Learn The Basics of Big Data Before Becoming Certified

Thanks to the advancing technology over the years, it’s now easier to collect data and store it. In this way, the generated and collected data doubles itself every year. Using the old, conventional methods to mine the information is insufficient as data grows more and more. 

In general terms, Big Data technology helps to analyze, process, and separate the meaningful information from a complicated pile of data sets. Analyzing the useful data helps businesses by offering better customer services, improving their operational efficiency, and making marketing more effective. Data scientists, predictive modelers, and other professionals in the field can make more accurate and data-based decisions for their business using Big Data analytics, especially compared to the traditional technique.

Are you wondering why Big Data certifications matter now? As the world grows bigger and bigger, so do businesses and the data they have collected, however, if you don’t know how to make it useful and interpretable, how big the database you have is not essential. Companies need skilled data analysts, who can understand what the data says and how to use it. That, the skill you need, is what CCC Big Data Foundation helps you acquire.

What to Know Before Big Data Certification?

The CCC has no required prerequisites for the exam. However, learning the basics of Big Data in advance can help you get one step forward in your path to be certified. You can check the syllabus before taking the exam.

Big Data 101: History, Benefits, and Characteristics

The term “Big Data” covers highly large data sets that can be analyzed to explain trends, patterns, and relations. Although it has gained popularity after the 21st century, the usage of Big Data dates back to the late 1880s when Herman Hollerith developed an electromechanical tabulating machine to organize census data. In 1937, when the Social Security Act became law, the government wanted to observe its citizens and employers using punched card reading machines. From this day to the 2000s, data processing methods changed and advanced until it gets the current shape. Roger Mougalas used the term for the first time in 2005.

Big Data Management systems allow businesses to add a range of data from hundreds of different sources in real-time. This means that companies can improve customer satisfaction because they can have more successful experiences with clients and better campaign strategies that eventually lead the business to create a longer and more productive customer relationship. Also, Big Data Analytics offers organizations full customer profiles that allow for more personalized customer experience at each point in the company’s journey. Thus, Big Data has countless benefits to businesses. This may include ideas from vast quantities of information from different sources, including data from external sources, the internet, social networks, those already held in databases of businesses. Moreover, it gives the ability to reduce risk by quicker optimization of complex decisions on unplanned events.

Big Data is characterized to organize the understanding of it. These characteristics bring some important questions that not only enable us to decode it, but also provide an insight into how to handle huge data within a reasonable time frame at a manageable pace to get value out of it, do some real-time analysis, and have a fast answer to it.

Volume: The quantity of generated and stored data. The size of the data determines the value and potential insight and whether it can be considered Big Data or not.

Variety: The type and nature of the data. This helps people who analyze it to use the resulting insight effectively. Big Data draws from the text, images, audio, video; plus, it completes missing pieces through data fusion.

Velocity: The speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big Data is often available in real-time. Compared to small data, Big Data are produced more continually. Two kinds of velocity related to Big Data are the frequency of generation and the frequency of handling, recording, and publishing.

Big Data Sources

What are the sources of this data? Where does it come from? Basically, there are three primary sources of Big Data: Enterprise data sources, social media data sources, and public data sources.

Data that is shared by the users of an organization, generally across departments and/or geographic regions, is enterprise data. This database contains three layers: raw staging, atomic and dimensional. The raw data accumulates in the first layer as in its original form, disconnected and unstructured. The connection between sets of data is made in the atomic layer. Here, the information is reorganized so that there is no duplication, and all the data is linked together. At the last layer, which is the dimensional layer, the regrouped information is put into schemes and charts to hold the contextual data associated. This is how the sources of enterprise data operate.

Businesses want personalized ads on the internet; but, ads can be personalized only at a certain level without using detailed information. However, people generally give more than enough information on social media and help companies without even noticing it. Every action we take on social media makes up social data: likes, posts, comments, uploads, friends we have.  And this data is not only making ads personalized but also helps social media networks to make link predictions, community detection, and influence propagation.

The information that can be freely used and distributed by anyone with no legal restrictions on access or usage is public data. This data set helps organizations and governments to get a better understanding of the local community, the country, and the world as a whole.

Data Mining

Data mining is the practice of finding correlations in large data sets at the intersection of machine learning, statistics, and database systems. Cleaning and integration mean that irrelevant or unnecessary data is separated from meaningful information using multiple data sources. Afterward, data relevant to the task of the analysis is extracted from the database by selection. It is then transformed or consolidated by summary or aggregation operations into forms suitable for mining purposes. Later, smart methods are applied to identify patterns of the data. Finally, trends of the data are evaluated so that the knowledge is represented. This is the complete process of data mining.

If after reading the basics of Big Data you want to become CCC Big Data-certified, feel free to contact us at any time. If you need more persuasion, we have more blog posts that may interest you.