Big Data is expanding every day, but how much data is generated each day? Big Data is only a term that encompasses all data, but how big is it really? What contributes to Big Data? This article gives a brief overview on the growth of Big Data and why Big Data consulting is necessary.
What is Big Data exactly?
First, it is important to analyze the path of defining Big Data. Only then one can how the data should be to be called as ‘Big Data’. There is no official definition of Big Data. What one analyst considers Big Data may be only a traditional dataset to another analyst. This doesn’t mean that data experts don’t offer diverse definitions of Big Data.
An example definition of Big Data would be any data that is distributed across different places. This can be a good definition in some respects. Distributed systems produce lots of data compared to local systems. Every machine and server on the distributed network generate logs contributing to data.
On the other side, there can also be a distributed system without involving much data. For example, a desktop computer’s hard disk can be mounted over the local network. And, the data can be shared with other computers on the network. This technically would form a distributed data environment, but not become an example of Big Data.
Another way to define Big Data is to compare it to “little data.” In this definition, it is any type of data that is processed using advanced analytics tools. On the other hand, little data is interpreted in less sophisticated ways. The size of the actual data sets isn’t important in this definition.
This is also correct when it comes to Big Data. The big problem is that there’s no clear line separating advanced analytics tools from basic software scripts. If Big Data is defined only as data that is analyzed using Hadoop, Spark or another complex analytics platform, then there is risk of excluding data sets that are processed using R instead, for instance.
So, there’s no universal definition, but there are multiple ways to think about it. And, Big Data cannot be defined in quantifiable terms alone.
Examples of Big Data
It’s wise to gain a sense of just how much data the average organization must store and analyze today. Here are some metrics that contribute to contribute to the grand scale of Big Data:
- It is predicted by 2020, there will be 5,200 gigabytes of data on every person on the planet.
- People send about 640 million tweets per day.
- An average American uses 31.4 gigabytes of data per month on his or her smartphone.
- Walmart handles one million customer transactions per hour.
- Amazon sells 600 products per second.
- Persons using email, on an average, receive 88 emails per day and send 34. This estimates to 200 billion emails each day.
- MasterCard processes 74B transactions every year.
- Commercial airlines make about 5,800 flights per day.
All the above are examples of sources of Big Data, irrespective of the definition of Big Data. Whether these types of data are analyzed using a platform like Hadoop, and regardless of whether the systems that generate and store the data are distributed, it’s a safe bet that data sets like those described above would count as Big Data in most people’s books.
The Big Data Challenge
It’s also clear that the data sets represented above are huge. Even if an organization doesn’t work with the specific types of data described above, they provide a sense of just how much data various industries are generating today.
To work with that data effectively, a streamlined approach is necessary. And, a way to move data from its source to an analytics platform quickly is required. With so much data to process, the company cannot waste time converting it between different formats or offloading it manually from an environment like a mainframe (where lots of those airline, banking and other transactions take place) into a platform like Hadoop.
That’s where solutions from Deevita come in. Deevita’s data integration solutions automate the process of accessing and integrating data from legacy environments to next generation platforms, to prepare it for analysis using modern tools.
Whether you want to integrate data from disparate sources, or just spin out detailed data reports, Deevita can help you lead the way. Right from data architecture and design to data testing, Deevita provides end-to-end services for your business requirements.
Send an email to email@example.com or contact us at +1-425-502-5094 to learn about our Data services.
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