Hadoop Admin Training Institute in Pune

What we offer for Hadoop Admin Training

Before stepping in to Hadoop Environment for the first time, we need to know why Hadoop came into existence. What were the drawbacks of traditional RDBMS in and why Hadoop is better?

We are going to learn about basic networking concepts. Along with networking terminologies we are also going learn about AWS Cloud. Why cloud at the first place? Now industries are migrating to cloud. Baremetals and VM’s don’t have the power to store the amount of data that is generated in today’s world. And it costs company a lot of money to store the data into the hardware, and the maintenance of those machines are also required on timely basis. Cloud provides solution to these problems, where an organization can store all its data that is generated without even worrying about the amount of data that is generated on the daily basis. They do not have to care about the maintenance and security of the machines, cloud vendors looks after all this. Here at seven mentors we will give you hands on exposure to the Amazon Web Services (AWS Cloud) as AWS is market leader in this field.

Now most important technology which is must for Hadoop Admin is Linux. We will provide exposure to Linux environment also. Hadoop Administrator gets a lot of tickets regarding the Hadoop cluster and those tickets have to be solved as per the priority of the tickets. In industry we call it troubleshooting. So, Hadoop Admin has to troubleshoot in Linux environment. We have designed our course in such a way that if you do not have any knowledge in Linux Environment we will give you enough exposure to this technology while covering the sessions of Hadoop Admin.

After Linux, Networking and AWS Cloud we will slowly begin with the Hadoop 1.x architecture first. Now why Hadoop 1x architecture at the first place when the industry is using Hadoop 2.x and the stable version of Hadoop 3.x has already been released.  We will learn Hadoop 1.x because this will allow us to know the core concept of Hadoop daemons such as Namenode, Secondary NameNode (Standby Namenode in Hadoop 2.x), Job Tracker (Resource Manager in Hadoop 2.x), Data Node and Task Tracker (Node Manager in Hadoop 2.x).  It will also allow us to understand how password less login architecture is achieved as we will be deploying Hadoop 1.x cluster in command line interface.

After we have deployed Hadoop 1.x cluster we will learn about the Hadoop ecosystem. We will learn about Pig, Hive, Sqoop and Flume first. These services we will deploy in command line interface and we will learn different components of these services.

Once we are familiar with Hadoop 1.x environment and the drawback of Hadoop 1.x we will learn what pre requisites needs to be done on Linux based OS (Ubuntu, RedHat, Centos) to deploy Hadoop 2.x cluster.

Once we have successfully completed Hadoop 1.x and Hadoop 2.x deployment in command line, we will have enough exposure of Linux Environment, AWS Cloud and HDFS Architecture and Hadoop Ecosystem.

Now we will move towards industrial grade i.e. deploying Hortonworks cluster and Cloudera Cluster. First we will start with hortonworks cluster. We will learn to deploy hortonworks cluster by installing Ambari Server and we will dive deep into hortonworks cluster by performing Admin tasks on the same cluster.

The Admin task will include commissioning, decommissioning, adding a service, removing a service, enabling Namenode HA (High Availability) environment, enabling Resource Manager HA (High Availability environment), why high availability environment is necessary in a Hadoop cluster, accessing UI of Namenode,  Resource Manager and Data Node. Submitting a job to the cluster.

After Hortonworks we will deploy Cloudera cluster by installing Cloudera Manager Server and we will perform Admin tasks on the Cloudera cluster.

The Admin task will include commissioning, decommissioning, adding a service, removing a service, enabling Namenode HA (High Availability) environment, enabling Resource Manager HA (High Availability environment), why high availability environment is necessary in a Hadoop cluster, accessing UI of Namenode,  Resource Manager and Data Node. Submitting a job to the cluster, allocating resources to a job.

Now once we have successfully deployed and performed Hadoop Admin tasks on both cloudera and hortonworks cluster we will proceed towards Cloudera Director. What is the need of Cloudera Director, discussion on this topic and we will deploy Cloudera Director through scripting.

After this we will move towards the most important part of training i.e. Hadoop security. Why a Hadoop cluster should be secured, concepts of authorization and authentication, why Kerberos is needed to secure a cluster will be discussed.

We will enable Kerberos using MIT and Active Directory. First we will understand Kerberos through MIT and while enabling Kerberos we will learn about the basics of Kerberos and Kerberos commands. Active Directory is industry grade and after MIT we will enable Kerberos through Active Directory.  Why authorization is important in a Hadoop cluster and for that how sentry should be used.

Now as a Hadoop Admin skillset of HBase and Kafka is a must to clear an interview today. We will deploy both the services into the cluster and while deploying we will discuss about each and every component of these two services. We will also learn about Hue, Oozie and Zookeeper and what are their roles in a cluster.

After all these sessions we will have our sessions on real time issues faced by Hadoop Admin and how those issues should be solved. Session on cluster planning and capacity planning will be taken to make each and every candidate interview ready as soon as this Hadoop Admin course is completed. Real Time projects will be discussed and what questions will the interviewer ask based on the project that we will be sharing in the interview.

What is Hadoop Admin?

Hadoop is an associate degree open supply package framework designed for storage and process of huge scale type of information on clusters of artifact hardware. The Apache Hadoop software library is a framework that allows the data distributed processing across clusters for computing using simple programming models called Map Reduce. It is designed to rescale from single servers to a cluster of machines and every giving native computation and storage in economical means. It works in a series of map-reduce jobs and each of these jobs is high-latency and depends on each other. So no job can start until the previous job has been finished and successfully completed. Hadoop solutions normally include clusters that are hard to manage and maintain. In many scenarios, it requires integration with other tools like MySQL, mahout, etc. We have another popular framework that works with Apache Hadoop i.e. Spark. Apache Spark allows software developers to develop complex, multi-step data pipeline application patterns. It also supports in-memory data sharing across DAG (Directed Acyclic Graph) based applications, so that different jobs can work with the same shared data. Spark runs on top of the Hadoop Distributed File System (HDFS) of Hadoop to enhance functionality. Spark does not have its own storage so it uses other supported storage. With the capabilities of in-memory data storage and data processing, the spark application performance is more time faster than other big data technologies or applications. Spark has a lazy evaluation which helps with optimization of the steps in data processing and control. It provides a higher-level API for improving productivity and consistency. Spark is designed to be a fast real-time execution engine that works both in memory and on disk. Spark is originally written in Scala language and it runs on the same Java Virtual Machine (JVM) environment. It currently supports Java, Scala, Clojure, R, Python, SQL for writing applications.

Why Should I take Hadoop Admin Training in Pune?

Apache Hadoop framework allows us to write distributed applications or systems. It is more efficient and it automatically distributes the work and data among machines that lead a parallel programming model. Hadoop works with different kinds of data effectively. It also provides a high fault-tolerant system to avoid data losses. Another big advantage of Hadoop is that it is open source and compatible with all platforms as it’s based on java. In the market, Hadoop is the only solution to work on big data efficiently in a distributed manner. The Apache Hadoop software library is a framework that allows the data distributed processing across clusters for computing using simple programming models called Map Reduce. It is designed to proportion from single servers to the cluster of machines and every giving native computation and storage in an economical manner. It works in a series of map-reduce jobs and each of these jobs is high-latency and depends on each other. So no job can start until the previous job has been finished and successfully completed. Hadoop solutions normally include clusters that are hard to manage and maintain. In many scenarios, it requires integration with other tools like MySQL, mahout, etc.

We have another popular framework that works with Apache Hadoop i.e. Spark.

Apache Spark allows software developers to develop complex, multi-step data pipeline application patterns. It also supports in-memory data sharing across DAG (Directed Acyclic Graph) based applications, so that different jobs can work with the same shared data. Spark runs on top of the Hadoop Distributed File System (HDFS) of Hadoop to enhance functionality. Spark does not have its own storage so it uses other supported storage. With the capabilities of in-memory data storage and data processing, the spark application performance is more time faster than other big data technologies or applications. Spark has a lazy evaluation which helps with optimization of the steps in data processing and control. It provides a higher-level API for improving productivity and consistency. Spark is designed to be a fast real-time execution engine that works both in memory and on disk.

Where Hadoop Admin can be used?

Machine Learning – Machine learning is that the scientific study of algorithms and applied mathematics models that pc systems use to perform a particular task while not mistreatment specific directions. AI – Machine intelligence which behaves like a human and takes decisions. Data Mining – Finding meaningful information from raw data using standard methods.

Data Analysis – information analysis could be a method of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making.

Social Network analysis – Facebook, youtube, google, twitter, LinkedIn data analysis. Graph and data visualization – Data representation through graphs, charts, images, etc.

Tools in Hadoop:

•  HDFS (Hadoop Distributed File System) basic storage for Hadoop.
• Apache Pig is an ETL (Extract Transform and Load) tool.
• Map scale back could be a programmatic model engine to execute man jobs.
• Apache Hive is a Data Warehouse tool used to work on Historical data using HQL.
• Apache Sqoop is a tool for Import and export data from RDBMS to HDFS and Vice-Versa.
• Apache Ooozie is a tool for Job scheduling to control applications over the cluster.
• Apache HBase is a NoSQL database based on CAP(Consistency Automaticity Partition) theory.
• A spark could be a framework will in memory computation and works with Hadoop. This framework is based on scala and java language.