Everything You Need To Know About Big Data in 2020

What is Big Data?

Big Data is described as a massive chunk of unstructured and structured data, that accompany a hardship for processing data using traditional methods.

Thus, Big data appears as “ a whole lot of data”.

Big Data concept is new and hasn't evolved too much for offering a comprehensive understanding to general people. However, the hype is making people acquainted with this technology.

Basically, Big Data is a collection of facts that represent both increasing and varied types of data (data being collected). Big Data experts and proponents assertively refer to it as “datification”.

Succinctly, the information of the world travels online, and everything gets upgraded into digitized information. In more simple words, digitization of information such as social media, music, videos, online books and more.

Every day, the internet is being flooded with a huge amount of information, which is consolidating the Big Data concept. The sensors available around the world also increase the accumulation of data over the web. All of this is imposing an astounding surge in the amount of data.

SImply, everything you do online now gets stored as data. Though you have to understand that Big Data isn’t about only data logs such as text, video, search, sensors. It is more about customer transactions and more about 4 industry V’s. It includes

Volume – Suring amount of data ( generated every second)

Velocity – Speed at which data is being generated

Variety – Dissimilar types of data being generated

Veracity – Messiness of data, ie. it’s unstructured nature

The amount, speed, variety and unstructuredness lead to unmanageable data using traditional methods. The traditional data analysis methods are not promising enough to give accurate insights. Thus, new technologies, processes, and tools to accommodate and analyze this vast amount of data.

Now let's talk about its Solution:

Yes, of course we have a solution to this big problem,.. i.e. Apache Hadoop.

What is Hadoop and what problems does it solve?

The story starts with the early days of Google. Engineers needed to design new ways to store and process and retrieve data that would scale to very large sizes. The published two papers on their design in 2003, and the highly regarded community-focused Doug Cutting produced an open source version of the software called Hadoop.

Along with that open source project came many other related open source capabilities, and soon an entire big data framework was created. New methods of storing, processing and retrieving data were now available, free, from the Apache Software Foundation. And innovation continued as a firm called Cloudera stood up to continue to accelerate innovation into the open source project.

Hadoop is a single data platform infrastructure that is more simplified, efficient, and runs on affordable commodity hardware.

How does Hadoop solved our problem?

Hadoop is designed to handle the three V’s of Big Data: volume, variety, velocity. First lets look at volume, Hadoop is a distributed architecture that scales cost effectively. In other words, Hadoop was designed to scale out, and it is much more cost effective to grow the system. As you need more storage or computing capacity, all you need to do is add more nodes to the cluster. Second is variety, Hadoop allows you to store data in any format, be that structured or unstructured data. This means that you will not need to alter your data to fit any single schema before putting it into Hadoop. Next is velocity, with Hadoop you can load raw data into the system and then later define how you want to view it. Because of the flexibility of the system, you are able to avoid many network and processing bottlenecks associated with loading raw data. Since data is always changing, the flexibility of the system makes it much easier to integrate any changes.

Hadoop will allow you to process massive amounts of data very quickly. Hadoop is known as a distributing processing engine which leverages data locality. That means it was designed to execute transformations and processes where the data actually exists. Another benefit of value is from an analytics perspective, Hadoop allows you load raw data and then define the structure of the data at the time of query. This means that Hadoop is quick, flexible, and able to handle any type of analysis you want to conduct.

Organizations begin to utilize Hadoop when they need faster processing on large data sets, and often find they save the organization some money too. Large users of Hadoop include: Google,Facebook, Amazon, Adobe, EBay, and LinkedIn. It is also in use throughout the financial sector and the US government. These organizations are a testament to what can be done at internet speed by utilizing big data to its fullest extent.