Introduction used for backup purposes and basic data

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For as long as we can remember, relational
databases were in use and worked efficiently in large scale industries to store
and work with data. In recent years, the cloud has started gaining popularity
and has come to light as the most efficient place for not just storing data,
but also sharing, accessing, and reliability. A cloud is defined as a parallel
and distributed system which has a number of virtualized and interconnected
computers. These are actively provisioned and presented as single or more united
computing resources depending upon the service level agreement. Cloud has three
popular paradigms – Infrastructure as a Service (IaaS), Platform as a Service
(PaaS), and Software as a Service (SaaS). These services constitute distributed
operating system, the distributed database and other services. Due to the
massive growth in digital data, changing requirements for data storage, qualified
broadband facilities and Cloud computing has led to the rise of cloud
databases. Cloud storage, Data as a service (DaaS) and Database as a service (DBaaS)
are the various terminology used for data management in the Cloud, each of
which differ in terms of how data is stored and managed accordingly.


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Cloud storage is virtual storage that
allows users to store documents and objects of various sorts. Some examples of
cloud storage are Dropbox, iCloud and so on, which are among the popular ones.
These are used for backup purposes and basic data management at a remote disk
available through the Internet. DBaaS offers complete functionality of a database
and lets users access and store their database at any point of time via the
internet. Some commonly used databases in the Cloud are: Amazon’s Simple DB,
Amazon RDS, Google’s BigTable, Yahoo’s Sherpa and Microsoft’s SQL Azure Database.
A Cloud Database is a database that typically runs on a Cloud Computing
platform, such as Windows Azure, Amazon EC2, GoGrid and Rackspace. There are
two deployment methods which are commonly used: users can run databases on the
cloud independently, using a virtual machine image, or they can purchase access
to a database service, maintained by a Cloud Database provider. Of the databases
available on the Cloud, some are SQL-based, and some use a NoSQL data model.

Cloud databases provide scalability, high
availability, optimized resource allocation and multitenancy. It can be a
traditional database such as MySQL and SQL server. These databases can be
installed, configured and maintained on a Cloud server by the user, such an
option is called the “DIY” approach. In this approach, the developers manually
ensure reliability and elasticity service. The fact that these services are all
provided at a lesser price makes it more favorable. Cloud databases support
changing storage requirements of Internet-savvy users who deal more with unstructured
data, user created content such as documents and photos. So, the big question
is, how is all this data stored and managed? In the cloud environment, data is
stored on multiple dynamic servers at data centers rather than on dedicated
servers like in traditional data storage. The user sees the virtual server when
storing the database, but in reality, the data is stored on any one or more of
the servers at data centers. On the cloud platforms, data is represented as
Key-Value pairs, and atomic access is provided only at the granularity of
single keys. Key-Value stores have emerged as a preferred choice for scalable
and fault tolerant data management, but lack the rich functionality, and
transactional guarantees of RDBMS.

Most database systems have scalability
limit, once users reach these limits, data migration and load balancing are the
only recourse. Scalability of a system indicates its ability to either handle
growing amounts of work in graceful manner when additional resources are added.
In cloud computing environment, there is a need to support virtually unlimited
number of users for web-based applications by making it scalable. Database
scalability in cloud can be achieved in two ways – Data Fusion and Data
Fission. The first approach – Data Fusion architecture maintains multi-key
atomicity while ensuring scalability. The second approach – Data Fission is
where database schema is partitioned instead of keeping individual tables. This
approach tries to minimize the distributed transactions. 

Categories: Management


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