Hadoop is an open source Apache project. It stores and processes huge volumes of Big data.
SK Trainings Hadoop Training From Hyderabad has been designed by the industry experts with an aim to provide the in-depth knowledge on Hadoop and Big Data Ecosystem tools such as Sqoop, Flume, Oozie, Spark, HBase, Pig, Hive, MapReduce, Yarn, and HDFS. As a part of Hadoop Online Training you will be working on multiple real-life industry use cases. This certification will also help you to clear the Cloudera CCA Spark and Hadoop developer certification exam. Learn from the industry trainers by enrolling into a Hadoop Certification Course.
Hadoop is an Open Source Apache project that stores and processes large volumes of Big Data. It stores Big Data in a distributed & fault tolerant manner on commodity hardware then after various Hadoop tools are used to execute parallel data processing on HDFS.
This Hadoop Training Online has been designed to make you learn the industry demand skills to perform real-world tasks. This Hadoop training provides you in-depth knowledge of the Hadoop ecosystem and covers all the concepts to make you a Hadoop developer. Get the Best Hadoop Training by joining SK Trainings.
Upon the successful completion of this Hadoop online certification course you will learn :
No as such there are no special prerequisites to learn Big Data Hadoop course. Having basic knowledge of Java, SQL and UNIX would help you learn Big Data Hadoop easily.
Following are the job roles who get benefited from learning this course :
You need not to worry about any system requirements. All the practicals will be executed in the cloud LAB environment.
Yes, upon the successful completion of the Big Data training you will receive the course completion certificate from SK Trainings. This certificate is valid across all the top MNCs and simplifies your job search process.
Yes, you will get all assistance from the Hadoop experts to clear your certification exam. Moreover you will be provided with the experts designed Big Data Hadoop course material to clear your certification exam.
When “Big Data” emerged as a problem, Apache Hadoop evolved as a solution to it. Apache Hadoop is a framework which provides us various services or tools to store and process Big Data. It helps in analyzing Big Data and making business decisions out of it, which can’t be done efficiently and effectively using traditional systems.
Tip: Now, while explaining Hadoop, you should also explain the main components of Hadoop, i.e.:
Generally approach this question by first explaining the HDFS daemons i.e. NameNode, DataNode and Secondary NameNode, and then moving on to the YARN daemons i.e. ResorceManager and NodeManager, and lastly explaining the JobHistoryServer.
One of the most attractive features of the Hadoop framework is its utilization of commodity hardware. However, this leads to frequent “DataNode” crashes in a Hadoop cluster. Another striking feature of Hadoop Framework is the ease of scale in accordance with the rapid growth in data volume. Because of these two reasons, one of the most common task of a Hadoop administrator is to commission (Add) and decommission (Remove) “Data Nodes” in a Hadoop Cluster.
NameNode periodically receives a Heartbeat (signal) from each of the DataNode in the cluster, which implies DataNode is functioning properly.
A block report contains a list of all the blocks on a DataNode. If a DataNode fails to send a heartbeat message, after a specific period of time it is marked dead.
The NameNode replicates the blocks of dead node to another DataNode using the replicas created earlier.
In brief, “Checkpointing” is a process that takes an FsImage, edit log and compacts them into a new FsImage. Thus, instead of replaying an edit log, the NameNode can load the final in-memory state directly from the FsImage. This is a far more efficient operation and reduces NameNode startup time. Checkpointing is performed by Secondary NameNode.
The smart answer to this question would be, DataNodes are commodity hardware like personal computers and laptops as it stores data and are required in a large number. But from your experience, you can tell that, NameNode is the master node and it stores metadata about all the blocks stored in HDFS. It requires high memory (RAM) space, so NameNode needs to be a high-end machine with good memory space.
Blocks are the nothing but the smallest continuous location on your hard drive where data is stored. HDFS stores each as blocks, and distribute it across the Hadoop cluster. Files in HDFS are broken down into block-sized chunks, which are stored as independent units.
Rack Awareness is the algorithm in which the “NameNode” decides how blocks and their replicas are placed, based on rack definitions to minimize network traffic between “DataNodes” within the same rack. Let’s say we consider replication factor 3 (default), the policy is that “for every block of data, two copies will exist in one rack, third copy in a different rack”. This rule is known as the “Replica Placement Policy”.
This question can have two answers, we will discuss both the answers. We can restart NameNode by following methods: 1. You can stop the NameNode individually using. /sbin /hadoop-daemon.sh stop namenode command and then start the NameNode using. /sbin/hadoop-daemon.sh start namenode command. 2. To stop and start all the daemons, use. /sbin/stop-all.sh and then use ./sbin/start-all.sh command which will stop all the daemons first and then start all the daemons. These script files reside in the sbin directory inside the Hadoop directory.
The three modes in which Hadoop can run are as follows: 1. Standalone (local) mode: This is the default mode if we don’t configure anything. In this mode, all the components of Hadoop, such NameNode, DataNode, ResourceManager, and NodeManager, run as a single Java process. This uses the local filesystem. 2. Pseudo-distributed mode: A single-node Hadoop deployment is considered as running Hadoop system in pseudo-distributed mode. In this mode, all the Hadoop services, including both the master and the slave services, were executed on a single compute node. 3. Fully distributed mode: A Hadoop deployments in which the Hadoop master and slave services run on separate nodes, are stated as fully distributed mode.
The main configuration parameters which users need to specify in “MapReduce” framework are:
The “InputSplit” defines a slice of work, but does not describe how to access it. The “RecordReader” class loads the data from its source and converts it into (key, value) pairs suitable for reading by the “Mapper” task. The “RecordReader” instance is defined by the “Input Format”.
This is a tricky question. The “MapReduce” programming model does not allow “reducers” to communicate with each other. “Reducers” run in isolation.
Custom partitioner for a Hadoop job can be written easily by following the below steps:
“SequenceFileInputFormat” is an input format for reading within sequence files. It is a specific compressed binary file format which is optimized for passing the data between the outputs of one “MapReduce” job to the input of some other “MapReduce” job.
Sequence files can be generated as the output of other MapReduce tasks and are an efficient intermediate representation for data that is passing from one MapReduce job to another.
Pig Latin can handle both atomic data types like int, float, long, double etc. and complex data types like tuple, bag and map.
Atomic data types: Atomic or scalar data types are the basic data types which are used in all the languages like string, int, float, long, double, char, byte.
Complex Data Types: Complex data types are Tuple, Map and Bag.
If some functions are unavailable in built-in operators, we can programmatically create User Defined Functions (UDF) to bring those functionalities using other languages like Java, Python, Ruby, etc. and embed it in Script file.
“Derby database” is the default “Hive Metastore”. Multiple users (processes) cannot access it at the same time. It is mainly used to perform unit tests.
HBase is an open source, multidimensional, distributed, scalable and a NoSQL database written in Java. HBase runs on top of HDFS (Hadoop Distributed File System) and provides BigTable (Google) like capabilities to Hadoop. It is designed to provide a fault-tolerant way of storing the large collection of sparse data sets. HBase achieves high throughput and low latency by providing faster Read/Write Access on huge datasets.
HBase has three major components, i.e. HMaster Server, HBase RegionServer and Zookeeper.
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