The concept of big data is a common entity in data science. As such, one is bound to write essays on big data as part of the requirements of a big data course. My Homework Writers understands that most data science students find it challenging to write an exploratory essay on data science. Hence, myhomeworkwriters.com provides a guide on how to write an exploratory essay on big data.
The task of writing an exploratory essay on big data becomes easy when you use a guide on how to write an exploratory essay on big data. My Homework Writers provides an easy way of understanding how to write an exploratory essay on essay on big data. Read on to expertise your skills on how to write an exploratory essay on big data.
An exploratory essay is a piece of writing that expounds on a topic without giving a personal opinion. The paper explains an event or a phenomenon based on an unbiased point of view. Such essays mean to remind us of the existence of different points of view on a specific phenomenon.
Exploratory writing is a writing technique that approaches a particular subject matter from an unbiased point of view. Exploratory writing aims to analyse the subject of discussion comprehensively. As such, exploratory writing does not aim at solving a problem but to examine the matter and clarify the diverse approaches and perspectives towards the topic.
It is vital to pay keen attention to the techniques of writing exploratory easy before learning how to write an exploratory essay on big data. Thus said, what are some exploratory writing techniques?
First, before writing your exploratory essay, make sure you understand the concept of exploratory writing. It would be pointless to write your essay using a format or an approach that best fits a different type of essay.
Secondly, before writing the explore essay, you must ensure that you write down an outline of the ideas you are planning to explore on. These ideas will help to give direction on how the to explore essay will progress.
Third, always note that the basic outline of exploratory essay conforms to the outline of any other essay. The essay must have an introduction, a body as well as a conclusion. Having this in mind, let’s tackle how you would develop an exploratory essay.
How do you Start an Exploratory Essay?
The start of your exploratory essay should have the following elements:
Typically, the body of an exploratory essay analyses the topic at hand. The body should contain two sections:
Section one: this part clarifies the subject matter issue introduced in the introductory part
Section two: this part which contains at least three paragraphs clarifies different perspectives of the subject matter. The writer of the essay has to provide supporting evidence of the different perceptive to substantiate the different claims.
The conclusion of your essay should close the phrase opened in the introductory part of the essay. The conclusion gives a chance for the writer to introduce their opinion on the issue.
A good example of an exploratory essay is an essay on big data. The article provides a guide on how to write an exploratory essay on big data. myhomeworkwriters.com provides the following information that can develop an exploratory essay on big data.
It is essential to understand the basis of big data before learning how to write an exploratory essay on big data. A big data essay is an essay that tackles different aspects of big data. In this article, My Homework Writers provides the different topics on big data starting from the definition, the uses to the importance. Anyone writing an exploratory essay on big data can develop their ideas in the following order:
Background information on big data such as the definition and the history
Big data refers to datasets, whose sizes range from terabytes to zettabytes. The sizes or big data are usually beyond the capacity of traditional data analysis tools. Typically, these traditional techniques of data analysis are unable to capture, manage or process big data. With the increase of technology, data analysts and researchers are now able to make faster and better decisions using data that was formerly unusable or even inaccessible.
Big data analysis refers to the use of advanced techniques to analyse very large and diverse sets of data. These types of data include unstructured, semi-structured and structured data from diverse sources and with different sizes. The analysis of big data enables scientists to discover correlations of data as well as hidden patterns which would give them an insight into the analysis.
Before the discovery of big data analytics, many businesses were using traditional forms of data analysis to discover hidden trends and insights. This analysis involved the use of statistical figures presented on spreadsheets for manual examination and analysis. On the discovery of big data analytics, many organisations can agree that big data analytics is more efficient than the basic forms in conducting businesses. These firms or organisations are now capable of capturing and analysing all data the streams into their businesses to make significant decisions.
The discovery of big data analytics brings with it the benefits of efficiency and speed. In the past, businesses would gather information to run analytics that would affect future decisions of the companies. With big data analytics, however, businesses owner or managers are capable of making instant decisions that would affect the business based on analytics made from big data analytics. This ability to make faster decisions gives most organisations a competitive edge that they could previously not be able to achieve.
There are several importances of big data. First off, the advancement of big data analysis techniques has made previously inaccessible data more accessible. Different fields use big data analysis to improve how they go about their businesses.
For entrepreneurs, the use of advanced techniques of data analysis makes business operations work significantly faster. These techniques include machine learning, data mining, predictive analysis, text analysis and natural language processing. Such systems help to quickly analyse formerly untapped data sources to get a better insight into their current data. As a result, businesses can formulate a faster decision that would build the business.
Sites such as Facebook and Quora also used big data analytical techniques to improve user experience on the sites. Such sites use these tools to provide users with feeds that they should find relevant and exciting.
For credit card companies, the analysis of big data comes in handy when they need to investigate fraud. These companies analyse millions of transaction information to detect fraud patterns.
Generally, the importance of big data is to provide a holistic system of information management with integrates different data and data management.
From the above explanation of big data and big data analytics, we can derive the following advantages of big data analytics:
Four Vs can characterise big data:
Volume refers to the size or amount of data. Even though the volume of the data indicates more information, the granular nature of the data is the exclusive aspect of the data. Big data processing requires data of high volume that have low densities and are unstructured Hadoop data. Such data include network traffic, web page and mobile app traffic, and Twitter data feeds, among others. Big data transforms such Hadoop data into important information. Such information usually comes in large volumes such as hundreds of petabytes or even tens of terabytes.
The velocity of big data refers to the speed of data processing. The velocity translates to how fast data is received and used. Typically, high-velocity data streams directly to memory without being written to a disk. Some internet applications have safety evaluations that require fast action. A good example of the velocity of big data is the operation of mobile apps. Mobile apps usually have several users who expect an immediate response when using apps. As a result, there is high network traffic on these apps.
Unstructured and semi-structured data such as audios, videos and texts need additional processing and supporting data to be meaningful. Once the processing makes the data more understandable, they become more like structured data. Furthermore, variety arises when data from other known sources change their form without notice.
All data have an intrinsic value, but the value must be discovered. There are several investigative techniques which help to derive value from different data. Such techniques could include investigating the preferences of users or customers and making offers which are relevant to the customer’s location.
In modern times, many organisations are using big data to make them more competitive in their field of business. For this reason, most organisations face the task of carefully choosing their open source big data tools. These tools aid in the analysis and processing of big data that circulates within and without the companies. Paraphrase tools aid in process interpretation of the processed data.
The choice of big data tool purchased by an organisation is dependent on the benefits of individual tools such as cost and ease of use. Most people are quite familiar with Hadoop as a big data tool. What most people or organisations do not realise is that several other open-source big data tools that are available in the market.
Many aspects come alongside the different types of data tool. For example, the data set sizes, the type of analysis done on the different sets as well as the expected outcome of the analysis. However, on a broader spectrum, the types of big data tools can be grouped as follows:
Most organisations use open source big data tools mainly because of the presence of Hadoop. Hadoop is an open-source big data tool that has largely dominated the world of big data. As such, most companies tend to familiarise themselves with open source big data tools. Furthermore, open-source data tools are quite easy to download and use. An additional benefit of open source data tools is that they are free of any licensing overhead.
Below are among the best open source big data tools that are ruling the big data industry:
Well, Hadoop tops the list of the most popular open-source data sources. Apache Hadoop is widely common in most companies due to its great ability in large-scale processing of data. The tool has a complete open-source structure and operates on commodity hardware in a data centre. The tool can also run on a cloud infrastructure.
As a big data tool, Hadoop has four separate parts:
Hadoop Distributed File System: this section is also known as HDFS. Typically, the section is a distribute file compatible that is compatible with bandwidths of high scales.
YARN: This is a section of Hadoop that manages and schedules all resources in Hadoop infrastructure.
Libraries: this section is a part that allows other modules to work with Hadoop
MapReduce: this section of Hadoop is like a ‘processing plant’ for big data
Apache Spark is also among the most popular big data tools in the big data industry. The purpose of the creation of Apache Spark is to fill the gaps left by Hadoop. Both big data tools are used in the processing of big data. One benefit of Spark over Hadoop is that it can handle both real-time data and batch data. The big data tool processes data at a faster rate compared to other traditional tools of disk processing. As such, Spark becomes handier for data analysts who need to achieve the results of their analysis faster.
Another one major benefit of Spark is its flexibility to integrate well with Hadoop Distributed File System (HDFS) and other data stores such as Apache Cassandra and OpenStack Swift. Furthermore, data analysts find it easier to run the tool on a single local system for easier development and testing.
The integral part of the big data tool is Spark Core. The functions of Spark Core include:
Most data analysts use Spark in place of MapReduce by Hadoop. Why? Because Spark can run tasks much faster (approximately 100 times faster) the MapReduce by Hadoop.
Apache Cassandra is a big data tool used to manage large sets of data across different servers. This type of big data tool mainly functions as a tool that processes unstructured sets of data. The data tool is very efficient and convenient since its services are highly available for use. Furthermore, Apache Cassandra is also special in its way in that it is capable of some capabilities which are not available in other NoSQL or relational database. These special capabilities include:
The unique feature about this big data tool is that it does not follow the master-salve architecture and that all of its nodes have the same functions. Furthermore, the tool can handle several synchronised users from different data centres. For this reason, the addition of a new model does not affect any existing cluster at any point.
Apache Strom is an open-source big data tool that is a distributed real-time framework. The tool is quite reliable in the processing of unbounded data stream. The real-time framework can integrate well with any programming language. As a big data tool, Apache Storm possesses the following unique features:
The topologies of Apache Storm are somehow similar to those of MapReduce. However, there is a slight difference between the two. For Apache Storm, the processing of data is real-time while for MapReduce, the processing of data is by batch processing. According to the configuration of Storm topology, its scheduler allocates workloads to nodes. Another prominent feature of Storm is its ability to incorporate the Hadoop Distributed File System through adapters when necessary.
The R programming tool is also a significant asset in the big data industry. Its major functionality is in the statistical analysis of big data. A significant advantage of R programming to is that it easy to use and you do not have to be an expert in statistical matters. Furthermore, the programming tool has its public library called the Comprehensive R Archive Network, which has more than 9000 module and algorithms used for data analysis.
R programming tool runs on servers such as Linux, Windows and inside SQL. Furthermore, the big data tool supports other tools such as Spark and Hadoop. R programming tool can also work on discrete data and attempt new algorithms for statistical data analysis. The tool is a transportable language which makes it easy to integrate an R tool built on local sources of data with other servers.
Other Open Source Programming tools that can be explored include:
From these major topics on big data, you can always develop an essay on big data. The above guide on how to write an exploratory essay on big data is just a guide on what you can write in the essay. My Homework Writers is well versed with these topics. In case you are still stuck on how to write an exploratory essay on big data, you can always contact My Homework Writers. Not only does My Homework Writers offer tips on how to write an essay on big data but also provides data science homework help.
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