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traditionally the software industries
use relational databases to store and
manage data persistently not only sequel
or no sequel is a new set of database
that has emerged in the recent past as
an alternative solution to relational
databases Karl Stroh G introduced the
term no sequel to name his file based
database in 1998 no sequel refers to all
databases and data stores that are not
based on the relational database
management systems or our DBMS
principles it relates to large data sets
accessed and manipulated on a web scale
no sequel does not represent single
product or technology it represents a
group of products and a various related
data concepts for storage and management
no sequel was a hash tag that was chosen
for a tech meetup to discuss the new
databases we will continue our
discussion on no sequel in the next
screen typically no sequel does not have
a prescriptive definition the common
characteristics of a no sequel database
are as follows it does not use the
relational model it runs well on
clusters it mostly has an open source it
is built for the new generation web
applications it is schema-less we will
discuss the purpose of using no sequel
in the next screen with the explosion of
social media user driven content has
grown rapidly and has increased the
volume and type of data that is produced
managed analyzed and archived in
addition new sources of data such as
sensors Global Positioning Systems or
GPS automated trackers and other
monitoring systems generate huge volumes
of data on a regular basis these large
volumes of datasets also called Big Data
have introduced new challenges and
opportunities for data storage
management and
and archival in addition data is
becoming increasingly semi-structured
and sparse this means that our DBMS
databases which require upfront schema
definition and relational references are
examined to resolve the problems related
to large volume and semi structured data
a class of new database products have
emerged these new classes of data based
products consists of column based data
stores key value pair databases and
document databases together these are
called no sequel the no sequel database
consists of diverse products with each
product having unique sets of features
and value propositions in the next
screen we will identify the difference
between no sequel and our DBMS no sequel
differs from our DBMS in terms of the
following features data storage in our
DBMS data is stored in a relational
model in tabular format with numerous
rows and columns rows contain
information about an item type and
columns contain various values
attributed to the item for example a row
may contain information about an item
and the columns will contain specific
information such as model date of
manufacture color and so on no sequel
comprises a host of different databases
with different data storage models
schemas and flexibility each record in
an our DBMS follows a fixed schema the
columns are defined and locked before
data entry in addition each row contains
data for each column although this
format can be modified it will require
altering the entire database and going
offline on the other hand schemas in no
sequel are dynamic you can add columns
anytime unlike our DBMS each row need
not contain data for each column
scalability our DBMS supports
vertical scaling typically to handle
more data a bigger server is required
however this will increase the cost
although you can scale in our DBMS
across multiple servers it is a
challenging and time-consuming process
scaling is horizontal in no sequel you
can scale across multiple servers
multiple servers are cheap commodity
hardware or cloud instances which make
scaling cost-effective compared to
vertical scaling many no sequel
technologies automatically distribute
data across different servers atomicity
consistency isolation durability or acid
compliancy relational databases are
mostly acid compliant however most no
sequel databases compromise acid
compliancy for performance and
scalability in the next screen we will
discuss the benefits of no sequel the
desired technical characteristics of an
enterprise class no sequel solution are
as follows
primary and analytic datasource
capability the first criterion of an
enterprise class no sequel solution is
it must serve as a primary or active
data source that receives data from
different business applications it also
must act as a secondary data source or
analytic database that enhances business
intelligence applications from business
perspective the no sequel database must
be capable of quickly integrating all
types of data structured semi-structured
or unstructured in addition it must be
able to execute high-performance queries
once all the required data is collected
in the database data administrators may
want to perform an analysis in real time
and in Map Reduce form an enterprise
class no sequel database can easily
handle such requests using the same
database it does not require loading the
data into a separate analytic database
for analysis
big data capability no sequel databases
are not restricted to working with big
data however an enterprise class no
sequel solution can scale to manage
large volumes of data from terabytes to
petabytes in addition to storing large
volumes of data it delivers high
performance for data velocity variety
and complexity continuous availability
or no single point of failure to be
considered enterprise class a no sequel
database must offer continuous
availability with no single point of
failure
moreover rather than providing the
continuous availability feature outside
the software the no sequel solution
delivers inherent continuous
availability the no sequel databases
must include the following key features
all nodes in a cluster must be able to
serve read requests even if some
machines are down must be capable of
easily replicating and segregating data
between different physical shelves in a
data center this helps avoid Hardware
outages must be able to support data
distribution designs that are multi data
centers on-premises or in the cloud
multi data center capability typically
business enterprises own highly
distributed databases that are spread
across multiple data centers and
geographic locales data replication is a
feature offered by all legacy our DBMS
however none can offer a simple mode of
data distribution between various data
centers without any performance issue
simple method includes the ability to
handle multiple data centers without
concerning about the occurrences of the
read and write operations on good no
sequel Enterprise solution must support
multi data center deployment and must
provide configurable option to maintain
a balance between performance and
consistency easy replication for
distributed location independent
capabilities to avoid data loss
affecting and
application a good no sequel solution
provides strong replication abilities
these include a read anywhere and write
anywhere capability with full location
independent support this means you can
write data to any node in a cluster
have it replicated on other nodes and
make it available to all users
irrespective of their location in
addition the write capability on any
node must ensure that the data is safe
in the event of a power failure or any
other incident no need for separate
caching layer a good no sequel solution
is capable of using multiple nodes and
distributing data among all the
participating nodes thus it does not
require a specific caching layer to
store data the memory caches of all
participating nodes can store data for
quick input and output or IO access no
sequel database eliminates the problem
of synchronizing cache data with the
persistent database thus it supports
simple scalability with fewer management
issues we will discuss some more
benefits of no sequel benefits in the
next screen following are a few more
benefits of no sequel cloud ready as
adaptation of cloud infrastructure is
increasing day by day an enterprise
class no sequel solution must be cloud
ready a no sequel database cluster must
be able to function in a cloud setting
such as Amazon ec2 and also must be able
to expand and contract a cluster when
necessary it also must support a hybrid
solution where part of the database is
hosted within the enterprise premise and
another part is hosted on a cloud
setting high performance with linear
scalability an enterprise class no
sequel database can enhance performance
by adding nodes to a cluster typically
the performance of database systems may
go down when additional No
are added to a cluster however a good no
sequel solution increase performance
revolt read and writes operations when
additional nodes are added these
performance gains are linear in nature
flexible schema support an enterprise
class no sequel database offers a
flexible or dynamic schema design to
manage all types of data structured
semi-structured and unstructured
therefore the need to have different
vendors to support the different data
types does not arise no sequel databases
may support various schema formats such
as columnar BigTable and document
therefore choosing an appropriate
database based on application
requirement is a key design decision the
flexible or dynamic schema support
ensures that you can make schema changes
to a structure without making the
structure offline this support is
critical considering the near zero
downtime and round-the-clock
availability for business applications
support key developer languages and
platforms ideally an enterprise class no
sequel solution must support all major
operating systems in addition it must
run on a product hardware that does not
require any tweaks or other proprietary
add-ons the no sequel database must
provide client interfaces and drivers
for all common developer languages it
must offer a structured query language
or sequel or a similar language that
helps store and access data in a no
sequel database easy to implement
maintain and grow a no sequel database
must be simple but robust in other words
it must be easy to implement and use and
must offer sturdy functionality to
handle various enterprise applications
in addition the no sequel vendor must
supply good management tools that assist
data professionals perform various
administrative tasks such as adding
capacity to a cluster
running utility tasks and so on the no
sequel database must allow easy growth
without making any change to the front
end of the business application thriving
open-source community for an open-source
no sequel database having a vibrant
community is essential to make regular
contribution to enhance the core
software
moreover open source communities
generally provide excellent quality
assurance or QA testing this sometimes
eliminates the need for software
companies to hire train and retain a QA
team to encourage a thriving open-source
community include activities on mailing
lists and technical forums initiate
technical discussions and participate in
conferences in the next screen we will
discuss the various types of no sequel
databases there are four basic types of
no sequel databases one key value
database it has a big hash table of keys
and values react Tokyo cabinet Reedus
server memcached and scholars or
examples of key value store to document
based database it stores documents made
up of tagged elements examples include
MongoDB CouchDB orient DB and rave and
eBay three column based database each
storage block contains data from only
one column
examples are BigTable Cassandra HBase
and hyper table for graph based database
it is a network database that uses nodes
to represent and store data
examples are neo4j info grid infinite
graph and flock DB the availability of
choices in no sequel databases has its
own advantages and disadvantages the
advantages it allows you to choose a
design according to your system
requirements however because
you have to make a choice based on
requirements there is always a chance
that the same database product may not
be used properly
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