With the technology that has already reached the pinnacle of its highest use implementation, you would be quite aware of its major functionalities, processes, uses, and overall importance. In August of 2015, it slipped off Gartner’s 2015 Hype Cycle for Emerging Technologies and created a huge buzz in the tech-driven world.
If you haven’t been all that tech-savvy and missed on crucial information on what is Big Data, this write-up will furnish you with details on all that you need to know at the outset to understand the technology better.
When John Graunt was researching the bubonic plague ravaging Europe in 1663, he had to cope with enormous volumes of information. This was the first instance of big data. The first individual to ever employ statistical data analysis was Graunt. The study of statistics later broadened to encompass gathering and analysing data in the early 1800s. In 1880, the world first became aware of the issue with abundant data.
According to the US Census Bureau's estimate, handling and processing the data gathered during that year's census operation would take eight years. Herman Hollerith, a Bureau employee, created the Hollerith Tabulating Machine in 1881, lessening the calculation required. Data developed at an unforeseen rate during the 20th century. Big data is now at the centre of evolution. At that time, magnetic information storage devices, message pattern scanning devices, and computers were also developed. To store millions of fingerprint sets and tax returns, the US government constructed the first data centre in 1965.
As Gartner defines it – “Big Data are high volume, high velocity, or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.”Let's dig deeper and understand this in simpler terms.
The term ‘big data’ is self-explanatory − a collection of huge data sets that normal computing techniques cannot process. The term not only refers to the data, but also to the various frameworks, tools, and techniques involved. Technological advancement and the advent of new channels of communication (like social networking) and new, stronger devices have presented a challenge to industry players in the sense that they have to find other ways to handle the data.
From the beginning of time until 2003, the entire world only had five billion gigabytes of data. The same amount of data was generated over only two days in 2011. By 2013, this volume was generated every ten minutes. It is, therefore, not surprising that a generation of 90% of all the data in the world has been in the past few years.
All this data is useful when processed, but it had been in gross neglect before the concept of big data came along.
Pro-Tip: To learn more about Big Data and get your foot in the Data Science industry door, consider professional certification training in Big Data or allied technologies, such as Impala, Cassandra, Spark, and Scala.
Now, as you have learned what is Big Data, let's get to know the source of Big Data.
We'll start with the one that is the most evident. Big data is all about quantity. data volumes that, in reality, may reach hitherto unimaginable heights. There will be 40 zettabytes of data generated by 2020, representing a 300-fold increase from 2005, according to estimates that 2.5 quintillion bytes of data are created every day. As a result, Terabytes and even Petabytes of data in storage and servers are now commonplace for big businesses. While tracking success, this data aids in shaping a company's future and activities.
The expansion of data and the significance it has taken on have changed the way we think about data. We used to underestimate the value of data in the business world, but because of changes in how we obtain it, we now often rely on it. Velocity simply gauges how quickly data is entering the system. While some data will be provided to us in batches, others will arrive in fits and starts. Additionally, since not all systems will process incoming data at the same rate, it's critical to avoid making assumptions before obtaining all the information.
Data used to be given in a single format from a single source. Previously given in database files like excel, csv, and access files, it is now being delivered through tech like wearable devices and social media in non-traditional formats, including video, text, pdf, and graphics. Although this data is helpful to us, it demands more labour and analytical abilities to interpret it, manage it, and make it function.
With the development and increase of apps and social media and people and businesses moving online, there’s been a huge increase in data. If we look at only social media platforms, they interest and attract over a million users daily, scaling up data more than ever before. The next question is how exactly is this huge amount of data handled and how is it processed and stored. This is where Big Data comes into play.
And Big Data analytics has revolutionized the field of IT, enhancing and adding added advantage to organizations. It involves the use of analytics, new age tech like machine learning, mining, statistics and more. Big data can help organizations and teams to perform multiple operations on a single platform, store Tbs of data, pre-process it, analyze all the data, irrespective of the size and type, and visualize it too.
Analytics of big data involves spotting trends, patterns, and correlations within vast amounts of unprocessed data in order to guide data-driven decisions. These procedures employ well-known statistical analysis methods, such as clustering and regression, to larger datasets with the aid of more recent instruments.
Every company has a distinct approach to data collection. Thanks to modern technology, businesses are now able to collect unstructured and structured data from a variety of sources, including cloud storage, mobile apps, in-store IoT sensors, and more.
For analytical queries to yield correct answers, data must be appropriately organised once gathered and stored, especially if the data is big and unstructured.
All data, regardless of size, must be scrubbed to increase data quality and produce more robust findings. Duplicate or unnecessary data must be removed or accounted for, and all data must be structured appropriately. Dirty data may conceal and deceive, leading to inaccurate findings.
It takes time to transform huge amounts of data into a usable form. Advanced analytics techniques may transform huge data into significant insights once available. Among these large data analysis techniques are:
