Database Design

Database Design

Database design essentially defines the various different processes and measures taken by database designers in development of ways in which data will be organized in the database, it's structural illustration, logical storing and retrieval methods along with the interrelation of data elements is included in database design.

Database designs are the most debated topics in the current context of computation and technical logic of data storing. Since the inception of databases, it has been widely discussed on various forums, conferences and general narrative what defines the precedence for selecting database design and should it be implemented in the organization based upon the precedence or should the goals take superiority in deciding the factor.

SQL & Big Data Adversary

SQL or Sequential Query Language was considered as the benchmark for implementing database design and methodology. The easy retiral, comprehensive logic and considerable amount of functionality provided by SQL had turned it to industry standard for deploying databases, managing businesses and innovating new idea. It was until Big Data Analytics, artificial intelligence, machine learning and other related fields of AI came banging and took the industry by storm. Big Data analytics and Machine Learning algorithms opened countless doors of possibility and innovation using minimal query syntax, without considering relational data storing and that too with considerable data logic. It was time for NOSQL syntax to enter the market and turn heads which were looking longtime to get rid of complex logical structure implemented in SQL.

SQL & NOSQL

SQL Database:

Sequential Query Language is the standard language used in manipulation of data such as access, retrieval, altering, removing from Relational Databases Management Systems or simply relational databases. In relational databases data is integrated according to the relations that exist between various data factions also known as data columns. The data to be accessed and managed is based upon the relations and structures SQL's success and it's effective management system paved way to be included and adopted as a standard in American National Standards in 1986 and subsequently in International Organization for Standardization in the following year 1987.

Pros:

• SQL query language has a comprehensive logical syntax which is flexible in drafting and execution. The powerful query syntax makes it easier to implement various factors, retrieving, removing and altering data in databases, based on various conditions.
• The implementation is comprehensively walked through and has several execution factors which makes it easier to implement faster query execution.
• Minimal query structure allows more work in less lines of codes through keywords and functions, thereby making it easier to be executed even if no coding skills are present.
• Standardized Structure makes it easier to access in case of any complications, similar standard is practised all around the world and helps in developing similar practices.

Cons:

• While the overall structure is quite competent with the rest of the working and blends with the structure, the complex interface makes it quite complications for users to navigate through.
• Costly Implementation in terms of Enterprise solutions make it a less desirable function and hence the industry looks for more effective and less costly solutions.
• SQL query language has semi open source documentation and thereby there are some restrictions in continuous development and restrictions.
  

NOSQL Databases

NoSQL databases are also known as not only SQL Databases and have variety of types in implementing solutions for the dynamic needs of organizations. These include non-tabular databases and solutions based on data models used for the implementation by database designers. Databases using NoSQL have high standards and complex security routines deployed for effective control and authority over the data storing, visualization, access and retrieval. Various types of the NOSQL databases developed using the syntax include

1. Graph
2. Document
3. Key Value
4. Wide Column

Graph Databases are the most popular among these NoSQL databases and have competitors in field such as MongoDB, Neo4j and related one. Graph Databases are used since they are highly scalable, stores data in nodes and edges provided with additional flexibility in querying data.

Pros

• NoSQL allows the data to be flexible in implementation which provides the option of defining schema features at time of inserting data alternate to the SQL theory where the schema constraints have to be defined before insertion of data.
• Scalability in the context of expanding the various functionalities as the user base grows is very functional and easily implemented in the graph databases as compared to SQL databases.
• Query run time is very small and results are retrieved in the most small time as compared to the related ones and thereby helps in faster access and more speed as compared to others.

Cons

• NoSQL databases mostly implement atomicity but some still stands with the eventual consistency which is relative consistency to the time of event however atomicity is required and arguably the most integral feature of database.
• NoSQL databases although promote the compatibility with legacy systems which utilize the SQL databases but there exists compatibility issue among the residuals which reduces their functionality.
• Relational databases had the head start of standardization as compared to the NoSQL databases which are still lacking the feature and various debates are conducted on selecting the standard.

SQL vs NoSQL:

SQL vs NoSQL database has been going on for quite some time now and there is no visible conclusion, Both have their own set of division and could be stated according to the need of the organization. Both have clear and visible difference as well as their own set of features which are implemented in entirely different logic. It would really be up to the need of the organizations, their principles policies and business requirements which would define which database pattern would they most likely go for.

References

This article "Database Design" is from Wikipedia. The list of its authors can be seen in its historical and/or the page Edithistory:Database Design. Articles copied from Draft Namespace on Wikipedia could be seen on the Draft Namespace of Wikipedia and not main one.

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