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Description

Take your career to the next level by becoming a skilled CCA Spark and Hadoop Developer. This can happen by enrolling into Tekslate’s Big Data Hadoop training, where you will become an expert in working with Big Data and Hadoop ecosystem tools such as YARN, MapReduce, HDFS, Hive, Pig, HBase, Spark, Flume, Sqoop, etc., through practical executions and real-time examples. Our training is designed by industry-expert trainers according to latest developments of Hadoop and learning them is esse

Key Features

  • 30 hours of Instructor Led BigData Hadoop Training
  • Lifetime Access to Recorded Sessions
  • Practical Approach
  • 24/7 Support
  • Expert & Certified Trainers
  • Real World use cases and Scenarios
Trusted By Companies Worldwide

Course Overview

After the successful completion of Big Data Hadoop training at Tekslate, the participant will be able to

  • Master the fundamentals of Hadoop and Big Data and its features.

  • Gain knowledge on how to use HDFS, and MapReduce frameworks.

  • Gain knowledge of various tools of Hadoop ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase.

  • Work with Pig and Hive to perform ETL operations and data analytics.

  • Perform Partitioning, Bucketing, and Indexing in Hive.

  • Understand Apache Spark and its Ecosystem.

  • Implement real-world Big Data Analytics projects in various verticals.

  • The demand for Big Data Hadoop developers is increasing rapidly in the industry with high CTC being offered to them.

  • On average, a certified Big Data Hadoop developer is earning 123,000 USD per annum.

  • Due to the high demand for Big Data Hadoop, there are numerous job opportunities available all over the world.

The following job roles will get benefited from learning this course:

  • Software Developers and Architects

  • Analytics Professionals

  • Senior IT professionals

  • Testing and Mainframe Professionals

  • Data Management Professionals

  • Business Intelligence Professionals

  • Project Managers

  • Aspirants who are looking to build a career in Big Data analytics.

There are no specific prerequisites for learning this course. Anyone who is looking to build a career in this domain can join this training.

Having prior knowledge of Core Java, and SQL will be helpful but not mandatory.

We will provide two real-time projects under the guidance of a professional trainer, who will explain you on how to acquire in-depth knowledge on all the concepts involved in these projects.

Course Curriculum

  • The Architecture Of Hadoop 2.0 Cluster

  • What Is High Availability And Federation

  • How To Setup A Production Cluster

  • Various Shell Commands In Hadoop

  • Understanding Configuration Files In Hadoop 2.0

  • Installing Single Node Cluster With Cloudera Manager And Understanding Spark, Scala, Sqoop, Pig And Flume

  • Introducing Big Data and Hadoop

  • What is Big Data and where does Hadoop fit in

  • Two important Hadoop ecosystem components, namely, MapReduce and HDFS

  • In-depth Hadoop Distributed File System – Replications, Block Size, Secondary Name node

  • High Availability and in-depth YARN – resource manager and node manager

  • Learning the working mechanism of MapReduce

  • Understanding the mapping and reducing stages in MR

  • Various terminologies in MR like Input Format

  • Output Format, Partitioners, Combiners, Shuffle and Sort

  • Introducing Hadoop Hive, detailed architecture of Hive

  • Comparing Hive with Pig and RDBMS

  • Working with Hive Query Language

  • Creation of database, table

  • Group by and other clauses

  • Various types of Hive tables, HCatalog, storing the Hive Results, Hive partitioning and Buckets

  • Indexing in Hive, the Map Side Join in Hive, working with complex data types, the Hive User-defined Functions

  • Introduction to Impala

  • Comparing Hive with Impala

  • The detailed architecture of Impala

  • Apache Pig introduction

  • Its various features, various data types and schema in Hive

  • The available functions in Pig, Hive Bags, Tuples and Fields

  • Apache Sqoop introduction

  • Overview

  • Importing and exporting data

  • performance improvement with Sqoop, Sqoop limitations

  • Introduction to Flume and understanding the architecture of Flume and what is HBase and the CAP theorem

 

  • Using Scala for writing Apache Spark applications

  • Detailed study of Scala

  • The need for Scala, the concept of object-oriented programming, executing the Scala code, various classes in Scala like Getters, Setters, Constructors, Abstract, Extending Objects, Overriding Methods, the Java and Scala interoperability

  • The concept of functional programming and anonymous functions

  • Bobsrockets package and comparing the mutable and immutable collections

  • Scala REPL, Lazy Values

  • Control Structures in Scala

  • Directed Acyclic Graph (DAG)

  • First Spark application using SBT/Eclipse

  • Spark Web UI

  • Spark in Hadoop ecosystem.

  • Detailed Apache Spark, its various features

  • Comparing with Hadoop

  • Various Spark components

  • Combining HDFS with Spark

  • Scalding

  • Introduction to Scala and importance of Scala and RDD

  • Understanding the Spark RDD operations

  • Comparison of Spark with MapReduce

  • What is a Spark transformation

  • Loading data in Spark

  • Types of RDD operations viz. transformation and action and what is a Key/Value pair

  • The detailed Spark SQL

  • The significance of SQL in Spark for working with structured data processing

  • Spark SQL JSON support

  • Working with XML data and parquet files

  • Creating Hive Context

  • Writing Data Frame to Hive

  • How to read a JDBC file, significance of a Spark Data Frame

  • How to create a Data Frame

  • What is schema manual inferring

  • How to work with CSV files, JDBC table reading

  • Data conversion from Data Frame to JDBC

  • Spark SQL user-defined functions

  • Shared variable and accumulators

  • How to query and transform data in Data Frames

  • How Data Frame provides the benefits of both Spark RDD and Spark SQL and deploying Hive on Spark as the execution engine

  • Introduction to Spark MLlib

  • Understanding various algorithms

  • What is Spark iterative algorithm

  • Spark graph processing analysis, introducing Machine Learning

  • K-Means clustering

  • Spark variables like shared and broadcast variables

  • What are accumulators, various ML algorithms supported by MLlib

  • Linear Regression, Logistic Regression, Decision Tree, Random Forest

  • K-means clustering techniques, building a Recommendation Engine

  • Why Kafka, what is Kafka, Kafka architecture, Kafka workflow, configuring Kafka cluster, basic operations, Kafka monitoring tools

  • Integrating Apache Flume and Apache Kafka

  • Introduction to Spark streaming

  • The architecture of Spark streaming

  • Working with the Spark streaming program

  • Processing data using Spark streaming

  • Requesting count and DStream

  • Multi-batch and sliding window operations and working with advanced data sources

  • Introduction to Spark Streaming, features of Spark Streaming, Spark Streaming workflow,

  • Initializing StreamingContext, Discretized Streams (DStreams), Input DStreams and Receivers, transformations on DStreams, Output Operations on DStreams

  • Windowed Operators and why it is useful, important Windowed Operators, Stateful Operators.

  • Create a 4-node Hadoop cluster setup

  • Running the MapReduce Jobs on the Hadoop cluster

  • Successfully running the MapReduce code and working with the Cloudera Manager setup

  • The overview of Hadoop configuration

  • The importance of Hadoop configuration file

  • The various parameters and values of configuration

  • The HDFS parameters and MapReduce parameters

  • Setting up the Hadoop environment

  • The Include and Exclude configuration files

  • The administration and maintenance of name node

  • Data node directory structures and files

  • What is a File system image and understanding Edit log?

  • Introduction to the checkpoint procedure

  • name node failure and how to ensure the recovery procedure, Safe Mode, Metadata and Data Backup, various potential problems and solutions

  • What to look for and how to add and remove nodes

  • How ETL tools work in the Big Data industry

  • Introduction to ETL and data warehousing

  • Working with prominent use cases of Big Data in the ETL industry and end-to-end ETL PoC showing Big Data integration with the ETL tool

FAQ's

We have a strong team of professions who are experts in their fields. Our trainers are highly supportive and render a friendly working environment to the students positively stimulating their growth. 

We will share you the missed session from our recordings. We at Tekslate maintains a recorded copy of each live course you undergo.

Our Trainers will provide the student with the Server Access ensuring practical real-time experience and training with all the utilities required for the in-depth understanding of the course.

We provide all the training sessions LIVE using either GoToMeeting or WebEx, thus promoting one-on-one trainer student Interaction.

Live training uncovers distinct benefits as they are mighty to reach your desired audience converting your prospects into customers in less time. Pre-recorded videos offer plenty of advantages for entrepreneurs to educate entertain and inspire your audience as long as you want.

You can contact our Tekslate support team, or you can send an email to info@tekslate.com for your queries.

Yes. We provide the course materials available after course completion.

There exist some discounts for weekend batches and group participants if the joiners are more than 2.

If you are enrolled in classes and have paid fees but want to cancel the registration for any reason, we will attain you in 48 hours will be processed within 30 days of prior request.

Certifications

For acquiring the Big Data Hadoop certification, submit  CCA Spark and Hadoop Developer (CCA175) exam online, and get certified in it.

By enrolling into Tekslate’s training, you will gain a strong foundation in Big Data analytics and implement projects which include major applications of Big Data Hadoop in real-time industries with ease.

Through this, you will be able to clear your Big Data Hadoop Certification Exam in first submit itself.