Data engineering is the process of extracting, transforming, and loading (ETL) data from various sources into centralized systems, serving as the foundation for data science, analytics, and AI. The instructor explains that despite different titles like ETL developer, big data engineer, cloud data engineer, or Snowflake engineer, all these roles perform the same core work of building data pipelines. The course emphasizes learning one cloud platform (Microsoft Azure) thoroughly, as the fundamental data engineering skills transfer across different cloud providers. Data engineering is essential because without it, there is no data science, analytics, or AI, making it a high-demand career path with significant salary potential.
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AI Data Engineering
Added:Hi D. Good evening. Uh we'll start the session in 5 minutes.
>> Okay sir.
>> Uh is this uh Santosh is not there today?
>> Yeah I'm there. Tell me.
>> Hello. Nothing.
>> Yeah, >> nothing. I thought you are absent today.
>> Okay.
This class is from Monday to Friday or weekends also. Is there such? It's >> a Monday to Friday classes.
>> Okay.
Santos sir is going to lead this course now sir >> yes I'll be there and this is a 6 months course and all six months I'll be with you every module will be covered by >> no one will come in between now no another teacher >> no for this particular course I'll be the mentor >> okay so thank For snowflake, you'll have to join another course where Saga will be the trainer and it will be a morning batch.
>> Okay, sir.
Ah, okay.
Uh for snowflake how much how much month is how how much is the duration for this course?
>> For this month for this course >> no this course is next month but snowflake one morning snowflake >> see same see python SQL pispark everything you learn only instead of learning azure you will be learning snowflake so it will be still five months. Oh, truck.
We'll wait for another 2 3 minutes.
This is a problem with uh Indian IT culture. Okay. In Indian IT, the weekend is considered as Friday afternoon to Sunday afternoon.
Do you all agree?
>> Yes. And >> true. True.
>> Sunday afternoon, your weekend is done.
You finish your lunch, that's it. You're done. Because you'll start thinking about Monday. That Monday stress will come. Yes or no?
>> Correct.
>> Yeah.
>> Correct. and Friday before even you complete the work by evening everyone will be chill that's fine we'll close it right so that's why I wanted to I mean if there is a chance for me to uh go and change the rule I would do this weekend is Friday afternoon to Sunday afternoon and that's why I can see some less strength as well anyway it's increasing so let's wait for another 2 minutes we'll start exactly at 85 before that any questions Any questions before that?
>> In this course, snaps anatics also is included.
>> Yes. Yes, it is included.
Uh whoever is your point of contact, reach out to them. Uh they will help you with uh brochure. Okay. The syllabus and there we will be mentioning all the topics, subtopics, whatever we are going to cover. Everything will be clearly mentioned. Go through it. Then if you have any questions you can ask me uh during the class or you can go through and ask me the next class as well.
>> Sure.
Uh uh yeah and uh regarding ADF and data bricks also it will be covered right just >> everything is mentioned. Yes.
>> Okay.
86 87 88 89 we wait for 100 91 >> yeah in this provided brochure they didn't included the synopsis they included fabric >> synopsis is there can you check it should be there in the services it will be there in the Azure services if you uh we have we have mentioned about uh 15 16 services it should be there.
>> Okay.
>> The fabric is also included right?
>> Yes fabric is also included.
Okay [gasps] so let's get started. Uh before we start let me give you a small recap for the guys who have joined late in the last session or those guys who have missed and today's the first session. So we are here to discuss about the master class of AI data engineering and for this AI data engineering we wanted to understand first what is data engineering don't get buzzed with this words okay what is this AI what is this engineer what is this data ops don't get too much into it we are doing the same old work the old school work only but these guys are changing the name and getting the fancy words so that's why I would say that we are already an ETL developer that was there from 5 to 15 and then it called as a big data engineer. Now this is a phase where the spark Hadoop everything came into picture and post that we started doing everything as you know onrem as well as the cloud. So we were called as a data engineers in the covid era followed by like we are being called as a cloud data engineer because all the work we are going to do it in cloud. So why cloud? Because you're going to do the projects in cloud. And if you're knowing more than one cloud, you will be called as a cloud data engineer.
Specifically, if you are going to work in one cloud in Azure, you'll be called as Azure data engineer. You work in AWS, you'll be called as AWS data engineer.
GCP, you'll be called as GCP data engineer. Or if you're working on a snowflake, you'll be called as a snowflake engineer. But but you know what? All these guys whomever you see on the screen, everyone are doing the same work. Okay, everyone are doing the same work. What is that work? It's none other than the data engineering work. Okay, building the pipelines and then working with data and all these are common. All these are common. Okay, say I'll ask you one question. Okay, I'll ask you one question.
Um actually I have interest in this. Uh probably maybe once I get rid of this uh it I'm thinking to get into that. Okay.
See my other interest is I want to become a chef. Okay. That's my interest.
Like I I usually like cooking, right? So just imagine this is a class where you have joined to learn cooking. Okay. Just listen. You have joined here to learn cooking and I am teaching you cooking all sort of cryines I'm teaching you and you're well prepared now you go to your house you go to your house I'll ask you one dish you will prepare and give right if I ask you any xyz you'll prepare and give because you have learned cooking from us so you can cook in your house now the question is if I ask you to cook in your neighbor's house if I ask you to cook in your friend's house. If I ask you to cook in your relative's house, will you again come and say me that no, you have not teached me to cook in their house. Will you come and tell me that?
>> No.
>> Why?
>> Cooking is the same process for everywhere.
>> Exactly. Cooking is same, right? The process is same. So what happens? I'll tell you. When I teach you cooking in your home, in your home kitchen, you know where the ingredients are. Where is a salt box, sugar box, honey box, whatever it is, you exactly know where it is. So you easily will cook it. But if I ask you to cook in your neighbor's house, you will have to ask them where are the ingredients? Show me where is the salt box? Show me where is the chili powder box. Show me where is this leaves. All this you will ask them. But cooking will never change. That is exactly happens here. And if you learn one data engineering, you know how to build the end toend pipelines. We will be learning with one cloud. Tomorrow if I ask you to do in AWS data engineering, let me tell you, you already know this much. Only this much you will have to ask them. Instead of asking in our cloud, you're going to ask them, hey, where is the data? Show me the data.
Show me the path for the data. I will go and cook. Data engineering is cooking data, right? As simple as that. So don't get burdened with all the clouds. One cloud you will learn. It's a process of cooking. You learn the cooking process.
You learn to cook in your house and irrespective of other places you can take help. Where is the ingredients?
Where is the storage? Where have you stored? Where have you placed it? But you are not going to change anything.
Your pro process is going to be remain same. So learn one cloud completely and other clouds automatically you'll be able to get that experience. You need not even join that earth course also for AWS separately I'll have to go and join another course for GCP I have to go and join another course no need you can do it by yourself but still as part of this cloud computing we will be giving you AWS and GCP as self-paced this will be completely live sessions this will be completely live we'll be completely learning one cloud one cloud will learn A to Z then for the other cloud we'll try to compare here where I'm storing in AWS where I'm storing in GCP where I'm storing this is what we are going to learn and in the recent days it has also become like a AI data engineer everywhere there is a AI AI AI right now with this AI how are we going to incorporate AI in our data engineering that is what we are going to learn so let's focus on these two roles And this is going to be your target. So tomorrow if you're going to apply you are a data engineer but slightly you need to know more on AI as well as I mean uh a bit of AI and more on cloud.
So both included will be applying for rules. Okay. So this is what we were talking about and I introduced you to the an example of how and suddenly if data comes in a huge capacity how are we going to handle this so that's what we had challenged right is your system capable of handling the heavy data is your system is capable of handling the variety of data so all of this we'll be checking it and for that we are going to build a pipeline and then we compared with our few examples Right? And we talked about a supermarket and in the supermarket our main objective is to build a supermarket.
That's all that is what your work but don't go and tell an interview that I'll have to build a supermarket. You can tell instead of supermarket I will build a data warehouse. Data warehouse is a collection of all the data. Your supermarket is a collection of all the products. Now you're going to build a data warehouse. Correct? Now then we were talking about who is a data architect and who is a data engineer.
Okay. Okay, I was relating to a design architect to build a house. I'm the builder who builds the house. So I'm the data architect. Okay, by profession, by it, I'm a data architect. I will be telling you how to build the project and you guys are going to build a project because you guys are going to join in a company as data engineer. Correct? Now with this then we were check we we talked about couple of use cases and in yesterday's session I shown you one demo how the realtime data swiggy data will be generated and how we are going to process that. So that I have shown you followed by then we entered into the subject what is data engineering. Data engineering is nothing but extraction transformation and load. You're extracting the data from the different parts of the system API website CSV files plat files excel files JSON files I can tell hundreds of files from everywhere you'll be collecting the data and you'll be doing a transformations.
When you say the transformations lot of duplications, abnormalities, special characters, white spaces, lot of unwanted things will be there. Those and all we will have to clean it and then we are going to load it into one machine and in the one machine we will be going and accessing it. Okay. I have a very close friend uh who is running a supermarket.
Okay. I usually go to supermarket and I have always have interaction with him.
Okay.
Supermarket opens at 9:00 in the morning for public and customers, right? Supermarket opens at 9:00 for public. But 8:00 when you go to the supermarket, it will be closed from outside. But if you peep through the class, people will be inside doing something.
Have you ever noticed this?
Have you ever noticed this?
>> Yeah. Yes.
>> Yes.
>> Yes. Yes.
>> What is that one hour going to do? You know what?
>> The things.
>> Yes. Excellent.
>> Excellent. The staffs will be arranging it. Any product which has crossed the expiry date will replace it. Any duplicate products or duplicate items they will store it in and they'll keep one for showcase. Right? So, they will replace it. They will arrange it properly. They will clean the floor.
They'll switch on all the lights.
They'll make sure that AC is working.
Right? This is called maintenance, right? So all of this we need to do correct. So similarly when you're building a data warehouse, you need to take care of the other things as well.
You need to take care of the additional things. How can I make sure this is working? So some sort of maintenance, everything will be involved right now with that with that we have seen an example. then we will be loading it. So all this removal of abnormalities, special characters, white spaces, everything will happen at the T layer.
The T layer is called transformation layer. Okay. Now is it really needed?
Why can't we just ignore it? Okay. Data is growing, growing, growing, growing.
Why cannot I ignore it? Okay, I was talking to our team.
When I say my team like we are all working as a team here and you would have been contacted with any of our team in terms of call you would have reached out to us asking for the course duration please would have contacted us right and we have a set of team right now we get students probably from YouTube Instagram um Facebook then some of them will will come from LinkedIn right we have all of this one day one day Just listen to the story. In Instagram, we got about 100 leads.
In Facebook, we got about 90 leads. In LinkedIn, we got about 140 leads. 140 students. Okay. So, Instagram, Facebook, and then I'll take a YouTube. In YouTube, we got about 150. Okay. And there is one thing called Twitter. In Twitter we got only one.
Okay. In Twitter we got only one. So all our guys, all our KSR employees, right?
They were very happy with these numbers.
They started contacting everyone in YouTube, Instagram, Facebook and LinkedIn. This person they ignored this person. The only one person who had reached out to us in Twitter asking what is a course what you guys are teaching we ignored as a team we did not contact him I asked our team hey why are you just ignoring him why can't you call him and they were all against me the team one leader why you are worrying here we are focusing on this you did not ask this why we I mean how well we reached 150 lead you did not ask me about this 110 you did not ask us about 19 you did not ask about this 140 and why are you asking only about this one lead this exactly our team responded now can I say okay let's focus on the other leads let's ignore that one lead can I do that >> no >> fortunately or unfortunately the one person who was actually reached out in Twitter was actually a a college HOD.
If we had contacted him, if we had responded to him today, we would have got 4,000 students from his college.
This is a lesson that KSR has learned the importance of one contact on overall we think that hey just one contact right ignore him. are we are getting in YouTube, Instagram, Facebook, LinkedIn but that one guy who has reached out in Twitter we did not contact him by chance we'd have contacted him he's an H of a college so probably you would have recommended all his 4,000 students to attend his class so that's why I'm saying data is important you can never ever ignore one data point can change the business upside Now it's a very important point that you need to consider. Business decisions are made with data.
The richest person is not the one which has money. The richest person is decided how you can make money with data. Learn the statement. Right? Data is wealth.
Data is wealth. You know how to play with data.
Literally, you can earn money. Get into your favorite roles. Enjoy your rest of your life.
Data. Importance of data. You ask me, I've been into the ocean of data for all these years.
12 years I've been into this field. I know the importance of data. Every single data point decides the business fate.
The one swipe you do in Instagram, just one swipe is actually a business, is actually decision making.
Right? So with all this importance, I cannot ignore more data, more prediction. Data engineering gives your data velocity, how it can be processed.
And please remember this.
Please remember the statement.
Please remember this statement. without engineers, data engineers, no data science, no analytics and I'll write a big no AI people are running behind AI. I'm telling you without data engineering there's no AI there's no AI to a college I asked them who wants learn AI everyone in the class is raising hand and I asked them who want to learn data engineer no one is interested no one is interested without data engineers there is no other roles It's a privilege to work under this role because you will have lot of dependencies but at the same time you'll be also under risk high paying job high risk. What do you mean by high risk?
There is no room for mistakes.
The margin of error is very less because every single data point is important for business. So we cannot make any mistakes, right? And we are the guys who are going to make use of this data and make it available for all the systems.
Now in real world we are getting the data from different different systems.
Okay. And for all of this we need one report.
That is why ETL comes into picture. We cannot go and do individually report for this, do one report for this, do one report for this, do one report for this, do one report for this. We cannot do that. We have to collect everything and place it at one place. And that is why ETL was introduced right now.
Then we talked about traditional systems like what we have and a big no to all of this because all this are not able to handle the big data. Then we entered into the big data world. What is the reason that why the data is growing bigger and bigger? Because of this internet users, smartphones, sensors, smart devices, IoT, right? It's all because of this.
The data is getting bigger and bigger.
And finally we saw an example of big data right big data is not like a size 1 GB is a big data 1 terabyte is a big data no when we are crossing the threshold of the capacity obviously our performance will go down there will be a water leak there will be a data leak the data will not be able to be processed so all of this leads to a big data and in order to solve the big data problem we have a cloud We have a cloud. I'll tell you one simple example of cloud. A very lame explanation I'll tell you. Okay.
So, what is the most important part of our current life to have a great life?
Do you guys understand the question?
What is the most important part in our current life to have a great life?
Anyone?
>> Happiness.
>> Happiness. Very good.
Money.
>> Health first is health. Health is wealth first. If you don't have wealth, we can't do anything.
>> Excellent. Well said. Okay. If you want to sit in the class, if you want to listen, if you want to focus, if you want to go and work in company, the first thing is you need to have energy.
You need to be able to ready to work right. So fitness is very important.
Now I want to go for gym. This is my goal to be a fit. I want to go for a gym. Okay.
Now what I'm doing I'm taking one room that is my own room and I'm buying all these things treadmill dumbbells all of this 39,000 7,000 27,000 1,000 I'm buying at home. Okay, in my one room is completely full of gym equipments. So what is the problem? Initial investments I need to do maintenance. Every day I have to call one maid, ask them to clean one complete room is gone. Maybe I can uh resale but it could be limited and uh one time I need to buy all these products, tumbles everything and the average cost is 1 to 1.5 lakhs.
Okay. Then uh why will I go and buy all of these things here? Why can't I go for a gym? Right? So go outside for a gym membership. No investments, no maintenance, no space required. You can cancel anytime. You can upgrade your plan as you like and you can use it and you can pay for it. So hardly you'll be spending 15 to 20k in India for subscription. Even if you don't go, no one is going to ask you. Even if you do it for 5 days, 6 days, even if from morning to night you stay in gym, no one is going to question you. No one is going to ever question you. Now tell me, buying gym equipment is easy or going to gym is easy.
>> Which is easy?
>> Going to gym is easy.
>> Going to gym is easy. Right. So if I purchase right I actually purchase one dumbbells here I purchase one dumbbells for that day I need to put I need to clean it sometimes it's that rotation is there right that also I need to oil it space issue suddenly my mom will come and ask why have we kept here throw it out this type of things and all happens but in gym the specially known for the equipments okay the ambience will be really good you'll hear loud noise sounds motivation songs, past songs, and you'll feel like doing something in the gym. Not at home here. Right now, I will tell you to run a project to run a project we need systems. We need laptops. So, I will tell you to run a project, we need something called computer lab.
We need something called computer lab.
So this is a computer lab. With this computer lab we can go and do a project.
Now if I want to run my own business that is let's say KSR for running our KSR collecting fees giving access making sure that everyone is getting benefited in terms of all this resume preparation interview questions guidance everything to do all of this 15,000 students record to process we need the seven systems we need this 7 to eight systems now in KSR we are actually worried about the space for classrooms. Okay, we need four classrooms, we need five classrooms, but if I am managing my own data, we need one room full of computers.
We need one room full of computers.
Who is going to buy this computers here?
10 laptops, 10 lakhs, space, power supply, internet supply, mouse, keyboard, speakers, AC.
Come on here, how can I run the show?
One complete room. I need a systems to process my data.
Right? Then I was thinking, is there any better option? Is there any better option? Yes, there is a better option.
there's a better option. So I'll tell you imagine there are like flats. Okay, imagine there are flats like this.
Imagine there are flats. Okay. Now in this block A, block B, block C, block D, we have four blocks. In each block we have 80 hours. Listen just listen the story.
Okay. In each fl each each block we have 80 houses. Okay. So let me go and write one small example. The first option to build a project you need one room and in that room you need to have a computer lab. You need at least uh 10 to 12 machines.
Okay. 10 to 12 PCs. And you need to provide all the facilities for this.
Okay. Now this is to build a project.
Option number one, you build your own room.
Build your own room with a computer lab.
And we need to have 15 plus this one.
And for this you need to provide power.
Uh you need to provide uh maybe internet and you need to provide all the equipments right and then you need to provide AC etc etc etc etc etc. This is option number one. Option number two what I will do is in this we have four uh imagine it's a flat okay I'm making it simpler when you say flat it's actually called a data center okay when it's a cl it's called data center so the meaning of flat is it's a data center like this it's a data centers like this okay so this is a data center instead of calling data center I'm calling it as flat okay now in this flat we have four blocks Each block each block 80 flat 80 houses. Okay.
Now in each house in each house you can have like two BHK 2 BHK or 3 BHK.
Okay. Now just imagine the entire flat they have computers computer lab.
Computer lab, computer lab, computer lab, computer lab, computer lab. In the entire house we have computer labs. So totally how many computer labs I can have? Each block 40 means uh each each block 80 means uh block A 80, block B 80, block C 80, block D 80, 80, 160, 240, 320, 320 houses, right? In each house it is 3 BHK on an average. Let's say even 2 BHK.
So 320 houses multiplied by 2 or 3 2 or 3 2 B square 3 BHK easily I will end up in 800 H rooms. Now this 800 rooms are computer labs.
Now tell me building your own room is easy or renting a computer lab is easy.
They have everything. 800 rooms are there for your project. You hire one room. They will only provide electricity. They will only provide power supply. They will only provide AC.
Everything they will do. And you work from here only. You go and connect to them to that machines virtually and you work. Now tell me building your own computer lab is easy or renting a computer lab is easy.
>> Renting a computer.
>> Renting easy is just a straightforward comparison of a cloud and onrem. Right.
>> Exactly. That's what we are going to do.
In reality I was talking about flat right that is nothing but data centers.
The data centers is already there that is managed by cloud.
The next time when you say flat the other name of flat should be your data center. You can never forget this for rest of your life.
We going to do a project but why will I go and build a computer labyard? Let me go ahead and make use of flat. This entire flat is managed by AWS.
This entire flat is managed by Azure.
This entire flat may be managed by GCP.
Like this there are different owners.
There are different builders. They are managing the house. They are managing all the infrastructure. What is your role? You go, you pay the rent. You build a project. Come out. Next tomorrow you want to build another project go take another room come finish it off like this the entire world is filled with flats that means the lot of data centers that is managed by companies those companies are AWS Azure GCP and why there are lot more cloud there are lot more than clouds like we have an even 10 to 12 clouds but we don't know all of that. We are only talking about 1 2 3 4. The reason why I wrote it in this order is because of its market share. The topmost cloud that most of the companies are using undoubtedly it is AWS. I need to agree that the topmost cloud is AWS. The second topmost cloud is Azure. The third topmost cloud is GCP. Now how can you say that and how can you give this ranking? If 10 companies are there, how many companies are using AWS? How many companies are using Azure? How many companies are using GCP? So it's called market share.
So the with respect to market share, AWS is having the top cloud services.
Okay, wait a minute. There is a confusion in your mind now. Why are you not teaching the top cloud? Yeah. Why are you going and teaching the second top?
Is that your question?
>> Yes.
>> Yeah. Yes.
>> Okay. Very good. Let me answer it. One of the top phone is the one of the top smartphone is iPhone.
Apple's iPhone.
No other phone can beat iPhone in terms of the performance.
Can I assume all 110 people have iPhone now?
>> No.
>> Why?
>> Why? It is one of the top perform top performing phone. Yeah.
>> Budget issue.
>> Not everyone can afford it.
>> Not everyone can afford it.
>> And not required also. So say you can get from Android also. No, that's what when same thing you can get it from Azure. Why are you going and buying AWS here?
Whatever services you're getting in the top cloud, you're getting in Azure. Why are you going to learn top?
Okay, another example. Another example in KSR, we have a problem that to count all the installments, right? How many installments a student is paying? How many full amount is paying? So KSR team is asking me to hire one a person is a accountant like at least he can be uh cautious of who is paying how much he can keep a track of it. Okay. So KSR has asked me to hire one accountant. This is a role that is was given to me. I went to a college. I went to a college IAT.
Okay. I went to a college. There were two students. Student A, student B. And KSR clearly told me one has to be really good at maths.
One has to be really good at maths because a person who's good at maths can do a calculation with his fingertips.
Very easy, very easy hiring.
So we went we I went to a college IAT.
There were two students A and B. This student in uh computer science he has scored 50 in data structures he has scored 25 failure in maths he [snorts] has got 100.
So totally score is 150 for 300 and his percentage is 50 or 50.
Now the other person uh computer science 100, data structures 100 and mathematics 50.
So his score is 250 by 300. He's a first rank and he has actually scored 70 80%.
Now tell me will I hire A or will I hire B?
A >> A is failure.
>> But >> but he's good at max and the requirement was max. He should be good at max.
>> Now tell me should I really want to justify why I'm still going with Azure?
It is a top services for data engineering. Your AWS is good. I'm not denying. But it is good for maybe a web application, mobile application, UI application and datadriven also it is good. But one of the top cloud services even though it is a second ranked top cloud service for datadriven there is no other matching cloud in this generation as of now. So that is why let's not worry about what is one, who is one, what is two, who is two. And we are preferring Microsoft Azure because it is a cloud which is improving for the last 10 years. Every other cloud market share has become stable. But this is only one cloud which we are seeing a tremendous growth because of their innovation. Right? And I'm telling you one point of time I can't predict postcoid I have stopped predicting but I can tell you in the future there's a high likely chance that Microsoft Azure can overtake AWS but it will happen not like one year two year down the line based on the features and the more and more services there is a chance but as I told you not everyone can afford iPhone. Similarly, not every company is going and dumping the data into AWS.
Literally, you will not believe I have done about seven projects migration from AWS to Azure. I have done this. I asked them I asked them, hey, everyone is migrating from on premises and old technology to cloud. Why are you moving from cloud to cloud? Hey, no, no, no, no. After joining there I got to know how much is the difficulty in maintaining. They're saying that this is exactly the same words told by the client and we are moving cloud to cloud migration which never happens.
Everyone is moving from on premises to cloud but literally in the last 3 years I have moved from cloud to cloud.
I was like surprised cloud is a better one. After buying iPhone only you will get to know the real pain. Have you observed it? The guys who hold iPhone right if a small scratch if a small breakdown then you will feel the heat until then everyone wants iPhone after purchasing only the real challenge comes do you all agree >> yes >> right >> the screen for your normal iPhone is like let's say 100 but again if you go and take a screen for iPhone it's 1,000 rupees again you'll go back to Android right that is exactly what happened literally I have worked seven projects in the last 3 years where I've moved the data from AWS to Azure so we need to see the affordability we need to see the flexibility we need to see how well the projects has been designed here what are the things that features that you can use it see even now I'm denying AWS is a top class cloud and before working in Microsoft I also worked in Amazon so I was an ex employee of Amazon so I know their cloud also. Okay, I know about their cloud also. One of the best but sometimes because of the cost, because of the other features, not every company will use AWS. Okay. And another challenge is another challenge is in this or this or this if you see right each company has their own fan base.
There are few companies who are always using AWS. There are few companies who are always using Azure. There are few companies who are already using GCP.
Right? For example, let's talk about uh there is one project uh there's one client called uh let's say JP Morgan.
So I'm giving an example only. Let's say JP Morgan. Let's say another bank City Bank.
Now City Bank may prefer to use AWS. JP Morgan may prefer to use Azure like this. They have their fan base. They all they get some benefits. They get some benefits, right? So for example here JP Morgan Azure may give some benefits for them. Hey, you need not pay full amount. You pay just only the partial amount like this. So City Bank also will have their own fan base. See very simple. Yeah. You have pizza. You eat pizza. Pizza you get it in two shops. One is in Domino's, one is in Pizad. But always you'll you'll go to Pizard, few people will always go to Pisad irrespective of however it is because you look for a lot of offers, discounts, cashbacks. A person who really believes in taste probably may go to Domino's. So they have the different fan base. It's not like because of Pizard, Domino's is coming down. It's not it's not like Domino's is coming up because of Pizard. No, each has their own fan base. And that is why with all of these corrections more opportunity, flexibility, learning, usage, features, data engineering, data related for all of this. Now we are going to choose Microsoft Azure. But again I'm telling you let's learn this one cloud and other clouds will become easy for us. Let's learn one cloud. As I told you, you learn to build cook and you can cook anywhere in the world, right? Similarly, you learn one and you get to know the other clouds easily. So that is a conclusion for which cloud you're going to choose. So we are going to choose Microsoft Azure.
So before I go to the next step, any questions on this?
>> I have one question. So >> like uh so when it comes to the Azure and micro Amazon so the processing cost wise and the storage wise the cost is most as compared to Azure uh in >> see when you when you're going and doing a project right it involves multiple things like not only process storing you'll have an email notifications you'll have to have a follow realtime streaming lot of things are there when we compare AWS is standard so it is with chargeable more chargeable when compared to Microsoft.
>> Okay. So I mean the storage wise also like so >> everything everything >> where we can store in Amazon like in the storage. So here here in Azure we have a storage accounts but what about in Amazon?
>> There also we have it is called S3 >> S3 buckets.
>> Yes. Here it is called container there it is called bucket. That's all.
>> Okay. What place uh the snowflakes is having like here I can see the four clouds. So I mean Snowflake is not a cloud.
>> Okay.
>> Okay. Snowflake is not at all a cloud.
>> Okay.
>> The three main clouds which you see on the screen for the fourth one I just filled the space with Y. Remove that.
The three cloud you see here.
>> Those are the three clouds. These are the top three cloud. Now what they're doing to manage this building, right?
>> See I'll tell you this building is actually owned by listen carefully.
Okay. This will this will answer your question. This will answer most of your doubts. This building is managed by Azure.
This building is managed by GCP.
This building is managed by Amazon.
Okay. Now, Azure, GCP and AWS, right?
They're feeling heat of collecting the piece from every people who is going to come and hire the I take a rent in the that room that computer labs. Okay. So in this right we have about how many we discussed about one block in one block we have about 320 right? So uh okay each block 80. So 80 into 2 let's say we have about 160. Okay. So 160 rooms are here.
So we have about 160 rooms here. Okay.
So in this 160 rooms the ground belongs to that is the land land belongs to Azure.
The building belongs to Azure right but Azure right they want to go and build another flatarier. That is what their goal is. They want to build another data center. So they will be looking for another land. They'll be looking for another computer labs right now suddenly one person came that person is called snowflake you know what he will tell hey you are finding difficult to collect the rent from 160 people you're you're finding it difficult if they have any issue they come and say that internet is not working power is not working AC is not working water is not coming electricity is not working they have a problem with billing food is not good like this they will come and complain right why are you taking var I will manage it I will manage it this person name is called contractor they don't have their own land they don't have their own building nothing they have they are just coming and managing your mine that is my azure they're managing my building they will take care of everything where I will run behind building another flat. This person will come and say hey don't worry I will manage this flat I will ask him what you need in return just give me 5% of commission I am also making money you're also making money here how Azure is benefited less work yeah less work I have given everything to the contractor let him take care right and snowflake will recede on one of these cloud and I keep saying it again and again. Snowflake is not a cloud which will reset on one or the other cloud. Maybe if he doesn't like this building, he will go to this building. GCP will pay money for the snowflake or AWS. AWS will pay money.
These guys are called contractors.
Did you understand?
Yes. Uh basically like uh uh the snowflake is handling the data like in case of data wise or like a >> data is receding on the cloud. Snowflake will help us to manage with the software a better UI design a better uh user interface easy drag and drop like this but in the back end it's still a cloud.
So what about uh the cost wise like as compared to like so managing few companies like uh so moving away from the snowflakes and they wanted to use only the azure >> see nothing like that the back end is cloud only the cloud is a back end snowflake will make your work easy with a good really beautiful user interface making my operation easy that's all so instead of having switches they'll go and replace smart switches so they're giving some more features snowflake is giving more features right end of the day cost is same for me whether I'm using snowflake or I'm using cloud cost is same but if if I go and opt for cloud probably I need to go and look for anything I need to go and raise a request uh anything I need to go and complain like this now snowflake is a point of contact for me so everything I can go and ask snowflake snowflake will take care of it >> just like just like Android and uh some just like Samsung UIE or OnePlus UIE Yes, that's correct. Yes, you can think that way because each phone has their own UI, right? Like that you can consider.
>> So he is a intermediate person between user and >> yes, very good. That's a right word.
Intermediate person between the cloud and the user >> like so we can choose uh like which cloud. So for example, >> yes, you can choose. You can choose.
It's your wish. If you want a building that is owned by AWS, go for it. If you want a building managed by AWS, go for it. If you want a building managed by Azure, go for it. That is your uh decision. Client will decide.
>> Can we choose multicloud? Uh like for example, for storage S3, Amazon and for computing my Azure service.
>> Yeah, you can do see I'm having a flat.
Okay. In one flat you take room. In another flat you take hall. What is the problem for me? Both the places you are giving rent, right? What problem will I have?
>> Yeah.
>> Are you getting it? Yeah.
>> Uh one question here like for example like so like I go with a snowflake uh with a azure uh with Azure cloud. So should I contact if any issues should I contact snowflakes directly like Azure directly like >> who is your point of contact?
>> Snowflake snowflake >> you pay you pay bill for snowflake only you any issues you contact snowflake only snowflake will take care he is your point of contact. He's an intermediate.
He's a contractor.
>> Mhm.
>> Why are you going and touching the cloud? Cloud Snowflake will take care.
He will manage it. Your point of contact with a snowflake. Any issues, you go and talk snowflake only.
>> Now I understand that topic.
>> I can say one more example.
>> Yes. Go ahead, please.
>> So we are using the sim Jio. So if you face any issues, we won't call directly to Amani. We will call to customer care.
>> Yeah, that's that's related example.
Yes. Very good. Yes. Right. Well said.
>> Can you also give a glance on uh MCP and fabric Microsoft fabric?
>> Uh we'll talk about that uh since we are covering as a master class. So this is about the cloud. Okay. Now I I'll again have one question.
>> Yeah.
>> Uh in the case of multicloud environment how the data pipeline will be established like uh data flow. Suppose we have some uh uh application in Amazon a AWS and some in Azure. Uh so in that case how the data flow will be happen.
See irrespective of whichever cloud you're using anyway when we are pulling the data we are quering it they will go and pull it from that particular computer lab only right that computer that uh server like for example if two data is one is present in AWS one is present in Azure when you query it they'll go and bring it from two different clouds and we can go it but usually we don't prefer that one cloud will be linked to one uh single cloud only that snowflake will either go with AWS or Azure but mixing up will again create a confusion Because snowflake also should pay bill for both. You guys are paying for snowflake but snowflake also should build two different billings right. So usually we avoid it. Usually we avoid it but it is possible. It is possible but we avoid it.
>> Okay. Because uh in my previous organization some applications were running in uh uh like uh Oracle cloud and some in Microsoft. So I was not sure like how the that's why it they they were migrating but before that it was running uh successfully. So I will ask how question so actually like uh so in even in our company like they are using the Oracle.
So we have a connectors in Azure. So that uh by using those connectors so we can connect to the clouds easily like different uh Oracle like whatever like SAP uh S3 bucket. So using that we can connect it and we can load the data we can get the data from there.
>> Yeah. The the one thing is if you are having different different like Oracle, SAP, CRM and all everything we can bring it to one uh cloud that is like Azure or GCP then snowflake can manage that. So basically behind Snowflake there is cloud. So the good thing about Snowflake is they don't need not worry about infrastructure uh they need not worry about all the uh storage and all every everything back end there is a they're still simply making use of uh the intermediate person. Okay, I will do the work for you. You go and build another building, you take care of building another house, I will go and manage this. That is what snowflake is doing.
Okay. Now here one one question sir here uh uh same similarly like uh ETL tool >> all whatever we are doing see in all the cloud we'll be doing ETL we'll be doing ETL is a data engineering work you'll be doing in AWS you'll be doing in Azure we'll be doing GCP we can do in snowflake also.
Okay. How we can tell this is intermediate in that in that con uh context?
>> See what I told you this is a computer lab that you're going to hire uh I mean you're going to take a rent and for that you're going to pay rent right >> you pay snowflake or you pay azure you pay GCP or you pay uh AWS what's your problem you're getting one room right?
What's your problem? Who is management?
What's your problem? No, right? I have no problem. Whoever is managing, what is my problem? I'm getting a computer lab.
I'm getting a room. I'm getting a server. I will go and build a project.
Instead of paying Azure, I was paying Snowflake. What is there? Room is a room. Server is a server. Paying rent.
You're going to pay bill either for Snowflake or you're going to pay bill either for Azure. That's all.
>> Okay.
So, I I have few more things to cover. I will give you another set of time for you to ask your questions but I want you to listen to this. So we will be focusing on one cloud which is going to be Azure and Azure it's just like an online portal you go and order something in Amazon right Amazon portal you go and adder something flipkart you go and add products right similarly you go to a online portal you request them that I need a computer lab I need a server and you just just go and log into internet and you'll be pushed into a Azure and they will assign some rooms for Those are all called servers. So they will assign room for you where you can go and operate. You need not go to that flat.
You can operate from your house. You can go and get that remote connections. You need not go to that flat and sit there.
You can take a rent. That's why I keep saying your flat what you're saying right? You can relate it with your data center. You will not go inside the data center and work. You'll be taking the connection and you'll be working. That is how I can relate your data centers and plat right now coming to this we have a online portal where you can go and request and you can manage all the resources and another point which I want to tell you is Microsoft Azure is the second top cloud with more than 80% of the fortune 500 companies are using.
They have about 60 plus regions and you have to just pay for what you use and they provide up to 200 services.
That means 200 services, 200 features you can go and add. Maybe you can build a web application, you can build a website, you can build a mobile application, you can build a data science, build AI model, data analytics, data engineering, PowerBI developers, or if you're going with a UI developers, Java coding, Python coding, you can do whatever you want. They support up to 200 services. But we are not going to learn 200 services, right? We are not going to build a website. We are not going to build a mobile application. So we'll be learning about 15 to 16 services which is going to be related to our role. What is our role? Data engineer. You go to a restaurant, you see 200 items on the menu card. Are you going to order all 200? Definitely no.
You went for breakfast, you will order only breakfast items. Which is your favorite item? Which food do you want to eat? Only that you will order.
Similarly, even though there are 200 services in each cloud, I am going to build only data engineering projects.
For data engineering projects, how much of services which I need only that I will be using it. So this is about your services. Now as in then last step maintenance. I told you right when you build a computer lab you also al to maintenance. So that maintenance is called devops. The last part we are developing an application and we are also taking care of deployment. That is whether all the things are working properly or not. Every 2 3 months you'll you'll give your vehicle for service.
You'll figure out what is wrong. Like this we will do maintenance. We'll do some operations. This is actually done by a DevOps team. This is actually done by DevOps team. Just like a data engineer, there's another person called DevOps engineer. But slightly we will learn small small things. Okay. For example, if something is gone wrong in your house, correct? For example, a small screw is loosen. Okay. Your your switchboard is there. Small screw is loosen. Will you call an electrician?
Will you call an electrician?
A small a small screw is been got loosened. Will you call an electrician?
No. No. No. We will.
>> We will do it. That small screwing we were going to learn. But we don't call it as devops we call it as data ops that instead of dev I'm making as data. Any any problem in data maintenance I will take care. So this also we will be learning which will be like deploying the project in production. How are we managing? How are we tracking? How are we deploying? All of this we learn. So this is a separate role called DevOps engineer. Don't be in a assumption that we will be learning complete DevOps. No small small work small work that we will be learning as part of the data ops. So with all this the complete road map will be this one. We'll start with SQL.
We'll go and learn another programming lo language called Python. Then we will see this is our main requirement here.
We have to go and build a data warehousing. But to build a data warehouse, we need to choose an option.
Right? But we are currently facing a problem called big data. This is a problem for this. These two are solutions. We'll go and learn the solutions. But the solution is what?
Setting up a computer lab. But how can I manage a computer lab? Right? So we will use a computer lab managed by cloud.
That's why cloud comes into picture. And in the recent days, recent days just like your snowflake which is residing on one or the other cloud, there is a fabric which is also residing on Azure cloud. So that is also new and and surface I would say it's like a contractor but this contractor is so honest and loyal that he will work only on Azure nothing else snowflake at least was receding on any of the cloud but this guy is very loyal like a RCB fan he's only receding on Azure so we will learn this also and finally after learning everything we need to deploy so we will go and deploy using data ops and All of this people are running behind AI, AI, AI, AI. Let's learn AI also.
So this will be the road map. Okay. Now last 5 minutes before I give time for you to ask a question. This course will be 6 months course.
Okay. And I'm sorry to say that I will never complete in 6 months. One or two weeks it will be extended. Okay? So one or two weeks it will be extended because we are running with multiple projects, multiple use cases. So we will be ending up in one or two weeks delayed but 6 months is a typical time that you can consider to cover all of this. Okay. So you can say now we are in June right so rest of the year it's only for upskilling because we'll be starting in June, July, August, September, October, November. By December 1st week or November end your course will be completed. So the entire year goes in learning this AI data engineer. Your number of classes will be total number of classes will be 130 and today is a second class.
So we have a 128 classes pending as of today. We have have 128 classes pending to complete your road map. So we are 2 kilometers crossed. We have 128 kilometers left in this journey. Right?
So with this I would also tell you the demand for data engineers is keep on increasing every year. I have been seeing this for the last 8 years the demand is increasing. So this is in terms of the openings as well as a package. I have seen people getting 50 to 60% Ike and some of them are even getting 100% Ike. If they are already below par the current city is very less they're also getting 100% Ike the maximum that you can get 100 120 140 also people are there but on an average for data engineering 80 to 100% hike is guaranteed that is what we have seen as part of the statistics right so this is a demand now if you ask me where data engineering is not there actually I don't have an answer every wherever data is involved opportunity for data engineers is higher. Banking, e-commerce, telecommunication, health, travel, retail, digital. I keep on telling this every single domain even a supermarket is actually doing a data engineering. Even KSR is doing a data engineering work. So every domain data will be there. The only one place where data engineering is not used is where data is not there. Can anyone tell me where data is not used?
Anywhere? Any domain where data is not used?
Can you think over it? Any domain where we don't use any data?
No, we I don't think so. For everywhere we are using.
>> So that's why I said opportunity for data engineers is everywhere. Right.
Everywhere. Now the expected salary it will be like four times your experience. Four to five times. So what do you mean by four to five times? If your experience is five you can target for 25 lakhs.
If your experience is 10 into five you can target for 50 lakhs.
If your experience is let's say 8 you can target for 40. But again uh this may not be final because your currencies also matters. For example, a person with 10 years, a person with 10 years, he is getting just 12 lakhs currently.
Okay. No company will give you 50 lakhs.
You cannot go and say that okay Santos from KSR sold at five times will be the salary. So you give me 50 lakhs. No one will give you. The maximum hike will be 100%. So because of your past I'm sorry to say this because of your past that you have not shifted or maybe you have not upskilled you may end up somewhere around 24 you may end up 24 because from 12 to 24 they'll easily give you but don't expect from 12 to 50 it's difficult probably they may still give 110%.
Or they may give you 120%. So in that case maybe you'll end up somewhere on 26 or 28. This is still possible. If your interview is exceptionally well, you can get this. [snorts] Okay. Now again I'm telling you don't just believe in this. Your current CTC, what is your role? What did you do in the past? How much of relevant experience you have in data engineering?
Everything matters to decide. A person with 10 years with current package is 25 have got 50 that we have seen because 100% is also there. five times your experience is also there. So like this we have seen but 12 lakhs person we cannot expect 50 that is impossible that is impossible even I go with 12 lakhs no one will hire me right so that way the demand for data engineers is higher and higher and I would say when it is a demand learn as much as you can right so next 5 years 6 years try to get into a role make your hands wet and start choosing this option now one more thing I want to tell you is uh as I keep saying it the market is actually having lack of talents. So when I say lack of talents, we need people.
We need people but people are not there.
The only time in my company where I am getting exhausted is I've been asked to take everyday interview. I'm being asked to take everyday interview and I'm I'm not able to hire a talent. They would have done all this 15 days course one day one month course and they just sit come in the sitter class and they'll by heart they have answers and they come and they're explaining how will I hire them. So the companies needs a talents but we don't have talents and banking, e-commerce and healthcare has the highest percentage of need of data engineers and the highest salary what we have seen for 10 years less than 10 years experience is 60 lakhs and I mean when I say median we have taken some statistics. So this is a job market. Now the last question that you guys will have is who can go for this course? Who can learn this course? The first thing is it is for the guys I'll go in the reverse I demand I pay. So this is a this is a technology this is a framework this is a role which is giving you the high pay. People who are shifting from non-technical to technical who are already exhausted with the low package this is a chance for you to get the high package. people who are already working in maybe IT roles, maybe PowerBI developers, Java developers, testers, you have a very high likely chance to get into this role. And if you want to learn more on the engineering side, big data analytics side, you have it.
Freshers, um a bit of um point I need to consider because freshers no one is hiring. Okay? They always think, "Oh, you're fresher. Ah, no, I will not hire you." Like this is happening. But everyone running behind Java, everyone running behind uh testing, why don't you choose a one which is going to in demand even with this knowledge even as a pressure you can get a job at least uh 5 to 10% more than what you are running behind Java and testing you can get it and data is everywhere. So I can literally tell you opportunities are very high and finally who are looking for career growth who feel like okay if I learn this at least for the next 5 years 6 years I don't want to think too much I want to earn well I package everything but I'll also tell you one thing I'll also tell you one thing who should go for this course fine okay but among all the subjects all the technologies where I have trained I have trained almost 20 plus technologies like SQL, NoSQL, Java, Python, testing, then AI, PowerBI, Tableau, like this.
I've trained about 20 plus courses. No course is as difficult as data engineer.
I'm not here to fear you. The toughest subject what I have gone through is data engineer. Lot of things, lot of things you need to remember. lot of times you need to practice.
So here it's really worth to take this because the difficulty level forget about difficulty it's it's really worth to take because end of the day you work for 20 days in a month and you'll end up getting 1.5 likes who will not put efforts who will not put efforts right so it's a tough subject but it is not a rocket science it is not a rocket science if you ask me how rocket price even I don't know but this is a very simple analogy where you have to relate each and every topic remember and practice right now and the last thing which I want to tell you is after learning this course you can become an expert level in SQL and Python provided you do all the assignments task we will be able to create a data ver system you'll be you'll be building an professional ETL pipelines by using the cloud service and we'll be focusing on more on Azure and then SQL Python big data pispar cloud computing CID and LLM models. So we'll be working for bit of uh LLM models that is AI. How this AI guys need data? See AI is not just something which has uh flown from the sky. No, it is built on data. So this guys will ask me give me that type of data, give me this type of data, give me that information. So they will keep asking for them. We will have to provide them the data. We are the guys who will provide the data. So we will understand how to provide the data for them. Right? and you'll be readily available to work in IT companies. So all these are the expectations from you after you complete 6 months of course.
Okay. Now you can learn in YouTube, you can learn in charge GPT itself, you can learn from any institute, you can even go and join UDMI course, you can even join cloud era course or Corsera course, whatever it is, you will learn from anywhere. But please learn but please learn because this is a technology which is in demand. Now one last point which I want to tell why you can join here. Okay, I'll give you one small example why you can join here.
It's a one-stop shop where you'll get every single topics, the latest technologies, updates and AI we have recently added. Initially, it was only multicloud, right? It was only a data engineering. Now we have made it AI data engineering. So we are updating the course every single year, right? And your batch is a second batch with AI.
The first batch with AI we started in the January and the second half of the year that is June we are starting with again AI you will be the second batch six projects 10 pipelines pip plus use cases every single day what I speak will be live examples I'm a working employee who is working in one of the organization until 7:00 8:00 I work and then I come to class so I don't have experience in teaching I have experience in covering what I've done in office. What I'm doing in office, I'm presenting here. That's all. I'm not a person who has who has learned everything and coming here as a trainer. No, I don't call myself as a trainer. I call myself as a mentor. What I work in real time, I come and teach here 120 hours of subject which is less. I usually take 140 plus hours. We will be giving you fabric credential which is a license which you need for working. For example, whatever the projects we are building, we can build everything in fabric. For that we need license. So that we are giving interview guidance, resume preparations. We will be having every alternative week as a guest lecture from one of the um an experienced person who's working in different organization. We bring them and we help them to help you. that will be from different trainer Monday to Friday I'll be in front of you I'll be the single point of contact and every alternative Sundays or Saturdays we help you with all the extra trainings soft skill in a sense like you'll learn more on the agile methodology project management tools then we'll focus on communication how you can send an email all of this we will cover as part of the soft skills 120 days 140 hours you listen more than everything the first thing you learn is confidence. [snorts] The more and more you are confident, you'll be able to clear. Okay. One more thing I'll tell you for the 5 minutes. For the next 5 minutes, I will tell you as a non-case or employee. I'm a case or employee. For the next 5 minutes, I will tell you as a normal person like you. Okay.
The fee structure is 45,000 for one installment, 46,000 for two installments, 48 48,000 for three installments. Okay? Now, I'm telling you from your side, please don't join here.
If you can't spare 1.5 hours in attending the class and 1.5 hours of practice, don't join here if you're not able to do this. And this is not for one day, 2 days. It comes for 6 months. for the next 6 months. I'm telling you again, it's this is your hard earned money.
Don't waste it. Only if you're able to sit in the class for 1.5 hours. Only if you're able to sit and practice for 1.5 hours on daily basis, only then I'm requesting you to join or else please don't join. It is not at all going to work out.
Okay? I can simply say it will never work out. If you want to do something, if you want, if you are okay with this amount, okay, well and good. But you have to be ready to do this. Okay. And one more thing I also tell you, I told you why to join KSR. But I always have a habit of speaking negatives also. Please don't join KSR.
If you're looking for completing the entire course in one to two months, please don't join here. If you are not able to spend for two two three hours in a day, please don't join here. If you are not ready to learn coding and multiple things, please don't join here.
We will be giving you lot of assignment task and homework. If you are like, hey, why should I do assignments? I cannot do. If you are in that mindset, please don't join. We will track your progress.
If you are okay with that, please join.
And we will challenge with your mock interviews. If you are not ready to take mock interviews, please don't join. And if you're looking like a classroom training where I will have to go and uh make some notes, prepare, give you the printed notes and I'll give a printed notes and you read it, that is not going to work. And if you're expecting like a Google tutorials, I'm not a Google person. Right? So all these are negatives.
This if you're not okay, I'm telling you please don't join. Please don't join here because all these are negatives.
Six months course every single day 3 hours of efforts, homeworks, assignments, projects. You will have to do it. You will have to do it. Okay? I told you we will be doing how many projects? Six projects. Right? If you listen to me, if you listen to me by end of your batch, you know how many projects you'll be doing? Any guess?
Six projects.
>> I'll be doing six projects. But how many you will be doing?
>> Anyone?
>> It depends on our practice on consistency. So 18 >> 10 >> 21 projects 21 projects if you're listening to me you will coming up with by end of 6 months if someone is asking how many projects you teach I can openly tell 21 one project I'll take we will do it in ADF we'll do it in datab bricks we'll do it in synapsis we'll do it in fabric that's a four projects but I will not do four I will do one in the class I will do in ADF I will ask you to do it in other three then you may ask me boss you're teaching me in ADF how can you tell me to do it in others that is why the second project I will do it in datab bricks I will do it in datab bricks then you will have to do it in others what I teach in class doing the same thing it's not a big deal yeah everyone can do it. What is the real challenge?
You should be ready to do other projects without me involved. That is why this level of detailed explanation. One project I'll be doing it in class. Other three you'll have to do. One I'll be doing it in fabric you'll have to do it in others like this. The way we have splitted is six projects we will be doing in 21 types. each and every uh method one in datab bricks, one in ADF, one in synopsis and one in fabric like this. I will be doing six projects but you will end up doing 21 but no one has ever done 21 in KSR history. The maximum a person has done is 18 projects.
Maximum one has done is 18 projects. So with that I would also tell you all these are please negatives. So if you're not okay with this please don't join and with that I would like to take up all your questions because from Monday onwards we are entering into the subject SQL will be a first topic where we have already gone through the road map. Now you can ask all of your questions I'm here to answer. Come on one by one please.
>> Hello sir.
>> Yes >> sir. Actually I have around 15 years experience. Okay. So I started my career with support only. Okay. So now I'm I'm I have resigned my job and uh I'm planning to do some course dedicatedly and looking for I mean work on that uh course.
>> What what did you do for 15 years? One is for support. What which technology work release engineer support work only.
It's not anything technical like development or testing like that.
>> Okay. All 15 years.
>> Yes.
>> Okay. When did the last time you worked like when did you resign? Uh so I will resign by this month end. Yeah.
>> You've still not resigned?
>> I mean I mean serving notice.
>> Okay. So you're ready to uh push your 6 months without salary.
>> Yeah.
>> Okay. Then please opt this by 2027 you'll be readily available to work as a data engineer. In your 15 years of experience 5 years you can work as a you can portrait as data engineer. Now you may ask me a proof. I've shown you can do 21 projects. If you do 21 projects, you are much better than a 5 year real experienced data engineer. So you can easily portrait as 5 years. So by 2027 in the next 6 months, you can dedicately practice. What about 3 hours? You can even spend 6 hours. So 6 hours of dedication work, you can even become an expert. You can even do more projects and you'll be readily available to work as a data engineer by 2027. You can opt this.
>> Okay.
>> Hi Satosh.
>> Yes.
>> Yeah. Satya. Uh my name is Satya. U uh in datab bricks are you going to explain data lakehouse concept also?
>> Yes data lake delta lake unity catalog everything we will be covering.
>> Okay. Okay. Thank you. Thank you.
>> Just one question from my side. uh uh whatever uh you provide in the class and whatever we follow I mean it should be more than enough to uh uh uh compete with the five year uh guy uh as per okay >> that's what I said right by by completing this course you can compete with five year exper real experienced person >> so we don't further it's not needed that I should go and further read any other things uh because uh most of the institute what they will just put the basic things okay uh if in in the SQL only assume just a basic thing of writing queries so to this level of whatever five years it will be have some expectations around this right so we'll be uh so we don't require any further thing whatever you follow whatever you say if you follow and uh keep notes and do everything uh end to end that should be more and what you are suggesting See whatever I'm teaching apart from that we will also give you some homeworks assignments task that you need to research because I I've done this data engine for 12 years I cannot teach for 12 years right so we have cut shorted in such a way that you'll be able to work as a five years experience but for that you need to put efforts you cannot say that I'll attend only class can I show 5 years not possible you need to practice so the five level experience you'll have to solve a rank you'll have to solve uh lead code you'll have you should be able to um solve any type of questions that foundation we will give you right based on the foundation you'll have to practice more >> okay >> so that level of teaching but again learning there's always an ever learn ever ending whatever I teach it's more than enough to work as a five years and it's like a uh maybe like you can work in real company but what if after course if there's a new topic which comes a new update >> then you'll have to go and learn that right that you have to learn from anywhere so that type of new features new things you will have to But this course will be completely enough to show us a five years experience.
>> Okay. Thank you.
>> Yeah.
>> Couple of questions like so one is like you said data ops right. So in devops like the creating the pipeline g repository and moving from one environment to another environment that will rely like any extra thing we will learn >> in devops right uh you'll be building a mobile application website application all this testing development java everything we'll push right in devops but we will be focusing only on pipelines data pipelines that is why we are calling as data ops still github will come integration will come main branch will come master branch will come pipeline will come everything will be here only instead of Java I'm using pipelines here that's all >> okay and uh regarding this uh uh like licensing part like like so earlier when uh like I started this aure uh data engineering like we got 1 month of free licensing right so uh you you said you were providing the fabric licensing right so that it it will include all the Azure services >> fabric is different Azure is different I mean when I say different for Azure they will give you 1 month of um 30 30 days uh they'll give you about 200 two two 200 credits which is about 20,000 we will utilize that okay and for fabric there is nothing like we'll give you free so that's why we are providing license >> okay so the fabric you will provide licensing and what about datab bricks so in data bricks also like there is a >> that is why we are we are using that free account right that free account we'll be getting $200 right we will utilize Okay. Like I mean like that is only for 30 days, right? So >> you will have to create two accounts 60 days, right? So see all 6 months is not Azure, right? SQL 1 month, Python 1 month, Pispark one month. In this four months will go. Last two months we'll be learning Azure that for 2 months we need two accounts. Two free accounts we will create 400 credits $400 40,000 amount we'll get practice in that.
Okay. Okay. Sure.
Uh yeah that's it Santo. So thank you.
Any other questions? Uh >> Santo sir if someone has 8 year of experience in reporting like uh PowerBI or business intelligence. So is this course benefit beneficial to him or uh need to join another course.
>> Data analyst with data engineer is one of the best combination you can have in datadriven technologies and I'm coming from the same field. I'm doing data analytics. I'm doing report. I'm building data engineer uh pipelines.
>> Yeah. Yeah, same problem with me because I am working with the PowerBI and the uh MSXL and GCEDs. But the thing is that I don't have exposure of uh how to connect data source with uh different uh servers, right? Because here our data source in in our local machines or flat files.
>> So I'll answer I'll answer your question in one line. A person 8 years experience in Java and testing learning data engineering and a person like you worked as a reporting for 8 years and learning data engineering you are 100 times better than the other person.
Okay, understood.
>> In this way, it's a good way, right?
Obviously having both roles in your bucket will always be putting you in the front seat >> and in this course I will complete whole fabric like because whenever we open PowerBI service so there is option for fabric data engineering machine learning so whatever means uh like you said earlier like snowflake is a contractor so it's for all the cloud services but Microsoft fabric is only for the as well right >> excellent yes correct >> uh but we will not do everything here okay we will only focus on fabric with data engineering. In fabric you can do machine learning models, you can do reporting that and all you can do but we will be teaching only fabric with data engineering role.
>> Okay. But in some roles like that when we apply in a no or other sites uh they ask for in a JD like they required a PowerBI with a fabric but in this course I can apply for those role.
>> Yeah, you can apply since you already know PowerBI right? You can apply. You mean like CI, CI, C, CI and CD pipelines all you will complete in this course, right?
>> Data ops is nothing but CI/CD pipelines.
>> Okay. Okay. Means fabric will be all complete. Right? Fabric.
>> See that's what I'm saying. Fabric all complete means fabric will be only focusing on data engineering. In fabric we can build a models also. We can do reporting also. We can do powerb also.
PowerB and all we are not teaching. In fabric what pipelines we are doing only that we'll be repeating.
>> Okay sir. Okay, thank you.
>> Hi Santo.
Yes, I >> I do have 15 year of experience in KPO operation.
>> Okay, so >> as a KPO manager >> KPO manager Okay, >> I'm getting a lot of noise. Whoever is just asking questions only you please unmute it and please ask your questions.
Please ask your questions.
>> Uh Santos, I want to know about that. Uh first good evening. Uh know about that is certifications mandatory for example we are learning as your so DP900 or >> see uh not mandatory uh I would I mean most of the companies they believe in skills than certificates but some companies are asking okay uh like uh why don't you uh uh have certification this type of questions are asking if you're coming from a career gap let's say 6 months 8 months 10 months then they will ask you what you did. So that time you can tell I was just I took a certification I've done this and all but if you're having a working employee you can still manage it. Okay. Uh and I mean see I personally don't have any certification but that does not test my knowledge right. So certification is just a like a presentation like you can present it on your resume. Okay. I'm a certified engineer. So it's not mandatory but if you're looking for any product based companies they're asking.
>> Okay. It's help for the p your spend time. Okay. Thank you. Hi Sund.
>> Yeah. Go ahead. One by one. Yes.
>> Uh so this course also help for the certification program.
>> Yes. You can easily pass.
>> Okay. Thank you Sus.
>> Su sir.
>> Uh I have uh 8 years of experience in business development. I'm into sales and uh does this course will be benefit for my career? Yeah, I see uh that's what I told you right the guys are who are working in nonIT career gap freshers for all of you guys it will be benefited for you if you're working in sales so 100% your communication will be good you'll be able to talk to your clients so if you are choosing this particular uh subject uh then you in the 8 years of experience you can say okay 3 to four years I worked with some projects PC projects and I worked with uh real projects all of this you can show and then you can get into the role >> okay sir thank Okay.
>> Are you providing any like document like uh for us like uh because like you said like the course content recording session will be only for one year. So what about the document wise? So are you providing any documents so that we can go through anything?
>> Uh 95% of this course is practical hands-on.
>> Okay.
>> Everything will be on screen. I'll open the software. I'll write the code in front of you. So the expectation is you will have to do the same thing at your home but what I practice right end of the day I will share it with you but it's not like any notes what I do I will share it with you >> okay like it's like a normal word document whatever it is >> correct yes >> sure and one more question like so uh so if we'll attend this course so we can crack those interviews like so berry boat so like we will uh uh recently like TCS is launched right and hackers com and few like SQL uh like questions uh that is like predeprimined for like uh some of few companies so that we can crack those questions as well.
>> Okay, just just a correction you're asking if I attend this course will you be able to clear the interview?
Yes, not only the clear not only that mock interview like uh like earlier so we will take few questions right like uh a few uh like berry board questions like which is predeployment questions are there right those questions >> uh that's what your your question is if you attend this course will you be able to answer all the questions in interview will you be able to clear >> yes yes yes >> okay the answer is no the reason only attending will not work okay you will have to do All sort of assignments, coding questions, whatever you are, we have been giving if you are able to do that then you can clear any interview any coding question also you should be able to try you should be able to implement the logic that way it will help you.
Oh okay just I get because not not only attending that course like that just after >> because I'm I'm always see I'm always worried some people will come and say if I attend your class will I get job by attending what you will get nothing right you need to practice you need to put efforts you need to do projects 21 projects you need to do you need to build 10 pipelines you need to do all the documentation work and you will have to listen the requirements so lot of things are there if you follow this 100% you'll work in any company you will clear even Amazon Google I mean um uh any any product company also you can clear but provided you should also practice. So to answer your question yes you should be able to do everything by attending and practicing.
>> Sure. Awesome. Thank you.
>> Hi Santosh.
>> Yes >> I have a query.
>> So as a data engineer see we are onboarded and we get a request for building the pipelines all that but it is always not we can expect for creating the new pipelines. So how much of effort the data engineers need to put on the maintenance because after if if any existing pipelines are available in a project if the if a data engineer is onboarded so much of effort they need to put it on a maintenance perspective >> see I wouldn't say maintenance maybe I can use a right word called uh maybe additional uh enhancements there is already a running project which has already been there by 2 years someone left a role and you are going to replace them so you'll be given a task of new enhancements, new additional things.
Maybe they'll say that all these days the data was coming in terabytes, now it is coming in pabytes. How do you handle it? Can you increase a table size? Can you scale it up? Can you go and optimize it? This job is running for last 2 hours. Can you go and optimize it? It was running good. This pipeline is there for last two years. Suddenly it has become slow. So like this you'll be given a s all sort of works. But we will be focusing on end to end the one project from starting to end. So when you do this projects right you will also know what type of challenges you'll get what type of new requirements they can ask so that we will be able to help you here and even when you go for a replacement role already the pipeline is ex already there you'll be asked to do maybe adding some sort of improvements optimize it those type of things you'll be able to do it >> okay yeah >> the activities uh uh here >> discussion is going on right activities >> are You're asking I'll be teaching CI/CD.
>> Yeah. Yeah. Continuous integration >> data ops is nothing but CI/CD.
>> Yeah. That activities uh lightly you are going to tell right >> lightly. Sorry.
>> It means uh not completely devops role but uh CI/CD part you are going to >> uh yes I will not be covering complete devops. I'll be covering data ops. Data ops means building CICD pipelines for engineering side. That's it.
>> Yeah. Yeah. It will be like about six to seven hours of syllabus.
>> Okay.
>> So like regarding this architect and like the manager tools like yesterday we asked question right? So that session is also completed uh with this course right? I mean extra sessions.
>> No this is only a engineering role. If you want to uh apply for a manager role then you'll have to reach our offline. I mean offline essence you can reach out to our team where I'll be interacting with you. I'll be like uh giving you some tips like about uh two three sessions we can meet and we can discuss as a manager what you'll be asked to do it. It's not a session. I'll not teach you I'll not teach you the management side. I'll help you what you can do as a manager. What you'll be able to do it, what what uh uh expectations you will they will have on you. Those type of things we'll be covering.
>> So um when one minute please one minute please. Uh Naven you're done with your question.
>> Sorry those are extra questions right?
Those are additional things apart from your engineering side that additional two three classes we can meet we can meet together we can sit and we can discuss about the management side.
>> Sure s >> but before that you should have a strong experience here without this being a manager is nothing it will not help you.
>> Yeah. So like that will be in like offline session or like like uh >> I mean once you finish the course once you complete all the course once you prepare everything you give us a call that I have already discussed with uh in the session that after after this course I'll be applying for a management role that is data engineer technical manager then our team will uh connect the call with me we will discuss what are the additional things you need to focus to become a a technical manager sure sorry thank you Yeah.
>> Yeah.
>> Yeah. Please.
>> Sir, recently uh you were conducted on one meeting I mean summit. So those who are attended taken the >> so in your KSR data center in Hyderabad you are conducted right? Vently >> only joined people right?
So what exactly the discussion? Uh >> okay you're talking about the alumni meet.
>> Yeah. Yeah.
I mean whoever has uh completed uh I mean whoever has got job right just a small meetup >> okay >> you can reach out to our team they can help you okay >> yeah yeah >> if you want to visit you can you can talk to our team they'll help you with more details >> okay >> your contact which companies go for snowflake and which companies go for this one >> uh we can't tell you uh there was a company was very happy with snow AWS suddenly they came with migrate to Azure so people are shifting see our our own people only are jumping here and there like what companies will do they'll always look for price a lot of companies even though they have a best fan base they're still moving from cloud to cloud because of pricing so we can't judge this companies only use AWS this companies only use snowflake we cannot tell >> uh but uh can I say like this so small companies It's supposedly enterprise I mean startup companies having less data right. So um so this snowflake came into picture like pay as you go. So how much you are using mean you need to pay that much right?
>> So we cannot say that because everything is pay as you go. You use Azure pay as you go. You use AWS pay as you go. You go snowflake pay as you go. Everything is pay as you go only. But provided what is happening in the market is they're looking for low low cost with best quality like this. So people keep jumping it but as I said right we don't have an answer like only this companies use snowflake this companies use if I really want to tell you that Amazon will use only Amazon web service Microsoft will use only Microsoft Google will use only Google GCP that's all is a strict one everything else we cannot judge because their own cloud they will use it but other companies they will look for benefits >> but definitely you said the snowflake has a contract of right so definitely Snowflake will provide the more widget than compared to uh Azure.
>> See uh see not like that. Uh maybe you may not happy with the contractor. You want to directly deal with the owner.
What you will do?
>> I you are raising some complaints that contractor is not even responding to you. What you will do? Hey, I cannot talk to you. You bring the owner. This is what you will tell, right?
>> This is what you will tell, right? uh directly go and deal with contra builder only. Who is the builder? AWS, Azure, GCP, you can go and directly work from there.
>> It all depends.
>> Is the snowflake is the top 15 companies uh having large market share, right? So when compared to other so this is the top one in like that. Okay. Um we can uh always shift also. It also depends on what is your need. It's not always snowflake is great and everyone will use snowflake.
And I am telling you once you learn Azure snowflake is also easy because most of them will be SQL queries. So you'll be able to do this.
Yeah. I mean they currently introduced the DBT as well right? So basically DBT will reduce the snowflake uh number of lines. Right? So >> see again I'm telling you different clouds have different features.
Snowflake also has a different feature.
Sometimes in snowflake there's a limitation. We cannot go and write a complex logic. We cannot go and write a pispark every complex logic and pispark logic. What you will do? Snowflake itself doesn't even provide a best service for writing spark. What you will do then obviously you'll have to go with datab bricks. Datab bricks will be one on the azure gcp and this. So there are some limitations. No tool is perfect here. Snowflake doesn't mean it can do everything. No, there are few things where snowflake cannot do. Azure can do >> vice versa is possible, right?
>> Yes, that is also possible.
And Santos like you >> in case of opportunity who has more opportunity snowflakes or uh data engineering with fabric >> data engineers is having more opportunities you work with AWS you work with Azure you work with snowflake you work with fabric doesn't matter data engineers are in demand >> and uh sus like I just want to understand like you said like you'll be covering a data engineering right so what uh in AI right because AI is huge I a lot of things right so just want to understand what in AI will you be covering as >> a people will come up with some sort of requirements I need data like that I need data like this I need in vector form I need in table form I need in data frame form they'll come with lot of requirements your responsibility is to provide them the data that's all >> okay >> for them for that we will have to understand how charg works how LLM model works, how the words are converted into text, uh sorry, how the text is converted into numbers, so what type of data they need. We will help them with certain data points.
>> Okay. So, you'll be covering that point.
>> We will not build any rag models. We will not build any agents but we will know how it works and as a role of data engine what type of data they need that type of data that how to [snorts] provide them the data those type of things we will learn. No, >> thank you.
Hey hi, I have an like u I have been working in IT since four and a half years but initially for two two and a half years I have been working on a support project but now I have been working in project where uh but I don't have much exposure on the de so do you think it's better to I mean on this data engineer or like >> see DevOps uh see if you're if you're already working in DevOps and if you want to choose between DevOps and uh data engineer. DevOps there's lot of tools you need to learn maybe it is a less coding if you are a person who wants to say I don't want to learn coding I want to continue you can learn AI ops so AI ops is rather than doing all the devops there is lot of AI things that you can add on and probably you can learn AI ops also okay that is also good option for you because you are already coming from that background but if you're feeling like no I would like to get more salary I want to like stabilize my career then this is the option >> yeah since I already completed four and a half here. So, uh it I mean effects or like uh which one is the question of like as of now I don't have much exposure on the dash I mean I just working on a flow infl see here we are teaching everything from scratch right >> yeah okay >> no everything from scratch means see you you're worrying about four years people with 15 years 16 years also are going to learn the same thing so they cannot think like oh 15 years I have worked in that one this one or why am I learning this they cannot think right So if you are if you're looking for a upgrade doesn't matter you have four years or 15 years you have to learn there's no other option.
>> Okay. So mean uh in the interview for like uh for the project level and so we just need to have the mean real time projects and all. So you sold for the 71 I mean you mentioned the trainers and but when it comes to the real science and all those things >> see I I'll um I don't know but uh I'll remember your question I'll remember your name after doing 21 projects if you are not able to clear the interview I will hire you for my role I will give you a job in our company if you're not able to clear the interview after 20 after doing 21 projects there's a you can take as a open challenge you're worried about whether you'll be able to clear or not right after doing 21 projects if you're not able to clear the interview I will hire you without interview so that's the confidence I'm giving you if you do the projects you'll be able to clear any interview >> thanks what about Sunday classes Santos like uh do you have like on what time it will start uh the Sunday classes the Sunday also will be the same time uh but initial 2 three months uh I will not disturb you uh because we need lot of practice on SQL and Python so first three months no Saturday and Sunday fourth month Saturdays will be there and fifth month or sixth month Sundays will be there you know why because projects we will be covering only on Sundays because it's a lengthy class it goes for 3 hours 4 hours so we'll be pro planning projects on Sundays we'll plan it if majority of the people are uh asking for morning class we'll shift it If lot a lot of people are joining from US, we'll keep it in the same time. So we'll plan it. But last one month we will have we will be having uh weekend uh weekend classes Saturday and Sundays.
>> Sure. Thank you.
>> Is that weekend classes is available?
>> No, this class only will go for weekend.
We don't have any separate batch for weekend. This batch only we will be covering most of the projects on weekends.
>> Okay. What is the usual time?
>> Ask per day.
>> How many hours you want?
>> No, per a day. How many hours you you teach? Actually, >> regular session one and a half hour.
Weekend classes 2 and a half hours.
>> Okay.
>> What can uh what about weekend?
>> Wicket class? That's what I said. It will be in the last one month.
It will be for two two hours per [laughter] day. Yeah. Santos tomorrow at what time we have class?
>> Tomorrow we don't have it. Uh that's what I said. First initial three months only classes will be Monday to Friday.
Next class will be on Sun uh Monday 8:00 SQL.
>> Monday 8:00. Okay.
>> Yeah.
>> Sorry, your voice is bit low.
>> One second.
>> I cannot hear you.
>> No. Are you able to hear me? No, I think you're speaking bit far from the mic. I cannot hear you.
>> Uh, okay. I hope now you are able to hear.
>> Yeah. Yeah. Yeah, I can hear you now.
Yes, tell me.
>> If I'm not wrong, uh, you have two courses in KS data vision. One is Azure and MS Fabric for PowerBI. The current one is Microsoft AI data engineer, right? Uh, I was already enrolled for Azure and MS Fabric for PowerBI. uh since you mentioned that in this course uh it will be cover BI related topics only but I'm looking for the data engineer part as well which is data lakehouse and warehouse Azure uh ADF and all since in your course you are covering these parts also if I'm not wrong >> see I was taking PowerBI and fabric uh and I was also taking data analytics and data engineering both I was taking now I moved on from data analytics to data engineering So in data engineering we'll be covering ADF activities. Um then again fabric everything we'll be covering. Only thing which will not be here is PowerBI. PowerBI we will not be covering here and not needed also.
>> I got it. If person have experience in PowerBI developer side like so then no need to go for the the course which I already opted. So is it okay if >> we can offer this?
>> Yes. uh how I can uh uh like uh request the uh >> you can talk to our team uh whoever was a point of contact who has helped you in getting registered with our team. So you can talk to them, they can help you.
>> Okay. Okay. Thank you. Thank you sir.
>> Okay. So with that we can stop here for today. Please fill the form which has been shared with you and our team will get back to you. And with that we are done with the master class to day two and 128 days left which will be the third class will happen on Monday which will be starting with SQL. Thank you all. Over the weekend please do research. Okay I'm telling you outside of KSR go and research outside also what they are teaching how they are teaching what is the syllabus they are covering how much they are charging everything you research. After research then you talk to our team then you pay. You pay after 5 days. After five free sessions only you pay only if you understand you pay. Else please join somewhere else.
Okay.
>> Yeah.
>> Just one last question.
>> Is it uh good to give this DP800 exam as now Microsoft is offering free coupons.
>> Yeah, we can opt for it. Uh but make sure you take up uh enough dumps uh and learnings before you take because it's a bit difficult. So you once you complete the course probably you may able to take but at this point of time go with the preparation because uh if you if you are able to take the certifications you have to come up with some dumps have some sort of solving then only you can take up the certification.
>> Yeah I just completed PL300 now again they are offering free one.
>> Yeah yeah go for it. Hi >> Shand.
>> Yes.
>> Hi Shandto.
>> Mhm. Actually sir I have a 9 year experience as a mobile developer but currently last two year I have a career gap. So can I join this course? Yes, you can join the course for two two years.
You need to do something like you've done some internship, you've done some sort of projects. You need to have a visibility. Okay. Whatever projects I'm doing, see if you tell 21 projects I've done in the two years, who will not hire you?
>> Okay.
So, you have to put efforts but you can get it.
>> Can I mention KSR in my resume?
>> Yeah, you can mention.
>> Okay. We will not give you any certificates when work experience but you can mention.
>> Okay. Okay. Thank you.
>> Um hello Soros sir.
>> Yeah.
>> Uh how much technology we'll be able to cover in 3 months? Like I'm not saying like 6 months. I'm saying like in 3 months like SQL, Python, Spark. How much we'll be able to cover in 3 months?
>> So in 3 months we will be able to cover SQL uh then Python followed by data wareousing, big data, Hadoop. So you'll be learning only this like 50% syllabus but you cannot apply with this role. I mean we cannot apply uh with three months of learning you cannot apply as a data engine because the second part is different. The second half is actually very important. So if you are having a 3 months uh period I'll give you one suggestion. Uh take the previous batch recordings uh attend the live here and you can drop an email or talk to our team saying that uh I'm running out of time. I don't have much time. I cannot wait for 6 months. So you uh what you do you attend live here in the morning time when you already in the notice period right go back and watch the previous batch recordings so you can finish it in 3 minutes >> actually sir I'm currently as a power developer and I'm on bench right now so I was thinking like if any other skills I can add on like find a job for this period of time and continue with this batch so if I add SQL Python big data and spark so that will help me or not >> no no what That's what I'm saying. You have to if you're joining here, you will have to apply as a data engineer only.
Okay. So what I am saying >> okay >> learn this 3 months you attend the live already or in notice period you're in bench. So this bench period you can make use of old batch recordings you do you go through it. So you will you'll be able to apply within 3 months only right. If you want a fast learning >> are you getting?
>> Okay sir.
Okay sir. Yes sir. Yes sir. Understood.
request our team. They will give you old batch recordings. So morning you watch the recordings, evening you attend the live batch.
>> Okay sir. Thank you sir.
>> Okay with that we'll stop here for today. will continue in the next
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