so Azure Databricks, what is databricks,I think the easiest to explain
databricks is... it's the big data,technology that Microsoft brought as one
of the services in Azure, it's a very,cool platform that is based on Apache
spark was it cool because it is created,and designed by the same people who
actually created a Apache spark and since,Apache spark is one of the leaders of
Big Data technologies on the market it,really promises fast transformations in
the cloud,so since it's based on Apac he spark,the key features that you would
get from this are first of all spark SQL,and data frames this is a library that
allows you to work on your structured,data as pretty much tables in any system that we've been working on,already
additionally you have some services that,allow you for streaming of the data so
if you're doing IOT or life even,applications this is one of the great
examples of how you can perform,transformations on the live system you
also have machine learning library which,allows you to do machine learning type
of transformations prepping and training,models using spark itself you also have
graphx so if you're doing some social,media type of applications then it is
also a great place to do so and,everything is basing on spark core API
which means you can use R, you can use,spark SQL which is a little bit
different than normal SQL it is more,limited but it's still very powerful so
you know SQL that could be a very good,feature for you to use without needing
to learn any other different language,you also have Python, Scala those are the
two main languages that you will be,using when developing in databricks but
you also have Java,if you need to do that databricks as a platform has a lot of features itself,besides being Apache spark based it also
has a runtime. Runtime combines all those,features together into singular platform which delivers you workspaces a,places where you can collaborate with
your friends and colleagues on your,scripts if you have multiple scripts you
can combine them into workflows,workflows are can be nesting of the
scripts... scripts coming another scripts,basically a simple ETL and also you have
a DB i/o which is databricks,input/output library allowing you to
easily connect multiple services both in,Azure and not only like Apache Kafka and
Apache and sorry in Hadoop but also,databricks has something called databricks serverless what this really means is,that when you work with databricks you
just specify what kind of server do you,want how powerful it is how many of
those servers do you want,what is the runtime that you want on it
and that's it... databricks as a platform,will manage handling and creation of
that clusters for you without you,needing to manage them at all and lastly
there is something called enterprise,security so databricks is integrated
very well with Azure and Azure Active,Directory so handling all those accesses
credentials,authorization everything is basing on
Azure AD so you can just use your,corporate credentials and identity to use
databricks itself there are a lot of,storage solutions that it can connect
to but the five main ones that has,native connectivity is a blob storage
data lake, data lake in both version 1,and 2, SQL data warehouse Apache Kafka
and Hadoop, we already mentioned some of,those but also there are several
applications that you can use,databricks for, the most common ones are
machine learning scenarios streaming,scenarios data warehousing so your
typical ETL prepping the data and power,bi which is very common case recently
but there are many,other applications that you can use databricks for since, this is a collaborative,platform it is really easy for users to
use it there's a UI that they can use,there's it's very... I would say it's very
simple... it's very simple once you know,the platform but since there is a UI you
don't really have to be technical savvy,in order to use it so your typical data
scientists engineer analyst once they,learn the platform is very easy for them
to use databricks as well,the typical,scenario that you would see databricks in
is during the prep and train for machine,learning or your typical prep which is
part of the ETL you normally have,ingestion layer so either data factory
Kafka IOT Hub, Event Hub or something,gathering your data from external
systems and putting it either on a blob,or data lake so this is where the databricks come in usually databricks will,grab the data from the blob or data lake
transform it or train the models if it's,machine learning scenario and put it in
some sort of database it can be their,SQL database cosmos DB data warehouse or
maybe even analysis services or you,of'course can put it back on a blob stored
if you want you tha
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Intro To Databricks - What Is Databricks
Intro To Databricks - What Is Databricks
what is going on guys and welcome back,to another video with me ben rogezon aka,the seattle daily guy well as i just got,back from the snowflake summit i feel,like it's only appropriate that i do a,video about data breaks so the focus of,this video is to answer the question,what is data breaks and why do people,use it when you look at the fact that,databricks is recording 800 million,dollars of revenue in 2021,it's got to make you stop and wonder,where in the world are they going to,grow to next and since there have been a,few times that databricks has,essentially passed the value of,snowflake based on their vc funding and,valuation,it makes you wonder,which tool is going to win out in this,battle of what people are calling data,lake houses now arguably that whole,concept i think was brought out a little,bit more from databricks but both,solutions are trying to sell themselves,as data platforms and not just you know,a data lake or not just you know a data,warehouse on the cloud,they want you to know that they are so,much more,so let's dive into databricks now,dataworks itself wasn't started till,about 2013 but much of the development,into spark itself happened,far prior there's actually a few,research papers you can pick up,including one on resilient data sets,which is kind of the focus or kind of,what spark is developed around um it's,basically processing and what you're,often gonna hear is rdds i'm going to,put up the paper here as well as link,for anyone who's interested in learning,more but basically it was developed by,some professors at uc berkeley and,eventually like anything else that is,difficult to manage people eventually,wanted an option,for managed spark services if you're,familiar with aws emr or,gcp's dataproc,that's essentially what you could do you,could set up spark jobs,using those managed services but,what if you went a few steps further,that's where databricks comes in,databricks is not just one open source,solution but in fact it's multiple,at its core in particular it's spark,delta lake and ml flow in particular,spark is pretty much unavoidable you're,going to use it whenever you're,processing data,delta lake and set up delta tables so,that's something that we can dive into,in a second video and ml flow again is,more of an option uh for those of you,who haven't worked with mlflow it's,basically going to take a lot of those,questions you have if you're a data,scientist in terms of how do i deploy,this model um,that's going to be your answer for a lot,of people it's going to take care of,like model registry model deployment,some model monitoring a lot of these,things that we don't always know what to,do with right like you're like i've,developed a model now what do i do with,it um mlflow is one option another,option you might have heard of is,kubeflow so ml flow is what databricks,uses as well as again delta lake and,spark but again most people are going to,most likely interact with the spark,layer mostly,but in a way that is very friendly for,any data scientist or data engineer,because they've set it up in such a way,that if you're familiar with jupiter,notebooks you're going to do great so,let's just dive into spark really,lightly so you can kind of understand,what it is what it's doing and what's,the whole focus you know what is an rdd,so apache spark was started in 2009 at,uc berkeley at amp labs with the goal of,balancing uh fault tolerance and,scalability that often you find with,hadoop,in a solution and the goal of spark was,to balance the fault tolerance,and scalability of hadoop while also,providing that ability to essentially,reuse sets of data across multiple,processes now i think it would be a miss,if i didn't go over data lake houses,because clearly uh databricks has,decided to bet uh on this horse and,basically every ad i've ever seen um for,databricks is often poking fun at the,concept of a data warehouse because what,they are viewing in terms of the future,of development and data management isn't,a data warehouse but instead a data lake,and both snowflake and databricks have,their definitions in terms of what is a,data lake house if you ask snowflake,what is a data lake house they're going,to define it again as a combination of,data warehouse and a data lake and,trying to find the benefits of both you,know the cost effectiveness of a data,lake with the data management kind of,benefits that you get in a data,warehouse things like security and just,clear table structures that make it easy,for analysts and future developers to,actually,approach the data and it's not just a,bunch of files that you know someone's,going to figure out what exists where,one thing i do think is interesting is,snowflake does seem to try to push more,towards the data science use case,for data lake houses whereas you know,databricks is clearly saying this is,everything this is sql this is business,intelligence this is real-time analytics,you know that's kind of the difference,that they're trying to
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What is Databricks? The Data Lakehouse You've Never Heard Of
What is Databricks? The Data Lakehouse You've Never Heard Of
in the digital age data is everywhere,we all leave a data footprint wherever,we go online,but with so much data it's sometimes,tricky to get your head around,how it's collected and why it's,collected given the vast quantities of,it,that's where databricks comes in one of,the market leaders when it comes to data,services,but what is the story of data bricks and,how have they reached a 28 billion,dollar valuation,here's how it happened first of all we,have to answer the question what is,databricks,well databricks is a san francisco,headquartered data and ai company,founded by ali godzii in 2013,who was one of the original creators of,apache spark,delta lake and ml flow built on a lake,house,architecture in the cloud which combines,the best elements of data lakes,and data warehouses delivering data,management and performance typically,found in data warehouses with the,low-cost,flexible object stores offered by data,lakes,they might seem like a boring business,that deals with data,and they even suggest they handle the,boring ai needs for their clients,but they're trusted by over 5 000,businesses around the world,some of which are huge institutions like,shell,hsbc t-mobile microsoft and amazon among,others,who rely on data bricks to help with,data engineering,science machine learning and analytics,to help data teams solve incredibly,difficult problems,essentially databricks helps clients,store clean,and visualize vast amounts of data from,disparate sources,some examples include finance firms,analyzing satellite data to understand,where to invest money,or shell even use data bricks to monitor,sensor data from,200 million valves to predict ahead of,time,if any will break there are limitless,uses for the software,the business was started in 2013 by the,team of engineers who launched apache,spark from the university of california,berkeley alongside one of the computer,science professors there,dave patterson apache spark is described,as a lightning fast,unified analytics engine for big data,and machine learning,and it has quickly become the largest,open source community in big data,with over a thousand contributors from,250 organizations,it's 100 open source hosted at the,vendor independent apache software,foundation,apache spark therefore laid the,foundation upon which databricks was,built,when they first set up godzi and the,team estimated they might be able to,sell their business one day for a few,hundred million dollars,far underestimating themselves they were,also often told that their idea wouldn't,take off,because people believed that the cloud,wouldn't work that you'd need an,on-prem or on-premise solution with some,companies investing billions into data,centers,but their bold stand which included,turning down 20 million dollars to build,an on-prem,version of their software has seen the,company rise to become,a giant today they make their money,through a model they call software as a,service open source,they offer a free open source version of,databricks but their software as a,service offering has more features that,would interest business clients like,reliability,scalability and availability with,everything being on the cloud,whilst their free version saw them grow,like a b2c company,databricks really needed to target,business customers who were more,reluctant when it came to paying for the,service,why pay when you can get something great,for free so they had to take a step back,and think what can we remove from the,free version that keeps it functional,and useful and what can we include in,our paid version,to make it good enough that people won't,mind paying for it and,customers have now been paying for it,freely so much so that data breaks have,surpassed,annual recurring revenues of 425 million,dollars which has attracted a lot of,attention from investors,and have raised over 1 billion dollars,in hard cash,which values the business at over 28,billion dollars with investments from,franklin templeton,fidelity cppib blackrock,alphabet and t rowe price among others,after an oversubscribed funding round,gotzi suggested that he's always left,some valuation on the table after each,funding round because,it's a long term game after all the goal,for the business,isn't just to get to ipo but to be,around for a long time,in a gigantic market in which they've,only just scratched the surface,the team is so ambitious that sometimes,they're keen to abandon yesterday's work,because today's innovative idea might be,even more successful,it's seen them partner closely with,microsoft which helps the tech giant,pass,lots of data quickly and databricks are,now an industry leader when it comes to,cloud-based data engineering and yet few,people have heard of them,perhaps with ipo fever around the corner,the public will soon know,who databricks are and understand the,importance of businesses like databricks,especially as technology continues to,improve,and that's how it happened thanks for,watching,you
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Designing Structured Streaming Pipelines—How to Architect Things Right - Tathagata Das Databricks
Designing Structured Streaming Pipelines—How to Architect Things Right - Tathagata Das Databricks
hello everyone welcome to another,session on structured streaming and if,you're looking to learn about how to,architect structure streaming right you,are in the right place,we have TD he's an apache spark emitter,and a member of the PMC he's the lead,developer behind spark streaming and,currently develops structured streaming,previously he was a grad student at UC,Berkeley and I am plop where he,conducted research about data-centric,frameworks and networks with Scott's,concur and young Stryker please welcome,TD thank you very much so people are,still coming in but nonetheless we can,start I guess so before I start let me,take a quick poll in the room on how,many people are not familiar with,structured streaming please raise your,hand quite a few I'm glad that I took,put a five-minute introduction on what,you're assuming is so now,okay let's start so structure streaming,at a very high level is a distributed,stream processing framework built on top,of the spark sequel engine so the same,engine that provides you with all the,fast queries etc also speeds up stream,processing and it's fault tolerant,exactly ones everything that you expect,out of a modern stream producing system,and it has got a great set of connectors,to work with all the ecosystem of,different storage systems around there,and the fundamental philosophy of,structure streaming is that you as a,developer should not have to think about,stream processing you should only think,about your query in terms of a batch,like query like ok here is my sequel,query it's on a table and you write that,code in as if you're writing it on a,some batch data and it's sparks job to,automatically figure out how to run that,incrementally as more and more data,comes in from the input streams to kind,of - now there are structuring has been,around for almost three years now and,the thousands of weed on data bricks,have thousand of streaming applications,over with hundreds of customers running,them on our platform we have the,collectively we have trillions of rows,well actually there's thousands of,trillions of rows all we process so it's,a pretty big huge eruption that we have,even within data breaks let go of the,community is much larger but to give an,idea of what structure streaming query,looks like or here is a brief example to,show you what the anatomy of a,structured semi query looks like so,let's say you these are pretty common,case let's say you are reading data from,Kafka your data is encoded in JSON and,what you want to do is store the data,finally in a structured table like,Parque,and you want to get end-to-end exactly,once guarantee that every record from,Kafka gets written out exactly once in,the pocket able to do this this is what,you will write basically you start by,creating a data frame on top of the,Kafka data to do that if you do spot or,read stream where you specify how to,connect to Kafka as an option how to,what topic to read etc and an option and,finally call load this creates a data,frame which is essentially a,programmatic KPI that represents a table,which has essentially columns and a,bunch of rows for those columns so for,every record in Kafka becomes like a row,in that conceptual table in the data,frame now when you're calling note on,this you're just defining that this is,you want the data in Kafka to be,conceptually present as a data frame it,doesn't actually start reading so,everything is lazy but what it returns,is this data frame now on this data,frame now you can do your standard data,frame operations in spark or either,using data frame operations itself or,you can directly use sequel on top of it,but but what you can do on this is use a,bunch a lot of built-in sequel functions,to actually process the data for example,in this case I have the binary payload,from Kafka I want to cast it as a string,treated as a parse out the JSON using,this inbuilt function call from JSON and,then find then finally I want to kind of,unnecessary to park' so I've done these,operations next step I say write out to,the stream of parsed data into parkade,given the with the given path and then,finally I want I specify that now that I,am the computation has been specified,how do I want to run this computation I,want I in this case I'm saying that,triggered this every one minute so that,basically this boils down to a micro,batch of every one minute of data every,one rate of data gets processed and then,the next one wind picks up you can you,have to you have to specify the,checkpoint location which is where the,query will save all the necessary,information to restart if there is any,fail,things like which cough cup offsets have,been processed all of that information,gets saved to the check point location,and then finally when everything has,been specified you can say start now,when you say start what a structure,streaming does underneath is that it,takes this code and converts it into a,logical plan which is a pure logical,representation of what is it that I want,to
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19. Databricks & Pyspark: Real Time ETL Pipeline Azure SQL to ADLS
19. Databricks & Pyspark: Real Time ETL Pipeline Azure SQL to ADLS
hello friends welcome to rajasthan,engineering in this video i am going to,explain one of the real time project,exercise,that is how to build etl pipeline to,load data from issues equal to ashu data,lake storage,this is one of the common requirement in,most of the issue data engineering,projects,this exercise consists of,three stages in the first stage i am,going to extract the data from associate,sql,so the extraction would contain fact and,dimension tables,we are going to use jdbc connection to,read the data from issue sql,so at the end of the read we are going,to create data frame,for dimension and,fact tables,in the next stage we are going to,transform the,extracted data from azure sql so in this,stage we are going to apply business,rules for this demo i am going to,perform simple transformations such as i,am going to replace the null values with,some default value in the dimension,table and i am going to remove the,duplicate records from the fact tables,then i will join,fact and dimension tables,based on joining keep,once the join is done finally i will do,some aggregation to get some meaningful,output at the end of transformation,stage,so moving further to next stage,this is the last stage i am going to,load the transform data into i should,sql i should data lake storage,so for that first we have to create a,moon point,once mount point is done,then we can load the transform data into,i should data lake storage in the form,of parquet file,i have already posted one video how to,read,read data from azure sql,if you haven't watched i highly,recommend to watch that video,similarly i have posted one separate,video how to integrate azure data lake,storage with data breaks how to create,mount point,and how to access files in data lake,storage that i have already covered in,that video if you haven't watched i,highly recommend to watch that video as,well,now let us jump into our demo,this is my assumed portal i have logged,into azure portal,coming to,my resources,i have created a storage account that is,called adls raja data engineering within,that i have created a container that is,called,container underscore raja data,engineering,within that i have added some,one simple test file that is world,population data anyway we are not going,to use this one for our exercise because,i should data lake storage that is a,target for us so we are going to load,the transform data into this location,coming to azure uh sql i have created,one sql database,that is using adventure work as a sample,database so this is the sql database,asql hyphen raja data engineering,let me login into the database,i have logged into the database,and there are many tables so for this,demo i'm going to use the dimension,table product,let me get the data from product table,here you can see the product table,and i'm going to use sales order detail,as a dimension sorry as a fact table the,previous one product that is a dimension,table and sales order detail that is a,fact table,so this is the data so basically i am,going to replace the null values in this,dimension table,here you can see now there are many,dimension many null values for columns,size and weight so i am going to,replace the null values with some,default value that is operation i will,perform in databricks and coming to,fact table i will,i will remove if there is any duplicate,once that is done then i will join these,two tables based on product id key here,we have product id same we have product,id,in product table also then after that i,will perform some aggregation to get ah,measure value such as you know total,sales i want to get sum of,a total line total so for that i will,write some logic,once these transformations are applied,then finally we will load the data into,azure data lake storage,let me jump into my databricks workspace,the cluster my cluster is up and running,so i have already created a notebook for,this pipeline this pipeline consists of,three stages as i told earlier step one,it will extract the data from azure sql,so for that we will use the,jdbc connection then we will read the,product table and creating a data frame,similarly we are reading sales table,creating a data frame once uh extraction,is done then the second step we are,going to transform the data,so for that first i am going to clean,the dimension data by replacing null,values with some default value,also i am dropping duplicates from the,fact table these are the cleansing,operations i am performing after that i,am performing join,okay so i am performing left outer join,and after that i am just,selecting the data that we are,selecting the columns that i am,interested in then after that i am,performing aggregation to get some,meaningful output to get a sum of line,total,once transformation is completed,i am going to load the data into azure,data lake storage so this is step 3,first i need to create a mount point in,order to integrate with i should get a,lag storage through data breaks so that,what is done he
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Working with Databricks and Writing a Python Script to Clean/Transform Data
Working with Databricks and Writing a Python Script to Clean/Transform Data
hey everyone welcome back this is nick,and in this video series we're building,an automated azure data pipeline,just to give a brief overview what this,pipeline does is it will upload a local,excel file,into an azure data lake storage account,which then launches a data factory,pipeline,via an event trigger which sends that,raw data into a data bricks notebook,there's a python script written that's,going to clean and transform it,and it's going to load that clean data,and write it back into a new,azure data lake storage folder and then,we're gonna it's gonna load,that clean data into an azure sql,database,in this particular video we're gonna,read in data in data bricks from the,data lake storage account,we're to work with the notebook and,clean transform the data in that python,script that i'll show you how to do,and then we're going to write the clean,data back into a new azure data lake,storage folder,so let's get started so if you remember,our last video,we mounted the data lake,storage account to data bricks and again,what mounting means is,we've just created a pointer that points,from data bricks to data lake so it kind,of connects the two,um so we can kind of pull in files or do,what we needed to do,and again we created that mount point,name as data lake new mount 11.,if you're familiar with what this is,please check out the previous video and,see how we did that,but we'll just be moving forward here so,uh and again just a quick overview of,what databricks is,it's just a cloud-based big data,engineering tool,it allows you to process and transform,massive amounts of data,and it does this because it's built upon,of the apache spark framework,and you can think of apache spark like a,big processing engine,and how apache spark works is it's able,to actually divide your work up,and you can think about it like,different computers that all processing,different parts of your work at the same,time,so just to give a quick kind of high,level overview of apache spark is,is uh let's say you have a csv,file with millions of rows right what,will happen is is there's going to be a,driver node,that's kind of the main hub and it's,going to send communication,to what's called different worker nodes,right remember on our cluster we had,one worker as a minimum and max is two,it's going to have at least one of these,or two of these,and what's going to happen is the driver,node it's going to send communication,and break up the files for these worker,nodes to work on which are called tasks,so in the csv file it'll break up let's,say a hundred thousand rows for this,worker in a different task,100 000 for this worker and you know and,kind of so on depending on how many you,have,how many do and then once that's,completed um it's able to process your,things all at the same time,called parallelization so all these,computers are working on your file,but in different parts of your file all,at the same time that's the power of,apache spark and why it's so it's so,uh important and so uh so great,so if we go back here we created the,link,from data bricks to data lake and then,at the end of the last video,we actually loaded in one of the files,so we loaded in our,csv file that we have here so what i did,is we're going to go to a new notebook,here,and really quick as well once you create,a mount point,you don't have to do it again for the,same storage account so if i'm in a new,notebook i don't have to rerun this code,it'll transfer over for any notebook,that's in the same cluster,if you create a new cluster you just,have to write one other piece of one,line of code,to transfer that mount point over to the,new cluster but using the same cluster,that we,are it's going to be able to use in any,notebook you can use the same mount,point which is great,so again it's just data lake new mount,11 is the mount point,so let's go into our new notebook here,this is the same thing we had before,where i'm going to show you where we're,just going to read in,that file,all right and just to give you again,familiar it's the cfv,csv point so it's going to be that mount,and slash and that's our,mount point that remember that,connection with data lake and in this,folder nine,if you remember if we go into our data,lake storage account here,we go to our containers we have a,container name container nine,and the full name folder nine so that's,where the slash and folder nine comes in,if you didn't have that if your data was,just stored right here,you can get rid of this folder nine and,just right type in the data name but,then inside of this folder nine,we have our csv file and it's called,data.csv,that's why we have that slash data.csv,so it's saying here's a storage account,it's inside of folder9 inside of folder9,is the actual name called data.csv,if your csv file was named john.csv,you'd put john.csv here,right and this display function just,displays it,just displays this data frame in pi,spark,and what play spark is is it's just,the python api to support ap
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hi everyone let's welcome to the second,episode and second video about the azure,data breaks and today we will speak,about the azure data breaks networking,specifically when we are integrating,educator breaks to our existing network,first I'm gonna go through the,whiteboard and I'm having here this from,the documentation this diagram to go,through it but I want to discuss first,what what are we trying to integrate the,integration part happens when you are,creating a workspace and a workspace as,you know it's just a place for authoring,and collaboration between the team for,any code in this workspace to run this,workspace you need to be attached to a,cluster you create a cluster you run the,cluster and then you run your code,inside this cluster this cluster is a,cluster of virtual machines and these,virtual machines will be inside the,v-net that's what we are integrating,here so typically before without before,this feature released when you are,creating a workspace what happened is,the workspace creation create and,managed resource group and the managed,resource group will have three resources,one is a storage account to act you as,your dbfs and second is a v-net and a,third is a network security group or NSG,now whether with the v-net integration,these two are not created and the,workspace will be connected to a minute,that you are having pre-existing in your,environment now this is critical because,we need to understand the web interface,itself for the,data breaks and the cluster management,basically all the control plane that,does not exist in your V net your V net,once you create the workspace will not,have anything once you look into your,workspace and you create your first,cluster your first cluster will exist,the network card for this cluster will,be attached to your V net but you but,for data breaks web interface that will,not be inside your own workspace as you,know and if you recall if you load them,before to the web interface for data,breaks you will find that it's the first,part the subdomain is the azure location,the azure region let's say it's Canada,Central and the domain name is as your,data breaks look net so it's it's always,eager to the breaks book net this is the,hope interface and all the control plane,is installed inside and Microsoft,managed subscription inside the V net,controlled by Microsoft only the data,plane or your own cluster will be the,one deployed inside your V net now for,the requirements for this one the,documentation lists some requirements,but I'm gonna go through the important,ones that's the second part the,requirements so you need to have two,subnets and if you have these two,subnets allocated to a workspace you,can't have another workspace for it so,if you require to have two different,workspaces you need to have four,different subnets does this mean that,the workspace will have exclusive access,and you can't have any other machines,inside this subnet know you can this,submit can have any other resources,other than data breaks workspaces so,it's two subnets per workspace however,these subnets can have other machines,attach it to these subnets now how it's,done,it's done when once you do this part of,the installation part of the,installation that the workspace will,have access to your subnets basically,you are doing delegation from you to the,workspace so this workspace or educator,plex workspace resource provider will,manage these subnets on your behalf if,you have already NSG attach it to these,subnets the the resource provider will,add new rules inside this NSG if you,don't have one and you are using the the,portal or Orion your arm template you,are creating a subnet that will be,created automatically so the portal,experience will take care of this will,create a subnet and then we'll add the,rules if you have your own subnet,already attached then this will be the,rules will be added automatically for,you and for the workspace to maintain,this situation and nothing will happen,that will risk the the workspace,configurations the workspace will create,on the on this subnet policy a virtual,network intent policy and this intent,policy will make sure that no one can,can be or be able to create the rules,created on the NSG that's the the how,it's done for routing and peering and,anything else that's allowed so your,v-net here,that's your v-net this v-net can be,peered to another v-net you can have,user-defined routing however user,different routing because the two,subnets one of them will have the public,IP s the other one will have private IP,s only the public IP s will be exposed,outside your V net and the communication,will be from the control plane to your,cluster through this public IP so if you,do routing you need to make sure that,you do exceptions for the control plane,so when the traffic comes here directly,to the to the public IP and you have,routing that means you have a file,all here for example that means all the,traffic going back will go back to first,to t
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