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[Audio] Hello Welcome to Module 3 In this module we will check how important it is to understand Big data and its solutions.

[Audio] The Objective of course is to understand the concept of big data on the following To understand why big data is important To understand the difference between RDBMS and Big Data.

[Audio] After learning this course learners will be able to : Differences between RDMS, Mining, and Big Data. Understand the use and application of case study.

[Audio] Why Bigdata is important? Big data is important because it provides businesses with valuable insights that can help them make better decisions and become more competitive in their industries. It gives them the ability to analyze large amounts of data quickly and identify patterns and trends that can be used to improve operations and customer service. Big data also helps companies create more targeted marketing campaigns and better understand customer behaviors. In addition, it can help organizations improve their predictive analytics, which enables them to anticipate customer needs and develop more effective strategies for meeting those needs. Improved Decision Making: Big data helps organizations to make better decisions by providing them with a broader and deeper understanding of their customer base and market trends. This can be used to create more targeted and effective marketing strategies, product development, customer service, and operational strategies. Increased Efficiency: By leveraging large amounts of data, organizations can identify potential areas of improvement in their operations. This can help them to reduce costs, improve efficiency, and make better use of their resources. Improved Customer Experience: Big data can be used to analyze customer behaviors and preferences, allowing organizations to create personalized experiences for their customers. This can help to build brand loyalty and increase customer satisfaction. Predictive Analysis: Predictive analytics uses data to identify patterns and trends that can be used to make predictions about future outcomes. This can help organizations to prepare for potential problems before they occur and make strategic decisions. Faster Insight: Big data provides organizations with real-time insights into their data, allowing them to quickly identify patterns and trends that can be used to inform their decisions. This enables organizations to make more informed decisions faster than ever before. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as: Determining root causes of failures, issues and defects in near-real time. Generating coupons at the point of sale based on the customer's buying habits. Recalculating entire risk portfolios in minutes. Detecting fraudulent behavior before it affects your organization. Big Data in Saving Money: Big Data is being used to match market offers with consumers buying habits and individual needs. Relevant offers from retailers you use and those who sell products that may be relevant to you. Feedback gives an opportunity to engage with businesses can pass these savings onto the customer. Monitor and reduce energy usage. Airlines have started to use customer data to improve customer service. Frequent fliers can soon expect the in-flight crew to know allergies; seat performance; birthday; how they like their tea or coffee. Monitor the condition of your car. Monitor mileage and fuel consumption. Insurance Telematics boxes can reduce insurance significantly. Loyalty schemes enable shops to track what their customers are purchasing and tailor coupons accordingly. In-store location trackers interacting with a smartphone as customers enter shops..

[Audio] Why RDBMS Fails with respect to big data RDBMS fail with respect to big data due to their limited scalability and lack of flexibility. They can only store and process structured data and cannot handle unstructured data. RDBMS are also difficult to scale as they require manual intervention to add more hardware or software resources. Additionally, they are not designed to work with real-time data and have problems with data updates and consistency. The data size has increased tremendously to the range of petabytes—one petabyte = 1,024 terabytes. RDBMS finds it challenging to handle such huge data volumes. Most of the data comes in a semi-structured or unstructured format from social media, audio, video, texts, and emails. Big Data is generated at a very high velocity. RDBMS lacks in high velocity because it's designed for steady data retention rather than rapid growth. Even if RDBMS is used to handle and store Big Data, it will turn out to be very expensive..

[Audio] Let Check the Netflix, from DVD to Big data Solution provider In next slide we will check about Netflix story and its growth with bigdata solutions.

[Audio] Netflix, Inc. is an American media-services provider headquartered in Los Gatos, California, founded in 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California. The company's primary business is its subscription-based streaming media service which offers online streaming of a library of films and television programmes, including those produced in-house. As of October 2018, Netflix has 137 million total subscribers worldwide, including 58.46 million in the United States. It is available worldwide except in Mainland China, Syria, North Korea, and Crimea. The company also has offices in the Netherlands, Brazil, India, Japan, and South Korea. Netflix's initial business model included DVD sales and rental by mail, but Hastings jettisoned the sales about a year after the company's founding to focus on the DVD rental business. Netflix expanded its business in 2007 with the introduction of streaming media while retaining the DVD and Blu-ray rental service. The company expanded internationally in 2010 with streaming available in Canada, followed by Latin America and the Caribbean. Netflix entered the content-production industry in 2012, debuting its first series Lilyhammer..

[Audio] Netflix has greatly expanded the production and distribution of both film and television series since 2012 and offers a variety of "Netflix Original" content through its online library. By January 2016, Netflix services operated in more than 190 countries. Netflix released an estimated 126 original series and films in 2016, more than any other network or cable channel. Their efforts to produce new content, secure the rights for additional content, and diversify through 190 countries have resulted in the company racking up billions in debt $21.9 billion as of September 2017, up from $16.8 billion from the previous year.$6.5 billion of this is long-term debt, while the remaining is in long-term obligations. In October 2018, Netflix announced it would raise another $2B in debt to help fund new content..

[Audio] Netflix has over 100 million subscribers and with that comes a wealth of data they can analyse to improve the user experience. Big data has helped Netflix massively in its mission to become the king of a stream. Big data helps Netflix decide which programs will be of interest to you and the recommendation system actually influences 80% of the content we watch on Netflix. The company even gave away a $1 million prize in 2009 to the group who came up with the best algorithm for predicting how customers would like a movie based on previous ratings. The algorithms help Netflix save $1 billion a year in value from customer retention..

[Audio] Netflix started collecting data from the time they were distributing the DVDs which later when they started their streaming service in 2007 shaped into something more. It took them 6 years to gather proper data to analyze find the result-driven data from it and use it. This big data analytics lead to the launch of their first show – "House of Cards" which they estimated to be a success through data analysis, proving how beneficial big data analytics has been for them. This also gives another reason why you should consider adding big data analytics to your business..

[Audio] Further Netflix use, Content rating as opinion Different content searches Number of time content was viewed and its date and times Type of device used by users ant its no of devices too Total time of paused and play Full viewing of content or drop-in between The credits of users and their renewal times are also an important factor of data collection by net flix.

[Audio] Around 80% of the content streamed on Netflix comes from the recommendation engine. The platform has developed a series of algorithms that consider an array of factors to deliver personalized recommendations to every user. Netflix built new data pipelines, worked on complex datasets, and invested in data engineering, data modeling, heavy data mining, deep-dive analysis, and developing metrics to understand what the users want. Netflix innovation relies on- Personalized Video Ranking Video-Video Similarity Ranker Trending Now Ranker Artwork Visual Analysis Continue Watching Ranker Project Cost Predicting Algorithm, etc. Netflix hasn't limited the use of big data analytics only to curate content for users. It uses algorithms to estimate and predict how much a new project would cost and find alternate ways to optimize the production and operations. By reducing bottlenecks in daily operations, Netflix could streamline the workflow and make better decisions about the projects. This is how Netflix used big data and analytics to generate billions and has won 22 Golden Globe awards in 2021 while having 42 total nominations..

[Audio] At first, analysts were limited by the lack of information they had on their customers – only four data points (customer ID, movie ID, rating and the date that the movie was watched) were available for analysis. As soon as streaming became the primary delivery method, many new data points on their customers became accessible. Data such as time of day that movies are watched, time spent selecting movies and how often playback was stopped all became measurable. Effects that this had on viewers enjoyment could be observed, and models built to predict the perfect storm situation of customers consistently being served with movies they will enjoy. Happy customers, after all, are far more likely to continue their subscriptions. Another central element to Netflix's attempt to give us films we will enjoy is tagging. Netflix is a data-driven company. They always gather a tremendous amount of data from their users to make smart decisions like providing them with the contents they like to ultimately make them happy. By analysing viewer data: 30 million plays 4 million ratings 3 million searches Here are some statistics about Netflix data pipeline: 500 billion events and - 1.3 PB per day 8 million events and - 24 GB per second during peak hours House of Cards is a direct product of Big Data analytics. It was the very first show that the streaming service used as a testing ground. It is one of the most successful marketing experiments in the history of Netflix. Netflix had used all the Big Data it collected on its viewers to determine what they might like to see. Netflix has a lot of data. It has many millions of customers worldwide and spreads a very wide net to collect data on them. Netflix gather a tremendous amount of data from their users and make smarter decisions that ultimately make their users happier, by providing content they like. The success of House of Cards is a perfect example of how data can be used to make multimillion-dollar decisions with a high degree of confidence. If taken in a timely manner, data-driven decisions can give organisations an advantage over those who run on intuition. Big Data brings new opportunities, but also new challenges. So next time you are watching Netflix, take a moment to think about what data you are giving them simply by what you are clicking, searching and watching. It is surprisingly more than you think! So this the story of Netflix which use big data as solution, Now let check what we learn till now..

[Audio] Let check our knowledge Which one of the following defines the feature of RDBMS? RDBMS stores semi-structured data RDBMS stores structured data RDBMS stores unstructured data All of the above Answer is D All options are correct.

[Audio] As companies move past the experimental phase with Hadoop, many cite the need for additional capabilities, including: Improved data storage and information retrieval Improved extract, transform and load features for data integration Improved data warehousing functionality Improved security, workload management and SQL support Answer is option D.

[Audio] According to analysts, for what can traditional IT systems provide a foundation when they're integrated with big data technologies like Hadoop? Big data management and data mining Data warehousing and business intelligence Management of Hadoop clusters Collecting and storing unstructured data Answer is Option A.