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Flashcards

Big Data and Cloud Computing Concepts

Inês Macedo Segurança Informática Mestrado Portuguese 41 cards

Cards in this deck (41)

  1. 1Big DataIt consists of extensive datasets primarily in the characteristics of volume, variety, velocity, and/or variability that require a scalable architecture for efficient storage, manipulation, and analysis
  2. 2Cloud ComputingA 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. 3On-Demand Self-ServiceProperty of cloud computing that states a consumer can provision computing resources without human intervention
  4. 4Broad Network AccessProperty of cloud computing that states the cloud services are available over the network through standard means (e.g. Internet connection, thin clients such as smartphones or PCs)
  5. 5Resource PoolingProperty of cloud computing that dictates resources are pooled by the cloud provider to serve multiple consumers using a multi-tenant model
  6. 6Rapid ElasticityProperty of cloud computing that dictates the amount of computing resources can be adjusted/scaled dynamically at the consumer’s request or automatically in some configurable manner by the consumer
  7. 7Measured ServiceProperty of cloud computing that states resource usage can be tracked and measured precisely by both the cloud provider and consumer, for instance to allow for a clear billing analysis
  8. 8Resource Abstraction and Control LayerLayer that mediates the use of physical resources by services through techniques such as virtualization, dynamic provisioning, access control, etc. It usually exposes virtual resources on top of the physical resource layer. In some cases (for higher quality of service) there is a direct mapping between virtual and (a priori reserved) physical (aka “bare metal”) resources
  9. 9Physical Resource LayerLayer that that manages the actual pool of concrete physical resources available for processing, networking, storage, etc. In some cases, (for higher quality of service) there is a direct mapping between virtual and (a priori reserved) physical (aka “bare metal”) resources
  10. 10Virtual ResourceProvides the illusion of a real resource (computer, storage, network) through diverse context-specific techniques
  11. 11VirtualizationThe process of multiplexing the same physical resource such that it actually provisions several virtual resources, e.g. a physical computer may host several VMs. It can also be the act of provisioning a virtual resource by several different physical resources at different times or even at the same time, e.g. storage service using several physical disks. Or it can be the dynamic provision of virtual resources by physical resources, e.g. through reconfiguration, replication, auto-scaling, migration, etc
  12. 12IaaS (Infrastructure-as-a-Service)Service model that allows customers to get an actual infrastructure in the form of computing resources (e.g., for processing, storage, or networking purposes)
  13. 13PaaS (Platform-as-a-Service)Service model that allows customers to get a platform that supports the development and/or deployment of cloud applications (e.g., database engines, data workflows, programming environments)
  14. 14SaaS (Software-as-a-Service)Service model that allows customers to access applications running in the cloud (e.g., Gmail, Google docs). The cloud is transparent to the user
  15. 15Public CloudDeployment model of a cloud that is open for use to the general public by a cloud provider. The cloud provider may be a business operator (e.g. Google) or a public institution
  16. 16Private/Community CloudDeployment model of a cloud that is used exclusively by a single organization (private) or a group of organizations (community)
  17. 17Hybrid CloudDeployment model of a cloud that results from the combination of two or more clouds that exist separately but are interoperable in some way
  18. 18QoS (Quality-of-Service)A kind of metric related to the functionality (e.g., requests-per-second, response time) or resources (e.g., computing power, network bandwidth, storage size) at stake
  19. 19ElasticityThe amount of computing resources that can be adjusted/scaled dynamically at the consumer’s request or, through auto-scaling, automatically in some configurable manner
  20. 20Local File SystemStorage model with an hierarchical organization of files within directories. It uses an entire disk or a disk partition with fixed capacity. Data is read and written by a single host. It provided programmatic access by standard APIs. It has the advantage of being the most common method of file storage
  21. 21Network File SystemStorage model where files are physically stored in a server host. The file system can be attached over the network by other hosts, and accessed transparently (i.e. the same way as local file systems). Data consistency is managed by a network protocol such as NFS (Networked File System) and SMB (Server Message Block). Advantages include data sharing by multiple hosts and typically higher storage capacity
  22. 22Object Store (Bucket)Container for unstructured binary objects that can be accessed over the Internet without any logical association to the specific storage facility. An object is identified by an URI and cannot be modified once created, only deleted or updated. The container bucket has no limit, so it can have a very large size
  23. 23Bucket Creation ParametersGlobally unique Name, Region placement and Storage class
  24. 24DatabaseStructured collection of data about entities and their relationships. It models real-world objects—both entities and relationships. It captures structure in ways that allow these entities and relationships to be queried for analysis
  25. 25Relational DatabaseStorage model that allows for the efficient storage, organization, and analysis of large quantities of tabular data. Table rows represent entities and columns represent attributes of those entities. The associated Structured Query Language (SQL) can then be used to specify a wide range of operations on such tables, such as compositions and joins
  26. 26NoSQL DatabaseStorage model that scales the quantities of data and number of users that can be supported. It deals with unstructured data that is not easily represented in tabular form, for example large amounts of text or different items with different properties
  27. 27Data WarehouseData management system optimized to support analytic queries that involve reading large datasets
  28. 28VM (Virtual Machine)Virtual computer running on top of a physical server machine. It is considered a guest machine that is provided with virtual resources corresponding to actual physical resources like memory, CPUs, disks, and network interfaces. Each VM also runs its own operating system (OS) and applications. For all purposes, a VM behaves like a regular computer
  29. 29VM Creation ParametersName, Region and Zone (define the hosting data center), Machine Type (base CPU + memory configuration) and Boot Disk (initial OS image + disk size). Instance templates can also be used for the creation of VMs
  30. 30Server MachineA host machine that pools its physical resources to run several independent VMs. VMs are created, started, stopped or destroyed on the fly. Each VM is guaranteed to have an isolated execution environment, so that if it crashes, the host and other VMs are not affected. For all these purposes, the host machine runs a hypervisor
  31. 31Fabric ControllerA system within the cloud data center, that manages the allocation of VMs to server machines
  32. 32Sole TenancyA client may reserve a server machine that is exclusively dedicated to running the client’s VMs. This mode can be managed entirely by the client providing a more isolated environment, a more stable performance and efficient resource sharing between VMs
  33. 33Instance GroupSet of VMs created from the same instance template, whose size may grow or contract over time
  34. 34Auto ScalingA group's size may contract or grow over time, according to the configurable criteria
  35. 35Load BalancingClient requests may be balanced across instances in the group
  36. 36Auto HealingHealth checks may be configured so that non-responding VMs are stopped and new ones are started
  37. 37Regional CoverageInstances may be allocated to different zones (data centers) in the same region
  38. 38GPU (Graphic Processing Unit)Specialised hardware processor for graphics processing. Since they are not general-purpose processors, they must be used in conjunction with CPUs. Howver, they provide Single Instruction Multiple Data (SIMD) instructions that perform an operation on multiple data operands concurrently. Originally developed for high-performance graphics (e.g., in games), GPUs are also heavily used in AI/machine learning (e.g. deep neural networks) and other HPC applications
  39. 39TPU (Tensor Processing Unit)Specialised hardware developed for AI applications. It is a type of AI accelerator developed by Google and designed to accelerate AI applications, in particular those that make use of the TensorFlow software library
  40. 40ContainerLayered on top of a guest OS rather than a full OS. The guest OS runs on top of a host OS enabled by a host machine (that may be a VM). By sharing the same guest OS, containers can be created and destroyed very fast. They use less resources than VMs
  41. 41Container ImageSpecifies the software packages to be available and programs that should be activated with each container instance, rather than the hardware characteristics and operating system. Multiple instances of different container images may be running on the same machine. Usually, containers in the same machine interact and may share resources like disk storage or network connections in the host machine

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Mestrado Segurança Informática Flashcards on Big Data and Cloud Computing Concepts, created by Inês Macedo on 15/06/2021.
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