MindsMapped is an online IT training institute that offers job oriented practical hands-on training and also assists individuals to achieve professional certification on various technologies. It offers both Instructor led live interactive online training and self-paced video learning.
One of the most successful course that MindsMapped has been offering is the Hadoop training program. Within this program participants are provided all the assistance to pass Hadoop certification exam.
The purpose of this certification training is to offer individuals that use Big Data and Hadoop with a means to proof their development skills on Hadoop applications for processing, storing, and analyzing data saved in Hadoop using the open-source tools of Cloudera, including Hive, Pig, Sqoop and Flume.
Some of the benefits of Hadoop Certification training program offered by MindsMapped:
- Hadoop certification training begins with the basic concepts including Java basics andgradually covers all the key concepts of Big Data and Hadoop
- You get to learn about the topics that are mandatory for passing Cloudera, MapR and HortonWorks.
- Topic based quiz are available to get batter insight about the topics that are already covered
- Online training is conducted in a very interactive and conducive environment.
- All the participants are provided high-quality assignments to develop better understanding of covered topics
- All the topics within the course are simultaneously covered in the project
- This online Hadoop certification training helps you perform various tasks on MapReduce, Sqoop, Hive and related subject with ease.
- Every class is recorded and archived in our video library. So, even if you miss any class you can easily go through it by going through our video library.
- Within the training program you would be provided study materials which has been developed by team of experienced Hadoop professionals.
- Our Hadoop instructors are IT professional with years of experience in various domains.
Apache Hadoop is an open-source software framework used for distributed storage and processing of dataset of big data using the MapReduce programming model. It consists of computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common occurrences and should be automatically handled by the framework.
The core of Apache Hadoop consists of a storage part, known as Hadoop Distributed File System (HDFS), and a processing part which is a MapReduce programming model. Hadoop splits files into large blocks and distributes them across nodes in a cluster. It then transfers packaged code into nodes to process the data in parallel. This approach takes advantage of data locality,where nodes manipulate the data they have access to. This allows the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking.
The base Apache Hadoop framework is composed of the following modules:
Hadoop Common – contains libraries and utilities needed by other Hadoop modules;
Hadoop Distributed File System (HDFS) – a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster;
Hadoop YARN – a platform responsible for managing computing resources in clusters and using them for scheduling users' applications; and Hadoop MapReduce – an implementation of the MapReduce programming model for large-scale data processing.
The term Hadoop has come to refer not just to the aforementioned base modules and sub-modules, but also to the ecosystem, or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Phoenix, Apache Spark, Apache ZooKeeper, Cloudera Impala, Apache Flume, Apache Sqoop, Apache Oozie, and Apache Storm. Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on their MapReduce and Google File System.
The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts. Though MapReduce Java code is common, any programming language can be used with "Hadoop Streaming" to implement the "map" and "reduce" parts of the user's program. Other projects in the Hadoop ecosystem expose richer user interfaces.
Hadoop consists of the Hadoop Common package, which provides file system and operating system level abstractions, a MapReduce engine (either MapReduce/MR1 or YARN/MR2) and the Hadoop Distributed File System (HDFS). The Hadoop Common package contains the Java ARchive (JAR) files and scripts needed to start Hadoop.
For effective scheduling of work, every Hadoop-compatible file system should provide location awareness – the name of the rack (or, more precisely, of the network switch) where a worker node is. Hadoop applications can use this information to execute code on the node where the data is, and, failing that, on the same rack/switch to reduce backbone traffic. HDFS uses this method when replicating data for data redundancy across multiple racks. This approach reduces the impact of a rack power outage or switch failure; if any of these hardware failures occurs, the data will remain available.
A multi-node Hadoop cluster
A small Hadoop cluster includes a single master and multiple worker nodes. The master node consists of a Job Tracker, Task Tracker, NameNode, and DataNode. A slave or worker node acts as both a DataNode and TaskTracker, though it is possible to have data-only and compute-only worker nodes. These are normally used only in nonstandard applications.
Hadoop requires Java Runtime Environment (JRE) 1.6 or higher. The standard startup and shutdown scripts require that Secure Shell (SSH) be set up between nodes in the cluster.
In a larger cluster, HDFS nodes are managed through a dedicated NameNode server to host the file system index, and a secondary NameNode that can generate snapshots of the namenode's memory structures, thereby preventing file-system corruption and loss of data. Similarly, a standalone JobTracker server can manage job scheduling across nodes. When Hadoop MapReduce is used with an alternate file system, the NameNode, secondary NameNode, and DataNode architecture of HDFS are replaced by the file-system-specific equivalents.
After completion of this Big Data and Hadoop Certification Training you would be able to pass any of the Hadoop professional certification exam including Cloudera certification, HortonWorkscertificationand MapR Certification. For information regarding MindsMapped Hadoop certification training, email at email@example.com or call at +1 (435) 610-1777 or +1 (801) 901-3035.
You can also visit below mentioned links for more information:
Job Oriented Hadoop Training: http://www.mindsmapped.com/big-data-hadoop-training.html
Hadoop Certification Training: https://mindsmapped.com/certification/big-data-hadoop-certifications/