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What are the differences between “Big Data” and Data Analytics?

The creation of data has kept on increasing with every passing day. The Business Intelligence terms such as Big Data, Artificial Intelligence, Data Analytics, Data visualization, and other terms are trending. Nowadays, most people will get confused between Data Analytics and Big Data. So, if you are one of them, let us understand what big data and data analytics and the major difference between these terms is. 

What is Big Data?

Big Data, as the name says, involves a huge amount of data sets. It is a technique to analyze data, including structured and unstructured, and arrive at useful insights. Big Data can be defined with 3 ‘V’ s. Velocity, Veracity, and Variety. Since it involves massive data from various sources, it requires high-end computing power to gather and process the data. The source of data can be the internet, mobile, social media, and all other sources. All these types of data are being analyzed and processed to get useful insights. 

What is Data Analysis?

Data analytics involves the analysis of structured data collected through various sources. It is also an important factor for businesses since it easily finds solutions for various business problems using historical data. The nature of data here is more organized and analyzed to get a solution for a specific problem. 

The difference between Big Data and Data Analytics

Nature

The understanding of the nature of these two terms is essential to get rid of the common confusion. Data analysis is analyzing the data from the cleaned set of data. Wherein Big Data has more scope, and as the name denotes, it consists of a huge amount of data coming from various sources and different file formats. To make you understand, let us assume an excel file as data analytics, and you can find the answers to your question with ease in a file. You can go through the entire file, or by searching, you can get the answer. But Big Data is like millions of files available in the cloud storage, where to find an answer for any question, one has to classify the files, sort it and apply the filters to arrive with the answers. Naturally, Big Data involves more work, and it needs more expertise. For the same reason, big data analytics training and placement in USA are becoming a trend among job seekers. 

Tools used

Since the data is already structured and cleaned in data analytics, it won’t demand the heavy tools to carry out the operations. One can use simple tools for predictive modeling and statistical modeling. The simple tools are enough to deal with structured and less complex data. But in Big Data, one has to utilize sophisticated tools and technologies such as automation tools and high-efficiency computing tools to handle the Big Data. The sheer data size and unstructured data demand such tools. You can learn more about the tools used in Big Data and its application by enrolling in a big data certification online course. 

Types of the industry using Data Analytics and Big data

Data analytics is used mostly by travel industries, It industries, and healthcare service industries. Data analysis helps them build new developments and strategies using the past data and analysis of the trends and patterns. On the other hand, Big Data is used widely by almost all industries. However, the application is limited in the current timeframe, but every sector is expected to be applied in the coming years. Big data techniques are mostly used by retail industries, the banking sector, IT companies, manufacturing industries, etc. This is why we are seeing big data analytics training and placement courses are getting more famous. 

Data Structure

In the field of data analytics, finding the answer to any of your questions easy. And you can also easily locate the data. But it is not the case in Big Data. Big Data handles a huge set of organized and unorganized data. Generally, it consists of unstructured data, and to find an answer to any question; one has to sort out these unstructured data. Hence, finding the answer is not an easy task in Big Data since it has to be classified, analyzed using numerous filters to arrive at a common answer. 

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