Learn Big Data Hadoop for beginners

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The market is flowing with Big data in every industry. Data is pouring in from multiple sources such as online transactions, social media feeds, and other sources. The exponential technology has changed the landscape. Candidates from IT background are following difference course for career growth. When Big data comes in the scenario, Hadoop is undeniable to not discuss. Hadoop is the topmost processing tool for handling Big data. Hadoop is continuously evolving with the development of technologies. It is an open-source framework which is popular to manage data. It is crucial for Big data Hadoop for beginners to get the knowledge of the basics. An increasing number of candidates are selecting Hadoop to build a lucrative career.

Before we begin explaining Big data Hadoop by starting explaining Big data 

Big data refers to humongous datasets from the structured and unstructured format. The data available is too complex to process with the use of complex processing tools. The difficulty faced by Big data is classified into the following factors.

  • Volume: Social media is tp most sources for contributing to the generation of data. It produces terabyte to a petabyte of data. There is a massive amount of data including machines, human and special social media platforms.
  • Velocity: There is a continuous flow of data from various sources, and at present, the amount of data is generated at the exploding rate. The number of social media users is increasing at a high rate. Every organization has a different time frame to process data. Thus, a capable framework is demanded computation of data at high speeds.
  • Variety: Data comes from varied sources and in many forms such as images, texts, audio, and videos. Big data should be competent to process data analytics using the data

Reasons for the origination of Hadoop:

  • Storage for large data sets: The traditional RDBMS is incapable to fulfill the data storage requirements. Apart from that price for RDBMS was comparatively very high than Hadoop.
  • Managing data from various sources: The conventional framework was limited to store and process data in a structured format. In the practical scenario, we deal with data in various formats such as structured, semi-structured or unstructured.
  • Processing data at high speeds: As the data is emanating, the demand to process this huge amount of data become very high. The conventional tools failed to process data at high speed

What is Hadoop

To solve the problems of managing and processing big data, Hadoop comes as the best solution. Hadoop is not a platform to store data but it also is used to process and manage data. Hadoop is an open-source Java-based framework for massive amounts of data and process data in distributed computer ecosystems.

Significance of Hadoop 

  1. Management of big data: In the present time, the market is exploding with massive amounts of data. The data is generated at higher speeds and volume. The needs to manage this amount of data has increased. Hadoop is an accurate framework to fulfill the demand to manage big data. Its robust feature makes it perfect to manage big data.
  2. Burgeoning big data: with the advent of time, companies are realizing the significance of data analysis and its benefits. This domain is going to grow in the future and will become widespread. with growing Big data; nee for Hadoop is also likely to rise. The companies are looking for Hadoop professionals to meet demands.
  3. Lack of Hadoop professionals: with the growing market demand, the need for Hadoop professionals is also stemming. It is the perfect time to take Hadoop training and certification to join the flourishing market.
  4. Easy to use: Hadoop is written in java which is the most widely used programming language among the developers. It is very easy to use and candidates with knowledge of java can learn hassle-free.
  5. Higher salaries: In the scenario, where the demand is higher than the availability of competent candidates, companies are prying handsome salaries to the candidates. The growing scarcity for Hadoop professionals leads to companies to offer higher salaries.

Challenges for Hadoop 

It is a strenuous task to find competent candidates with knowledge of Java at entry level who has experienced in working with MapReduce. The availability of candidates with SQL knowledge is more common than MapReduce.Even new technologies are evolving for the security of data, the challenge for security still pertains for fragmented data security issues.

Map reduces cannot be used for all of the problems: 

  • The issue with small files
  • Vulnerable by nature
  • Processing overhead
  • Supports only batch processing
  • Repetitive processing
  • Security

Prerequisite to learn Hadoop 

  1. Java: Candidates must possess the knowledge of Java as Hadoop is written in Java language.
  2. SQL: Linux will give a head start to learn and start working in Hadoop ecosystem tools such as Pig, Hive and many more.
  3. Linux: Hadoop primarily runs on Linux to capitulate better outcomes over windows. It is easier for candidates with an understanding of Linux, to learn Hadoop.
  4. Big Data: Though learning bIg data is quintessential for learning Hadoop, it is better to have an understanding of what you have to work with.

Career for Hadoop learned professionals

  • Hadoop administrator
  • Hadoop/Bigdata Developer
  • Data engineer
  • Big data architect
  • Data scientist
  • Data Analyst
  • Big data consultant
  • Future of Hadoop

Applications of Hadoop in various sectors

  • Banking Sector
  • Communication, media, and entertainment
  • Healthcare sector
  • Education
  • Insurance sector

In this age of continuous emerging technologies, it is of utmost important to stay up to date with the market trends. The best way to learn Big data Hadoop is to get hands-on training by the expert professionals hands-on projects provide real-time practice on actual projects.and give an authentic industrial scenario. The instructor-led training is useful to build a grip in technology. There is a huge increase in Big data startups, as the market is bending towards data analysis.

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