Foundation ITO

149 This domain includes services related to management and monitoring. The services under this are as follows: • Cloud Deployment Manager • Cloud Console • Cloud Shell • Cloud APIs 10. API Platform-The few services under this are as follows: • Maps Platform • Developer Portal • API Analytics • Apigee Sense • Cloud Endpoints Difference between AWS, Azure, and Google Cloud Platform Parameter AWS Azure Google Cloud Platform App Testing It uses device farm It uses DevTest It uses Cloud API Management Amazon API labs Test labs. gateway Kubernetes EKS Azure API gateway Cloud endpoints. Management Kubernetes service Kubernetes engine

150 Git Repositories AWS source Azure source Cloud source Data warehouse repositories repositories repositories. Object Storage Redshift Big Query Relational DB S3 SQL warehouse Google cloud storage. RDS Block Blobs and Google Cloud files SQL Persistent disks Relational DBs G suite ZFS and Avere Block Storage EBS Page Blobs Cloud video intelligence API Marketplace AWS Azure Subnet Per minute File Storage EFS Azure Files 96 Media Services Amazon Elastic Azure media 1433 transcoder services CloudCDN Cloud Load Virtual network VPC VNet Balancing Pricing Per hour Per minute Cloud Interconnect Maximum 128 128 G suite processors in VM ZFS and Avere Cloud video Maximum memory 3904 3800 intelligence API in VM (GiB) Subnet Per minute Catching ElasticCache RedisCache Load Balancing Elastic Load Load Balancer Application Configuration Balancing Gateway Global Content CloudFront Content Delivery Network Delivery Networks Marketplace AWS Azure File Storage EFS Azure Files Media Services Amazon Elastic Azure media transcoder services Virtual network VPC VNet Pricing Per hour Per minute

151 Maximum 128 128 96 processors in VM 3800 1433 Maximum memory 3904 in VM (GiB) RedisCache CloudCDN Load Balancer Cloud Load Catching ElasticCache Application Balancing Gateway Load Balancing Elastic Load Content Delivery Cloud Network Interconnect Configuration Balancing Global Content CloudFront Delivery Networks 10.5 Types of Clouds Cloud computing is Internet-based computing in which a shared pool of resources is available over broad network access, these resources can be provisioned or released with minimum management efforts and service provider interaction. Types of Cloud • Public cloud • Private cloud • Hybrid cloud • Community cloud Public Cloud-Public clouds are managed by third parties which provide cloud services over the internet to the public, these services are available as pay-as-you-go billing models. They offer solutions for minimizing IT infrastructure costs and become a good option for handling peak loads on the local infrastructure. Public clouds are the go-to option for small enterprises, which are able to start their businesses without large upfront investments by completely relying on public infrastructure for their IT needs. The fundamental characteristics of public clouds are multitenancy. A public cloud is meant to serve multiple users, not a single customer. A user requires a virtual computing environment that is separated, and most likely isolated, from other users.

152 Public cloud Private Cloud-Private clouds are distributed systems that work on private infrastructure and provide the users with dynamic provisioning of computing resources. Instead of a pay-as-you-go model in private clouds, there could be other schemes that manage the usage of the cloud and proportionally billing of the different departments or sections of an enterprise. Private Clou Advantages of using a private cloud are: • Customer information protection: In the private cloud security concerns are less since customer data and other sensitive information do not flow out of private infrastructure. • Infrastructure ensuring SLAs: Private cloud provides specific operations such as appropriate clustering, data replication, system monitoring, and maintenance, and disaster recovery, and other uptime services. • Compliance with standard procedures and operations: Specific procedures have to be put in place when deploying and executing applications according to

153 third-party compliance standards. This is not possible in the case of the public cloud. Hybrid cloud: A hybrid cloud is a heterogeneous distributed system formed by combining facilities of public cloud and private cloud. For this reason, they are also called heterogeneous clouds. A major drawback of private deployments is the inability to scale on-demand and efficiently address peak loads. Here public clouds are needed. Hence, a hybrid cloud takes advantage of both public and private clouds. Hybrid cloud Community cloud: Community clouds are distributed systems created by integrating the services of different clouds to address the specific needs of an industry, a community, or a business sector. In the community cloud, the infrastructure is shared between organizations that have shared concerns or tasks. The cloud may be managed by an organization or a third party.

154 Community cloud Sectors that use community clouds are: • Media industry: Media companies are looking for quick, simple, low-cost ways for increasing the efficiency of content generation. Most media productions involve an extended ecosystem of partners. In particular, the creation of digital content is the outcome of a collaborative process that includes the movement of large data, massive compute-intensive rendering tasks, and complex workflow executions. • Healthcare industry: In the healthcare industry community clouds are used to share information and knowledge on the global level with sensitive data in the private infrastructure. • Energy and core industry: In these sectors, the community cloud is used to cluster a set of solution which collectively addresses management, deployment, and orchestration of services and operations. • Scientific research: In this organization with common interests in science share a large distributed infrastructure for scientific computing.

155 Sectors used in Community clouds 10.6 Clouds in The Market Three Cloud Providers Cloud Providers 10.7 Summary • Cloud computing is the delivery of different services through the Internet, including data storage, servers, databases, networking, and software. • computing is a technology that uses the internet for storing and managing data on remote servers and then access data via the internet. • Cloud computing one of the examples is Google cloud. • Cloud computing used for data backup, disaster recovery, email, virtual desktops, software development and testing, big data analytics, and customer-facing web applications.

156 10.8 Glossary • Cloud Computing: A type of computing in which shared computing resources, software, or data are delivered as an on-demand service through the internet. • Cloud Types: There are three types of clouds: private, public, and hybrid. • Cloud Service Provider (CSP) Lock-in: The ease of moving data between providers or services. • Hybrid Cloud: A cloud computing environment that uses a mix of on-premises private cloud and public cloud services with orchestration between the two platforms. 10.9 References • https://www.javatpoint.com/cloud-service-models • https://aws.amazon.com/what-is-cloud-computing/ • https://www.scality.com/solved/the-history-of-cloud-computing/ • https://www.livemint.com/technology/tech-news/india-is-a-key-growth-market-for- google-cloud-bikram-singh-bedi-11645466115411.html

157 UNIT -11: INTRODUCTION TO ANALYTICS Structure 11.0 Learning Objectives 11.1 Introduction 11.2 Basics of DataScience 11.3 Basics of Data Analytics 11.4 Summary 11.5 Glossary 11.6 References 11.0 Learning Objectives After studying this unit, you will be able to • Outline Data Science basics • Recall fundamentals of Data Analytics 11.1 Introduction Data analysis involves sorting through massive amounts of unstructured information and deriving key insights from it. These insights are enormously valuable for decision- making at companies of all sizes. Data analytics is the science of analyzing raw datasets in order to derive a conclusion regarding the information they hold. It enables us to discover patterns in the raw data and draw valuable information from them. Data analytics processes and techniques may use applications incorporating machine learning algorithms, simulation, and automated systems. The systems and algorithms work on the unstructured data for human use. These findings are interpreted and used to help organizations understand their clients better, analyze their promotional campaigns, customize content, create content strategies, and develop products. Data analytics help organizations to maximize market efficiency and improve their earnings.

158 11.2 Basics of Data Science Data Science is a multi-disciplinary feld that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from tremendous amount of data. Theories and techniques from many fields and disciplines are used to investigate and analyze a large amount of data to help decision makers in many industries such as science, engineering, economics, politics, finance, and education Data Science Essentials Data Science Essentials Data Science Life Cycle • Business Understanding-Every domain and business work with a set of rules and goals. In order to acquire the correct data, we should be able to understand the business. • Data Collection-The source of data could be logs from webservers, data from online repositories, data from databases, social media data, data in excel sheet, so in short data can come from any source. • Data Preparation-To analyze the data, data needs to be in certain format. Data might have missing values which will cause obstruction in analysis and model building.

159 • Data is to be cleaned before processing any further. Thus, this step is also known as Data Cleaning or Data Wrangling. • Exploratory Data Analysis (EDA) plays an important role at this stage as summarization or clean data helps in identifying the structure, outliers, anomalies and patterns in the data Data Science Life Cycle Data Science Applications • Fraud and Risk Detection. • Healthcare. • Internet Search • Targeted Advertising. • Website Recommendations • Advanced Image Recognition. • Speech Recognition • Airline Route Planning 11.3 Basics of Data Analytics Data analytics is the process of collecting data in raw form, processing is based on the needs of the user and utilizing it for decision-making purposes. Data analysts and data

160 scientists perform data analysis. This process involves data cleaning, inspection, transformation, modelling to understand data from its raw form. Business intelligence needs data analytics to perform its operations. A large amount of data is unstructured and it has to be collected from various sources. • Data analytics helps to acquire problem-solving skills in any type of business as it is used by many professionals and students. This helps to approach the problems analytically and solve them in the most logical way. This helps the user in daily life as well. • The skill is in high demand right now as there is skills’ shortage globally. Learning basics is the starting point of learning data science and hence machine learning. • Data is available everywhere and it has become the need of the hour to know how to analyze the data in our hand and to understand how our data is being analyzed for various businesses. Types of Data Analytics Data analytics is a broad field. There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and a different place in the data analysis process. These are also the primary data analytics applications in business. • Descriptive analytics helps answer questions about what happened. These techniques summarize large datasets to describe outcomes to stakeholders. By developing key performance indicators (KPIs,) these strategies can help track successes or failures. Metrics such as return on investment (ROI) are used in many industries. Specialized metrics are developed to track performance in specific industries. This process requires the collection of relevant data, processing of the data, data analysis and data visualization. This process provides essential insight into past performance. • Diagnostic analytics helps answer questions about why things happened. These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why they got better or worse. This generally occurs in three steps: ➢ Identify anomalies in the data. These may be unexpected changes in a metric or a particular market. ➢ Data that is related to these anomalies is collected. ➢ Statistical techniques are used to find relationships and trends that explain these anomalies.

161 • Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future and their techniques include a variety of statistical and machine learning techniques, such as neural networks, decision trees, and regression. • Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made. This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large datasets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated. Applications of Data Analytics Basics • It helps in health care to know various causes of different diseases and to analyze the data to help the patients understand the risk involved and to recognize the disease easily. This helps in giving proper treatment to the patients. • Database analytics helps to do the internet search as various data helps to collect information in searching the data. • The analytics helps in determining the images and doing speech recognition. Clustering the images and determining their groups is one of the applications of Data analytics. • Data analytics is easy to learn and can be mastered by anyone with the basics of computer programming and analytics. This helps the users to grasp more about data. • Data analytics helps in recommending the websites to the clients and this is helpful to the users who do data analytics in a perfect manner. 11.4 Summary • Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. • Data science can be used to gain knowledge about behaviours and processes, write algorithms that process large amounts of information quickly and efficiently, increase security and privacy of sensitive data, and guide data-driven decision- making

162 • Data analytics helps individuals and organizations make sense of data. • Data analytics helps individuals and organizations make sense of data. • Data analytics use various tools and techniques to help organizations make decisions and succeed. 11.5 Glossary • Fuzzy Algorithms: Algorithms that use fuzzy logic to decrease the runtime of a script. Fuzzy algorithms tend to be less precise than those that use Boolean logic. • Data Analysis: This discipline is the little brother of data science. Data analysis is focused more on answering questions about the present and the past. • Data Wrangling: The process of taking data in its original form • Data Science: discipline of using data and advanced statistics to make prediction 11.6 References • https://www.crcpress.com/Introduction-to-Data-Science-Data-Analysis-and- Prediction-Algorithms-with/Irizarry/p/book/9780367357986↩︎ • https://leanpub.com/datasciencebook • https://github.com/rafalab/dsbook • https://www.scribd.com/document/178275229/Introduction-to-Data-Science