Which Hadoop Certification is the best?

There are several Hadoop Certifications, and one such popular certification is the Cloudera Hadoop Developer Certification. You can prepare for the same by enrolling in the Big Data Hadoop Certification Training by Intellipaat.

Are advanced programming skills necessary to learn Hadoop?

No, you do not require advanced-level programming knowledge to learn Hadoop. The basics of programming are, however, necessary.

Why should I take the Big Data Hadoop Certification Training?

Big Data is a promising platform for processing vast quantities of data for data mining. Besides, large multinationals are making a switch towards Big Data Hadoop, certified Big Data professionals are in huge demand. Thus, the Big Data Hadoop Training and Certification allows you to be up and running with the most demanding technical skills.

Which Big Data Hadoop Certification Training model is the better- self-paced or online classroom?

For the Big Data Hadoop Certification Training course, you can opt for either self-paced or online classroom training. However, the online classroom training has additional features over self-paced training such as one-on-one query resolution and doubt clearance that make it more desirable.

Is Python better than Hadoop?

While Hadoop is a database framework, Python is a programming language. The Hadoop Ecosystem is unrelated to Python in these terms. Several companies prefer using Python with Hadoop to write its framework.

Big Data Hadoop Course by Intellipaat: Fee, Duration, How to Apply

Big Data Hadoop Course

Intellipaat

Join Intellipaat’s Big Data Hadoop Certification Training to ace Cloudera Big Data Certification (CCA175). Learn Hadoop, Big Data Analytics, and Apache Spark.

Online

₹ 20007

particular	details
Collaborators IBM	Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based	Frequency of Classes Weekdays, Weekends

particular

details

                                    Collaborators
                                    
                                        IBM

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

                                    Frequency of Classes
                                    Weekdays, Weekends

Hadoop Installation and Setup

The architecture of Hadoop cluster
What is High Availability and Federation?
How to setup a production cluster?
Various shell commands in Hadoop
Understanding configuration files in Hadoop
Installing a single node cluster with Cloudera Manager
Understanding Spark, Scala, Sqoop, Pig, and Flume

Introduction to Big Data Hadoop and Understanding HDFS and MapReduce

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

Deep Dive in MapReduce

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

Introduction to Hive

Introducing Hadoop Hive
Detailed architecture of Hive
Comparing Hive with Pig and RDBMS
Working with Hive Query Language
Creation of a database, table, group by and other clauses
Various types of Hive tables, HCatalog
Storing the Hive Results, Hive partitioning, and Buckets

Advanced Hive and Impala

Indexing in Hive
The ap 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

Introduction to Pig

Apache Pig introduction and its various features
Various data types and schema in Hive
The available functions in Pig, Hive Bags, Tuples, and Fields

Flume, Sqoop and HBase

Apache Sqoop introduction
Importing and exporting data
Performance improvement with Sqoop
Sqoop limitations
Introduction to Flume and understanding the architecture of Flume
What is HBase and the CAP theorem?

Writing Spark Applications Using Scala

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.

Use Case Bobsrockets Package

Introduction to Scala packages and imports
The selective imports
The Scala test classes
Introduction to JUnit test class
JUnit interface via JUnit 3 suite for Scala test
Packaging of Scala applications in the directory structure
Examples of Spark Split and Spark Scala

Introduction to Spark

Introduction to Spark
Spark overcomes the drawbacks of working on
MapReduce
Understanding in-memory MapReduce
Interactive operations on MapReduce
Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, and YARN Revision
The overview of Spark and how it is better than Hadoop
Deploying Spark without Hadoop
Spark history server and Cloudera distribution

Spark Basics

Spark installation guide
Spark configuration
Memory management
Executor memory vs. driver memory
Working with Spark Shell
The concept of resilient distributed datasets (RDD)
Learning to do functional programming in Spark
The architecture of Spark

Working with RDDs in Spark

Spark RDD
Creating RDDs
RDD partitioning
Operations and transformation in RDD
Deep dive into Spark RDDs
The RDD general operations
Read-only partitioned collection of records
Using the concept of RDD for faster and efficient data processing
RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions

Aggregating Data with Pair RDDs

Understanding the concept of key-value pair in RDDs
Learning how Spark makes MapReduce operations faster
Various operations of RDD
MapReduce interactive operations
Fine and coarse-grained update
Spark stack

Writing and Deploying Spark Applications

Comparing the Spark applications with Spark Shell
Creating a Spark application using Scala or Java
Deploying a Spark application
Scala built application
Creation of the mutable list, set and set operations, list, tuple, and concatenating list
Creating an application using SBT
Deploying an application using Maven
The web user interface of Spark application
A real-world example of Spark
Configuring of Spark

Project Solution Discussion

Working towards the solution of the Hadoop project solution
Its problem statements and the possible solution outcomes
Preparing for the Cloudera certifications
Points to focus on scoring the highest marks
Tips for cracking Hadoop interview questions

Parallel Processing

Learning about Spark parallel processing
Deploying on a cluster
Introduction to Spark partitions
File-based partitioning of RDDs
Understanding of HDFS and data locality
Mastering the technique of parallel operations
Comparing repartition and coalesce
RDD actions

Spark RDD Persistence

The execution flow in Spark
Understanding the RDD persistence overview
Spark execution flow, and Spark terminology
Distribution shared memory vs. RDD
RDD limitations
Spark shell arguments
Distributed persistence
RDD lineage
Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey

Spark MLlib

Introduction to Machine Learning
Types of Machine Learning
Introduction to MLlib
Various ML algorithms supported by MLlib
Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

Integrating Apache Flume and Apache Kafka

Why Kafka and what is Kafka?
Kafka architecture
Kafka workflow
Configuring Kafka cluster
Operations
Kafka monitoring tools
Integrating Apache Flume and Apache Kafka

Spark Streaming

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 and stateful operators

Improving Spark Performance

Introduction to various variables in Spark like shared variables and broadcast variables
Learning about accumulators
The common performance issues
Troubleshooting the performance problems

Spark SQL and Data Frames

Learning about Spark SQL
The context of SQL in Spark for providing structured data processing
JSON support in Spark SQL
Working with XML data
Parquet files
Creating Hive context
Writing data frame to Hive
Reading JDBC files
Understanding the data frames in Spark
Creating Data Frames
Manual inferring of schema
Working with CSV files
Reading JDBC tables
Data frame to JDBC
User-defined functions in Spark SQL
Shared variables and accumulators
Learning to query and transform data in data frames
Data frame provides the benefit of both Spark RDD and Spark SQL
Deploying Hive on Spark as the execution engine

Scheduling/Partitioning

Learning about the scheduling and partitioning in Spark
Hash partition
Range partition
Scheduling within and around applications
Static partitioning, dynamic sharing, and fair scheduling
Map partition with index, the Zip, and GroupByKey
Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions

Hadoop Administration – Multi-node Cluster Setup Using Amazon EC2

Introduction to various variables in Spark like shared variables and broadcast variables
Learning about accumulators
The common performance issues
Troubleshooting the performance problems

Hadoop Administration – Cluster Configuration

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?
Understanding Edit log

Hadoop Administration – Maintenance, Monitoring and Troubleshooting

Introduction to the checkpoint procedure, name node failure
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

ETL Connectivity with Hadoop Ecosystem (Self-Paced)

How ETL tools work in Big Data industry?
Introduction to ETL and data warehousing
Working with prominent use cases of Big Data in ETL industry
End-to-end ETL PoC showing Big Data integration with ETL tool

Hadoop Application Testing

Importance of testing
Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end-to-end tests, Functional testing, Release certification testing, Security testing, Scalability testing, Commissioning and Decommissioning of data nodes testing, Reliability testing, and Release testing

Roles & Responsibilities of Hadoop Testing Professional

Understanding the Requirement
Preparation of the Testing Estimation
Test Cases, Test Data, Test Bed Creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, Hive and HBase) while loading the input (logs, files, records, etc.) using Sqoop/Flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges, etc.), reporting defects to the development team or manager and driving them to closure
Consolidating all the defects and create defect reports
Validating new feature and issues in Core Hadoop

Framework Called MRUnit for Testing of MapReduce Programs

Report defects to the development team or manager and driving them to closure
Consolidate all the defects and create defect reports
Responsible for creating a testing framework called MRUnit for testing of MapReduce programs

Unit Testing

Automation testing using the OOZIE
Data validation using the query surge tool

Test Execution

Test plan for HDFS upgrade
Test automation and result

Test Plan Strategy and Writing Test Cases for Testing Hadoop Application Preview

Test, install and configure

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Self-paced Training	Rs. 20,007 (plus GST)

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Big Data Hadoop Course

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₹ 20007

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