Hadoop Training

Upon the successful completion of this Hadoop online certification course you will learn :

• Hadoop Architecture 
• Hadoop ecosystem and components
• Hadoop cluster management 
• Scheduling, monitoring and troubleshooting Hadoop Cluster
• Working with Flume, Zookeeper, Sqoop, MapReduce, HDFS, Pig and Hive. 
• Testing Hadoop clusters with multiple automation tools. 
• Integrating various tools such as with MapReduce, Pig and Hive.

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 :

• Software Developers
• Software Architects
• ETL and Data Warehousing Professionals
• Project Managers
• Senior IT Professionals
• Data Engineers
• Mainframe professionals
• DBAs
• Data Analysts & BI Professionals
• Testing professionals
• Freshers interested in starting their career in Big Data. 

• The Global Hadoop market is expected to witness massive growth. 
• To leverage the benefits of growing data, organizations across the globe are looking for the skilled Hadoop experts. 
• According to Indeed, the average salary received by a Hadoop profession is around $123,000

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.

• Intro to PIG
• Why PIG?
• The difference between MAPREDUCE and PIG
• When to go with MAPREDUCE?
• When to go with PIG?
• PIG data types
• What is field in PIG?
• What is tuple in PIG?
• What is Bag in PIG?
• Intro to Grunt shell?
• Different modes in PIG
• Local Mode
• MAPREDUCE mode
• Running PIG programs
• PIG Script
• Intro to PIG UDFs
• Writing PIG UDF using Java
• Registering PIG UDF
• Running PIG UDF
• Different types of UDFs in PIG
• Word Count program using PIG script
• Use cases for PIG scripts

• Intro to HIVE
• Why HIVE?
• History of HIVE
• Difference between PIG and HIVE
• HIVE data types
• Complex data types
• What is Metastore and its importance?
• Different types of tables in HIVE
• Managed tables
• External tables
• Running HIVE queries
• Intro to HIVE partitions
• Intro to HIVE Buckets
• How to perform the JOINS using HIVE queries
• Intro to HIVE UDFs
• Different types of UDFs in HIVE
• Running HIVE queries for Word Count example
• Use cases for HIVE

• Intro to HBASE
• Intro to NoSQL database
• Sparse and dense Concept in RDBMS
• Intro to columnar/column oriented database
• Core architecture of HBase
• Why Hbase?
• HDFS vs HBase
• Intro to Regions, Region server and Hmaster
• Limitations of Hbase
• Integration with Hive and Hbase
• Hbase commands
• Use cases for HBASE

• Intro to Flume
• Intro to Sink, Source, Flume Master and Flume agents
• Importance of Flume agents
• Live Demo on copying LOG DATA into HDFS

• Intro to Sqoop
• Importing and exporting the RDBMS into HDFS
• Intro to incremental imports and its types
• Use cases to import the Mysql data into HDFS

• Intro to Zookeeper
• Zookeeper operations

• Intro to Oozie
• What is properties
• What is xml
• Scheduling the jobs in Oozie
• Scheduling MapReduce, HIVE,PIG jobs/Programs using
• Setting up the VMware for Hadoop
• Installing all Hadoop Components
• Intro to Hadoop Distributions
• Intro to Cloudera and its major components

• Getting started With
• Scala Background, Scala Vs Java
• Introduction to Scala – REPL
• Scala data types, variables, simple
• Intro to Scala compiler
• Installing Scala on Linux
• Intro to Functional Programming Language
• Differences between OOPS and FPP
• Word count pgm, file handling
• Running Scala script
• Intro to Maps, Sets, groupBy, Options, flatten, flatMap and more

• What is Spark Ecosystem
• Batch vs real time data processing
• Intro to Spark Architecture
• Installing Scala on Linux
• Scala utility in Spark
• Spark Cluster Managers
• Spark -Standalone mode Installation
• Spark on YARN
• Spark on MESOS
• What is SparkContext
• Intro to RDDs
• Intro to DAG
• RDD’s lineage
• How to work on RDD in Spark
• What is transformations and Actions
• Intro to Spark Streaming(SS)
• Intro to Discretized Streams RDD
• Applying Transformations and Actions on Streaming data
• Intro to Spark Streaming Architecture
• Applying transformations and Actions on SS data
• How to run a Spark Cluster
• Comparison of MapReduce vs Spark
• Integration of Hadoop and Spark

• Tableau Fundamentals
• Tableau Analytics
• Visual Analytics
• Creating different types of WorkSheets,Dashboards and
• Connecting with different data sources
• Hadoop Integration with Tableau

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.

• NameNode: It is the master node which is responsible for storing the metadata of all the files and directories. It has information about blocks, that make a file, and where those blocks are located in the cluster.
• Datanode: It is the slave node that contains the actual data.
• Secondary NameNode: It periodically merges the changes (edit log) with the FsImage (Filesystem Image), present in the NameNode. It stores the modified FsImage into persistent storage, which can be used in case of failure of NameNode.
• ResourceManager: It is the central authority that manages resources and schedule applications running on top of YARN.
• NodeManager: It runs on slave machines, and is responsible for launching the application’s containers (where applications execute their part), monitoring their resource usage (CPU, memory, disk, network) and reporting these to the ResourceManager.
• JobHistoryServer: It maintains information about MapReduce jobs after the Application Master terminates.

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.

• Hadoop 1 default block size: 64 MB
• Hadoop 2 default block size: 128 MB

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:

• Job’s input locations in the distributed file system
• Job’s output location in the distributed file system
• Input format of data
• Output format of data
• Class containing the map function
• Class containing the reduce function
• JAR file containing the mapper, reducer and driver classes

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:

• Create a new class that extends Partitioner Class
• Override method – getPartition, in the wrapper that runs in the MapReduce.
• Add the custom partitioner to the job by using method set Partitioner or add the custom partitioner to the job as a config file.

“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.

• Region Server: A table can be divided into several regions. A group of regions is served to the clients by a Region Server.
• HMaster: It coordinates and manages the Region Server (similar as NameNode manages DataNode in HDFS).
• ZooKeeper: Zookeeper acts like as a coordinator inside HBase distributed environment. It helps in maintaining server state inside the cluster by communicating through sessions.