Introduction to Big Data & Hadoop
Hello World! In this article, I am going to talk about an overview of Big Data & Hadoop. The first thing we should know, What is Big Data? What Comes Under Big Data? Benefits of Big Data? About Big Data Technologies? What is Hadoop? About Hadoop Architecture? How big MNC’s like Google, Facebook, Instagram, etc store, manages, and manipulates Thousands of Terabytes of data with High Speed and High Efficiency?
“90% of the world’s data was generated in the last few years.”Due to the advent of new technologies, devices, and communication means like social networking sites, the amount of data produced by mankind is growing rapidly every year. The amount of data produced by us from the beginning of time until 2003 was 5 billion gigabytes. If you pile up the data in the form of disks it may fill an entire football field. The same amount was created in every two days in 2011, and every ten minutes in 2013. This rate is still growing enormously. Though all this information produced is meaningful and can be useful when processed, it is being neglected.
What is Big Data?
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, techniques, and frameworks.
What Comes Under Big Data?
Big data involves the data produced by different devices and applications. Given below are some of the fields that come under the umbrella of Big Data.
- Black Box Data − It is a component of helicopter, airplanes, and jets, etc. It captures the voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.
- Social Media Data − Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe.
- Stock Exchange Data − The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers.
- Power Grid Data − The power grid data holds information consumed by a particular node with respect to a base station.
- Transport Data − Transport data includes model, capacity, distance and availability of a vehicle.
- Search Engine Data − Search engines retrieve lots of data from different databases.
Thus Big Data includes huge volume, high velocity, and extensible variety of data. The data in it will be of three types.
- Structured data − Relational data.
- Semi Structured data − XML data.
- Unstructured data − Word, PDF, Text, Media Logs.
Benefits of Big Data?
- Using the information kept in the social network like Facebook, the marketing agencies are learning about the response for their campaigns, promotions, and other advertising mediums.
- Using the information in social media like preferences and product perception of their consumers, product companies and retail organizations are planning their production.
- Using the data regarding the previous medical history of patients, hospitals are providing better and quick service.
About Big Data Technologies?
Big data technologies are important in providing more accurate analysis, which may lead to more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business.
To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in realtime and can protect data privacy and security.
There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology −
Operational Big Data
This includes systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored.
NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement.
Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure.
Analytical Big Data
These include systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data.
MapReduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL and a system based on MapReduce that can be scaled up from single servers to thousands of high and low-end machines. Analytical Big Data
These include systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data.
MapReduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL and a system based on MapReduce that can be scaled up from single servers to thousands of high and low-end machines.
What is Hadoop?
Hadoop is an open-source distributed processing framework that manages data processing and storage for big data applications in scalable clusters of computer servers. It’s at the center of an ecosystem of big data technologies that are primarily used to support advanced analytics initiatives, including predictive analytics, data mining, and machine learning. Hadoop systems can handle various forms of structured and unstructured data, giving users more flexibility for collecting, processing, analyzing, and managing data than relational databases and data warehouses provide.
Hadoop’s ability to process and store different types of data makes it a particularly good fit for big data environments. They typically involve not only large amounts of data but also a mix of structured transaction data and semistructured and unstructured information, such as internet clickstream records, web server, and mobile application logs, social media posts, customer emails, and sensor data from the internet of things (IoT).
Hadoop runs applications using the MapReduce algorithm, where the data is processed in parallel with others. In short, Hadoop is used to develop applications that could perform a complete statistical analysis of huge amounts of data.
About Hadoop Architecture?
- Processing/Computation layer (MapReduce), and
- Storage layer (Hadoop Distributed File System).
How big MNC’s like Google, Facebook, Instagram, etc store, manages, and manipulates Thousands of Terabytes of data with High Speed and High Efficiency?
Facebook:
Facebook designs its own servers and networking. It designs and builds its own data centers. Its staff writes most of its own applications and creates virtually all of its own middleware. Everything about its operational IT unites it in one extremely large system that is used by internal and external folks alike.
This is probably because not too many IT companies, especially young ones, have had to serve upwards of 950 million registered users — including a high percentage on a real-time basis — daily. Not many have to sell advertising to about 1 million customers or have dozens of new products in the works, all at the same exact time.
Facebook, which has a clear do-it-yourself IT approach, also designs its own servers and networking. It designs and builds its own data centers. Its staff writes most of its own applications and creates virtually all of its own middleware. Everything about its operational IT unites it in one extremely large system that is used by internal and external folks alike.
For example, Facebook’s human resources group, the accounting office, Mark Zuckerberg on email, and even you at your laptop checking your status are all using exactly the same gigantic, amorphous data center system that circles the globe in its power and scope.
Even though Big Data is just a small portion of all the generated data in every company, it is widely used for integration and analytics in a great variety of industries: from media, entertainment, telecom, and government to retail, healthcare, energy, and hospitality.
In fact, 97.5% of big and expanding organizations are already investing in Big Data and AI.
Gmail & Google Drive have over 1.5 billion users. Google Play has over 1 billion users, it has had over 100 billion app downloads and approx. 3.5 million apps published. Google Maps has over 1 billion users. Google Analytics the website analytics service is the most widely used analytics service on the web. Google Assistant is installed on over 400 million devices. Google Chrome is the most used web browser in the world. Besides these, there are several other add on services offered by Google such as google docs, sheets, slides, calendars, etc. For a complete list of products offered by Google, here you go.
Conclusion:-
So, we learned how the MNC’S like Google, Facebook, etc solve the challenges of the Big Data this concept.
this much I learned just in 2 days of the ARTH Journey
“Thanks! to Mr. Vimal Daga sir for giving the great information of Big data”
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