What is Big Data?
The answer may surprise you! You may have already heard about what is big data from popular media or perhaps by experts, but for those who are new to the concept, there is an introduction to some of its characteristics and common uses.
Big Data is literally a set of huge data which is growing exponentially with time-accelerated growth. It is graphically dense data with so many dimensions and complexity that all traditional data processing tools cannot store or process it effectively.
So how does Big Data visualization designers create such visually stimulating charts which allow users to interactively explore the data? Two types of graphical tools have evolved to solve this challenge: Web-based apps and Cap Focus (dataflow) technologies.
Since it can be accessed from anywhere with an internet connection, web-based apps enable users to not only explore what is big data but also to visualize it in their browser. Examples include Apache Hadoop and Twitter Streaming. In addition to being easy to use, they are highly flexible since data can be shared and manipulated in real-time without additional technical knowledge. Examples include Textbench and Plotly. Of course, the most famous ones such as Yahoo!, Amazon and Google use these to power their own analytics platform, while others provide web access to much more content including complex analytical models.
Basically, Cap Focus enables developers to visualize and analyze massive amounts of data analytics in real-time. A typical user can do this by running visual tools such as InfluxDB, Hadoop, Spark and Kibana in the background. With these, data analysts can examine volume trends, latency, throughput, latency, average consumption, and other characteristics. They can also compare the historical performance against target outcomes. As a result, the user gets the insight they need on how to optimize deployment, and adapt to any business context.
Characteristics of Big Data
In order to understand the importance of big Data, it is necessary to have a basic knowledge of the various dimensions of Data Set Variances.
Understanding the 5V’s of big Data (volume, velocity, value, variety and veracity), enables data scientists to extract more value out of their analyzed data while at the same time allowing the scientist’s organization to be more consumer-centric.
A lot of people are asking “what is the volume in big data” and what to do with all this new information that is coming out of Facebook, Twitter and the rest of these sites. Well, volume is certainly part of it, but it is not the whole story.
Hence, the volume of data refers to the size of the data sets that need to be analyzed and processed
Velocity essentially measures how fast the data is coming in. Some data will come in in real-time, whereas other will come in fits and starts, sent to us in batches.
There have been attempts to measure velocity, but they have been unsuccessful. The measurement of velocity is difficult because it depends on all the different measurements of speed. The only way to determine if the velocity can be measured is to make every possible assumption that can be made, and then average the results. This can seem like a very tedious process.
Once we have this information, we can then normalize this data and see what kind of shape it takes into the future, before it changes, in a mathematical model. However, even with this simplified description, it is still very difficult to find an answer to what is the velocity in big data. For an answer to this question, it is often necessary to take a more involved statistical approach.
people are still confused as to what is value in big data, and therefore, have not engaged in much related debate. Unfortunately, this lack of engagement may be the best possible argument for not putting more effort into the r&d in the new age of information technologies. More companies are already seeing the value in big data-based decision making, and as a consequence are embarking on large-scale research initiatives to better understand the intricacies of big data and its implications for management.
One issue at the core of the value in big data argument is what is lost and what is the gain in terms of business. Many have argued that certain types of data are unimportant or even obsolete. However, value in big data must necessarily begin with a definition of what is a loss in business. After all, it is impossible to measure what is value in big data if one is not sure what is the loss in business?
An example would be measuring the usefulness, safety, and value of a particular product. If a new product has been developed that outperforms all prior products currently on the market, then by definition, this product is valuable.
However, if current reports indicate that this product is unsafe and/or defective, then obviously, it is of no use whatsoever. To address the issue of value in big data, therefore, it is necessary to have an agreed-upon definition of what is a loss in business. Without such an agreed upon definition, it is easy for product quality to be changed mid-course because of economic necessity, and it is also easy for a company to change course mid-course when it comes to product quality due to external pressures.
A recently published paper by UC Berkeley researchers suggests that managers may be able to exploit information about plant motion sensors for a more efficient way of keeping track of plant productivity. “What is variety in big data?” is a question many people have asked in the wake of the massive amounts of data that are available to analysts every day.
Managing this enormous amount of data is not only a challenge for IT managers, but also a technical one. Computers are designed to process massive amounts of data and it takes a trained computer mind to master the task. There are solutions, however, which will allow even an IT novice to become proficient at identifying the relevant data and then pulling it together to make sense of it.
What is Veracity in Big Data? It is all about the importance of verification in big data analytics. What exactly does this mean? Verification is really about being able to trust that the results that you are getting from your analytic queries are actually derived from the real data sets. When a system can’t be verified as accurate, then we run into trouble with accuracy.
When it comes to Verification in big data, you cannot really make an educated guess or go by theory. What needs to be the case is when we want to know if a given piece of information is accurate and meaningful, then we have to make sure that the data sets that the query is coming from actually contain the kind of information that we’re after.
For instance, if you are a real estate agent and you get a lot of leads from Google and you want to make some quick analytics queries on them, then you have to know if they are in fact leads from Google or are they random visitors. Or how about a medical researcher who want to understand the relationship between a virus and a disease, and then he performs lots of experiments on patients, which could also be simulated by simulation using a probability model? There are so many things that we want to verify in these situations, and the only way that we can do it is through modeling and simulation.
So what is veracity in big data? It is about making sure that the results that you are getting are indeed derived from the actual data sets. Without proper models and simulations, you will always be left guessing and you’ll never be able to trust the accuracy and truthfulness of your results. The only way that you can make sure that these results are indeed derived from the actual data sets is through modeling and simulation
Big Data Analytics
This is the biggest issue when discussing what is big data in the context of these three different types. The first type is traditionally known as traditional data processing, which refers to the analysis, display and visualization of this data. Traditional data analysis is typically implemented on a server-side and requires expertise in programming languages such as Java, C/C++, MATLAB, Python, and R.
Examples of traditional types include streaming, batch, and other system-wide approaches. Streaming involves high volumes of input/output (I/O) data, where one piece of information is processed right away and all following pieces are processed at the same time. Examples include real-time web feeds, streaming audio, video, and video-streaming systems. Batch processing involves an even larger volume of input/output data, where different types of results are accumulated and analyzed at different stages of the operation. Examples include medical and retail surveys.
They are unstructured and structured data, where the data available represents the physical form of items. As opposed to streaming, this is unprocessed and represented in their raw or ordered state.
Examples of structured data are temperature, geographic location, and other specific types. Unstructured data can also come in the form of images, text, sounds, video, and other categories.
While unstructured data is useful for certain applications, it is usually used as part of a more complex system.
Use case of Big Data
Big Data provides tremendous business opportunities because of its ability to analyze the volume and quality of data available. Companies now have access to the financial, social, and operational characteristics of every customer, client, or product they may deal with.
By exploiting the capabilities of Big Data, companies gain a greater understanding of their customers’ behaviors and preferences, which allow them to create or modify effective marketing strategies. In addition, companies can apply this knowledge to improve the efficiency and quality of everyday operations. Companies who embrace Big Data will be able to address their customers’ needs by gathering and organizing the volume and types of information that are relevant to their customers.
Most IT managers favor a data warehouse over relational databases because of its ability to store volume, provides accurate availability, is cost effective, and improves efficiency. Volume refers to the amount of data processed during any given time frame. Relational databases store information about individual records, while a data warehouse collects, analyzes, and interprets data from multiple sources and stores it in a consistent and reliable manner across multiple machines.
In addition, it is also important to determine the characteristics of a data warehouse versus a relational database so that IT managers can better understand which tool is best for their organization. Relational databases store data about one, maybe two, tables while a data warehouse contains many tables, potentially hundreds of them. Since the amount of tables increases with the size of the database, it is easier to predict the amount of traffic the system will incur.
Although a relational system allows users to access stored data directly, it tends to be inefficient if all the data needed is known at run time. Big data analytics provide insight into new opportunities because it enables fast insights and rapid decision making.
Big data analytics enables companies to make more informed decisions about product design, staffing, advertising, and customer relationships. One example is unstructured data, which can be extremely valuable for unstructured data warehouse queries. Unstructured data often comprises web logs, email addresses, URLs, and other types of static information. This type of data is not normally thought of as a part of a data warehouse but when combined with traditional business intelligence techniques, it can provide tremendous value.