Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud

Start Date: 08/09/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/cloud-applications-part2

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About Course

Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data! In this second course we continue Cloud Computing Applications by exploring how the Cloud opens up data analytics of huge volumes of data that are static or streamed at high velocity and represent an enormous variety of information. Cloud applications and data analytics represent a disruptive change in the ways that society is informed by, and uses information. We start the first week by introducing some major systems for data analysis including Spark and the major frameworks and distributions of analytics applications including Hortonworks, Cloudera, and MapR. By the middle of week one we introduce the HDFS distributed and robust file system that is used in many applications like Hadoop and finish week one by exploring the powerful MapReduce programming model and how distributed operating systems like YARN and Mesos support a flexible and scalable environment for Big Data analytics. In week two, our course introduces large scale data storage and the difficulties and problems of consensus in enormous stores that use quantities of processors, memories and disks. We discuss eventual consistency, ACID, and BASE and the consensus algorithms used in data centers including Paxos and Zookeeper. Our course presents Distributed Key-Value Stores and in memory databases like Redis used in data centers for performance. Next we present NOSQL Databases. We visit HBase, the scalable, low latency database that supports database operations in applications that use Hadoop. Then again we show how Spark SQL can program SQL queries on huge data. We finish up week two with a presentation on Distributed Publish/Subscribe systems using Kafka, a distributed log messaging system that is finding wide use in connecting Big Data and streaming applications together to form complex systems. Week three moves to fast data real-time streaming and introduces Storm technology that is used widely in industries such as Yahoo. We continue with Spark Streaming, Lambda and Kappa architectures, and a presentation of the Streaming Ecosystem. Week four focuses on Graph Processing, Machine Learning, and Deep Learning. We introduce the ideas of graph processing and present Pregel, Giraph, and Spark GraphX. Then we move to machine learning with examples from Mahout and Spark. Kmeans, Naive Bayes, and fpm are given as examples. Spark ML and Mllib continue the theme of programmability and application construction. The last topic we cover in week four introduces Deep Learning technologies including Theano, Tensor Flow, CNTK, MXnet, and Caffe on Spark.

Course Syllabus

In Module 1, we introduce you to the world of Big Data applications. We start by introducing you to Apache Spark, a common framework used for many different tasks throughout the course. We then introduce some Big Data distro packages, the HDFS file system, and finally the idea of batch-based Big Data processing using the MapReduce programming paradigm.

Deep Learning Specialization on Coursera

Course Introduction

Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud In the second course of this specialization, we continue our exploration of the cloud computing industry with a look at the real-world applications of cloud computing. This time around, we look at the cloud computing landscape as it relates to a wide variety of cloud-related businesses. In the first half of this course, we will explore how cloud computing is being used in the real world. We will learn about the different types of cloud services and how they are created and managed. We will also take a look at the various cloud types that are available and explore the different pricing models that are used. We will also take a look at applications that are created for the cloud. The second course in this specialization will focus on business. We will look at the different ways that business is served through the cloud. We will explore the different pricing models for cloud services, commercial and open-source, that are used within companies. Finally, we will take a look at the cloud as a whole. We will explore how the cloud is applied in different types of businesses. We will look at the different cloud types and services that are created for the cloud. At the end of this course, you will be able to: •�Describe the business side of cloud computing applications. • Compare cloud services. • Describe the business side of cloud computing applications. • Look at the cloud as a whole. • Expl

Course Tag

Graphs Distributed Computing Big Data Machine Learning

Related Wiki Topic

Article Example
Cloud computing Users access cloud computing using networked client devices, such as desktop computers, laptops, tablets and smartphones and any Ethernet enabled device such as Home Automation Gadgets. Some of these devices—"cloud clients"—rely on cloud computing for all or a majority of their applications so as to be essentially useless without it. Examples are thin clients and the browser-based Chromebook. Many cloud applications do not require specific software on the client and instead use a web browser to interact with the cloud application. With Ajax and HTML5 these Web user interfaces can achieve a similar, or even better, look and feel to native applications. Some cloud applications, however, support specific client software dedicated to these applications (e.g., virtual desktop clients and most email clients). Some legacy applications (line of business applications that until now have been prevalent in thin client computing) are delivered via a screen-sharing technology.
Cloud computing Cloud computing also leverages concepts from utility computing to provide metrics for the services used. Such metrics are at the core of the public cloud pay-per-use models. In addition, measured services are an essential part of the feedback loop in autonomic computing, allowing services to scale on-demand and to perform automatic failure recovery. Cloud computing is a kind of grid computing; it has evolved by addressing the QoS (quality of service) and reliability problems. Cloud computing provides the tools and technologies to build data/compute intensive parallel applications with much more affordable prices compared to traditional parallel computing techniques.
Cloud computing architecture Software as a service provides the equivalent of installed applications in the traditional (non-cloud computing) delivery of applications.
SAP Cloud Platform SAP Cloud Platform is a platform as a service developed by SAP SE for creating new applications or extending existing applications in a secure cloud computing environment managed by SAP. The SAP Cloud Platform integrates data and business processes.
Cloud computing In cloud computing, the control of the back end infrastructure is limited to the cloud vendor only. Cloud providers often decide on the management policies, which moderates what the cloud users are able to do with their deployment. Cloud users are also limited to the control and management of their applications, data and services. This includes data caps, which are placed on cloud users by the cloud vendor allocating certain amount of bandwidth for each customer and are often shared among other cloud users.
Carrier cloud In cloud computing a carrier cloud is a class of cloud that integrates wide area networks (WAN) and other attributes of communications service providers’ carrier grade networks to enable the deployment of highly demanding applications in the cloud. In contrast, classic cloud computing focusses on the data center, and does not address the network connecting data centers and cloud users. This may result in unpredictable response times and security issues when business critical data are transferred over the Internet.
IEEE Cloud Computing IEEE Cloud Computing is a global initiative launched by IEEE to promote cloud computing, big data and related technologies, and to provide expertise and resources to individuals and enterprises involved in cloud computing.
Cloud computing security Cloud computing security or, more simply, cloud security refers to a broad set of policies, technologies, and controls deployed to protect data, applications, and the associated infrastructure of cloud computing. It is a sub-domain of computer security, network security, and, more broadly, information security.
HP Cloud A way to mitigate cloud migration difficulties is to architect applications for the cloud that reduce or eliminate the dependencies between the cloud application stack and the capabilities of the cloud service provider. Another way is to select only generic and higher-level applications to move to the cloud in the first place. Another method is to select open standards for cloud computing.
Cloud computing security Cloud computing and storage provides users with capabilities to store and process their data in third-party data centers. Organizations use the cloud in a variety of different service models (with acronyms such as SaaS, PaaS, and IaaS) and deployment models (private, public, hybrid, and community). Security concerns associated with cloud computing fall into two broad categories: security issues faced by cloud providers (organizations providing software-, platform-, or infrastructure-as-a-service via the cloud) and security issues faced by their customers (companies or organizations who host applications or store data on the cloud). The responsibility is shared, however. The provider must ensure that their infrastructure is secure and that their clients’ data and applications are protected, while the user must take measures to fortify their application and use strong passwords and authentication measures.
Visual Cloud Visual Cloud is the implementation of visual computing applications that rely on cloud computing architectures, cloud-based graphics processing, and ubiquitous broadband connectivity between connected devices, network edge devices and cloud data centers. It is a model for providing visual computing services to consumers and business users, while allowing service providers to realize the general benefits of cloud computing, such as low cost, elastic scalability, and high availability while providing optimized infrastructure for visual computing application requirements.
Cloud computing IaaS-cloud providers supply these resources on-demand from their large pools of equipment installed in data centers. For wide-area connectivity, customers can use either the Internet or carrier clouds (dedicated virtual private networks). To deploy their applications, cloud users install operating-system images and their application software on the cloud infrastructure. In this model, the cloud user patches and maintains the operating systems and the application software. Cloud providers typically bill IaaS services on a utility computing basis: cost reflects the amount of resources allocated and consumed.
Cloud computing In the mobile "backend" as a service (m) model, also known as backend as a service (BaaS), web app and mobile app developers are provided with a way to link their applications to cloud storage and cloud computing services with application programming interfaces (APIs) exposed to their applications and custom software development kits (SDKs). Services include user management, push notifications, integration with social networking services and more. This is a relatively recent model in cloud computing, with most BaaS startups dating from 2011 or later but trends indicate that these services are gaining significant mainstream traction with enterprise consumers.
IEEE Cloud Computing In early January 2012, IEEE Cloud Computing began partnering with established conference to develop a matrix of events targeted to specific geographic regions. IEEE GLOBECOM 2013, held in Atlanta, Georgia, was co-located with the North America Cloud Congress. The initiative is organizing additional Cloud Congresses in the European, Asia Pacific, and Latin America regions to encourage greater focus on cloud computing via existing, well-established conference such as IEEE Signature Conference on Computers, Software, and Applications (COMPSAC), IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom), and IEEE Global Communications Conference, Exhibition, and Industry Forum (GLOBECOM), or to create new cloud computing-related events in specific geographic regions.
Cloud computing Another example of hybrid cloud is one where IT organizations use public cloud computing resources to meet temporary capacity needs that can not be met by the private cloud. This capability enables hybrid clouds to employ cloud bursting for scaling across clouds. Cloud bursting is an application deployment model in which an application runs in a private cloud or data center and "bursts" to a public cloud when the demand for computing capacity increases. A primary advantage of cloud bursting and a hybrid cloud model is that an organization pays for extra compute resources only when they are needed. Cloud bursting enables data centers to create an in-house IT infrastructure that supports average workloads, and use cloud resources from public or private clouds, during spikes in processing demands. The specialized model of hybrid cloud, which is built atop heterogeneous hardware, is called "Cross-platform Hybrid Cloud". A cross-platform hybrid cloud is usually powered by different CPU architectures, for example, x86-64 and ARM, underneath. Users can transparently deploy and scale applications without knowledge of the cloud's hardware diversity. This kind of cloud emerges from the raise of ARM-based system-on-chip for server-class computing.
Big data Multidimensional big data can also be represented as tensors, which can be more efficiently handled by tensor-based computation, such as multilinear subspace learning. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data mining, distributed file systems, distributed databases, cloud-based infrastructure (applications, storage and computing resources) and the Internet.
Cloud computing Cloud computing is a type of Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., computer networks, servers, storage, applications and services), which can be rapidly provisioned and released with minimal management effort. Cloud computing and storage solutions provide users and enterprises with various capabilities to store and process their data in either privately owned, or third-party data centers that may be located far from the user–ranging in distance from across a city to across the world. Cloud computing relies on sharing of resources to achieve coherence and economy of scale, similar to a utility (like the electricity grid) over an electricity network.
Cloud testing Cloud testing is a form of software testing in which web applications use cloud computing environments (a "cloud") to simulate real-world user traffic.
Cloud computing Varied use cases for hybrid cloud composition exist. For example, an organization may store sensitive client data in house on a private cloud application, but interconnect that application to a business intelligence application provided on a public cloud as a software service. This example of hybrid cloud extends the capabilities of the enterprise to deliver a specific business service through the addition of externally available public cloud services. Hybrid cloud adoption depends on a number of factors such as data security and compliance requirements, level of control needed over data, and the applications an organization uses.
Cloud computing In the software as a service (SaaS) model, users gain access to application software and databases. Cloud providers manage the infrastructure and platforms that run the applications. SaaS is sometimes referred to as "on-demand software" and is usually priced on a pay-per-use basis or using a subscription fee. In the SaaS model, cloud providers install and operate application software in the cloud and cloud users access the software from cloud clients. Cloud users do not manage the cloud infrastructure and platform where the application runs. This eliminates the need to install and run the application on the cloud user's own computers, which simplifies maintenance and support. Cloud applications differ from other applications in their scalability—which can be achieved by cloning tasks onto multiple virtual machines at run-time to meet changing work demand. Load balancers distribute the work over the set of virtual machines. This process is transparent to the cloud user, who sees only a single access-point. To accommodate a large number of cloud users, cloud applications can be "multitenant", meaning that any machine may serve more than one cloud-user organization.