Owing to the advent of new technologies, the amount of data produced is rising rapidly every year. Storing and processing of the data becomes a challenging task. This growing data is Big data – a collection of large datasets that cannot be processed using traditional computing techniques, within the given time frame. There are many big data tutorials for beginners available, including self-study guides online where professionals and students can learn more about this new concept.
As the traditional techniques are not sufficient for processing and storing of huge volumes of data, there came a need for a new tool. Hadoop is an open source software framework that manages data processing and storage for big unstructured data applications running on clusters of commodity servers. Hadoop has successfully carved out its niche for itself as it is efficient, inexpensive, immutable, stores data reliably has the ability to tolerate faults, scalable, block structured, and is capable of processing a large amount of data simultaneously.
The Hadoop framework is based on Java programming with some native code in C and shell script. There are four important Hadoop modules that play a vital role in the Hadoop architecture are ––
- Hadoop Distributed File System (HDFS): HDFS Architecture follows a Master/Slave Architecture, where a cluster consists of single Name node which stores the metadata of all the files and all the other nodes are Data Nodes which stores application data.
- Hadoop YARN (Yet Another Resource Negotiator): It is a framework for job scheduling and cluster resource management. It consists of two components – Resource Manager that tracks the available nodes and resources and Node Manager that is responsible for the execution of assigned tasks.
- Hadoop MapReduce: It provides the method for parallel processing of large data sets (on distributed servers), which means it breaks a massive task into smaller chunks and processes them in parallel.
- Hadoop Common: It is also known as Hadoop core. It is a set of utilities and Java libraries required by other Hadoop modules. These libraries provide filesystem and OS level abstractions and contain the necessary Java files and scripts required to start Hadoop. Additionally, it also manages common hardware failure.
In order to increase knowledge of new tools and technologies, beginners can adopt different methods. Big data tutorial for beginners, Hadoop hands-on tutorial, self-study guides, and paid courses are some of the ways novices can wet their feet in this area. There are several Hadoop online training resources available – free and paid that will help learners get an idea of the Hadoop ecosystem.
- Big data tutorial for beginners: There are many big data hands-on tutorials for beginners that can help enthusiasts understand the fundamental concepts and challenges of big data.
- Webinar Series on Hadoop Essentials: It is an interactive live session that allows the user to have discussions and to learn how Hadoop works.
- Hadoop hands-on tutorial: Along with theoretical concepts, the hands-on tutorial will help to get the acumen for it through practical experience.
- Hadoop for dummies:This guide gives insights into big data and Hadoop so new learners can understand the concepts easily.
As Hadoop is growing in full bloom career opportunities in Hadoop are endless. It is a good idea to upgrade skills or just stay in touch with the new technologies in the market. Whatever your motivation to learn might be, a good big data/Hadoop hands-on tutorial for beginners can help you get familiar with the challenges of big data and the solution.