Emerging IT Trends and Innovation Concepts.pptx

Emerging IT Trends and Innovation

1. Unit – 8 Emerging IT Trends and Innovations

2. Cloud computing Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction

3. Three ClouD Service Models Infrastructure as a Service (IaaS) The infrastructure cloud provides storage and compute resources as a service which can be used by developers and IT organizations to deliver business solutions. IaaS has evolved from virtual private server ( vps ) concept. Basic characteristics of IaaS: Resources distributed as a service Dynamic, on-demand scaling of resources Utility based pricing model Concurrent users on a single piece of hardware

4. Platform as a Service (PaaS) Platform as a Service (PaaS) Next level up in the pyramid is Platform cloud. PaaS delivers development/operating environments as a service. It includes set of tools and services designed to make coding and deploying the applications quickly and efficiently. Single environment to develop, test, deploy, host and maintain applications Web based UI designing tools to create, modify, test and deploy different UI scenarios Multi-tenant architecture facilitating concurrent users Load balancing, security and failover capabilities for application to be deployed OS and Cloud programming APIs to create new apps for cloud or to cloudify the current apps Tools to handle billing and subscription

5. Software as a Service (SaaS) Top most layer of pyramid is functional layer or SaaS layer. This type of cloud delivers a single application through the browser to multiple users using a multitenant architecture. With SaaS, a provider sells an application to customers on license basis, in a “pay-as-you-go” model. Centralized web based access to company and commercial software Entire business process shifting to cloud giving superior services to client No hassle of software upgrades and patches as they are managed by Service provider Application Programming Interfaces (APIs) allow integration with different applications

6. Architecture

7. EXAMPLE

8. WHO PROVIDES CLOUD SeRVICES

9. Who/HoW Uses These Cloud Services

10. Who is DEVops ?

11. Different USERS

12. VISIBILITY

13. WHO LEAds the market ?

14. What is Virtualization ? Virtualization is a process that efficiently utilizes physical computer hardware, forming the basis of cloud computing. It creates an abstraction layer over hardware, dividing a single computer into multiple virtual computers called virtual machines (VMs).

15. Benefits of Virtualization Resource Efficiency : Virtualization eliminates the need for each application server to have its own dedicated physical CPU, allowing for more efficient use of hardware. Easier Management : Software-defined VMs are easier to manage, simplifying the implementation and enforcement of policies. Minimal Downtime : Running multiple redundant VMs allows for failover between them, minimizing downtime caused by crashes. Faster Provisioning : Provisioning virtual machines is significantly faster than purchasing, installing, and configuring physical hardware for each application.

16. What is a Hypervisor ? A hypervisor is a small software layer that enables multiple instances of operating systems to run alongside each other, sharing the same physical computing resources. It manages these virtual machines (VMs) as they run, separating them logically and assigning each its own resources.

17. Characteristics of Hypervisor

18. Types of Hypervisors Type 1 (Bare-Metal) : Runs directly on physical hardware, interacting directly with the CPU, memory, and storage. Efficient and secure due to direct access to hardware. Type 2 (Hosted) : Runs as an application in an existing OS. Suitable for individual PC users needing to run multiple operating systems but introduces latency issues affecting performance.

19. Type 1 vs Type 2 Hypervisors Feature Type 1 Hypervisor (Bare-Metal) Type 2 Hypervisor (Hosted) Placement Directly on physical hardware Runs as an application in an existing operating system Performance Typically higher due to direct access to hardware Lower due to access to resources through the host OS Security More secure as there is no intermediary OS layer Less secure as it relies on the security of the host OS Complexity More complex to set up and manage Easier to set up and use, ideal for desktop environments Use Cases Data centers, server virtualization Desktop virtualization, development and testing Examples VMware ESXi, Microsoft Hyper-V, XenServer VMware Workstation, Oracle VirtualBox, Parallels Desktop

20.   Big Data Analytics 

21. What is  big data? 01 What is big data analytics? 02 Types of big data analytics 03 Characteristics of big data analytics 04 05 Process of big data analytics? Big data application domains 06 Benefits of Big Data Analytics 07 Some Realtime applications of big data analytics 04 Big Data

22. What is big data? Big Data refers to a huge volume of data, that cannot be stored and processed using the traditional computing approach within a given time frame.

23. Example of big data? For example, if we try to attach a document that is of 100 megabytes in size to an email we would not be able to do so. As the email system would not support an attachment of this size. Therefore this 100 megabytes of attachment with respect to email can be referred to as Big Data.

24. What is big data Analytics? Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences

25. Types of Big Data Analytics Structured Data Unstructured Data Semi-Structured Data

26. Structured Data Structured Data  refers to the data that has a proper structure associated with it. For example, the data that is present within the databases, the CSV files, and the excel spreadsheets can be referred to as Structured Data.

27. Unstructured Data Un-Structured Data  refers to the data that does not have any structure associated with it at all. For example, the image files, the audio files, and the video files can be referred to as Un-Structured Data.

28. Semi-structured Data Semi-Structured Data  refers to the data that does not have a proper structure associated with it. For example, the data that is present within the emails, the log files, and the word documents can be referred to as Semi-Structured Data.

29. Characteristics of Big Data Big Data is categorized into  3 important characteristics. Volume Velocity Variety

30. Variety  refers to the different types of data that is getting generated. Volume  refers to the amount of data that is getting generated. Volume Velocity Variety Velocity  refers to the speed at which the data is getting generated.

31. Which is able to help making decisions in easier way Process of Big Data Analytics Case study and evaluation Identification of particular data Filtering  Data Data extraction Aggregation of Data Visualization of data data analysis Final analysis Result

32. Tools used in Big Data Analytics MongoDB Hadoop Talend Cassandra Storm Spark Tools Hadoop helps in storing and analyzing big data Tools used in big data analytics MongoDB is used on datasets that change frequently Tools used in big data analytics Talend is a tool used for data integration and management Tools used in big data analytics It is a distributed database that is used for handling chunks of data It is used for real time processing and analyzing large amount of data It is an open source real time computational system

33. Big Data application domains Education Healthcare Agriculture Weather Forecast Manufacturing

34. Education Enhancing Student Results A better Grading System Gaining Attention Customized Programs Reducing The Number of Dropouts

35. HealthCare Health Tracking Improve the care delivery system / machinery Fraud detection and prevention  Real-time alerts

36. Weather Forecast Estimates of areas where flooding is likely to be most severe The strength and direction of tropical storms The most likely amount of snow or rain that will fall in a specific area The most likely locations of downed power lines Estimates of areas where wind speeds are likely to be greatest The locations where bridges and roads most likely to be damaged by storms The likelihood of flights being cancelled at specific airports

37. Agriculture Boosting productivity Access to plant genome information Predicting yields Risk management Food safety Savings

38. Manufacturing Product quality  Defects tracking Supply planning Manufacturing process defect tracking Testing and simulation of new manufacturing processes

39. Benefits Big Data Analytics Big data analytics is used for risk management Big data analytics is used to improve customer experience Big data analytics is used for product development and innovations Big data analytics helps in quicker and better decision making in organizations  Google has mastered the domain of big data analytics and it has developed several tools and techniques to capture the data of users which includes their preference, their likes, dislikes, the area of specialization, their requirement etc.

40. Some Real Time Applications of Big Data Rohith

41. Google Big Data Google developed several open source tools and techniques that are extensively used in big data ecosystem. With the help of different big data tools and techniques, Google is now capable of exploring millions of websites and fetch you the right answer or information within milliseconds.

42. Google Big Data The first question that comes to our mind is how can Google perform such complex operations so efficiently? The answer is Big data analytics.  Big Data tools and techniques to understand our requirements based on several parameters like search history, locations, trends etc.  Google effortlessly displays the complex calculations which are designed to match the user’s requirement.  Google always wanted to develop a search engine that has the ability to think like a human and understand the phrase, logic, and goal of any search query. Semantics has helped Google to accomplish this task to look beyond the literal meaning of any phrase of a search query.

43. Google Has adopted the following techniques using Big Data Analytics Indexed pages Real-time Data Feeds Tracking Cookies Sorting Tools Knowledge Graph Pages Literal & Semantic search Google+ Synonyms Google Translate Ranking and Prioritizing the Search Results Google Adwords

44. IBM's Weather Forecasting One example of an application of big data to weather forecasting is IBM’s Deep Thunder. Unlike many weather forecasting systems, which give general information about a broad geographical region, Deep Thunder provides forecasts for extremely specific locations, such as a single airport, so that local authorities can get critically important information in real time. 

45. A single Jet engine can generate 10+terabytes of data in 30 minutes of flight time. With many thousand flights per day, generation of data reaches up to many Petabytes. The New York Stock Exchange generates about one terabyte of new trade data per day. New York Stock Exchange Social Media Air Industry 500TB+ data is generated everyday only on Facebook .This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc.