The history of big data

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The history of big data

Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and ‘70s when the world of data was just getting started with the first data centers and the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open-source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.

The development of open-source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data—but it’s not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.

The term «big data» refers to data that is so large, fast, or complex that it is difficult or mandalasystem.com impossible to process using conventional methods. Accessing and storing large amounts of data for analysis has been around for a long time. But the concept of big data was widespread in the early ’80s. 2000 When industry analyst Doug Laney gave the now understood definition of Big data consists of three Vs:

Volume : Organizations collect data from a variety of sources. This includes business transactions. Smart devices (IoT), industrial devices, video, social media, and more. In the past, data storage was a big problem – but when it comes to storage costs on platforms like central storage (data lakes) and Hadoop decreased, so this burden was alleviated.

Velocity (Speed) : With the growth of the Internet of Things, data is being delivered to businesses. With unprecedented speed and need to be managed in a timely manner, RFID tags, sensors and smart meters drive the need to manage these data streams in real time.

Variety : Information is available in all formats. Since the structured data numbers in traditional databases to text documents, emails, video, audio, stock information and financial transactions.

Why is big data important?

The importance of big data isn’t just the amount of data you have. But it’s how you deal with it. You can get information from multiple sources and analyze them. To find solutions that will help 1) reduce costs, 2) reduce time, 3) develop new products and find the best deals, and 4) make smart decisions. When you combine big data with powerful analytics . You will be able to accomplish business-related tasks. For example:

Identify the cause of the error Issues and mistakes in near real time

Assign promotional coupons at points of sale based on consumer purchasing behavior.

Recalculate the risk of the entire portfolio in minutes.

Detect fraudulent behavior before it affects your organization.

Who focuses on big data?

Big data is vital for many industries with the advent of IoT and other connected devices. This has greatly increased the collection, management and analysis of corporate data. Big data comes with the potential to unlock big data insights – for every industry. from large to small