Hadoop Admin Training in Pune

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 Admin Training 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 include clusters that are difficult 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 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.

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.

Why Hadoop
Discussion about the drawbacks of traditonal RDBMS and why
Hadoop is better than traditional RDBMS.

Introduction to Hadoop
Discussion about HDFS Architecture. Namenode,
Secondary Namenode, Resource Manager,
Data node and Node manager.

Introduction to AWS Cloud Brief discussion about cloud technologies in Industry. Why cloud is better than baremetal and VM’s.
Discussion about various components which we are going to learn through the course duration ( AWS EC2, AWS S3, EMR, VPC, Snapshots, AMI, IAM)

Basic Networking concepts
Discussion about basic networking concepts that are going to be required in Hadoop.

Introduction to Linux
Discussion on why Linux skillset is important for Hadoop.
Overview of Linux and practising basic commands.

AWS Cloud (AWS EC2)
Hands on practise on AWS EC2. Deployment of an instance using
AWS EC2 and connecting to that instance using terminal and putty.

Hands on Practise of Linux Concepts
When connected to the instance deployed through AWS Ec2,
Hands-on practice of Linux commands will be done to understand linux concepts.

Single Node Architecture (Hadoop 1x)
Deployment of Single Node Hadoop Cluster using command line interface and discussing hadoop daemons.
Accessing Namenode UI, Resource Manager UI
Running a mapreduce job.

Multi Node Architecture (Hadoop 1x)
Deployment of Multi Node Hadoop Cluster using command line
interface and discussing hadoop daemons.
Setting up password less login architecture.
Accessing Namenode UI, Resource Manager UI

AWS S3 and EMR
Creating a S3 bucket and storing the data into it.
Creating a cluster in EMR and processing the data stored in
S3 bucket, after processing storing the result back into the S3 bucket.

Hadoop Eco-system (Hive, Flume, Sqoop and Pig)
Installing, configuring and using Apache Hive on hadoop cluster.
Installing, configuring and using Apache Pig on hadoop cluster.
Installing, configuring and using Apache Flume on hadoop cluster.
Installing, configuring and using Apache Sqoop on hadoop cluster.

Multi node hadoop 2x cluster, ( AWS: Snapshots and AMI)
Creating an image of hadoop 2x prerequistes on AWS cloud
Deploying hadoop 2x multi node architecture using that image.

Hortonworks cluster.
Deploying hortonworks cluster using Ambari
Performing basic admin tasks on Hortonworks cluster

Cloudera cluster
Deploying Cloudera cluster using Cloudera Manager
Performing basic admin tasks on Cloudera cluster

Hadoop 2x
Why hadoop 2x is better than hadoop 1x
Discussion on Prerequisites of hadoop 2x
Deploying Single Node hadoop 2x cluster