Big Data Analytics | Big Data Analytics Use-Cases | Big Data Tutorial | Edureka Rewind

Key Takeaways

a variety of enormous data is being generated at an extremely fast pace in various sectors therefore analyzing big data has become extremely crucial and inevitable as a result big data analytics is being adopted all throughout the globe in order to gain numerous benefits from the data being produced so hello everyone this is anushree from edureka and I will be walking you through this interesting session on big data analytics so guys let us quickly view the topics for today's discussion so the first topic for today's session would be why we need big data analytics and why it has become so important after analyzing that we'll move to the next topic which is what is exactly big data analytics where we'll be defining what big data analytics exactly is after that we'll see what are the different kind of tools which are required for big data analytics then moving on we'll explore the various domains and use cases which are you know using big data analytics and lastly I'll be ending this session by telling about the different Trends which are prevalent in the field of big data analytics so now without further Ado let us move forward to our first topic of this session which is why big data analytics so guys why do you think big data analytics is so important and why do you feel that we need to study this topic or we should know what exactly it is so now let me tell you why so just like the entire universe in our galaxy said to have formed it to the Big Bang explosion similarly data has also been growing exponentially which is leading to the explosion of data so this can simply be termed as big data and do you know that we are creating about 2.5 quintillion bytes of data every day and one quintillion amounts to around 10 raised to the power of 18 bytes so you can do the math and imagine the amount of data that we are creating every day and this data as you can see from the image that I have depicted here is coming in from various sources whether it is from social media from banking sectors from governments from various other institutions all right and this data is not in the same format so it is coming from various sources so it is in different formats so now guys what do you think that is Big Data only limited to the volume or the enormous amount that is being generated or does it Define with various other characteristics that you know exactly Define what big data is so let us see what are the different characteristics associated with big data so here I have represented five such characteristics so first is volume so volume is nothing but the huge amount of data that is being generated or the enormous amount of data as we previously saw in the section that how data was coming in from various sources like social media banking sectors governments Etc so this is what volume is now moving on to the next characteristic which is variety so variety is nothing but the different formats of data from coming in from various sources so big data has three different formats one is structured other is semi-structured and then unstructured so what is structured data so structured data is basically in the form of relational databases which comes in the form of tables which has rows and columns coming to unstructured data so unstructured data is in the form of audio files video files images Etc now coming to semi-structured data so semi-structured data is in the form of Json and XML files so these were the basic you know formats of data now coming to the next characteristic which is value so value is nothing but the deriving meaningful data from this entire collection of Big Data so the next characteristic that we have stated here is velocity so velocity is nothing but the rate at which the data is being generated now coming to veracity so veracity is the inconsistencies and the uncertainties which are present in the data so these are the basic five ways of big data but these V's keep evolving as and when the data is going to grow over the period of time so I have put down four such reasons here to tell you that why it is so important and how it is helping many organizations all around the globe so the first reason here that I've stated is for making smarter and more efficient organizations so big data analytics is basically highly contributing to this factors and organizations are adopting this to basically lead them to faster decision making so one such example that I you know came across that I wanted to share with you guys is about the New York police department in short which is the NYPD so big data and analytics are helping the NYPD and the other large police departments to anticipate and identify the criminal activity before it occurs so what they do is that they analyze the entire Big Data technology to geolocate and then analyze the historical patterns and they map these historical patterns with sporting events paydays rainfalls traffic flows and federal holidays so essentially what the NYPD is doing that they're utilizing these data patterns scientific analytics technological tools to do their job and ensuring that by using these different tools they're doing their job to the best of their ability so by using a big data and analytics strategy the NYPD was able to identify something called crime hotspot so basically where crime occurrence was more so they were able to identify these hot spots and then from there they deployed their local officers so that they could reach their own time before it was actually committed so this is how NYPD basically utilizes entire you know this field of big data analytics so that they can prevent crime and make New York a boar safer place so now after exploring the first reason let's move on to the second reason and see what is it the second reason here is to optimize business operations by analyzing customer Behavior so the best example for this is Amazon we all know how much Amazon is popular and how much we use it on our daily basis so Amazon basically uses our click stream data that is the customers so they use our click stream data and the historical purchase data of more than 300 million customers which have you know signed up for Amazon and then they analyze age users data how they are clicking on different products and how they're navigating through their site so basically they show each user customize results on customized web pages so after analyzing all these clicks of every visitor on their website they're able to better understand their site navigation Behavior the parts that people are taking to buying their products and services and what else a customer looked on while buying that product and also the parts that led a customer to leave their page so this information basically helps Amazon to improve their customer experience and hence expand their customer base so guys let's see what the third reason is now so big data Technologies like Hadoop and cloud-based analytics they basically reduce your cost significantly for storage of Big Data because for storing Big Data if you buy like huge servers and you know huge Machinery so that is going to cost you a lot so by using Hadoop technology so what Hadoop does basically stores big data in a distributed fashion so that you can process it parallely so it reduces your cost a lot so by using commodity Hardware they are reducing their costs significantly so which brings us to our third reason you must have gauged what the third reason is it is cost reduction so now let us see how Healthcare is using big data analytics to curb their costs so using new data tools that's an automatic alerts when patients are due for immunizations or lab work more and more Physicians could reduce the hospitalizations by practicing better preventive care so you know what the patient started using these new sensor devices at home and on the go so these new sensor devices they basically you know deliver constant streams of data that can be monitored and realized in real time so they help the patients avoid hospitalization by self-managing their conditions now for hospitalized patients Physicians can use Predictive Analytics to optimize outcomes and then reduce the readmissions so Parkland Hospital in Dallas Texas is one such example which has been using analytics and predictive modeling to identify these high risk patients and then they predict likely outcomes once the patients are sent home so as a result Parkland has been able to reduce its 30-day readmissions back to Parkland and all area hospitals for Medicare patients with heart failure by around 31 so for Parkland that you know estimates about a savings of 500 000 annually and of course not to mention that the savings which patients are also realizing by avoiding these readmissions so this is how Healthcare is you know widely using big data analytics to reduce their cost significantly now let's move forward to see the last reason for why big data analytics is so essential so our last reason is next Generation products that how big data analytics is really really contributing to generate more such you know high-tech products so you know to see how customers needs can be satisfied and how they can use these new generation products for their own benefit so I have cited three such examples here for you guys so the first example here is Google self-driving car I'm very sure that most of you guys must have heard about it what Google self-driving car basically does it makes millions of calculations on every trip that help the car decide when and where to turn whether to slow down or speed up and when to change their Lanes so the same decision a human driver is making behind the wheel Google self-driving car is also doing that with the help of big data analytics another example of a sales diamond car is the Toyota prize which is fitted with cameras GPS as well as powerful computers and sensors to safely drive on the road without the intervention of human beings so this is how it is you know really really contributing to making such high-tech products which in the long run we'd be using probably and it will make our life more easier now moving on to the second product that I'm going to site here so it's really fascinating product let me ask you a question how many of you all love watching TV shows and how many of you all prefer spending your weekends doing nothing with Netflix and chill um let me guess almost all of us do I mean I love binge watching shows over the weekend so I know by now you would have guessed what example I'm arriving to so it is Netflix so Netflix committed for two seasons of its extremely popular show House of Cards without even seeing a single episode of the show guys and this project of you know House of Cards of two seasons it costed Netflix about 100 million dollars so guys how do you think that Netflix was able to you know take such a big risk monetarily so the answer to this my friends is Big Data index so by analyzing the viewer data the company was able to determine that the fans of the original house of cards which aired in the UK they were also watching movies that start Kevin Spacey who was playing the lead in the show House of Cards and they were directed by David Fincher who's also one of the show's executive producers so basically Netflix is analyzing everything so from what show you are watching to when you pause it or to when even you turn it off so last year Netflix grew it subscriber us subscriber Base by around 10 and then they added nearly 20 million subscribers from all around the globe so how fascinating is that I mean this is brilliant I am sure that the next time you guys are watching a show on Netflix you'll be really happy because you already know how the back end is working and how Netflix is recommending you new shows and new movies so now moving on to the third example that I've cited here so it's one of the really cool things that I've come across so this is a smart yoga mat now this has sensors embedded in the mat which will be able to provide feedback on your posters score practice and even guide you through an at-home practice so the first time you use your smart mat it will take you through a series of movements to calibrate your body shape size and personal limitations so this personal profile information of yours is then stored into your smart matte app and this will help the smart mat detect when you're out of alignment or balance so over time it will automatically evolve with updated data as you improve your yoga practice so now I'm sure that with these you know very interesting and exciting examples you would have got an idea about what exactly big data analytics is doing and how it is improving various organizations in their sales and marketing sector so now let's move forward and finally you know formally Define what big data analytics is [Music] so guys what is big data analytics big data analytics examines large and different types of data to uncover hidden patterns correlations and other insights so basically what big data analytics is doing it is helping large companies to facilitate their growth and development so this majorly involves applying various data mining algorithms on a given set of data which will then Aid these organizations in making better decisions so now that you know why we need big data analytics what is exactly big data analytics now you just see and explore what are the different kind of stages which are involved in this procedure of Epic data analytics [Music] so these are the different stages involved in this entire procedure so the first stage is identifying the problem so what is our problem that we need to solve this is the most important step of course and this is the first step of the process the second step is to design our data requirements so of course after identifying the problem we need to decide what kind of data is required for analyzing this particular problem the third step is pre-processing so in the pre-processing step basically cleaning of data takes place and you perform some sort of processing now after the processing stage we come to the fourth stage which is the analytics stage so in this stage you would be basically analyzing the process data using various methods after the analytic stage we'll move to the final stage which is data visualization so in visualization of data stage you will basically visualize the data using tools like Tableau angularjs but the visualization of data will only take place in the end so these are the basic five stages in this entire procedure now that you've understood this let's move forward and understand what are the different types of big data analytics foreign types one is descriptive analytics second is Predictive Analytics third is prescriptive analytics and fourth is diagnostic analytics so let us understand the first type which is descriptive analytics so descriptive analytics basically answers your question what has happened and how does descriptive analytics answer this question it uses data aggregation and data mining techniques to provide insight into the past and then it answers what is happening now based on the incoming data so basically descriptive analytics does exactly one what the name implies it describes or summarizes the raw data and it makes it something which is interpretable by humans and the past which I just referred in this context it basically can be one minute ago or even a few years back so the best example that I could side here for descriptive analytics is basically the Google Analytics tool so Google analytics basically is aiding organizations or different businesses by analyzing their results through Google analytics tools so the outcomes that help the businesses understand what actually has happened in the past and then they validate if a promotional campaign was successful or not based on the basic parameters like page views so basically descriptive analytics is therefore an important source to determine what to do next another example is what we saw earlier in the new generation product which is Netflix so Netflix basically uses descriptive analytics as I told you guys to find the correlations among the different movies that a subscriber is watching and to improve their recommendation engine they use historic sales and customer data so this is what descriptive analytics is now let's move forward to the second type which is Predictive Analytics so the second type which is Predictive Analytics basically uses statistical models and forecast techniques to understand the future and answer what could happen so basically as the word suggests it predicts we are able to understand through Predictive Analytics that what are the different future outcomes so basically Predictive Analytics provides the companies with actionable insights based on the data so through sensors and other machine generated data companies can identify if I when a malfunction is likely to occur so then the company can preemptively order parts and make repairs to avoid downtime and losses so an example of this type of analytics is the Southwest Airlines so Southwest analyzes their sensor data on the planes to identify the potential malfunctions or safety issues so basically this allows the airline to address the possible problems and then make repairs without interrupting the flights or putting the passengers in danger so this is a very great use of you know Predictive Analytics to how basically reduce their downtime and losses and as well as you know prevent delays and various other factors like accidents so now let's move forward to the third reason which is prescriptive Analytics prescriptive analytics uses optimization and simulation algorithms to advise on the possible outcomes and answer the question what should we do so basically it allows the users to prescribe a number of different possible actions and then guide them towards the solution so in a nutshell these analytics are all about providing advice so prescriptive analytics they use you know a combination of techniques and tools such as business rules algorithms machine learning and computational modeling procedures so then these techniques are applied against input from many different data sets including historical and transactional data real-time data feeds and then big data so these analytics go beyond descriptive and Predictive Analytics by recommending one or more possible courses of action and the best example for this is the Google self-driving card this example also we have already seen in the new generation products section so basically Google self-driving car analyzes the environment and then decides the direction to take based on the data so it is besides whether to slow down or speed up to change the lanes or not to take a long cut to avoid traffic or prefer short routes Etc so in this way it functions just like a human driver by using data analytics at scale now prescriptive analytics is a little complex type of analytics and it is not yet adopted by all the companies but when implemented correctly they can have a large impact on how the businesses make their decisions so now let's move on to our last type which is diagnostic Analytics so diagnostic analytics is used to determine why something happened in the past so it is characterized by techniques like drill down data Discovery Data Mining and correlations so diagnostic analytics it takes a deeper look at the data to understand the root cause of the events it is helpful in undermining what kind of factors and events contributed to a particular outcome so mostly it uses probabilities likelihoods and the distribution of data for the analysis so for example in a Time series data of sales diagnostic analytics will help you to understand why the sales of a company has decreased or increased for a particular year and so on so examples for Diagnostic analytics could be a social media marketing campaign so you can use diagnostic analytics to assess the number of posts mentions followers fans page views reviews pins Etc so and then you can analyze the failure and the success rate of a campaign at a fundamental level so therefore they can be thousands of online mentions that can be distilled into a single view to see what worked in your past campaigns and what did not so now that we have seen all the four types I hope that you've understood the different examples of all the four types and the difference between them now let's move forward and have a look at the tools which are required for big data analytics so these are some of the tools that I have listed down here there are more such tools which are used for big data analytics but let's explore the ones which I have mentioned over here so let me name them Hadoop Pig Apache Edge base Apache spark Talent Splunk Apache Hive Kafka so now let me start with the first one which is Hadoop so Hadoop is basically a framework that allows you to store big data in a distributed fashion so that you can process it parallely Apache pick is a platform that is majorly used for analyzing large data sets and then represent these data sets as data flows so basically ping is used for scripting and the language is Pig Latin now coming to Kafka so Kafka is a messaging system now guys what is a messaging system a messaging system is basically something which is responsible for transferring data from one application to another so the applications can focus on the data and they do not need to worry about how to share it so this is what Kafka does now coming to Apache hi no Apache Hive is a data warehousing tool so it allows us to perform big data analytics using Hive query language which is similar to SQL coming to Splunk so Splunk is a log analysis tool now what are logs so logs are generated on Computing as well as non-computing devices and they are stored in a particular location or directory so they contain details about every single transaction or operation that you guys have made so next is talent talent is an open source software integration platform which helps you to analyze effortlessly and then turn the data into business insights so it helps the company in taking real-time decisions and become more data driven next is Apache spark so party spark is an in-memory data processing engine that allows us to efficiently execute streaming machine learning and SQL workloads and it requires fast iterative access to data sets so basically it is used for real-time processing now moving to the last one which is Apache Edge base so Apache Edge base is a nosql database it allows you to store unstructured and semi-structured data with ease and provides real-time read or write access so these were the tools that I could list down and I have also told you about the different functions in brief that they perform [Music] so now I'll just move forward and explore the different kind of domains which are you know using big data analytics so these are some of the domains that I've listed out for you guys to understand how they're using big data analytics and how widely it is being used in different kinds of domains so Healthcare we've already discussed previously has been using big data analytics to you know reduce costs predict epidemics avoid preventable diseases and then improve the quality of life in general so one of the most widespread application of big data in healthcare is electronic health records which is ehrs I'm sure that most of you must have heard about it it basically stores the patient's entire data now coming to Telecom industry so Telecom industry is one of the most significant contributors to Big Data so Telecom industry basically analyzes all our call data records in real time and then they identify fraudulent behavior and acts on them immediately now the marketing division of Telecom industry it basically modifies their campaign to better Target its customers and then use these insights which are gained by them to develop new Production Services coming to insurance companies so insurance companies use big data analytics for risk assessment fraud detection marketing customer insights customer experience and much more now governments across the world are also adopting big data analytics the Indian government for example had used big data analytics to get an estimate of the trade in the country so the economists used central sales tax invoices for trade between two states to estimate the extent to which the states were trading between each other coming to Banks and financial firms now Banks and financial services firms they use analytics to differentiate fraudulent interactions from legitimate business transactions so by applying analytics and machine learning they're able to define the normal activity of a user or a customer based on their history and then distinguish it from the unusual behavior indicating fraud so then the analysis systems they suggest immediate actions such as blocking the irregular transactions which stop the fraud before it occurs and improves the profitability now moving on to the next domain which is automobile so many automobile companies are utilizing big data analytics and one example is Rolls Royce so Rolls-Royce embraced big data by fitting hundreds of sensors into its engines and propulsion systems and these sensors basically record every tiny detail about the operation of these engines and propulsion systems so then the changes in the data in real time are reported to the engineers who will then decide the best course of action such as scheduling a maintenance or dispatching the engineering teams if the problem arises now the next domain is education so education is one field where big data analytics is very slowly and gradually being adopted but it is very important that we utilize big data analytics in this field because so by opting for Big Data power technology you know as a learning tool instead of the traditional lecture methods we can enhance the learning of a student as well as it can Aid a teacher to basically track the performance in a better manner now coming to the last domain which is retail so retail includes both e-commerce and in-stores and they are widely using dictate analytics to optimize their business strategies so we already saw that with the example of Amazon so now that we have explored the various domains let me show you the use cases that I have taken here to explain you about how big data analytics is widely being used so I've taken two such use cases here's the first use case is of Starbucks so the leading Coffee House chain makes use of Behavioral analytics by collecting the data on its customers purchasing habits in order to send personalized ads and coupon offers to the customers mobile phones so the company also identifies Trends indicating whether the customers are losing interest in their product and then the direct offer specifically to those customers in order to regenerate their interest so I came across this article by Fox which reported how Starbucks made use of big data to analyze the preferences of their customers to enhance and personalize their experience so they analyzed you know every member's coffee buying habits along with their preferred ranks to what time of the day they are usually ordering so even when people visit a new Starbucks location that stores point of sale system is able to identify the customer through their smartphone and then the Barista gives them their preferred order so in addition based on ordering preferences their app which is a Starbucks app will suggest new products that the customers might be interested in drawing so this is how Starbucks is basically optimizing their business strategies and improving and basically increasing their customer base now let's move on and see what is the Second Use case that I want to share with you guys the Second Use case is of PNG Proctor and Gamble so Procter and Gamble uses Market Basket analysis and price optimization to optimize their products so Market Basket analysis analyzes customer buying Habits by finding associations between the different items that the customers place in their shopping baskets so this is what exactly Market Basket analysis does so apart from this Market Basket analysis may be performed on the retail data of customer transactions at your store so stores like Target Walmart Etc that they use Market Basket analysis to basically increase their sales and marketing so you can then use the results to plan marketing and advertise your strategies or even design a new catalog so for instance Market Basket analysis may help you design different store layouts in one strategy items that are frequently purchased together can be placed in close proximity to further encourage the combined sales of such items so example I'm going to a store I want to buy bread then I also you know cite butter so I will want to buy butter as well so that's how you know stores optimize their sales so they place all these products like butter bread milk eggs in close proximity because they know when a customer comes to buy bread they might also want to buy butter or milk or eggs all right so this is one such example so how PNG basically utilizes it is the company uses simulation models and Predictive Analytics in order to create the best design for its products so it creates and sorts through thousands of iterations in order to develop the best design for example for a disposable diaper and then they use Predictive Analytics to determine how moisture affects fragrance molecules in a dish soap so that the right amount of fragrance comes out at the right time during the dishwashing process I mean so we can't even imagine that a simple product like a dish soap also has so much thought process behind it and also has so much strategies or you know analytics applied behind it so I hope that you guys found both these you know use cases really interesting and how more such companies are utilizing big data analytics in a more proficient manner in order to basically increase their sales and marketing after looking at the use cases let us go forward and see our final topic for this discussion which is the trends in big data analytics so basically this entire image depicts the statistics for the growing Market revenue of big data in billion US dollars from the year 2011 to the year 2027. so in the current year which is 2018 as you guys can see the current market revenue of big data is about 42 billion US Dollars and it is going to exponentially increase to about 103 billion US dollars in the year 2027 which is a massive amount so now let's move forward and see the next one which is facts and statistics by Forbes so I've collected some of these so four which I found really interesting and I wanted to share with you guys so the first one here basically states that nearly 50 percent of respondents to a recent McKenzie analytic survey say that analytics and big data have fundamentally changed business practices in their sales and marketing functions so we also have seen examples of this by you know like by Starbucks of PNG of Amazon so these are such companies which are responding to such surveys now the next one is showing that how big data applications and analytics is projected to grow from about 5.3 billion dollars in 2018 to 19.4 billion dollars in 2026 which attains about a compound aggregate of 15.49 percent so the next one here is an extremely important fact or a stat which I found out and it is basically an eye-opener so according to an Accenture study 79 of Enterprise Executives agree that companies that do not Embrace big data will lose their competitive position and could face Extinction even more 83 percent have pursued Big Data projects to seize a Competitive Edge so this very fact guys tells you that how important this field is and if your particular organization or company is not adopting big data analytics in the future it is going to lead to obsoletion so now let's see which is the last fact that I have stated here so according to new Vantage Venture Partners big data is delivering the most value to Enterprises by decreasing their expenses by about 49.2 percent and creating new avenues for Innovation by about 44.3 percent an example of both of these facts we saw in New Generation why we need big data analytics section where we spoke about cost reduction as well as new generation products so this is an example of that so now let's move forward and look at the career prospects in big data analytics so the first one here that I've stated here is there's a storing demand for an analytics professional so technology professionals who are experienced in big data analytics are in high demand as organizations are looking for ways to exploit the power of big data so therefore there's a store in demand for analytics professional and as and when the data is going to grow more such people will be required to analyze that data so that leads us to our second point which is huge job opportunities so there are more job opportunities in big data management and analytics than there were last year and many IIT professionals are prepared to invest time and money for the training so now that companies under various domains are adopting big data analytics so there are definitely more huge job opportunities so now let's see what are the salary aspects I think is one of the most important ones again because we need to know that what kind of salary are we going to draw if you become a big data analytics professional so six analytics and data science jobs are included in glassdoors 50 best jobs in America for the year 2018. these include data scientist analytics manager database administrator data engineer data analyst and business intelligence developer and the average salary of the six analytics jobs that I just stated along with data science jobs is about 95 000 which is absolutely amazing and data scientist has been named the best job in America for about three years running with a median based salary of hundred and ten thousand dollars and four thousand five hundred and twenty four job openings I mean how wonderful is that so you guys can see that how great the prospects are in this field and if you guys are interested then you should definitely learn more about this field and you know who knows that you might be drawing such kind of a salary so but in India the percentage of analytics professionals commanding the salaries lesser than 10 lakhs it has gone lower which is great so the percentage of analytics professionals earning more than 15 lakhs has increased from about 17 in 2016 to 21 in the last year 2017 and to the current 22.3 percent in this year 2018. now let me tell you what kind of job titles are there in this field so the first one here is big data analytics business consultant second is big data analytics architect third is Big Data engineer fourth is Big Data solution architect fifth is big data analyst sixth is analytics associate seventh is business intelligence and analytics consultant and the last one is metrics and analytics specialist so I've just stated eight over yours so these might be addressed in different names and different you know job titles and there are more such job titles I'm sure so you can explore that so now let's move on to see what are the skill sets that you require if you want to become an analytics professional so these are the few skill sets that I've mentioned over here and there can be more depending on the role that you're going to play or maybe even you know restricted to one particular skill set so it depends upon what role are you going to play in this field of big data analytics so the first one is that I've put down here is basic programming so you would obviously be expected to know some kind of a general purpose programming language the second one here is statistical and quantitative analysis so it is preferable if you know about the statistics and quantitative analysis now moving on to data warehousing so knowledge of you know SQL and nosql database languages such as MySQL and nosql has mongodb Apache Edge base and Cassandra so knowing these databases is also very important so next one is data visualization which is I think one of the most important skill sets which are required so as a analytics professional you should know how to visualize the data in order to you know basically improve your business so you need to know what kind of Trends are going to be there in the data and how it is increasing and what kind of insights this data is going to provide you so you should be able to visualize the data you should be able to understand what the data is indicating the next one is specific business knowledge so this is extremely necessary according to me because if you're an analytics professional and you don't know what business your company is basically working on and you're not aware about it you won't be able to apply your knowledge of analytics to basically increase the fails and marketing of the company all right so the business knowledge of a particular company or the area which you're working on is extremely important the last skill set that I've mentioned over here is computational Frameworks so out of the tools that we discussed in the previous section one is expected to know at least more than one so if you know Apache spark Hadoop Pig also again that is depending upon the job role that you're going to play so it is important that you are aware about at least one or more tools which are you know required for big data analytics and one or two such computational Frameworks because it is going to of course help you and you will have a basic knowledge about how these tools are used for analyzing the data so guys this is all for today's session and I hope that the world of big data analytics really fascinated you and I hope that you really enjoyed watching this session so thank you very much for attending it and goodbye