SQL Full Course 2026 [Free] | SQL Tutorial for Beginners | SQL for Data Analytics | Simplilearn

Key Takeaways

Hey everyone, welcome to this course on data acquisition and manipulation using SQL by simply where we will learn how data is stored, accessed and manipulated using one of the most essential languages in data analytics SQL which stands for structured query language. It's the backbone of almost every datadriven application. Whether you're working with analytics, business intelligence, data science or back-end system, SQL is the skill that allows you to retrieve meaningful data, transform it efficiently and answer real business questions. So in this video, first we will dive into databases and SQL foundations where we will cover the evolution of data storage, core database concepts and the asset properties along with an overview of SQL commands and setup. Next, we'll explore SQL basics and crude operations including MySQL workbench, SQL syntax and best practices. Moving on, we will learn about filtering functions and query control focusing on logical operators, string functions and handling null values. Then we'll cover grouping conditions and time functions diving into advanced grouping conditions filtering and date time functions. Finally, we will wrap up with joins and advanced data manipulation where we will learn about SQL joins, table creation, and advanced quering techniques. Now, if you're looking to advance your career in data analytics and generative AI, then the professional certificate course in data analytics and generative AI from ENIC Academy IT Kpool is designed to equip you with the skills you need to succeed in today's techdriven world. With master classes delivered by IT Kpur faculty and a program certificate that boost your professional profile, this course offers hands-on learning through over 215 exercises and 12 plus real world projects. You will deep dive into data analysis, Python, SQL, generative AI and AI powered visualization tools like PowerBI and Tableau. With live interactive sessions, you will not only gain expertise in these tools but also earn Microsoft Azure data fundamental certification along the way. So what are you waiting for? Hurry up and enroll now and you can find the course link below. So before we get started, here's a quick quiz to test your understanding. Which SQL clause is used to filter aggregated date? Your options are where having group buy or order by. Let me know your answers in the comment section below. Let's get started certification along the way. So what are you waiting for? Hurry up and enroll and I can find the course link below. So before we get started, here's a quick quiz to test your understanding. Which SQL clause is used to filter aggregated date? Your options are where, having, group by, or order by? Let me know your answers in the comment section below. Let's get started. Let's start with SQL. Now, uh let's get introduced to SQL or sometimes you also got a SQL there. Now, let's understand this here. So, for example, here, okay, um Fix is a global leader like Netflix, we have got Amazon and several other streaming u subscription based uh companies out there, right? And they are very uh they will give you recommendations. This isn't a new movie, but this is the recommended movie based on what you have seen so far. Right? It means uh it collects the data. It collects the data that when you are watching it I'm pretty sure most of you have done the uh okay the binge watching on Netflix or anywhere else and after a while okay this actually ask you that are you a are you still there right so how do they know that so they are actually collecting our data now just imagine millions of people out there they are looking at Netflix right now and whatever they are looking at and whatever media they are uploading there and however the user is interacting with that Netflix application or the website there they are collecting the database there. Now that collected content or that collected data all together here they are saving somewhere inside the database they are they are saving somewhere inside the database. Right? So when they have such information here all right where they are going to save it. So wherever they going to save it okay that becomes a database. Now that database can be anything. A database here can be anything. These are databases. They can be your servers. Okay they can be your cloud or third party server or server or onremise server. Ultimately they all are your databases. Ultimately they all are the databases. So first uh what we are going to start with today is we will first start here with database and its types right and first we'll talk about that first we will only talk about that only database and their applications after database and their applications here I'm going to talk about the TOC after this here I will go ahead and I will talk about TOC it means that what we are going to cover in this SQL program all right after the TOC here we will go ahead and learn about SQL okay that how this language is used what are the clauses out there what are the commands out there that we should be looking out right so we'll go ahead and learn about that too once we will learn about that then we'll go ahead and get introduced to lab practice lab that is available online right that runs on a virtual machine that runs on a virtual machine right so let's go ahead and get introduced to database Yeah. So let's go ahead and get introduced to database here. But before I go ahead and start talking about database everyone, let's have some few uh small things straighten out. Right? What are small things I'll be talking about here because not everyone is using snowflake right a few percentage of the companies use that here. Now why are we learning SQL here? because uh SQL here is the base or foundation language to retrieve data from any database out there. Okay. So since you are using a snowflake so you uh we think that that oh my god everyone is using that here but that is not the case. People are using Oracle also people are connecting with AWS those who are working with PowerBI they're working with Azor there and most of the companies those are the older companies right there their data is still lies in Oracle and they are still utilizing that perfectly. So if we are about to learn any kind of language here or towards data engineering just simply retrieving or connecting my PowerBI to any other system or connecting Tableau to any other system out there or it can be a cloud storage or database there at least I should know what I'm doing. So as a data analyst or a business analyst here we should be aware about this that okay how am I connecting my data there. Even if you're a data scientist, you're far away from a analyst or and the data engineer there, you should know how the data is getting here. So if someone provides you a code somewhere or you are having little bugs out here, you will be able to at least understand what is going on. So that's how learning SQL language will help you. It will also help you to understand that how database is actually created. And once you understand that it will be far much easier to actually analyze that data set and understand from where this will be coming from and it it will actually help you to write the calculations uh or use the functions effectively. So that's how it works. Okay. Now let's understand here. uh now we say that we have social media and everything and the data is getting collected and we need to save data somewhere here but uh we didn't had the cloud technology before 2000s it is not available widely spread to all the businesses uh likewise there in later 2000s here we get to have our cloud technology and it was a big boom and everywhere after 2010 we had uh social media and too many and two different types of data set there but before that also data existed business existed clinical research were happening then back there too meaning everyone has the data with them. So where will the saving that when we didn't had any databases like that at the back end okay we were saving everything in the plat files we were saving everything in the plat piles so whatever data we had it was getting saved in the plat files here right now you must be thinking that why I'm taking you through here okay for the understanding about what is database here because nowadays we think about database or it's a cloud it's a database but what is actually happening at the packing let's understand that here I will be very quick don't worry about that. So here let's say we are saving the employee records here. All right. Now these are all going to be simple flat files as I'm typing it right now in the notepad. So for example if this is the uh employee data here maybe I'm going ahead and uh here we are saving the employee data. So I need my headers like ID, name, designation, employee status and a lot more. inside that here I now we will give keep going here and type in the row by row details of the employees here row by row details of the employee here likewise and like this we have to go ahead uh I will go ahead and write all the thousand entries right here now of course people are leaving people are joining back again okay so uh um this data will keep on getting edited so if the five people are there and those who are responsible to update this data every or have the access to this data here. You'll keep on accessing this data and they keep on adding and deleting that. But everything has to be very manual here and they have to be very very careful that what they are doing right here. So if this is a notepad file, simple notepad file here and we are collecting our data set right here. What do you think here? What can go wrong in this case? If it's a simple notepad file, it is a simple text file and we are manually entering the data here. What do you think can go wrong here? There is no data type here. There is absolutely no data type here. Okay. So for example, look here. It is using thousand separators. This is not using thousand separators. Okay. Here we are using upper case. Here we are using lower case. Again we are using upper case. So there is absolutely no data validation. There is absolutely no data validation. Meaning your data type has no meaning. Okay? We cannot uh enforce that this is the way you should be entering the data set to plus there will be issues with the data integrity because we have no idea what someone is typing. Okay, there can be manual errors here. So here comes the question for data integrity. Now here if uh someone will ask us here that can you get me the list for all the managers out there. It is going to be very difficult here because it is a simple text file. I cannot simply put a filter and get the data out. So here comes uh that retrieving the data becomes very painful. You have to go line by line, line by line and try to figure out that okay this is uh and uh copy paste manually to figure out that okay what's going on. So these are the few limitations that we were going to see there. So they have faced these limitations of course and then they came up with the database management system. Then they came up with database management system. database management system. They understood that as the technology has progressed. Okay, they understood that. But flat files are a nogo. Okay, that is very very difficult here. We have got tons of files saved in a computer drive there and that drive that is my server. I'm connecting via LAN there. Those who sit there, they can only go ahead and access that file and still it can have multiple wrong entries there. And of course, it was very very uh difficult to update this here. For example, if someone leaves right now. Okay, if let's say the Pali is leaving right now. So, should we simply go ahead and delete this line that the Pali has left the company? Should we simply go ahead and delete this line here? Why not? She's not working with us anymore. Should we delete this since she since the Pali has resigned? So, for example, right now we have employee status here, right? So we have to update the employee status. And with that we have to give date of joining and date of resignation too or date of leaving. We have to update that date too here. We cannot simply go ahead and delete the thing here. This is why this is required. Okay. So the now they will keep adding DOJ and date of leaving here. So let's say now this person has resigned. So they have to go ahead and update the status resigned. Now here they have to write DOJ. They have to write again one more status there and uh keep on writing the dates. If this person has resigned, this person is active, of course, they have to go ahead and type na anywhere. So, of course, there is going to be a lot of duplication again. So, that's where they came up with the database management system that okay, this is not working for us. So, let's go ahead and uh they created a database management system. What was the good thing here? Okay, they decided that there are no uh simple plat files here. Now we are going to go ahead and save everything in the table structure. Now they actually defined the table structure. They said that everything will be saved in the table structure which is going to contain rows and columns and the first row first row that is there. Okay, the the first row there it has to be a header. So they came up with the rules there. The first it should have rows and columns. First row there should be always be a header there and uh while you're reading that data set okay you should read from top to bottom plus each column header that we are giving or each column name that we are giving there each column header should have its own data type and data written inside it should be written according to that so if I'm saying it is data of joining it means inside that column only dates can be written. If I'm writing this is the employee name only the name of the employee can be written. So they came up with uh the table structure here that this is the table structure that should be followed in DBMS. However in DBMS here okay they are simply going ahead and creating a standalone tables. So for example if I have department table here right. So a department table will be there. So for I have an entire database with but that entire database will have multiple tables but they will be simple tables there. So employee table is there, department is there, customer is there and vendor is there right connected to each other. Now if I'm collecting employee data I'm simply collecting the employee data as I'm doing there. If I'm collecting department wise data I'm collecting department data. If I have customer details there I have got customer details there. So they were huge. Just imagine your MSXs data or MSXL files that you have here. So that kind of tables you have. So for example here, okay now we have employee table and department table in the rows and columns like this. If this is a row and column table here with the proper definition inside a proper MSXS or MSXL or that kind of software there it means we can define the data type data validation everything. So all the limitations of the file system there they became advantage in the database management system. The only drawback here was that if I will ask you for example this is department table right now this was uh our employee this is our employee table. This is our employee table. I'm simply going to get this employee table here. Let's just uh assume right now that both are in the table format. Both are in the table format. This is our employee table. And let's imagine here that both are in the same table format. Now if I will ask you right now that can you go ahead and get me the manager from marketing department. How we will how you will do that? See here we cannot do the join here. Why? Because tr is I have department name here. I have a department code here. I have a department code here too. But since these are two these two are not connected here. Right? What will I do here? I will find that okay this is my department code here go ahead and find the matching department code here and okay this is the marketing now once I know that this is the marketing department code here now I can go ahead and find okay which is one which is one which is one it means we can filter it out now we can filter it out so this is this is what we could have done but the only issue is okay we cannot go ahead and uh we we have standalone tables they were actually not connected to each other the way they were getting collected the way they they were getting saved. So with the DBMS here when they saw this is the problem here and plus the uh hu the when the data got bigger okay they keep kept on accumulating more data there it was becoming difficult for them to keep on saving multiple different files there and if they want to find anything there okay they cannot simply join it together like we do nowadays and move on with it why because they did not have those common columns actually created inside it if I am selecting the vendor data there okay if I'm having the transaction slips in front of me. Only codes are written there. Okay. Or if there are no codes there, they are simply using their own names uh for the product there. So it was becoming very difficult because they there we have to manually go ahead and figure out okay find out that out of these 30 40 columns which one is the common to which table there. So they came up with relational database to figure out these limitations here. Okay. Now they came up with the relational databases that is your RDBMS now. Okay, people started converting their DBMS into the RDBMS here. So what was the objective and why the RDBMS was there? First of course everything was saved in the tables here. Okay, everything was in the table structure as before in the DBMS. However, now these table structures were getting connecting connected based on the common columns. Now from where those common columns are coming from. So now we were creating those relationship between the tables. We were creating those relationship between the tables here by con if there were no common columns here we were creating the common codes. Okay. So that we can relate the tables together. And this is the work of data engineer. This is the work of data engineer. They are the ones those who will interact with the data and make sure in your database they are extracting data from multiple sources there and then they uh then they are making your data ready to be used by the end user by simp for simply simple extraction of data tables or reports out of the server there. So this is how the relational database came into view. Now whichever database you're working with no matter what it is it is a snowflake or something else out there right it is will have basic three entities or three components there what are the three components here three components here of uh any uh RDBMS any RDBMS here or three entities are going to be very simple first thing that you need here is of course data that you will be extracting from multiple different sources so in the MySQL here whichever database you're working with it is always based on three entities and three components. What are the three entities here? Three entities here is to create any database. Now we have the we have the definition of database, right? We have the definition of database here. Let's look at the proper definition of the database right now and then we will see here. So within the database first word is data and we have already discussed this word before. What is data here? Data is just the facts, numbers or any kind of information whether it's an image, an audio file, a video file, anything out there. Any form of information is that is a structured in a specific way and is stored for a particular purpose that is data. Plus this data can be in several forms. It can be numbers, text, bits or bytes. It can be stored in several forms too. That is data. Remember that it's structured in a specific way. It is not saying tabular right now. It is saying any specific way and then this is stored for a particular purpose. If I'm collecting the uh data from YouTube comments, it is for a particular purpose. If we are collecting data from my LinkedIn profile or for my GitHub profile there, that is for a particular purpose. Right? So that is the data there. Now what is database then? How do we define a database here? Database here is the structured collection of multiple data set. They are generally generally stored together. Okay. In one single entity so that we can access it, manage it and update it. So what is a database here? Database here has always three entities. What these three entities are? Look at the three entities here. A structured collection. Structured collection everyone. Right? Then it is saying that is stored on a computer. It means it has to be stored on a computer some hardware there and we so need to access that hardware manage that and update that data there by using uh this hardware here. So we have always three entities. First is your data right second is your hardware and the third component is the software through that you can access manage that data easily. So whenever we talk about three components here or three entities here, this is what we're talking about. We are saying that you have what whichever database you're using here, you always have three entities. First is your data. First is your data. Second is any kind of hardware, any kind of container that can contain data or any kind of hardware there to access that hardware. We will also have an application program through which we can contact and communicate with that. Right? And where are we saving this here? So we have got data, we have got a container in which we are saving our databases. And third is the application program here through which we are going ahead and communicating with that container so that we can retrieve our data as you want. So three components will be coming here right of course here what are the three components? First is of course data. you are importing data maybe from uh some SAP maybe you are collecting from Google forms maybe some other forms or excel sheets there or maybe your data is actually somewhere in from an ERP system and you're trying to extract it from there or somewhere website or APIs you're trying to get your data here basically you're trying to collect data from multiple sources right here now to do all of those tasks here you need hardware you need hardware to make sure that you have somewhere to save that particular data and then you need a software application to access that. So you can call them entities or uh components here but this is what you need to create any kind of database there. Since we have got three different components here we have got three different types of users based on the aritecture of any database that is out there. Right? I'm going to take you through a very simple one here. Okay. every time no matter where you say for this uh it is a layered architecture why I'm calling it layered architecture here because it is based on data obstruction what is data obstruction here layered architecture means based on these three components here please understand here that not everyone will work on all the three components it is uh why because they are not going to get the access to each and everyone to view in out of everything. Okay, that's why it is always based on obstruction. So, uh if there are the three components here, one person will be using the one component. Okay, they will not have the access to do something else. So, they will be obstructed to view that particular data or view that particular side of the entire database. So on the basis of that obstruction here, okay, there are three types of users. The first user here is the end user. First user here is the end user. What does this end user want? This end user is only interested in accessing the data, creating the reports here. Okay. Uh use as minimal coding as possible. All right. This is called as external view or application view here. So this is your third component where we are in we are going ahead looking at the application program the end uh end view there and we are simply using that to create reports extracting data or accessing data that we want there that person is an end user. Second person here is your application programmer or a developer or you right now we also call them data engineers right you also call them data engineers nowadays what do they do here they go ahead they interact with the with the data here they are the ones who will decide uh the that the data is in the temporary structure it should have common columns it should it is having common columns there relationship is there and the entire database is secured data types are properly given. All right. And plus it is optimized to to access it. So they are the ones who will take care of the entire application program at the back end. Plus they are also responsible to get uh what kind of data should be stored, what kind of relationships should be there. They will go ahead and decide the schemas databases uh inside the logical structure of database that is called as schema. So they will go ahead create a data model and they get the entire structure of the multiple data tables that we're saving inside it. They are the ones responsible for this. This is why this is called as logical or conceptual. This is why this is called as logical or conceptual level. Now the third here is your best friend in your company. Third one here is your best friend in your company. if you're working on any kind of research project there. Okay. Now, third person is your database administrator. Third person is your database administrator. Now, what does this DBA do? DB is the one okay that is responsible for maintaining the operability here. Meaning this is the person who will go ahead and make sure that uh you are connected to the server properly. You have your uh you have your access properly given. Okay. You have the permission to data set whatever you want permission with and this person will be um responsible for any kind of recovery any kind of restriction has to be placed on you. Meaning you should be given access to only the data that you are working with. If you're a finance person there you will have the access only to finance data. If you're the person who are looking at the accounts or you're the person looking in the HR, you will have only the accessory data which you're responsible for and nothing else. So this is decided by the DBA here. Usually in a proper uh setting of uh proper setting of the corporate here these three roles are properly defined separately. Okay. In some cases if you go to a startup okay they might ask you to do everything that is out there. So you're m everyone who is working out there. Okay. If you are going for a business analyst or a data analyst, what do you supposed to do there? You're supposed to access the data, get that data into some uh software and actually create the reports out of it, create the charts there, analyze what is happening in the business, know about that domain and then go ahead and give that here. Right? The two here looks very fascinating right now. But this is the pure coding here and this person should not know just one simple language. This person should know everything about the APIs. They should know how to work with any other system too. Okay. They should be wellvered with Java, JavaScript. They should be wellvered with C, C++ so that they can go ahead and use Python also if it meant to extract the data in a proper table format. So these people are the application programmer and the developer. You are not that. Right? They are not concerned with what is happening in the business now. They're only concerned with that data should be there. Are you that person whose work stops at okay my data is here now I'm done? No. Our work starts from there. Our work work starts when we have the access to the data. After we have the data there then we have to move forward and analyze the data and work forward with that. Okay. So we are the end users. Do we understand that? Do we understand this part here that yes we are the end users? No. Right. So uh have you ever seen uh Google forms? When you go ahead and look into the Google forms here. Okay. Google forms suddenly give you uh the uh some some pie charts here and there and it will give you uh the analysis as per itself. Right? It gives you a little bit of ability right now to simply do some tweaks here and there and that's it. Okay. So you are as an end user, you might have to hurt them, right? But it is not the end here. That's where you start your journey as a data analyst. So please understand here as a data analyst what you are supposed to do here. You are supposed to analyze it data set here. Okay. This I uh almost discuss in every SQL here. Even though it is not related to SQL here, I still need to tell you this. And as a data analyst what you're supposed to do once you have accessed the data set right first thing you have to think first thing you should know that what is your objective step number one is always here step number one is always here to know the objective for the research or the analysis know the objective for the research or analysis for example okay you're uh you're a branch manager for a bank all right and you get the call there from your manager uh from the area manager that's uh I want 5% increase in next quarter in the insurance sales go ahead and get it now you have to it that how I'm going to get the 5% there so you have to first know the objective so for example they will ask you that sell 5% more insurance in the next quarter or maybe they want to figure out here that why the our assembly line is having uh too many defects in the uh end product figure out how to optimize the cost for a certain project right or maybe go ahead and optimize the logistics out there. So you have to first know the objective that what you are supposed to do here. Once you that is clear here you go ahead and collect that what are the data tables here which data tables you need from where do you need that maybe you need customer data maybe you need employee data or ship data from someone you might need to ask access from DBA and they will provide you the data set with them or you may have to ask the data from some other department too. So it is all based on what is your objective. Once you have collected the entire data set here, you go ahead and you create a blueprint that what do you want to analyze? You'll figure out what is happening in this process, where it is happening, who is responsible, which particular process or part of the process is hampering it. Now you go and deep dive into it and figure out why is it happening. You see tendencies, trends and patterns out there. You create graphs and reports out of that. Here that is your step number two. Okay. Now after end what is your step number three here? After step number three here whatever reports you have built you have to go ahead and represent the solutions. So whoever the uh decision makers are right all the uh business process managers those are there director CEOs uh CEOs bodies whoever is the decision maker you have to present your solution to them and then they will select a solution and then they will run a test run of your solution again you will collect the data again you will analyze it you'll fix a solution then you will apply that solution to everywhere and and once you apply the solution everywhere you again collect the data. You will see that how much effective was your solution. So this is a a cyclical process. It will keep on going. Okay. So as a data analyst, this is your entire work. So this is uh what we're supposed to do. So SQL, where do we use it? Here. So this is uh the part here. Now talking about the RDPMS here. Let's go ahead and figure out the different types of databases everyone. Let's see the types of databases that we have here. So we understood the file system. Then we have understood about uh the DBMS. Uh now here we have got RDBMS. So with RDBMS here it is in two parts here. So you see here we have got RDBMS, NoSQL database and graph database. Right? These three databases here, relational, NoSQL and graph databases here, it is based on what type of data we are saving. Okay, these three these three are based on the type of type of data we are saving. Type of data that we are trying to save and this distributed and centralized database here is that where are we saving? Where are we saving it? basically location. So let's uh understand them one by one here. So relational database we are all aware about the relational database here we are all aware that we have just seen it. Okay, there's a collection of data objects that are linked together by predefined relationships. Plus uh in the RDBMS here every data is connected to each other and it is connect creating a proper logical structure there. It means we have got we can call we here we'll be we will be calling columns attributes here and rows are called as records and each attribute is going to have uh we are going to have common attributes that is common columns here that will establish the relationship among the data points there. Few examples here are okay MSQL, MySQL, Postgress SQL and what so PL/SQL is also there. There are multiple examples for RDBMS here and most of the time we are using RDBMS right simple data set here simple data set here that is RDBMS here. Now let's talk about NoSQL database. What is a NoSQL database here? No SQL database is responsible here for saving a not structured or unstructured database. What is unstructured database here? Meaning any type of geographical data with a lot of relationships or lot of hierarchy or maybe you have images that you want to save there. Okay, which in which they are in pixels or something. So whenever you have a data here which is a non-tabable database which cannot be saved inside a table only in that case no SQL is used used here. So whenever your data is huge it cannot be saved in a proper table there we go ahead and we saved in the JSON documents and instead of relational tables here we'll have JSON documents here. What is JSON everyone? That is JavaScript object notation documents. Now these documents here they follow a certain notation. I will show you how does it look like. And uh there they are going to simply save the key value and graph databases inside it. And it is very uh no matter how huge your data is, the files is going to be very very small and it is simply going it is going to save your data in a very normal format. Now these JSON files they are really good when it comes to geographical data set here. What I meant by this? So for example, let's take an example here. Okay. Let's take an example here. Uh have ever seen a geographical data set? A geographic data set. Let's go ahead and create one. So let's imagine here we want to go ahead and list all the countries a geographical data set there. And in this geographical database here, right? What we need? Let's say we want all the countries there. So we need a column called as country. In front of country here what we need? What we will go ahead uh we need a states what states are inside it. And then here we will we will need cities that are inside it. Now if I will go ahead and write the country a for example in front of the country a here I will type uh the state here right? I will go ahead and type the state for country is here. Let's say A1 done. Now let's say that country A has 50 states. It means that the country has to be repeated 50 times. Country name will be repeated 50 times because the I have got 50 states here. We have got 50 states here. Now just imagine here just imagine here if each state here has let's say 100 cities or let's say in a very nominal way here let's say it has got uh 30 cities or 13 cities out there. So that many times country will be repeated state also will be repeated. So simply to simply save a hierarchical structure here that inside this country these many states are there and this city is there we have to actually go ahead and save thousands and millions of rows there and that were a lot of redundant data a lot of redundant data out there. So because of this reason here we have got JSON documents and JSON documents are very very easy to save your data set here. So I will go ahead and open up a document here to show you out. But before I go ahead and show you a JSON document and how does it look like? Okay, the major examples here as you can see MongoDB, Amazon and HBR BSON is used. Great. But I think the objective remains same that we are the not Tableau databases. >> Great. Thank you very much. So this is where we use it. So basically you're saving the databases in these kind of formats. Now comes the graph database here. Now what is a graph database? So for uh okay uh have you ever visit um you see you have seen LinkedIn and Facebook right? So they whenever you visit somebody's profile on the right hand side they always tell you that people have visited these people also whoever has visited this profile okay they have visited these profiles also. Have you ever seen that in LinkedIn, Facebook or anywhere else? Especially I've seen that in LinkedIn a lot. Have you seen that before? Now, how do they know that? How do you think they are noticing that if you're going ahead and actually visiting a certain profile, right? If you you are the person here and you're and how you know that you're visiting a certain profile and how they do they know that someone else is also visited that certain profile and uh if they're visiting this profile here if you you are also visiting someone else is also visiting this profile here. Okay, they should go ahead and uh visit other profiles too. How do they know that? Do you think they can save that in a table structure? Do you think they can save this in a table structure format the entire path that you're taking there or anyone is taking there whether your parts are get getting crossed at a certain point how will they know that that's where comes the data uh graph databasing basically to figure out the social networking and recommendation systems here. So for example is this is you this is you okay and this is some other person one this is other person one and you have been visiting multiple LinkedIn profiles there this person and you have visited the same profile here same profile here afterwards afterwards okay you went ahead and you look for some other profiles back here you went ahead you went in a separate direction you were looking at some other profiles and this person is looking at other profiles here Right? So now what will happen next it what it is what they are going to do here they are going to simply tell you that uh why don't you view these profiles why because this person uh has also seen this profile but he went on to see these profiles and to person one here they will tell that why don't you see these profiles why because you came here and you went ahead in different direction and you were viewing the profile of these persons So basically so they are simply tracking here okay and they are making sure that you understand what is happening there. So instead of saving that in a table yes it is going to create huge data records and very complex ones too. So instead of managing a managing that here okay they will go ahead and use these graph structures or to store the data where they will they going to have notes edges attributes and parent child kind of relationships out there. So these are the graph databases here. If you're interested in that go ahead and read more about it. H it is quite interesting that how do they actually build these systems at the back end and how they are saving that kind of options with us. It is quite interesting. Okay, go ahead and read about that. And this is your graph database there. So these are the three databases based on what kind of data we are saving inside it. Okay, now you have centralized database and distributed database here. So what is centralized database here? Centralized database says that that uh whatever database you have here, whatever hardware system you have where you are saving your data ultimately, okay, it is maintained at a single location. And it is maintained at a single location such as mainframe computers. And physically also it is going to be stay at one single location there. And if anyone wants to visit uh if anyone wants to access the access the database from anywhere from anywhere of course via LAN or van connection there okay they have they will simply connect with the one centralized database here. So most of the servers that we see here usually are centralized like this. Now uh uh centralized is used a lot. Major banks here uses the centralized system, right? For example, whatever users they are getting fetching the data here, they save the entire customer transactions in one central server. Even the airlines reservation systems, the few are out there, they save in one single uh they will go ahead and save that in one single server at one single location. Even whatever ERP system or SAP systems we have there right they will all for especially created for the enterprises that is also saving entire data in one single place. So most of the time okay we are using these central but yes these are the disadvantages right here. Now what is the distributed database here? What is the distributed database here? Distributed database is uh quite uh it is just opposite to centralized here right what what we will do here in distributed database in distributed database here okay uh it says that you're going to have databases at several different locations at several different locations here however okay it is going to have the unified collection of linked databases meaning even though databases are saved at multiple different locations there. They're still going to be linked to each other. They're still going to be linked to each other. So, it will form a distributed database. They will be logically interconnected but physically they will be distributed over several sites. Now, whoever wants to access this here. Now, whoever wants to access this here from the multiple uh locations here, okay, they can access wherever the data set is. So, maybe they are accessing the data set from here. Okay, they want to access data from here. And likewise now what are the advantages here? So uh first thing is there is no single failure point. Your data is properly distributed across uh the multiple locations. Sometimes it can be slower if you're getting data from everywhere. It can be slower there but yes it is quite uh expensive to maintain. Yes. Yes. Let's say the hardware will be distributed here. It they can be at multiple locations physically. So that location can be uh geographic locations there of course and they will be on entirely different hardware systems too but they will be interconnected there. They will be interconnected there. So these are the few basic differences between centralized and decentralized here and failure risk is very low in decentralized here. In centralized it is going to be higher. Okay. Uh decentralized might be slower. Why? because we do not have a local access to it. So, data locality is in the centralized database there because locally it is available at one single access. However, when it is at the decentralized database, it is a little bit slower than the centralized database because you're getting data from multiple locations at once. Okay. So, in these cases here decentralized will happen. Usually the Google cloud that we have here, okay, they use the distribute database and across the data centers they actually distribute the database there. And there are MNC's also there. Okay. They use the local databases for operation but they will they use local databases. For example, if I have a office in Bangalore, their database will stay there. Another office at Mumbai, the data will stay there. So they once the uh the uh corporate wants to analyze that data set or get that data set there, they will simply there uh have different data centers, they have to go ahead and get a database from the different data centers too. But yes, they will be logically interconnected. So yes, that happens. And that is your distributed database. So geographically also they will be at several different places. So these are the different systems everyone. Uh so let's try to answer this here. Uh which system is characterized by having its data structured, logically interconnected and physically distributed over several sites within one computer network. Which one do you think it is? Yes, it is. It it is in the question itself. The C the distributed database system here. Distributed database system is unique. Okay. It involves multiple linked databases which are physically dist dispersed across different location within a computer network. Right? It is going it is uh it still remain interconnected and structured. It can enable efficient data management and retrieve across diverse geographical locations. Right now here what is SQL and uh what are we going to work on so when we are talking about yes very good thank you very much for the answers and the participation everyone. Thank you very much. Now let's go ahead and uh understand here uh we understand about databases how we are collecting everything and why we are doing that here. But when we say that I want to access the data here that's where we need SQL as a language. Now what is SQL here? SQL here is a structured query language. Right? So let's divide it by everyone. Let's divide it by so we have got a structured right and then we have got query lang query and the language. What is language? medium of communication. Yes, very good. Uh it's a means of communication with whom we want to communicate. In this case, we want to communicate with the machine. We want to communicate with the machine because machine contains the data. So, I want to ask through the machine there that can you give me my data. That's it. The system that we have developed the application that we have developed there. We want to talk with that particular database specifically the container that is holding all the data set. So, what is it? Uh so, we have to use a language. So, it's a language. Now what kind of language it is? It is a query language. What kind of language it is? It is a query language. What is meant by the term query here? What is meant by query here? That we're asking a question, right? We're asking a question there. We are querying something out. So this particular language is simply meant for asking the questions to the database. It is not a programming language. Programming language is the one where you create something. Okay. uh from uh when something doesn't exist you create out of nothing. So we are not creating anything there. We are not developing an application here. Absolutely not. We are simply communicating to the application. Let's see I want you to create this. Can you give me this? Can you give me that? So basically we are simply requesting yes data or information here. So we are simply going to request that data information here. We're simply going to ask questions. But since we want to ask questions and it's a language here, right? It has to be structured. It will have its own grammar. It will have its own rules and regulations and certain keywords and letters that it should be used using there. Okay, that is why it is called as a structured query language. So, it's a language through which we ask questions to the system. But since it's a language, it will have its own rules there. Okay, and this particular system here, it allows for uh any specific data query that we want to extract. Right. Through this function here, we can go ahead and we can create, read, update or delete. What does that mean here? Meaning we can go ahead and we can create databases. We can create tables out there and we can read or extract whatever we need. We can update whatever already exist. Maybe I want to update my data set. Maybe I want to update more columns inside it. Maybe rows inside it. Multiple different kind of updates can be done inside existing table here. Plus maybe we want to simply go ahead and delete whatever we want here. So these are the four type of operations that can be performed by using SQL. Now in SQL here we have learned about here our DBMS right. So what we will be using there we will be using MySQL there. So the lab that we have available right that lab is going to access the MySQL for us. Why are we using MySQL here? Because it is a very specific software product. It uh implements the functions of database server and it is the most fundamental and easiest tool to learn SQL. Okay. Whatever we are going to work here, whatever we are going to work within it, it will be you will find the commands the concept almost everywhere else. Right? So that is uh that is MySQL that we are going to use here. Now we are seeing that our uh this MySQL here is RDBMS meaning this is the relational database management system. This relational database management system here it is going it is the most popular one right now. Even though we have big data not everyone has the big data we is still using the RDBMS here. So what is RDBMS? In the RDBMS here okay you will we will have here. So what will be the RDBMS here? Okay, in the RDBMS here you have multiple data tables. Okay, you will have multiple data tables here and all these data tables will be interconnected will be interconnected to each other based on common columns will be interconnected to each other based on common columns. Okay