Introduction to Big Data

So you have heard the phrase “Big Data” and want to know what Big Data really is. Well, you have come to the right place. Perhaps you are a business owner and you want to know what the benefits of Big Data are or you are interested in pursuing a career as a data scientist and want to know more about the popular data trend. Whatever your purpose, inside this article, we will discuss the basics of Big Data, how it differs from traditional data, and some of the challenges of Big Data, as well as its benefits.

Data vs Big Data

The goal here is not necessarily to define what data is in general, but what exactly is “Big” Data and why is it so different and important? Of course, as you may know, data is simply defined as items of information, facts, or statistics that are gathered on a set of values. It can consist of qualitative or quantitative variables. However, It was agreed upon (by and large) in the technology community that it includes any amount of small or reasonably big sets of data that any usual professional with common tools and computers can analyze.

Now, when it comes to the newly emerging amount of increasing data – such as data gleaned from the Internet, networks, written content on websites, search engine indexing, the global social media network, and so forth, we are in front of blobulous monster that can overwhelm not only personal capabilities but even any business IT server’s database infrastructure. The amount of data is so huge that it can not truly be measured except in terms of terabytes or petabytes.

Big Data, in fact, is a huge collection of very massive and extremely complex data sets and volumes produced from many sources. The phenomenon started with Google spiders and did not end in Twitter and Facebook personal users’ data. With the emergence of smartphones and the Internet of things (IoT), it is exploding more and more. It has become the pinnacle of what lovers of great records and giant archives dream of. Big Data is also like the superstitious blob monster referenced above in that, the more data you covet, the bigger and greedier it gets!

Big Data Challenges and Promises

Now that we have discussed what Big Data is, let’s discuss some of the challenges Big Data and data scientists face:

  • Capturing
  • Storing
  • Accuracy
  • Curating
  • Analysis
  • Searching
  • Visualization
  • Transferring
  • Privacy securing

It is obvious that dealing with enormous sets of data can be impossible via traditional software such as Excel and even Access, not to mention SQL queries or other relational database management systems (RDBMS). Therefore, various scalable and cloud database tools and approaches have been established. They are continuously evolving to keep track of the avalanche that is big data.

Read: Best Database Software for Developers.

For instance, Hadoop as a big data processing software and batches of NoSQL databases were enough at first for big data releases, but now you need to at least run frameworks of an additional combination of technologies, such as Apache Spark.

You can ask, then, if Big Data is challenging to this degree, why should we take care of it? What are the benefits? Yes, that is also a great question. Let’s now see some of the big data benefits and promises:

  • Used by companies systems to enhance operations and decision-making.
  • Enhance provision of customer service based on more data feedback.
  • Empower creating more targeting marketing campaigns to increase income.
  • Gaining possible competitive points over other competitors in the market.
  • Improving differentiation in marketing and being more conscious of customer needs.
  • Increase health care organizations’ ability to identify the disease and medicines better.

Big Data Characteristics

Most references talk a lot about 3-6 “Vs characteristics of big data”. They were first introduced as “three Vs” by Gartner’s Doug Laney in 2001. But it seems also these Vs are stretching, from just Volume, Velocity, and Variety to also include Veracity, Value, and Variability. They could also technically reach up to ten Vs, to further include Validity, Vulnerability, Volatility, and Visualization. In extreme case, you may even hear of 17 Vs, but that is beyond the scope of this article.

For now, let’s focus on six of the Big Data Vs:

Volume: Big volume differentiates big data from usual data, it’s a critical character.
Velocity: The speed of data through the system, especially processed in real-time.
Variety: Diversity and uniqueness are some of big data nurture due to diverse sources.
Veracity: The accuracy of big data is a real challenge since its Magnitude and complexity.
Variability: The Variety of data makes diversity of its quality which needs filtration.
Value: This is the main goal and purpose of how to provide value from the data.

How Does Big Data Work?

Now you may be wondering, how exactly does Big Data work? There are a lot of approaches to take advantage of big data (such as the above mentioned), but today we will focus on just three main options:

  • Integration: The usual data methods of extraction, transforming, and loading (ETL) can’t be done for Big Data. Instead, you need more powerful technologies to integrate data to be useful.
  • Management: Between cloud and on-premises solutions, you can store and manage the data as suitable for your project needs.
  • Analysis: This is known as “the picking fruits” phase of the whole process. Get smarter actions and decisions based on the outcome of petabytes of Big Data and explore future possible data sets.
Zaher Talab
As a technology writer at TechnologyAdvice, Zaher B. Talab tries to help readers learn more about cloud computing and digital emerging technologies, and inform them in detail about how to put these technologies in use, as a technology, and as a business.

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