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HADOOP DEV + SPARK & SCALA

Hadoop Developer / Analyst / SPARK + SCALA / Hadoop (Java + Non- Java) Track

Best Bigdata Hadoop Training with 2 Real-time Projects with 1 TB Data set

Duration of the Training : 8 to 10 weekends 

How we are Different from Others : Covers each topics with Real Time Examples . Covers 8 Real time project and more than 72+ Assignments which is divided into Basic , Intermediate and  Advanced . Trainer from Real Time Industry with 9 years experience in DWH. Working as BI and Hadoop consultant having 3+ years in Bigdata & Hadoop real time implementation and migrations.
 
This is completely hands own training , which covers 90% Practical And 10% Theory .Here in Radical Technologies , we will take all prerequisite like Java ,SQL, which is required to learn Hadoop Developer and Analytical skills. This way We will accommodate technology illiterate and Technical experts in the same session and at the end of the training , they will gain the confidence  that , they got up-skilled to a different level. 
 
  • 8 Domain Based Project With Real Time Data
  • 5 POC 
  • 72 Assignments 
  • 25 Real Time Scenarios On 16 Node Clusters
  • Smart Class 
  • Basic Java 
  • DWH Concept 
  • Pig|Hive|Mapreduce|Nosql|Hbase|Zookeeper|Sqoop|Flume|Oozie|Yarn|Hue|Spark |Scala 

42 Hours Classroom Section

30 Hours of assignments

25 hours for One Project and 50 Hrs for 2 Project

350+ Interview Questions

Administration and Manual Installation of Hadoop with other Domain based projects will be done on regular basis apart from our normal batch schedule .

We do have projects from Healthcare , Financial , Automotive ,Insurance , Banking , Retail etc , which will be given to our students as per their requirements .

Hadoop Certifications : Radical is accredited with Pearson Vue and Kriterion , We do conduct Exams in every  month and we have 100% Passing record for all the students who completed course form Radical technologies . most demanding Hadoop Exams are Hortonworks  and Cloudera certifications . 
Exam Preparation : After the course We provide all of our candidates free exam preparation session , which will guide them to pass the Respective modules of Hadoop exams. 

Registration Process : We never take any registration fee from the candidate without experiencing our training quality.Once you satisfied with the demo , you can register with full payment and avail discount . We have installment facility also.
 
 
Placement : Hadoop placement record crossed 500+ as per Jan 2016 record . 30 percent Freshers and 70 percent up-skilled resources placed in different MNC’s and Startups at various locations all over India and Overseas

 

had

Bigdata Hadoop Syllabus

For whom Hadoop is?

IT folks who want to change their profile in a most demanding technology which is in demand by almost all clients in all domains because of below mentioned reasons-

  •  Hadoop is open source (Cost saving / Cheaper)
  •  Hadoop solves Big Data problem which is very difficult or impossible to solve using highly paid tools in market
  •  It can process Distributed data and no need to store entire data in centralized storage as it is there with other tools.
  •  Now a days there is job cut in market in so many existing tools and technologies because clients are moving towards a cheaper and efficient solution in market named HADOOP
  •  There will be almost 4.4 million jobs in market on Hadoop by next year.

Please refer below mentioned links:

http://www.computerworld.com/article/2494662/business-intelligence/hadoop-will-be-in-most-advanced-analytics-products-by-2015–gartner-says.html

Can I Learn Hadoop If I Don’t know Java?

Yes,

It is a big myth that if a guy don’t know Java then he can’t learn Hadoop. The truth is that Only Map Reduce framework needs Java except Map Reduce all other components are based on different terms like Hive is similar to SQL, HBase is similar to RDBMS and Pig is script based.

Only MR requires Java but there are so many organizations who started hiring on specific skill set also like HBASE developer or Pig and Hive specific requirements. Knowing MapReuce also is just like become all-rounder in Hadoop for any requirement.

Why Hadoop?

  • Solution for BigData Problem
  • Open Source Technology
  • Based on open source platforms
  • Contains several tool for entire ETL data processing Framework
  • It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools. 

 

Training Syllabus                                                   ,

 Big data

  • Distributed computing
  • Data management – Industry Challenges
  • Overview of Big Data
  • Characteristics of Big Data
  • Types of data
  • Sources of Big Data
  • Big Data examples
  • What is streaming data?
  • Batch vs Streaming data processing
  • Overview of Analytics
  • Big data Hadoop opportunities

Hadoop                                      

  • Why we need Hadoop
  • Data centres and Hadoop Cluster overview
  • Overview of Hadoop Daemons
  • Hadoop Cluster and Racks
  • Learning Linux required for Hadoop
  • Hadoop ecosystem tools overview
  • Understanding the Hadoop configurations and Installation.

HDFS (Storage)

  • HDFS
  • HDFS Daemons – Namenode, Datanode, Secondary Namenode
  • Hadoop FS and Processing Environment’s UIs
  • Fault Tolerant
    • High Availability
    • Block Replication
  • How to read and write files
  • Hadoop FS shell commands

 

YARN (Hadoop Processing Framework)

  • YARN
  • YARN Daemons – Resource Manager, NodeManager etc.
  • Job assignment & Execution flow

 Apache Hive

  • Data warehouse basics
  • OLTP vs OLAP Concepts
  • Hive
  • Hive Architecture
  • Metastore DB and Metastore Service
  • Hive Query Language (HQL)
  • Managed and External Tables
  • Partitioning & Bucketing
  • Query Optimization
  • Hiveserver2 (Thrift server)
  • JDBC , ODBC connection to Hive
  • Hive Transactions
  • Hive UDFs
  • Working with Avro Schema and AVRO file format

Apache Pig

  • Apache Pig
  • Advantage of Pig over MapReduce
  • Pig Latin (Scripting language for Pig)
  • Schema and Schema-less data in Pig
  • Structured , Semi-Structure data processing in Pig
  • Pig UDFs
  • HCatalog
  • Pig vs Hive Use case

Sqoop

  • Sqoop commands
  • Sqoop practical implementation
    • Importing data to HDFS
    • Importing data to Hive
    • Exporting data to RDBMS
  • Sqoop connectors

Flume

  • Flume commands
  • Configuration of Source, Channel and Sink
  • Fan-out flume agents
  • How to load data in Hadoop that is coming from web server or other storage
  • How to load streaming data from Twitter data in HDFS using Hadoop

Oozie

  • Oozie
  • Action Node and Control Flow node
  • Designing workflow jobs
  • How to schedule jobs using Oozie
  • How to schedule jobs which are time based
  • Oozie Conf file

Scala

  • Scala
    • Syntax formation, Datatypes , Variables
  • Classes and Objects
  • Basic Types and Operations
  • Functional Objects
  • Built-in Control Structures
  • Functions and Closures
  • Composition and Inheritance
  • Scala’s Hierarchy
  • Traits
  • Packages and Imports
  • Working with Lists, Collections
  • Abstract Members
  • Implicit Conversions and Parameters
  • For Expressions Revisited
  • The Scala Collections API
  • Extractors
  • Modular Programming Using Objects

Spark

  • Spark
  • Architecture and Spark APIs
  • Spark components
    • Spark master
    • Driver
    • Executor
    • Worker
    • Significance of Spark context
  • Concept of Resilient distributed datasets (RDDs)
  • Properties of RDD
  • Creating RDDs
  • Transformations in RDD
  • Actions in RDD
  • Saving data through RDD
  • Key-value pair RDD
  • Invoking Spark shell
  • Loading a file in shell
  • Performing some basic operations on files in Spark shell
  • Spark application overview
  • Job scheduling process
  • DAG scheduler
  • RDD graph and lineage
  • Life cycle of spark application
  • How to choose between the different persistence levels for caching RDDs
  • Submit in cluster mode
  • Web UI – application monitoring
  • Important spark configuration properties
  • Spark SQL overview
  • Spark SQL demo
  • SchemaRDD and data frames
  • Joining, Filtering and Sorting Dataset
  • Spark SQL example program demo and code walk through

Introduction to Kafka (Optional*)

  • What is Kafka
  • Cluster architecture
  • Basic operation
  • Integration with spark
  • Usecase

Key Features

  • This training program contains multiple POCs /exercises/ assignments on each topics and two real time projects with problem statements and data sets
  • This training will be conducted in workshop mode with full hands-on in class
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Hi friends, i join hadoop course in Radical technologies. Trainer is one of the best hadoop trainer. He explains each and every concepts with real time example, so its really easy to understand. He also provide online supports, you can asked any quires/questions over mail and definitely within hour you will get reply from him.
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