With the evolution of the internet, things are getting more changed and complicated. There are certain techniques to combat these complexes and in the field of data handling and storage in its proper place. Many terminologies in computer science assignment have been used since before but now in this report we will discuss the characteristics of the NoSQL data base management system which has made millions of operators to handle and manage data in a very easy and effect way.
Why have NoSQL models evolved?
There are two aspects that NoSQL has been dominant than a relational database procedure. The first aspect is that its supports inly general operations and secondly the practices of data handling are distributed in a very uniform way which is often called as ‘’horizontally scalable’’. This technique is for gathering a huge amount of data and then splitting over to various servers which makes it easy for processing smaller tasks and hence the objective is accomplished. The relational database technique lacks this feature (Kim, 2016).
Implications of Using a NoSQL
Another significant point is the types of NoSQL system in which each has its own way of processing various data types in an attempt to process data faster and gives an output in very less time. The problem with traditional database systems is the lack of replication and consistency with repeating the amount of data. These databases make use of MVCC which makes old data expire and creates a new edition of data in replacement of expired data. Several models of NoSQL has been introduced which are the following. The first model is the RDBMS databases which make sits easy for the user to understand and comprehend complex data (Kim, 2016).
Comparing NoSQL Approaches
The key elements used in this model is based on Amazon’s dynamo, data model for gathering of information and document-oriented databases. The document-oriented databases includes Lotus notes, collection of various documents such as mongo DB and couch DB plus the graphs theoretical procedures. It also includes data nodes and their relationships such as we can have an example like Vertex DB, Allegro Graph and Info Grid (Mullins, 2015).
By getting deep into the NoSQL structures, we first consider the key-value stores which is quite similar to memcached system architectures which maps the keys to values. They support various atomic procedures like addition, reading the data and deleting it to the storage area. They are used in complex applications and projects. The following projects include project Voldemort which is based on dynamo paper and is built with the help of LinkedIn through MVCC model. The main feature of this project is to support sectors and to do lists (Mullins, 2015).
Another related project is the Tokyo cabinet which mentions two components. The first component is the Tokyo cabinet which accounts for central database work and other component is the Tokyo Tyrant which considers the interfacing between user and the data. Along with these components it also supports B-tree databases and hash. It consist of a single thread for editing data by suing very minimum paperwork. The third project is the Redis which is also known as remote dictionary server and it is an open source which is more complex than the first project (Mullins, 2015).
It makes use of a rich set of commands and instructions and the biggest advantage is that it is being sponsored by VMware. So we see that Redis is suitable for handling complex data files and so it is used in various complex applications. It is fast and agile which has the capability of executing a good communication and documentation process (Stephan, 2015).
The next structure of NoSQL is the document-oriented data stores which make use of Mongo DB and Couch DB models. These models deals with several web applications. Considering the Mongo DB which make use of binary JSON documentation and is written in the form of C++ or C language code form. With the help of the drivers which connects the data store which make sits easy to access the builders and developers. In also possess Java script client feature. The query written in Mongo DB is very easy to read and interpret for the personal who has the experience with SQL background. It is very easy and fast to deploy with an enhanced documentation for its clients (Stephan, 2015).
Whereas the Couch DB makes use of JSON documentation which is used in map-reduction techniques and is widely used in complex data files and procedures. It makes use of MVCC for updates and regarding the document-oriented databases have the ability to balance a solution between two extremes. Those extremes were Bigtable clones and key value stores. These are applicable for small scale applications. So we see that Couch DB is well suited for no internet based applications whereas the Mongo DB is suited for online web applications. The document oriented databases have the capacity to peer to peer replication and synchronization between multiple databases. It makes it appropriate to go offline and come back to online in order to resolve conflict situations (Stephan, 2015).
Similarly another structure is the Bigtable clones and this has the richest data model. It finds its applications in production environments and it is applied on HBase deployment which runs on multiple nodes. The last structure of NoSQL is the graph databases. It is very much similar to a document oriented data model and it deals with relationships between quantities. The graphs have different abstractions and its stores nodes and key value characteristics rather than storing tables with rows and columns (Stephan, 2015).
Business Requirements and Deployment
Implications of Business Requirements and Deployment on Database Selection Policy
Regarding the implications of SQL and NoSQL in terms of business development, we see the importance of it in database management systems. So considering the IoT concept which is growing faster and faster in recent years and we see how it has been able to solve the problems related to handling and storage of data efficiently. Likewise we have already discussed the SQL which has provided support for the users in managing & transferring the database due to its robust performance (White Paper, 2013).
We have also discussed the main limitation which comes to light is the static schema in which it handles only a limited number of applications and so due to this reason it has resulted to build a terminology which will be equivalent to this SQL language with the ability to execute numerous applications in a very less time. That terminology is NoSQL language which came into being. It has been approved by experts and many others about the performance of the NoSQL which seems to be better than the traditional SQL language (White Paper, 2013).
It finds its application in emerging markets with no joins, no schema free, easy replication support and proved to be horizontally scalable. There is a lot of research in figuring out different applications which have the capacity to report through the internet which is also called the internet of things or IoT. In this regard, the object or a device is able to communicate through the internet with help of onboard sensors. These sensors take physical measurements such as temperature, speed & velocity, displacements etc. So the data collected is recorded and processed. It is then stored and properly managed with help of NoSQL in order to meet user requirements. The NoSQL databases store data in a very uniform manner as compared to conventional methods of managing the data (White Paper, 2013).
This new class of database which is emerging globally throughout the corporate sectors is known as NoSQL database. The purpose is to have schema-less structure and to implement these properties which are actually required to handle the huge amount of IoT data. By combining both the IoT applications and the NoSQL database terminology has enhanced the generation of real data which has made the availability to store data in a very effective and efficient way. There are several mother technologies which have the main function of database management systems or DBMS in which we see that it has enhanced growth and improved the overall system performance (White Paper, 2013).
If we go deep in the NoSQL database structures there are six factors which must be considered in order to fully understand the NoSQL database concept. There are different architects which exist in the NoSQL DBMS systems. The first factor is the rapid change which deals with the realm of NoSQL. It defines the performance parameters of this management system along with new functionality which is recently inducted. The second factor is the growing support for ACID capabilities which specifies the ACID transactions which is one of the supporting elements of NoSQL DBMS. It is significant for those services which rely on ACID transactions (White Paper, 2013).
The third factor is the limitation of multi-platform support. This feature deals with capability of DBMS which is required for reviewing numerous commercial products and services. After this then this feature comes into effect in deploying on multiple platforms. The fourth factor is the increased support for SQL. This is very useful which is required when dealing with SQL databases using the tools such as Apache Hive and Pig. Considering the fifth factor which is the ability to exploit multiple types of databases. In this case the NoSQL allows you to model and implement various types of data using the flexible combinations of key value pairs and graphical methods. This makes it easy for the organization to induct polyglot persistence (White Paper, 2013).
The last factor is to beware of pre-V1 technology. The V1 terms is used for the version 1 and in this case the NoSQL has the capacity to perform V1 technologies in a very fine way without affecting other running programs which define its agility and robustness. Now we consider the advantages and disadvantages of NoSQL. Talking about the advantages we see that the user has the capacity to customize your personal data with the help of database tools in an easy way. They are very light programs which do not hamper the existing system in which it is been running. It is very easy to comprehend and implement it with using the need of much technical knowledge and additionally it requires a very less amount of coding procedures (Burtica, Mocanu, Andreica, & Tapus, 2012).
Coming to the disadvantage, we see that in the case of ACID support, the NoSQL has limitations when it comes to flexibility of it. The next is the limitation which comes when dealing with SQL applications by not providing full support for learning various programming techniques in accessing the data. The last drawback of this program is the confusion regarding the market strategies as there is no formal model of data and at times it becomes very confusing which make it very problematic to proceed with the help of this NoSQL approaches unless and until a proper investigation and intelligence is practiced. The main confusion comes when differ NoSQL database offers same similar types of services (Rautmare & D.M., 2016).
Burtica, R., Mocanu, E., Andreica, M., & Tapus, N. (2012). Practical application and evaluation of no-SQL databases in Cloud Computing,". Systems Conference (SysCon), IEEE International.
Kim, D. (2016). NoSQL technologies are built to solve business problems, not just “wrangle big data”. O’REILLY.
Mullins, C. S. (2015). Key considerations for determining if a NoSQL DBMS meets your IT needs. SearchDataManagement.
Rautmare, S., & D.M., B. (2016). MySQL and NoSQL database comparison for IoT applications. IEEE International Conference on Advances in Computer Applications (ICACA)
Stephan, T. (2015). 10 use cases where NoSQL will outperform SQL. NETWORKWORLD.
White Paper. (2013). Implementing a NoSQL Strategy. DATASTAX CORPORATION.
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