UNIT-1
1 Impact of emerging database standards
Analysts caution that the much-hyped technologies are still very much in their early adopter phases and that they aren’t
right for everyone. But interest in them is expected to continue growing as information management requirements
become even more complex and time-sensitive – and as the performance and scalability ceilings associated with
traditional relational database software drive users to seek alternative options. A handful of unconventional database
technologies are starting to have a noticeable impact on the IT industry while challenging the traditional ways
that relational database management system (RDBMS) software is developed and used, according to analysts and IT
professionals. And the main reason for the growing interest in the emerging database technologies can be summed up
in one word: speed.
1.1 Emerging database technologies in action columnar DB opens new doors it’s a highly data-intensive process that
generally involves searching for a set of people with a specific illness, then comparing the records in an effort to find
individuals or geographic areas that meet certain criteria. It is said that queries were taking longer than usual to run and
that performance bottlenecks were popping up all over the place. Instead of throwing more row-based relational
databases at the problem, people opted to purchase a columnar database and some new servers. It took a great deal of
effort to get ready for the new hardware and software combination, This new technology allowed to reduce the number
of tables in its database from about 230,000 to just 12, and it is said that queries that used to take 10 minutes to run now
take 30 seconds.
1.2 NoSQL among key emerging database technologies, despite lack of frills NoSQL databases includes DBMS
products that don’t make use of the popular SQL programming language or that simply differ from traditional relational
databases in a significant way. Some are entirely non-relational, while some avoid selected relational functionality such
as fixed table schemas and join operations. In NoSQL databases users can often get up and running for a relatively low
cost. But he cautioned that there is still much room for innovation in the NoSQL realm, particularly in areas such as ease
of use and manageability. NoSQL databases are primarily designed for very high-performance situations, such as online
financial services or gaming uses, The technology is best suited to companies that have to process data so quickly and
at such high volumes that they can’t incur any additional overhead beyond just exactly what they need to be able to do.
2 Study of DDBMS architecture
Distributed databases can be broadly classified into homogeneous and heterogeneous distributed database environments,
each with further sub-divisions, as shown in the following illustration.
Homogeneous Distributed Databases: In a homogeneous distributed database, all the sites use identical DBMS and
operating systems. Its properties are −
The sites use very similar software.
The sites use identical DBMS or DBMS from the same vendor.
Each site is aware of all other sites and cooperates with other sites to process user requests.
The database is accessed through a single interface as if it is a single database.
,Types of Homogeneous Distributed Database: There are two types of homogeneous distributed database −
Autonomous − Each database is independent that functions on its own. They are integrated by a controlling
application and use message passing to share data updates.
Non-autonomous − Data is distributed across the homogeneous nodes and a central or master DBMS co-
ordinates data updates across the sites.
Heterogeneous Distributed Databases: In a heterogeneous distributed database, different sites have different operating
systems, DBMS products and data models. Its properties are −
Different sites use dissimilar schemas and software.
The system may be composed of a variety of DBMSs like relational, network, hierarchical or object oriented.
Query processing is complex due to dissimilar schemas.
Transaction processing is complex due to dissimilar software.
A site may not be aware of other sites and so there is limited co-operation in processing user requests.
Types of Heterogeneous Distributed Databases
Federated − The heterogeneous database systems are independent in nature and integrated together so that they
function as a single database system.
Un-federated − The database systems employ a central coordinating module through which the databases are
accessed.
Distributed DBMS Architectures: DDBMS architectures are generally developed depending on three parameters −
Distribution − It states the physical distribution of data across the different sites.
Autonomy − It indicates the distribution of control of the database system and the degree to which each
constituent DBMS can operate independently.
Heterogeneity − It refers to the uniformity or dissimilarity of the data models, system components and databases.
Some of the common architectural models are −
Client - Server Architecture for DDBMS
Peer - to - Peer Architecture for DDBMS
Multi - DBMS Architecture
Client - Server Architecture for DDBMS
This is a two-level architecture where the functionality is divided into servers and clients. The server functions primarily
encompass data management, query processing, optimization and transaction management. Client functions include
mainly user interface. However, they have some functions like consistency checking and transaction management.
The two different client - server architecture are −
Single Server Multiple Client
Multiple Server Multiple Client (shown in the following diagram)
, Peer- to-Peer Architecture for DDBMS
In these systems, each peer acts both as a client and a server for imparting database services. The peers share their
resource with other peers and co-ordinate their activities.
This architecture generally has four levels of schemas −
Global Conceptual Schema − Depicts the global logical view of data.
Local Conceptual Schema − Depicts logical data organization at each site.
Local Internal Schema − Depicts physical data organization at each site.
External Schema − Depicts user view of data.
1 Impact of emerging database standards
Analysts caution that the much-hyped technologies are still very much in their early adopter phases and that they aren’t
right for everyone. But interest in them is expected to continue growing as information management requirements
become even more complex and time-sensitive – and as the performance and scalability ceilings associated with
traditional relational database software drive users to seek alternative options. A handful of unconventional database
technologies are starting to have a noticeable impact on the IT industry while challenging the traditional ways
that relational database management system (RDBMS) software is developed and used, according to analysts and IT
professionals. And the main reason for the growing interest in the emerging database technologies can be summed up
in one word: speed.
1.1 Emerging database technologies in action columnar DB opens new doors it’s a highly data-intensive process that
generally involves searching for a set of people with a specific illness, then comparing the records in an effort to find
individuals or geographic areas that meet certain criteria. It is said that queries were taking longer than usual to run and
that performance bottlenecks were popping up all over the place. Instead of throwing more row-based relational
databases at the problem, people opted to purchase a columnar database and some new servers. It took a great deal of
effort to get ready for the new hardware and software combination, This new technology allowed to reduce the number
of tables in its database from about 230,000 to just 12, and it is said that queries that used to take 10 minutes to run now
take 30 seconds.
1.2 NoSQL among key emerging database technologies, despite lack of frills NoSQL databases includes DBMS
products that don’t make use of the popular SQL programming language or that simply differ from traditional relational
databases in a significant way. Some are entirely non-relational, while some avoid selected relational functionality such
as fixed table schemas and join operations. In NoSQL databases users can often get up and running for a relatively low
cost. But he cautioned that there is still much room for innovation in the NoSQL realm, particularly in areas such as ease
of use and manageability. NoSQL databases are primarily designed for very high-performance situations, such as online
financial services or gaming uses, The technology is best suited to companies that have to process data so quickly and
at such high volumes that they can’t incur any additional overhead beyond just exactly what they need to be able to do.
2 Study of DDBMS architecture
Distributed databases can be broadly classified into homogeneous and heterogeneous distributed database environments,
each with further sub-divisions, as shown in the following illustration.
Homogeneous Distributed Databases: In a homogeneous distributed database, all the sites use identical DBMS and
operating systems. Its properties are −
The sites use very similar software.
The sites use identical DBMS or DBMS from the same vendor.
Each site is aware of all other sites and cooperates with other sites to process user requests.
The database is accessed through a single interface as if it is a single database.
,Types of Homogeneous Distributed Database: There are two types of homogeneous distributed database −
Autonomous − Each database is independent that functions on its own. They are integrated by a controlling
application and use message passing to share data updates.
Non-autonomous − Data is distributed across the homogeneous nodes and a central or master DBMS co-
ordinates data updates across the sites.
Heterogeneous Distributed Databases: In a heterogeneous distributed database, different sites have different operating
systems, DBMS products and data models. Its properties are −
Different sites use dissimilar schemas and software.
The system may be composed of a variety of DBMSs like relational, network, hierarchical or object oriented.
Query processing is complex due to dissimilar schemas.
Transaction processing is complex due to dissimilar software.
A site may not be aware of other sites and so there is limited co-operation in processing user requests.
Types of Heterogeneous Distributed Databases
Federated − The heterogeneous database systems are independent in nature and integrated together so that they
function as a single database system.
Un-federated − The database systems employ a central coordinating module through which the databases are
accessed.
Distributed DBMS Architectures: DDBMS architectures are generally developed depending on three parameters −
Distribution − It states the physical distribution of data across the different sites.
Autonomy − It indicates the distribution of control of the database system and the degree to which each
constituent DBMS can operate independently.
Heterogeneity − It refers to the uniformity or dissimilarity of the data models, system components and databases.
Some of the common architectural models are −
Client - Server Architecture for DDBMS
Peer - to - Peer Architecture for DDBMS
Multi - DBMS Architecture
Client - Server Architecture for DDBMS
This is a two-level architecture where the functionality is divided into servers and clients. The server functions primarily
encompass data management, query processing, optimization and transaction management. Client functions include
mainly user interface. However, they have some functions like consistency checking and transaction management.
The two different client - server architecture are −
Single Server Multiple Client
Multiple Server Multiple Client (shown in the following diagram)
, Peer- to-Peer Architecture for DDBMS
In these systems, each peer acts both as a client and a server for imparting database services. The peers share their
resource with other peers and co-ordinate their activities.
This architecture generally has four levels of schemas −
Global Conceptual Schema − Depicts the global logical view of data.
Local Conceptual Schema − Depicts logical data organization at each site.
Local Internal Schema − Depicts physical data organization at each site.
External Schema − Depicts user view of data.
