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Who Can Do Hadoop course?

Hadoop is a Java-based programming framework which is open source. This framework is useful in processing and storage of huge data sets in a distributed computing environment. It handles thousands of terabytes of data and runs applications on systems with thousands of hardware nodes. The data transfer rates among the nodes are very high and even allows the system to continue working in case a node fails. Hadoop admin training lets you understand the Hadoop framework, HDFS and every related technology. It has four industry-based projects and is suited to data engineers, IT professionals, cloud administrators and system administrators. There will be different pros & cons in hadoop and more details about the course are given towards the end of the article. Who is eligible to do Hadoop course? Planning to learn Hadoop is a good decision if you are working in the Information Technology industry. There are no specific prerequisites to start learning the framework. But, it is recommended to know the basics of Java and Linux if you want to become a Hadoop expert and opt for a career in it. What if you don’t have much knowledge about Java and Linux? Experts say that you can still learn Hadoop course. While learning the framework, you can side by side learn Java and Linux for a few hours every day. Role of Java Though Java is not an exact prerequisite for Hadoop, knowing the basics is an added advantage. But, tools like Pig and Hive built on Hadoop have their own high-level languages to work with data. Writing code can be done in any language like Python, C, Ruby, Perl etc. These languages enable reading from the standard input and writing to the standard output with Hadoop streaming. In addition, the frameworks like Pig and Hive provide high level abstractions which do not require you to know Java. While Hive is programmed using HiveQL, Pig can be programmed in Pig Latin.  Both these are automatically converted to MapReduce programs in Java. Benefits of knowing Java Though you can use streaming to write the map and reduce the number of functions in the language you choose, there exists some advanced features that are only available in Java API at present. At times, you may have to check the Hadoop code in detail to find out why something is behaving like it is or to get more idea about the functionality of a particular module. This is again one situation when Java becomes useful. Many Hadoop projects come with roles like developer, tester, network administrator etc. some of which need Java knowledge while some don’t. If you are keen to dive into Hadoop administration then you can do so without any hesitation even if you don’t know Java. In course of time, you can improve your knowledge of Java so that you can be an expert in Hadoop. Role of Linux As you must be aware, Hadoop was initially built on Linux though it now runs fine in Windows. So, Linux is preferred for installing and managing Hadoop. Having a good foundation in Linux will help you grasp Hadoop better especially, the HDFS command line parameters. Role of C/C++ If you know to program in languages like C or C++, it would be easy for you to learn Java as they are not too different. So, learn some Java with your C/C++ knowledge and then move on to Hadoop training. You can also straight away start learning Hadoop with the C/C++ knowledge. There are many parts of the Hadoop system written in C and C++. If you can write map-reduce applications in C++ then Hadoop has pipes which is the library that permits C++ source code to be used for Reducer and Mapper code. Hadoop administrator certification Hadoop administration training prepares you for Cloudera’s CCAH certification and helps you build all the skills required for Big Data admin assignments. This specific course covers the Core Hadoop distributions which includes Apache Hadoop and Vendor specific distribution. You will learn about Cloudera manager and its capabilities along with the need for cluster management solutions. You will understand how to set up Hadoop cluster and its components like Pig, Hive, Sqoop etc. While taking the Hadoop admin certification, you will come to know about Hadoop’s Distributed File System and its computation/processing framework. You will also be taught to plan, secure, monitor and safeguard a cluster. Thus, the course will build a solid base in the advanced concepts of Big Data and the related technologies of Hadoop stack and the components in the Hadoop Ecosystem. After the completion of the course, you will have basic knowledge of Master Big Data with hadoop certification and its characteristics and the options used by companies to manage Big Data. You will understand the architecture of Hadoop framework and its distributed file system. Thus, learning Hadoop is one of the best options to go up in the admin career.

Who Can Do Hadoop course?

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Who Can Do Hadoop course?

Hadoop is a Java-based programming framework which is open source. This framework is useful in processing and storage of huge data sets in a distributed computing environment. It handles thousands of terabytes of data and runs applications on systems with thousands of hardware nodes. The data transfer rates among the nodes are very high and even allows the system to continue working in case a node fails.

Hadoop admin training lets you understand the Hadoop framework, HDFS and every related technology. It has four industry-based projects and is suited to data engineers, IT professionals, cloud administrators and system administrators. There will be different pros & cons in hadoop and more details about the course are given towards the end of the article.

Who is eligible to do Hadoop course?

Planning to learn Hadoop is a good decision if you are working in the Information Technology industry. There are no specific prerequisites to start learning the framework. But, it is recommended to know the basics of Java and Linux if you want to become a Hadoop expert and opt for a career in it.

What if you don’t have much knowledge about Java and Linux? Experts say that you can still learn Hadoop course. While learning the framework, you can side by side learn Java and Linux for a few hours every day.

Role of Java

Though Java is not an exact prerequisite for Hadoop, knowing the basics is an added advantage. But, tools like Pig and Hive built on Hadoop have their own high-level languages to work with data. Writing code can be done in any language like Python, C, Ruby, Perl etc. These languages enable reading from the standard input and writing to the standard output with Hadoop streaming.

In addition, the frameworks like Pig and Hive provide high level abstractions which do not require you to know Java. While Hive is programmed using HiveQL, Pig can be programmed in Pig Latin.  Both these are automatically converted to MapReduce programs in Java.

Benefits of knowing Java

Though you can use streaming to write the map and reduce the number of functions in the language you choose, there exists some advanced features that are only available in Java API at present. At times, you may have to check the Hadoop code in detail to find out why something is behaving like it is or to get more idea about the functionality of a particular module. This is again one situation when Java becomes useful.

Many Hadoop projects come with roles like developer, tester, network administrator etc. some of which need Java knowledge while some don’t. If you are keen to dive into Hadoop administration then you can do so without any hesitation even if you don’t know Java. In course of time, you can improve your knowledge of Java so that you can be an expert in Hadoop.

Role of Linux

As you must be aware, Hadoop was initially built on Linux though it now runs fine in Windows. So, Linux is preferred for installing and managing Hadoop. Having a good foundation in Linux will help you grasp Hadoop better especially, the HDFS command line parameters.

Role of C/C++

If you know to program in languages like C or C++, it would be easy for you to learn Java as they are not too different. So, learn some Java with your C/C++ knowledge and then move on to Hadoop training. You can also straight away start learning Hadoop with the C/C++ knowledge.

There are many parts of the Hadoop system written in C and C++. If you can write map-reduce applications in C++ then Hadoop has pipes which is the library that permits C++ source code to be used for Reducer and Mapper code.

Hadoop administrator certification

Hadoop administration training prepares you for Cloudera’s CCAH certification and helps you build all the skills required for Big Data admin assignments. This specific course covers the Core Hadoop distributions which includes Apache Hadoop and Vendor specific distribution. You will learn about Cloudera manager and its capabilities along with the need for cluster management solutions. You will understand how to set up Hadoop cluster and its components like Pig, Hive, Sqoop etc.

While taking the Hadoop admin certification, you will come to know about Hadoop’s Distributed File System and its computation/processing framework. You will also be taught to plan, secure, monitor and safeguard a cluster. Thus, the course will build a solid base in the advanced concepts of Big Data and the related technologies of Hadoop stack and the components in the Hadoop Ecosystem.

After the completion of the course, you will have basic knowledge of Master Big Data with hadoop certification and its characteristics and the options used by companies to manage Big Data. You will understand the architecture of Hadoop framework and its distributed file system. Thus, learning Hadoop is one of the best options to go up in the admin career.

Usha

Usha Sunil

Blog Author

Writing is Usha's hobby and passion. She has written widely on topics as diverse as training, finance, HR and marketing, and is now into technical writing and education. She keeps an interested eye on new trends in technology, and is currently on a mission to find out what makes the world go around.

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1 comments

Ananthi 09 Jul 2017

Great and interesting article to read.. i Gathered more useful and new information from this article.thanks a lot for sharing this article to us..

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Spark is capable of accessing data sources like HDFS, Cassandra, HBase, and S3, for distributed storage. It also supports a pseudo-distributed local mode that can be used for development or testing. The foundation of Spark is Spark Core, which relies on the RDD-oriented functional style of programming to dispatch tasks, schedule, and handle basic I/O functionalities. Two restricted forms of shared variables are used: broadcast variables, which reference read-only data that has to be available for all the nodes, and accumulators, which can be used to program reductions. Other elements included in Spark Core are: Spark SQL, which provides domain-specific language used to manipulate DataFrames. Spark Streaming, which uses data in mini-batches for RDD transformations, allowing the same set of application code that is created for batch analytics to also be used for streaming analytics. Spark MLlib, a machine-learning library that makes the large-scale machine learning pipelines simpler. 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