Examen 3. Presentacion 4

Question 1

What is cloud computing?

Answer 1

Cloud Computing (CC) 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

Question 2

public cloud

Answer 2

when it is made available in a pay-as-you-go
manner to the general public

Question 3

private cloud

Answer 3

when the cloud infrastructure is
operated solely for a business or an organization

Question 4

Cloud Computing services

Answer 4

Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), Infrastructure-as-a-Service

Question 5

Software-as-a-Service (SaaS)

Answer 5

Applications are accessible from several client devices.
The provider is responsible for the application for example : Email, CRM, Collaborative, ERP.

Question 6

Platform-as-a-Service (PaaS)

Answer 6

The client is responsible for the end-to-end life cycle in
terms of developing, testing and deploying applications
Providers supplies all the systems (operating systems,
applications, and development environment)
For Example: Application Development, Decision Suppport, Web, Streaming

Question 7

Infrastructure-as-a-Service (IaaS)

Answer 7

In this type of service the client manages the
storing and development environments for Cloud
Computing application such as the Hadoop
Distributed File System (HDFS) and the MapReduce
development framework.
For example: Caching, Legacy, Netoworking, etc.

Question 8

NoSQL

Answer 8

Not Only SQL, refers to an
eclectic and increasingly familiar group of nonrelational data management systems; where
databases are not built primarily on tables, and
generally do not use SQL for data manipulation.

Question 9

NoSQL Focus

Answer 9

NoSQL databases focus on
analytical processing of large scale datasets,
offering increased scalability over commodity
hardware.

Question 10

Why NoSQL Databases?

Answer 10

1.- The exponential growth of the volume of data
generated by users, systems and sensors.
2.-The increasing interdependency and complexity of
data accelerated by the Internet, Web2.0, social
networks and open and standardized access to
data sources from a large number of different
systems.

Question 11

The CAP-Theorem

Answer 11

postulates that only two of
the following three different aspects of scaling
out can be achieved fully at the same time

Question 12

Aspects of scalling

Answer 12

Strong Consistency, High Availability, Partition-tolerance

Question 13

CAP theorem and NoSQL

Answer 13

Many of the NoSQL databases above all have loosened
up the requirements on Consistency in order to achieve
better Availability and Partitioning (AP).

Question 14

primary uses of NoSQL Database

Answer 14

1. Large-scale data processing (parallel processing over
   distributed systems).
2. Embedded I-R (basic machine-to-machine information
   look-up & retrieval).
3. Exploratory analytics on semi-structured data (expert
   level).
4. Large volume data storage (unstructured, semi-structured,
   small-packet structured).

Question 15

Classification of NoSQL Databases

Answer 15

Key-Value stores, Document databases, WideColumn, Graph databases

Question 16

Key-Value store

Answer 16

these Data Management Systems (DMS) store items as alphanumeric identifiers (keys) and associated values in
simple, standalone tables (referred to as ―hash
tables‖). The values may be simple text strings or
more complex lists and sets.

Question 17

Document databases

Answer 17

designed to manage
and store documents which are encoded in a
standard data exchange format such as XML,
JSON or BSON

Question 18

Key-Value store values

Answer 18

The values may be simple text strings or
more complex lists and sets.

Question 19

Document Databases Value

Answer 19

he values may be simple text strings or
more complex lists and sets.

Question 20

Wide-Column

Answer 20

This type of
NoSQL Database employs a distributed,
column-oriented data structure that
accommodates multiple attributes per key

Question 21

Graph Databases

Answer 21

Graph databases replace
relational tables with structured relational
graphs of interconnected key-value pairings

Question 22

Hadoop HBase

Answer 22

HBase is a column-oriented
database management system that runs on top
of Hadoop Distributed File System (HDFS).

Question 23

The reason to store values on a per-column
basis

Answer 23

for specific queries, not all of the values are
needed

Question 24

HBase Architecture

Answer 24

Consistency, Atomic Read and Write, Sharding, High Availability, Client API, Scalability, Distributed Storage, HDFS/Hadoop integration, Data Replication, Load sharing and Support for Failure, API Support, MapReduce Support, Sorted Row Keys, Real Time Processing of Data,

Question 25

Consistency

Answer 25

HBAse provides consistent read and write operations and thus can be used for high speed requirements. This also helps to increase the overall throughput of the system.

Question 26

Atomic Read and Write

Answer 26

Atomic read and write means that only one process can perform a given task at a given time. For example when one process is performing write operation no other processes can perform the write operation on that data.

Question 27

Sharding

Answer 27

HBase offers automatic and manual splitting of regions. This means that if a region reaches its threshold size it automatially splits into smaller sub regions.

Question 28

High Availability

Answer 28

HBase provides Local Area Network(LAN) and Wireless Area Network(WAN) which supports failure recovery. There is a master server which monitors all the regions and metadata of the cluster

Question 29

Client API

Answer 29

HBase offers access through the Java API which helps to programmatically access HBase.

Question 30

Scalability

Answer 30

This is one of the important characteristics of non-relational databases. HBase supports scalability both in linear and modular form

Question 31

Distributed Storage

Answer 31

This feature of HBase helps usage of distributed storage such as HDFS

Question 32

HDFS/Hadoop integration

Answer 32

HBase can run on top of various systems such as Hadoop/HDFS

Question 33

Data Replication

Answer 33

The data in HBase are replicated over a number of clusters. This helps to recover data in case of any loss and high availability of data

Question 34

Load sharing and Support for Failure

Answer 34

HDFS is internally distributed and supports automatic recovery. As HBase runs on top of HDFS it is also automatically recovered.

Question 35

API Support

Answer 35

HBase supports Java API which makes it easily available programmatically using java

Question 36

MapReduce Support

Answer 36

HBase supports map reduce which helps in parallel processing of data.

Question 37

Sorted Row Keys

Answer 37

HBase uses keys and stores it in lexicographical order thus optimizing the requests.

Question 38

Real Time Processing of Data

Answer 38

HBase performs real time processing of data and supports block cache and bloom filters

Question 39

Denormalization, Duplication, and Intelligent Keys (DDI)

Answer 39

It is about rethinking how data is stored in Bigtable-like storage systems, and how to make use of it in an appropriate way

Question 40

table row keys

Answer 40

are byte arrays so almost anything can serve as a row key from strings to binary representations of longs or even serialized data structures

Question 41

Design Aspects

Answer 41

Tables are declared up front at schema definition time, Rows are lexicographically sorted with the lowest order appearing first in a table, Columns are grouped into column families, The column family prefix must be composed of printable characters, The qualifying tail, the column family qualifier, can be made of any arbitrary bytes, Column families must be declared up front at schema, A {row, column, version} tuple exactly specifies a cell in HBase.