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In this text, we will explore what are the primary tasks of Hadoop MapReduce and explore a course to become a skilled Big Data Developer or more.
Skills you'll gain: Apache Hadoop, hadoop mapreduce, Map and Reduce, More..
MapReduce is a part of the Apache Hadoop system. Hadoop MapReduce is a framework that helps in processing large amount of data, big data easily and efficiently. MapReduce using Hadoop allows the efficient processing and analysis of large datasets within the Hadoop ecosystem.
It makes it easier for companies to maintain their data by providing a simple, scalable and structured approach such as distributed computing and parallel processing, which helps in handling big data efficiently.
As the data is growing exponentially nowadays especially through social media and eCommerce platforms. This approach of handling data is commonly used by these social media and e-commerce companies to analyze the massive amount of data collected from online users.
In addition to Map Reduce, Apache Hadoop includes other important components like the Hadoop Distributed File System (HDFS), Yarn, and Apache Pig.
In this text, we will explore what are the primary tasks of Hadoop MapReduce and explore a course to become a skilled Big Data Developer, Hadoop Administrator, MapReduce Developer or Big Data Scientist.
MapReduce is a part of the Hadoop framework, which is used for creating applications that can handle large amounts of data on extensive clusters of computers. It’s like a programming model that allows us to process extensive datasets across multiple computer clusters.
This approach enables data to be stored in a distributed manner, simplifying the management of massive volumes of data and large-scale computing.
MapReduce involves two primary tasks: Map and Reduce. The mapping task is done before the reducing task. During the mapping job, we divide the input dataset into smaller pieces, and these pieces are processed in parallel. The output from the mapping stage serves as input for the reducing stage. The reducers take the intermediate data from the mapping phase and transform it into smaller sets, ultimately producing the final output of the framework.
The MapReduce framework also helps in scheduling and monitoring tasks. If any task fails, the framework automatically retries it.
Even programmers with limited experience in distributed processing can use Hadoop MapReduce easily. It can be implemented using various programming languages such as Java, Hive, Pig, Scala, and Python.
=> Comprehensive Hadoop MapReduce is a self-paced course designed by Edureka. It is designed and created by Hadoop Experts, to provide you the skills and knowledge in the field of MapReduce Framework. This course will help you to solve the problems and use cases by efficiently utilising MapReduce skills and concepts.
With this course you will understand the fundamental and deeper concepts Hadoop MapReduce framework and how MapReduce works with the data stored in HDFS. You will learn fundamental concepts such as MapReduce Input Splits, Combiner and Partitioner, MapReduce Demos and MapReduce YARN fundamental concepts.
You will be taught Advance MapReduce concepts such as Counters, Cache Distribution, Reduce Join, Customised Input Format, MRunit and how to build complex and sophisticate Hadoop MapReduce programs.
Every class consists of practical tasks and assignments, which should be complete before the next class starts. This helps you in applying the theoretical and practical concepts, you learned during the course.
👉 If you wish to learn MapReduce using Hadoop. You can explore the “Comprehensive Hadoop MapReduce Course“, and check whether it suits your requirement -> Link is provided below:
