Learn SQL: A Universal Language for Structured Data - Research Paper

Introduction

The Structured Query Language (SQL) is a specific language that is often used in programming. The language is also created to assist in managing data held in the relational database management system (RDBMS). The SQL is mostly used in the structured types of data. In 1986, the standard query language became a standard of the American National Standards Institute (ANSI). One year later, SQL became a standard of the International organization of standardization (IOS). The RDBMS also relies on modern data basis such as Oracle, IBM DB2, MS SQL Server, and microsoft access. Information in the RDBMS is stored in database objects referred to as tables.

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The Structured Query Language was first developed in the 1970s by Raymond Boyce and Donald Chamberlain who were both IBM developers. The SQL programming language was known as SEQUEL. After SQL was developed, it was only used by the two developers to retrieve data stored in IBM's original relational database management system (Agarwal & Rajan 2017). After a few years, the use of the structured query language was made public hence other firms were free to use the programming language. In 1979, the oracle company released its own version of SQL software which was called the oracle V2. Since its development, the SQL has been considered to be the industry standard.

Oracle company has, however, developed a language known as MYSQL which relies on SQL's database management system. The MYSQL was developed to ensure that it would be publicly used without any fee required. MYSQL has been considered to be among the most powerful tools used by websites to change content in a fast manner (Agarwal & Singh 2017). In most cases, MYSQL is used together with a web scripting language like Python, PHP, Perl. Currently, a majority of big brands like Facebook, Twitter, Google, Adobe, and Zappos rely on MYSQL. Apart from MYSQL, there are other SQL data management systems such as Ingres, Firebird, and PostgreSQL.

Use of SQL in the Finance Industry

SQL is essential in the finance industry in various ways. First, the Structured query language ensures that an analyst can transfer and store data in a more safer manner. When coupled up with cloud sources, the process becomes more manageable. In a majority of the cases, people in the finance industry always shy away from learning the database program which requires basic knowledge of mathematics (Gennick 2011). The structured query language can help in making work easier especially in case one needs to retrieve data faster and anytime. In most financial institutions, employees are faced with a massive burden of searching places to store data.

Unlike Python, SQL is known to assist the financial analysts in tackling complex maths. Thus, complex mathematics can be simplified by only adding a little bit of coding to the program. SQL can also be used in the manipulation of data or even deleting information from the tables. In banking institutions, SQL is used to act as a prevention against big losses (Gennick 2011). Thus, in banks, there is small process which is known to act as a single unit. In such a case, there is a higher possibility that when a short process fails, the bank is most likely to experience huge losses. This is where the SQL comes handy in that it helps in the processing process.

The structured query language in corporate finance is used in instances where an individual needs to find data that can not be found manually. SQL gives the analyst a clue of where specific information can be found. After familiarizing oneself with the programming language, an individual can be assured of never relying on anybody in cases where they may require assistance. Knowing how to use SQL speeds up the process of manipulating data and information (Gennick 2011). Knowing SQL requires a lot of sacrifice and expertise in coding. After that, one is considered to be employable in any financial field. Many corporate finance institutions prefer employing people with knowledge in SQL usage along with basic experience in Python, C++, or java. SQL is used to look into data that were saved into the systems in the last decade. It is a powerful tool when it comes to keeping or the information in an organized manner.

Programming Languages in Finance Analysis

Python is among the most used programming languages in the highly burdened financial institutions. Python syntax is known to be much closer to mathematics syntax thus making it suitable for institutional financial usage. Python is used for building secure and highly professional banking softwares (Agarwal & Singh 2017). In a majority of the banks worldwide, python is used to building their gateways. In more than a decade, Python has been used to create offline modes of money transactions such as ATMs which makes money transactions to be fast. Quartz which is position management, risk management platform of Merrill Lynch's joint trading and bank of America was built from the start using Python only.

Currently, Cryptocurrencies are becoming the significant modes of exchange in online platforms. Very versatile python is known to be effective in depicting the buy and sell trends in the market. The python programs are at some point programed to help in analyzing the past and present market trends (Stokinger et al., 2019). Anaconda which is a python package is known to be more helpful when it comes to analyzing data. There are various reasons as to why an individual should learn programming using python. The programming application is versatile, has more tutorials on the website in case one experiences difficulties, and it is more straightforward as compared to the Structured Query Language.

Java and C++ are other programming languages used in a majority of the financial institutions. In the past few years, C++ was and is still considered to be a faster and is suitable in terms of convenience. Java, on the other hand, appears to be quick and efficient as C++ though one has to put a lot of effort to ensure it runs efficiently (Agarwal & Singh 2017). Java which is owned by Oracle is often associated with handling big data projects. C++, on the other hand, is suited for institutions that require to handle multiple tasks at a go. C++ is mainly recognized for its ability to handle projects in a fast manner.

Benefits of Structured Query Language

One of the pros associated with the structured query language is that it is fast in retrieving large chunks of data. Also, as compared to Java and C++, it is easier to learn using SQL. Thus, SQL does not have nor require any knowledge in coding for one to use the programming language (Stokinger et al., 2019). The program is free to use and download make it one of the most referred program languages. SQL is easily accessible through mediums such as mobile phones, laptops, tablets, and servers. Another con is that the programming language is entirely interactive. By interactive, language programming software can get answers to questions fast.

SQL are used by other DBMS vendors such as IBM, Oracle, and Microsoft. This gives it a major platform to be used by prominent companies and brands such as Facebook, Google, and Adobe. By being recognized by such brands, it gets easier for it to be recognized as the industry-standard programming language (Gennick 2011). Unlike many of the programming languages applications, SQL works in a manner where it can easily connect clients' computers to the servers. The SQL programming language can be moved from a computer to another without any complication. Such benefits make it stand out from languages such as C++, C, python, and Java. Individuals with vast knowledge in SQL possess an added advantage when it comes to the possibility of getting employed in financial institutions.

Disadvamntages of SQL

One of the common disadvantages of the structured query language is that one needs some sought of training to use it. Trainings, especially carried out in an organization might result in a lot of time spent and resources which include spending of money. Its database has been previously criticized for its difficulty in use (Fotache 2015). As compared to coding which is also a difficult task, SQL Interference in the past has received negative remarks from other language programming users. In comparison to Python, C++, and Java, it was concluded that a majority of the new programmers did not an have interest in gaining knowledge regarding SQL usage.

Another demerit of SQL is that it requires an individual to have a powerful machine. The structured query language is known to use a lot of random access memory (RAM) space. Moreover, the programming language requires a machine to have enough speed and enough space to run effectively(Agarwal & Rajan 2017)(Agarwal & Rajan 2017) (Agarwal & Rajan 2017)(Agarwal & Rajan 2017). This is one of the reasons as to why various companies and individuals have preferred using java, C++, and python. Another challenge that affected SQL is that is has a low implementation of variable data types. Even though it is a free ware, SQL has been prohibited for practical usage. It is required to be used professionally after the license is acquired.

Structured query language requires training. Unlike other programming languages that require basic knowledge, SQL requires one to be professionally trained. In some instances, users of python, java, and C++ can look out for help online through tutorials in case they need any assistance. For SQL users, they have to seek assistance from professionals. That means that they get to spend more money seeking further assistance (Agarwal & Rajan 2017). The overall cost of maintaining the SQL programming language is often high. SQL only requires handling by a professional. Thus, in financial institutions one has to undergo various lessons to ensure that they become well versed with the language. The language also uses DBMS which also requires a lot of knowledge to run it effectively. Despite the fact that it is free, one has to spend a lot of money maintaining the services. Moreover, an individual is forced to purchase high-quality machines that have enough space to handle every saving.

How SQL Fits Organizations' Strategy

There are four types of organizational strategies. The four strategies include defender, analyzer, prospector, and reactor. Each strategy serves a different purpose in ensuring that a company runs effectively. The defender strategy entails an organization finding the appropriate market while still focusing on maintaining it. Organizations that use the defender strategy always use it to protect their operations from future technological changes (Fotache 2015). Companies utilizing prospector strategies rely on creativity to run their business. This strategy is known to be effective in instances where there is a fast change in technology. The reactor organizational strategy entails the top management's role in discerning every change. Reactor strategy is a major contributor to the downfall of businesses. This is due to the fact that they are often unable to offer fast response.

In the case of banks and other financial institutions, the analyzer strategy is the most appropriate. In analyzer stratagies, organizations implement new ways through which they get to retain current customers by ensuring their services are efficient. For instance, banks use languages such as SQL to ensure that all data and information are stored in an appropriate manner. SQL helps banks to be fast during the withdrawals and depositing of large chunks of money. Nowadays, ATMs use the SQL which ensures that the banks meet their customers' needs at any given place without any challenge. As highlighted by Fotache (2015), the use of SQL, python, C++, C, and java have b...