The world’s technological per-capita capacity to store information has almost doubled, making the future of Big Data and Hadoop bright. According to Forbes, Multinational companies are hiring for Hadoop technology as the market is expected to reach $99.31B by 2022.

When it comes to handling large data sets in a safe and cost-effective manner, Hadoop has the advantage over relational database management systems, and its value for any size business will continue to increase as unstructured data continues to grow. While large Web 2.0 companies such as Google and Facebook use Hadoop to store and manage their huge data sets, Hadoop has also proven valuable for many other more traditional enterprises.

Here we will see how Hadoop as a part of Big Data has evolved and become a quintessential part of our day to day activities.

What is Big Data?

Big data environments typically involve not only large amounts of data but also various kinds, from structured transaction data to semi-structured and unstructured forms of information. These include internet clickstream records, web server and mobile application logs, social media posts, customer emails and sensor data from the internet of things (IoT). Volume, Variety, Velocity, and Variability are some of the characteristics of Big Data.

What is Hadoop?

Hadoop’s name derived from a cute toy elephant but Hadoop, in reality, is very unlike a soft toy. Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from a single server to thousands of machines, each offering local computation and storage.

Evolution of Hadoop

2002 – Hadoop started when Doug Cutting and Mike Cafarella began working on Apache Nutch project. With the Apache Nutch project, you could build a search engine system that could index 1 billion pages. After a lot of research on Nutch, they concluded that such a system would cost around half a million dollars in hardware, along with a monthly running cost of $30, 000 approximately. They realized that the Nutch was turning out to be very expensive. After realizing that their project architecture will not be capable enough to work around with billions of pages on the web, they started looking for a feasible solution. They wanted something that could reduce the implementation cost as well as solve the problem of storing and processing large datasets.

2003 – They came across a paper that described the architecture of Google’s distributed file system, called GFS (Google File System) which was published by Google, for storing the large data sets. This paper solved half their problem of storing very large files which were being generated because of web crawling and indexing processes.

2004 – Google published one more paper on the MapReduce technique, which was the solution to processing those large datasets. This paper was another half solution for Doug Cutting and Mike Cafarella’s Nutch project. Both these techniques – GFS and MapReduce were just on white paper at Google who didn’t implement these techniques. Doug Cutting knew from his work on Apache Lucene that open-source is a great way to spread the technology. It is a free and open-source information retrieval software library that was originally written in Java by Doug Cutting in 1999. Hence, together with Mike Cafarella, he started implementing Google’s techniques (GFS & MapReduce) as open-source in the Apache Nutch project.

2005 – Cutting found that Nutch is limited to only 20-to-40 node clusters. He soon realized two problems:

(a) Nutch wouldn’t achieve its potential until it ran reliably on the larger clusters

(b) And that was seemed impossible with just two people (Doug Cutting & Mike Cafarella).

The engineering task in Nutch project was much bigger than he realized when he began searching for a company that was interested in investing in their efforts. And he found Yahoo! That had a large team of engineers and was eager to work on their project.

2006 – Doug Cutting joined Yahoo along with Nutch project. He wanted to provide the world with an open-source, reliable, scalable computing framework. At Yahoo, he separated the distributed computing parts from Nutch and formed a new project Hadoop – the name of a yellow toy elephant which was owned by his son. and it was easy to pronounce. He also found the name very unique. He then planned to make Hadoop in such a way that it can work well on thousands of nodes. Thus, with the help of GFS and MapReduce, he started to work on Hadoop.

2007 – Yahoo successfully tested Hadoop on a 1000 node cluster and started using it.

2008 – In the month of January, Yahoo released Hadoop as an open source project to ASF (Apache Software Foundation). In July, Apache Software Foundation successfully tested a 4000 node cluster with Hadoop.

2009 – Hadoop was successfully tested to sort a PB (PetaByte) of data in less than 17 hours for handling billions of searches and indexing millions of web pages. The same year, Doug Cutting left Yahoo to join Cloudera to fulfill the challenge of spreading Hadoop to other industries.

2011 – Apache Software Foundation released Apache Hadoop version 1.0 in November.

2013 – Version 2.0.6 was available in August

2017 – Apache Hadoop version 3.0 released in December.

Companies using Hadoop

With Hadoop, data distribution has become simplified that several companies are adopting this technology. Some popular ones among them are Yahoo, IBM, Samsung, HP, New York Times, Facebook, Intel, eBay, Netflix, Twitter, Groupon, and JP Morgan Chase.

Day to day areas where Hadoop is used

Once you begin storing data in Hadoop, the possibilities are endless. Companies across the globe are using this information to solve big problems, answer pressing questions, improve revenue, and more. Here are some real-life examples of ways other companies are using Hadoop to their advantage.

1. Analyze life-threatening risks

Suppose you are a doctor in a busy hospital, it could be challenging for you to quickly identify patients with the biggest risks. How can you ensure that you’re treating those with life-threatening issues, before spending your time on minor problems? Here’s a great example of one hospital using big data to determine risk–and make sure they’re treating the right patients and at the right time.

In a New York-based hospital, patients with suspicion of heart attack were asked to go through a series of tests, and the results were analyzed with use of big data – by comparing to the history of previous patients. Whether a patient was to be admitted or sent home depended on the algorithm. This method turned out to be much more efficient than human doctors.”

2. Identify warning signs of security breaches

Did you know: There is a hacker attack every 39 seconds, 43% of cyber attackers target small business and the average cost of a data breach in 2020 will exceed $150 million? Also, since 2013 there are 3,809,448 records stolen from breaches every day.

Now, imagine, what if you could stop security breaches before they happened? What if you could identify suspicious employee activity before they took action? Data has the solution to all your problems.

As explained below, security breaches usually come with early warning signs. Storing and analyzing data in Hadoop is a great way to identify these problems before they happen.

Data breaches like never just happen; there are typically early warning signs like unusual server pings, even suspicious emails, IMs or other forms of communication that could suggest internal collusion. Fortunately, with the ability to now mine and correlate people, business, and machine-generated data all in one seamless analytics environment, we can get a more complete picture of who is doing what and when. This includes the early detection of collusion, bribery, or an Ed Snowden in progress even before he has left the building.

3. Prevent hardware failure

Machines generate a wealth of information–much of which goes unused. Once you start collecting that data with Hadoop, you’ll learn just how useful this data can be.

Capturing data from HVAC systems helps a business identify potential problems with products and locations.

One power company combined sensor data from the smart grid with a map of the network to predict which generators in the grid were likely to fail, and how that failure would affect the network as a whole. Using this information, they could react to problems before they happened.

4. Understand what people think about your company

Do you ever wonder what customers and prospects say about your company? Is it good or bad? Just imagine how useful that data could be if you captured it. You could improvise on products or develop some based on the demands.

With Hadoop, you can mine social media conversations and figure out what people think of you and your competition. You can then analyze this data and make real-time decisions to improve user perception.

One company used Hadoop to track user sentiment online. It gave their marketing teams the ability to assess external perception of the company (positive, neutral, or negative), and make adjustments based on that data.

5. Understand when to sell certain products

Data can help companies uncover, quantitatively, both pain points and areas of opportunity. For example, tracking auto sales across dealerships may highlight that red cars are selling and blue cars or not. Knowing this, the company could adjust inventory to avoid the cost of blue cars sitting on the lot and increase revenue from having more red cars. It’s a data-driven way to understand what’s working and what’s not in business and helps eliminate ‘gut reaction’ decision making.

Of course, this can go far beyond determining which product is selling best. Using Hadoop, you can analyze sales data against any number of factors.

For instance, if you analyzed sales data against weather data, you could determine which products sell best on hot days, cold days, or rainy days.

Or, what if you analyzed sales data by time and day. Do certain products sell better on specific weeks/days/hours?

If you know when products are likely to sell, you can better promote those products.

6. Find your ideal prospects

You may know what makes a good customer. But, do you know exactly who they are and where are they located? What if you could use freely available data to identify and target your best prospects?

One company compared its customer data with freely available census data. They identified the location of their best prospects and ran targeted ads at them. The results: Increased conversions and sales.

For example: If you have an e-commerce site that caters to customers across the globe – you would announce a Thanksgiving sale for your American clients and a Diwali sale for your Indian clients.

7. Gain insight from your log files

Just like your hardware, your software generates lots of useful data. One of the most common examples: Server log files. Server logs are computer-generated log files that capture network and server operations data.

How will this data help you?

Security: What happens if you suspect a security breach? The server log data can help you identify and repair the vulnerability.

Usage statistics: Server log data provides valuable insight into usage statistics. You can instantly see which applications most popular, and which users are most active.

How can you get the Big Data and Hadoop Certification?

Graspskills conducts the Big Data and Hadoop Development certification training course in Bangalore, India which is delivered by a Big Data Professional. The course comprises of 3-days classroom or 32-hours online training along with a complimentary e-learning course that will help you earn 32 PDUs. You will also get 3-simulation tests, extensive practical exercises along with a live project and tips to will help you ace the Big Data and Hadoop Development examination with confidence.

Conclusion

With the advancement in technology, the scope of use for Big Data and Hadoop is increasing. Companies across sectors are using the data accumulated to perform better and attract their target audience by providing them with the kind of products they require. This has lead to an increase in demand for professionals. Thus you should consider the Big Data and Hadoop Certification and Training Course.

The post Evolution of Big Data and Hadoop and its application in the real world appeared first on Graspskills.