Shubham Sinha
Published on Dec 06,2018
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In today’s fast-paced IT world, where the technologies are evolving rapidly, a voluminous amount of data is generated every day. Because of this increase in data, more and more organizations are adopting the Big Data technologies like Hadoop, Spark, Kafka etc for storing and analyzing the Big Data. Therefore, the job opportunities regarding these technologies are also increasing at a faster pace. This results in demand for the professionals with Hadoop Certification. Let’s take a look at some predictions:

  • Big Data & Hadoop Market is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 – Forbes
  • McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts – McKinsey Report
  • The average salary for Big Data Hadoop Developer is $135k – Indeed.com Salary Data.

But, why do you need Hadoop Certification?

If you complete Edureka Big Data Hadoop Certification Training, you are recognized in the industry as a capable and a qualified Big Data expert. It would give you a preference and add value to your resume, which will help you in grabbing job opportunities in the field of Big Data & Hadoop. There are two major Hadoop certifications, namely Cloudera and Hortonworks.

Along with these two, Edureka also provides Hadoop Training, that covers up the similar curriculum, updated as per industry de facto and helps you in clearing the Cloudera & Hortonworks Hadoop certifications quite easily. 

In this Hadoop certification blog, I will be discussing in detail about different Big Data Hadoop certifications offered by Edureka, Cloudera and Hortonworks in the following sequence:

 

Big Data Certification | Hadoop Certification | Cloudera Certification | Edureka

Edureka Certification Training

Edureka provides 3 Hadoop certification training related to Big Data & Hadoop. 

Edureka Certification Training - Hadoop Certification - Edureka

Edureka Big Data Hadoop Certification Training

This Hadoop training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. This course is stepping stone to your Big Data journey and you will get the opportunity to work on multiple Big data & Hadoop projects with different data sets like social media, customer complaints, airlines, movie, loan datasets etc.

You will also get Edureka Hadoop certification after the project completion, which will add value to your resume. Based on the Edureka certification training and it’s aligned de facto curriculum, you can easily clear Cloudera or Hortonworks Hadoop certification.

Big Data Hadoop Certification Training

Big Data Hadoop Course Description

During this course, our expert instructors will train you to:

  • Master the concepts of HDFS and MapReduce framework
  • Understand Hadoop 2.x Architecture
  • Setup Hadoop Cluster and write Complex MapReduce programs
  • Learn data loading techniques using Sqoop and Flume
  • Perform data analytics using Pig, Hive, and YARN
  • Implement HBase and MapReduce integration
  • Implement Advanced Usage and Indexing
  • Schedule jobs using Oozie
  • Implement best practices for Hadoop development
  • Understand Spark and its Ecosystem
  • Learn how to work in RDD in Spark
  • Work on a real-life Project on Big Data Analytics

Big Data Hadoop Course Curriculum

The course is divided into modules and in each module, you will be learning new tools & architectures. Let us know what topics are covered in which module:

  • Understanding Big Data and Hadoop – In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, the common Hadoop ecosystem components, Hadoop Architecture, HDFS, Anatomy of File Write and Read, how MapReduce Framework works.
  • Hadoop Architecture and HDFS – In this module, you will learn the Hadoop Cluster Architecture, Important Configuration files in a Hadoop Cluster, Data Loading Techniques, how to setup single node and multi-node Hadoop cluster.
  • Hadoop MapReduce Framework – In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different datasets.
  • Advanced MapReduce – In this module, you will learn Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing.
  • Pig – In this module, you will learn Pig, types of use case we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, PIG running modes, PIG UDF, Pig Streaming, Testing PIG Scripts. Demo on healthcare dataset.
  • Hive – This module will help you in understanding Hive concepts, Hive Data types, Loading and Querying Data in Hive, running hive scripts and Hive UDF.
  • Advanced Hive and HBase – In this module, you will understand Advanced Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, optimizations in Hive. You will also acquire in-depth knowledge of HBase, HBase Architecture, running modes and its components.
  • Advanced HBase – This module will cover Advanced HBase concepts. We will see demos on Bulk Loading, Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper.
  • Processing Distributed Data with Apache Spark – In this module, you will learn Spark ecosystem and its components, how Scala is used in Spark, SparkContext. You will learn how to work in RDD in Spark. The demo will be there on running application on Spark Cluster, Comparing the performance of MapReduce and Spark.
  • Oozie and Hadoop Project – In this module, you will understand the working of multiple Hadoop ecosystem components together in a Hadoop implementation to solve Big Data problems. We will discuss multiple datasets and specifications of the project. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.

Big Data Hadoop Course Projects

As we looked at the Hadoop certification exams, so you need a good hands-on practice to clear the Hadoop certification exams. Thus, we provide various projects that you can work on and get a clear idea about the practical implementation. Towards the end of the course, you will work on a live project where you will be using PIG, HIVE, HBase, and MapReduce to perform Big Data analytics. Few Hadoop certification projects you will be going through are:

Project #1: Analyze social bookmarking sites to find insights

Data: It comprises of the information gathered from sites like reddit.com, stumbleupon.com which are bookmarking sites and allow you to bookmark, review, rate, search various links on any topic.reddit.com, stumbleupon.com, etc. 

Problem StatementAnalyze the data in the Hadoop ecosystem to:

  • Fetch the data into HDFS and analyze it with the help of MapReduce, Pig, and Hive to find the top rated links based on the user comments, likes etc.
  • Using MapReduce, convert the semi-structured format (XML data) into a structured format and categorize the user rating as positive and negative for each of the thousand links.
  • Push the output into HDFS and then feed it into PIG, which splits the data into two parts: Category data and Rating data.
  • Write a fancy Hive Query to analyze the data further and push the output into a relational database (RDBMS) using Sqoop.

Project #2: Customer Complaints Analysis

Data: Publicly available dataset, containing a few lakh observations with attributes like; CustomerId, Payment Mode, Product Details, Complaint, Location, Status of the complaint, etc.

Problem Statement: Analyze the data in the Hadoop ecosystem to:

  • Get the number of complaints filed under each product
  • Get the total number of complaints filed from a particular location
  • Get the list of complaints grouped by location which has no timely response

Project #3: Tourism Data Analysis

Data: The dataset comprises attributes like City pair (combination of from and to), adults traveling, seniors traveling, children traveling, air booking price, car booking price, etc.

Problem Statement: Find the following insights from the data:

  • Top 20 destinations people frequently travel to, based on the given data we can find the most popular destinations where people travel frequently, based on the specific initial number of trips booked for a particular destination
  • Top 20 locations from where most of the trips start based on booked trip count
  • Top 20 high air-revenue destinations, i.e the 20 cities that generate high airline revenues for travel, so that the discount offers can be given to attract more bookings for these destinations.

Project #4: Airline Data Analysis

Data: Publicly available dataset which contains the flight details of various airlines such as Airport id, Name of the airport, Main city served by airport, Country or territory where the airport is located, Code of Airport, Decimal degrees, Hours offset from UTC, Timezone, etc.

Problem Statement: Analyze the airlines data to:

  • Find list of airports operating in the country
  • Find the list of airlines having zero stops
  • List of airlines operating with code share
  • Which country (or) territory has the highest number of airports
  • Find the list of active airlines in the United States

Project #5: Analyze Loan Dataset

Data: Publicly available dataset which contains complete details of all the loans issued, including the current loan status (Current, Late, Fully Paid, etc.) and latest payment information.

Problem Statement:

  • Find the number of cases per location and categorize the count with respect to the reason for taking a loan and display the average risk score.

Project #6: Analyze Movie Ratings

Data: Publicly available data from sites like rotten tomatoes, IMDB, etc.

Problem Statement: Analyze the movie ratings by different users to:

  • Get the user who has rated the most number of movies
  • Get the user who has rated the least number of movies
  • Get the count of total number of movies rated by user belonging to a specific occupation
  • Get the number of underage users

Project #7: Analyze YouTube data

Data: It is about the YouTube videos and contains attributes such as VideoID, Uploader, Age, Category, Length, views, ratings, comments, etc.

Problem Statement:

  • Identify the top 5 categories in which the most number of videos are uploaded, the top 10 rated videos, and the top 10 most viewed videos.

MASTER HADOOP WITH EDUREKA

 

Here is a testimony by an Edureka learner on Big Data Hadoop Training course: 

The second Hadoop certification training provided by Edureka is Hadoop Administrator.

Edureka Hadoop Administration Certification Training

Hadoop Administration Training from Edureka provides participants an expertise in all the steps necessary to operate and maintain a Hadoop cluster, i.e. from Planning, Installation and Configuration through load balancing, Security and Tuning. The Edureka’s training will provide hands-on preparation for the real-world challenges faced by Hadoop Administrators.

Hadoop Admin Course Description

The course curriculum follows Apache Hadoop distribution. During the Hadoop Administration Online training, you’ll master:

  • Hadoop Architecture, HDFS, Hadoop Cluster and Hadoop Administrator’s role
  • Plan and Deploy a Hadoop Cluster
  • Load Data and Run Applications
  • Configuration and Performance Tuning
  • How to Manage, Maintain, Monitor and Troubleshoot a Hadoop Cluster
  • Cluster Security, Backup and Recovery 
  • Insights on Hadoop 2.0, Name Node High Availability, HDFS Federation, YARN, MapReduce v2
  • Oozie, Hcatalog/Hive, and HBase Administration and Hands-On Project

Big Data Hadoop Certification Training

Hadoop Admin Training Projects

Towards the end of the Course, you will get an opportunity to work on a live project, that will use the different Hadoop ecosystem components to work together in a Hadoop implementation to solve big data problems.

1. Setup a minimum 2 Node Hadoop Cluster

  • Node 1 – NameNode, ResourceManager, DataNode, NodeManager
  • Node 2 – Secondary NameNode, DataNode, NodeManager

2. Create a simple text file and copy to HDFS

  • Find out the location of the node to which it went.
  • Find in which data node the output files are written.

3. Create a large text file and copy to HDFS with a block size of 256 MB.

  • Keep all the other files in default block size and find how block size has an impact on the performance.

4. Set a spaceQuota of 200MB for projects and copy a file of 70MB with replication=2

  • Identify the reason the system is not letting you copy the file?
  • How will you solve this problem without increasing the spaceQuota?

5. Configure Rack Awareness and copy the file to HDFS

  • Find its rack distribution and identify the command used for it.
  • Find out how to change the replication factor of the existing file. 

CHECK OUT HADOOP ADMIN BATCHES

These are some of the scenario based questions, there are many more problems tackled by you while going through Hadoop Admin Certification Training. The last certification training provided by Edureka is solely based on Apache Spark. Lets us know the details.

Edureka Apache Spark Certification Training

This Apache Spark Certification Training will enable learners to understand how Spark executes in-memory data processing and runs much faster than Hadoop MapReduce. Learners will master Scala programming and will get trained on different APIs which Spark offers such as Spark Streaming, Spark SQL, Spark RDD, Spark MLlib and Spark GraphX.

Apache Spark Course Description

This Edureka course is an integral part of Big Data developer’s learning path. After completing the Apache Spark training, you will be able to:

  • Understand Scala and its implementation
  • Master the concepts of Traits and OOPS in Scala programming
  • Install Spark and implement Spark operations on Spark Shell 
  • Understand the role of Spark RDD
  • Implement Spark applications on YARN (Hadoop) 
  • Learn Spark Streaming API 
  • Implement machine learning algorithms in Spark MLlib API 
  • Analyze Hive and Spark SQL architecture 
  • Understand Spark GraphX API and implement graph algorithms 
  • Implement Broadcast variable and Accumulators for performance tuning
  • Projects

Apache Spark Training Projects

In Spark Hadoop certification Training, Edureka has multiple projects, few of them are:

Project #1: Design a system to replay the real-time replay of transactions in HDFS using Spark. 

Technologies Used: 

  1. Spark Streaming 
  2. Kafka (for messaging) 
  3. HDFS (for storage) 
  4. Core Spark API (for aggregation)

Project #2: Drop-page of signal during Roaming 

Problem Statement: You will be given a CDR (Call Details Record) file, you need to find out top 10 customers facing frequent call drops in Roaming. This is a very important report which telecom companies use to prevent customer churn out, by calling them back and at the same time contacting their roaming partners to improve the connectivity issues in specific areas.

So while going through Edureka training, you will be working on multiple use-cases as well as real time scenarios, which will help you in clearing various Hadoop certifications offered by Cloudera and Hortonworks.

MASTER SPARK WITH EDUREKA

If you would ask me personally, I would recommend you to go for Cloudera certification. So, let us advance ahead and look at the required skills to clear Cloudera certifications.

Cloudera Certifications

CCA exams test your basic foundation skills and set forth the groundwork for a candidate to get certified in CCP program. Cloudera has 3 certification exam at CCA level (Cloudera Certified Associate).

Cloudera Certifications - Hadoop Certification - Edureka

  1. CCA Spark and Hadoop Developer 
  2. CCA Data Analyst
  3. CCA Administrator 

CCA Spark and Hadoop Developer (CCA175)

The person clearing the CCA Spark and Hadoop Developer certification has proven his core skills to ingest, transform, and process data using Apache Spark and core Cloudera Enterprise tools. The basic details for appearing CCA 175 are:

  • Number of Questions: 8–12 performance-based (hands-on) tasks on Cloudera Enterprise cluster
  • Time Limit: 120 minutes
  • Passing Score: 70%
  • Price: USD $295

Each CCA question requires you to solve a particular scenario. In some cases, a tool such as Impala or Hive may be used and in other cases, coding is required. In order to speed up development time of Spark questions, a template is often provided that contains a skeleton of the solution, asking the candidate to fill in the missing lines of functional code. This template is written in either Scala or Python.

It is not mandatory to use the template. You may solve the scenario using a programming language. But however, you should be aware that coding every problem from scratch may take more time than is allocated for the exam.

Your exam is graded immediately upon submission and you are e-mailed a score report the same day of your exam. Your score report displays the problem number for each problem you attempted and a grade on that problem. If you pass the exam, you receive a second e-mail within a few days of your exam with your digital certificate as a PDF, your license number, a LinkedIn profile update, and a link to download your CCA logos for use in your social media profiles.

Now, let us know the required skill set for clearing CCA 175 certification.

Required Skills:

Data Ingest

The skills to transfer data between external systems and your cluster. This includes the following:

  • Import data from a MySQL database into HDFS using Sqoop
  • Export data to a MySQL database from HDFS using Sqoop
  • Change the delimiter and file format of data during import using Sqoop
  • Ingest real-time and near-real-time streaming data into HDFS
  • Process streaming data as it is loaded onto the cluster
  • Load data into and out of HDFS using the Hadoop File System commands

Transform, Stage, and Store

The skill to convert a set of data values, which is stored in HDFS into new data values or a new data format and write them into HDFS.

  • Load RDD data from HDFS for use in Spark applications
  • Write the results from an RDD back into HDFS using Spark
  • Read and write files in a variety of file formats
  • Perform standard extract, transform, load (ETL) processes on data

Data Analysis

Use Spark SQL to interact with the metastore programmatically in your applications. Generate reports by using queries against loaded data.

  • Use metastore tables as an input source or an output sink for Spark applications
  • Understand the fundamentals of querying datasets in Spark
  • Filter data using Spark
  • Write queries that calculate aggregate statistics
  • Join disparate datasets using Spark
  • Produce ranked or sorted data

Let’s move ahead and look at the second Cloudera certification i.e., CCA Data Analyst. 

CCA Data Analyst

Person clearing CCA Data Analyst certification has proven his core analyst skills to load, transform, and model Hadoop data in order to define relationships and extract meaningful results from the raw input. The basic details for appearing CCA Data Analyst are:

  • Number of Questions: 8–12 performance-based (hands-on) tasks on CDH 5 cluster
  • Time Limit: 120 minutes
  • Passing Score: 70%
  • Language: English

For each problem, you must implement a technical solution with a high degree of precision that meets all the requirements. You may use any tool or combination of tools on the cluster. You must possess enough knowledge to analyze the problem and arrive at an optimal approach given the time allowed. 

Below are the required skill set for clearing CCA Data Analyst certification.

Required Skills:

Prepare the Data

Use Extract, Transfer, Load (ETL) processes to prepare data for queries.

  • Import data from a MySQL database into HDFS using Sqoop
  • Export data to a MySQL database from HDFS using Sqoop
  • Move data between tables in the metastore
  • Transform values, columns, or file formats of incoming data before analysis

Provide Structure to the Data

Use Data Definition Language (DDL) statements to create or alter structures in the metastore for use by Hive and Impala.

  • Create tables using a variety of data types, delimiters, and file formats
  • Create new tables using existing tables to define the schema
  • Improve query performance by creating partitioned tables in the metastore
  • Alter tables to modify existing schema
  • Create views in order to simplify queries

Data Analysis

Use Query Language (QL) statements in Hive and Impala to analyze data on the cluster.

  • Prepare reports using SELECT commands including unions and subqueries
  • Calculate aggregate statistics, such as sums and averages, during a query
  • Create queries against multiple data sources by using join commands
  • Transform the output format of queries by using built-in functions
  • Perform queries across a group of rows using windowing functions

Candidates for CCA Data Analyst can be SQL developers, data analysts, business intelligence specialists, developers, system architects, and database administrators. There are no prerequisites.

Now, let us discuss the third Cloudera Hadoop certification i.e. CCA Administrator. 

CCA Administrator Exam (CCA131)

Individuals who earn the CCA Administrator certification have demonstrated the core systems and cluster administrator skills sought by companies and organizations deploying Cloudera in the enterprise.

  • Number of Questions: 8–12 performance-based tasks on pre-configured Cloudera Enterprise cluster
  • Time Limit: 120 minutes
  • Passing Score: 70%
  • Language: English
  • Price: USD $295

Each CCA question requires you to solve a particular scenario. Some of the tasks require making configuration and service changes via Cloudera Manager, while others demand knowledge of command line Hadoop utilities and basic competence with the Linux environment. Evaluation & Score Reporting are similar as CCA 175 certification. The required skill set is as follows:

Required Skills:

Install

Demonstrate an understanding of the installation process for Cloudera Manager, CDH, and the ecosystem projects.

  • Set up a local CDH repository
  • Perform OS-level configuration for Hadoop installation
  • Install Cloudera Manager server and agents
  • Install CDH using Cloudera Manager
  • Add a new node to an existing cluster
  • Add a service using Cloudera Manager

Configure

Perform basic and advanced configuration needed to effectively administer a Hadoop cluster

  • Configure a service using Cloudera Manager
  • Create an HDFS user’s home directory
  • Configure NameNode HA
  • Configure ResourceManager HA
  • Configure proxy for Hiveserver2/Impala

Manage

Maintain and modify the cluster to support day-to-day operations in the enterprise

  • Rebalance the cluster
  • Set up alerting for excessive disk fill
  • Define and install a rack topology script
  • Install new type of I/O compression library in cluster
  • Revise YARN resource assignment based on user feedback
  • Commission/decommission a node

Secure

Enable relevant services and configure the cluster to meet goals defined by security policy; demonstrate knowledge of basic security practices

  • Configure HDFS ACLs
  • Install and configure Sentry
  • Configure Hue user authorization and authentication
  • Enable/configure log and query reduction
  • Create encrypted zones in HDFS

Test

Benchmark the cluster operational metrics, test system configuration for operation and efficiency

  • Execute file system commands via HTTP-FS
  • Efficiently copy data within a cluster/between clusters
  • Create/restore a snapshot of an HDFS directory
  • Get/set ACLs for a file or directory structure
  • Benchmark the cluster (I/O, CPU, network)

Troubleshoot

Demonstrate ability to find the root cause of a problem, optimize inefficient execution, and resolve resource contention scenarios

  • Resolve errors/warnings in Cloudera Manager
  • Resolve performance problems/errors in cluster operation
  • Determine reason for application failure
  • Configure the Fair Scheduler to resolve application delays

LEARN HADOOP WITH EXPERTS

These were the three Hadoop certifications of Cloudera related to Hadoop. Further moving on, let us discuss the Hortonworks certifications.

Hortonworks Certifications

There are five Hadoop certifications provided by Hortonworks related to Hadoop:

Hortonworks Certifications - Hadoop Certification - Edureka

  • HDP CERTIFIED JAVA DEVELOPER (HDPCD-Java): for developers who design, develop and architect Hadoop-based solutions written in the Java programming language.

The cost of the exam is $250 USD per attempt and the duration is 2 hours. Hortonworks has a dynamic marking scheme based on the question you are attempting and the approach taken by you. So, now we will focus on the required skill for clearing different Hortonworks certification.

Required Skills:

HDPCD Exam

Data Ingestion

  • SQOOP-IMPORT: Import data from a table in a relational database into HDFS
  • FREE-FORM QUERY IMPORTS: Import the results of a query from a relational database into HDFS
  • IMPORTING DATA INTO HIVE: Import a table from a relational database into a new or existing Hive table
  • SQOOP-EXPORT: Insert or update data from HDFS into a table in a relational database
  • FLUME AGENT: Given a Flume configuration file, start a Flume agent
  • MEMORY CHANNEL: Given a configured sink and source, configure a Flume memory channel with a specified capacity

Data Transformation

  • Write and execute a Pig script
  • Load data into a Pig relation without a schema
  • Load data into a Pig relation with a schema
  • Load data from a Hive table into a Pig relation
  • Use Pig to transform data into a specified format
  • Transform data to match a given Hive schema
  • Group the data of one or more Pig relations
  • Use Pig to remove records with null values from a relation
  • Store the data from a Pig relation into a folder in HDFS
  • Store the data from a Pig relation into a Hive table
  • Sort the output of a Pig relation
  • Remove the duplicate tuples of a Pig relation
  • Specify the number of reduce tasks for a Pig MapReduce job
  • Join two datasets using Pig
  • Perform a replicated join using Pig
  • Run a Pig job using Tez
  • Within a Pig script, register a JAR file of User Defined Functions
  • Within a Pig script, define an alias for a User Defined Function
  • Within a Pig script, invoke a User Defined Function

Data Analysis

  • Write and execute a Hive query
  • Define a Hive-managed table
  • Define a Hive external table
  • Define a partitioned Hive table
  • Define a bucketed Hive table
  • Define a Hive table from a select query
  • Define a Hive table that uses the ORCFile format
  • Create a new ORCFile table from the data in an existing non-ORCFile Hive table
  • Specify the storage format of a Hive table
  • Specify the delimiter of a Hive table
  • Load data into a Hive table from a local directory
  • Load data into a Hive table from an HDFS directory
  • Load data into a Hive table as the result of a query
  • Load a compressed data file into a Hive table
  • Update a row in a Hive table
  • Delete a row from a Hive table
  • Insert a new row into a Hive table
  • Join two Hive tables
  • Run a Hive query using Tez
  • Run a Hive query using vectorization
  • Output the execution plan for a Hive query
  • Use a subquery within a Hive query
  • Output data from a Hive query that is totally ordered across multiple reducers

  • Set a Hadoop or Hive configuration property from within a Hive query

HDPCA EXAM

Installation

  • Configure a local HDP repository
  • Install Ambari-server and Ambari-agent
  • Install HDP using the Ambari install wizard
  • Add a new node to an existing cluster
  • Decommission a node
  • Add an HDP service to a cluster using Ambari

Configuration

  • Define and deploy a rack topology script
  • Change the configuration of a service using Ambari
  • Configure the Capacity Scheduler
  • Create a home directory for a user and configure permissions
  • Configure the include and exclude DataNode files

Troubleshooting

  • Restart an HDP service
  • View an application’s log file
  • Configure and manage alerts
  • Troubleshoot a failed job

High Availability

  • Configure NameNode HA
  • Configure ResourceManager HA
  • Copy data between two clusters using distcp
  • Create a snapshot of an HDFS directory
  • Recover a snapshot
  • Configure HiveServer2 HA

Security

  • Install and configure Knox
  • Install and configure Ranger

  • Configure HDFS ACLs

HDPCD: JAVA EXAM

  • Write a Hadoop MapReduce application in Java
  • Run a Java MapReduce application on a Hadoop cluster
  • Write and configure a Combiner for a MapReduce job
  • Write and configure a custom Partitioner for a MapReduce job
  • Sort the output of a MapReduce job
  • Write and configure a custom key class for a MapReduce job
  • Configure the input and output formats of a MapReduce job
  • Perform a join of two or more datasets

  • Perform a map-side join of two datasets

HDPCD: SPARK EXAM

Core Spark

  • Write a Spark Core application in Python or Scala
  • Initialize a Spark application
  • Run a Spark job on YARN
  • Create an RDD
  • Create an RDD from a file or directory in HDFS
  • Persist an RDD in memory or on disk
  • Perform Spark transformations on an RDD
  • Perform Spark actions on an RDD
  • Create and use broadcast variables and accumulators
  • Configure Spark properties

Spark SQL

  • Create Spark DataFrames from an existing RDD
  • Perform operations on a DataFrame
  • Write a Spark SQL application
  • Use Hive with ORC from Spark SQL
  • Write a Spark SQL application that reads and writes data from Hive tables

Now, as we know the required skill sets and exam pattern to clear various Hadoop certifications. Thus, you can choose among three Edureka’s Hadoop Certification Training programs based on the Hadoop certification you want to pursue. Edureka Training curriculum is aligned with Cloudera & Hortonworks Hadoop certifications.

I hope this blog was informative and helped in gaining an idea about various Hadoop certification and their training. Now go ahead, choose a Hadoop Certification and get certified in Big Data Hadoop which will boost your professional career. All The Best!

CHECK OUT HADOOP BATCHES

Edureka is a live and interactive e-learning platform that is revolutionizing professional online education. It offers instructor-led courses supported by online resources, along with 24×7 on-demand support. Edureka courses are specially curated by experts who monitor the IT industry with a hawk’s eye, and respond to the expectations, changes, and requirements from the industry and incorporate them into the courses.

Now that you know various Hadoop Certification, check out the Hadoop training by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. The Edureka Big Data Hadoop Certification Training course helps learners become expert in HDFS, Yarn, MapReduce, Pig, Hive, HBase, Oozie, Flume and Sqoop using real-time use cases on Retail, Social Media, Aviation, Tourism, Finance domain.

Got a question for us? Please mention it in the comments section and we will get back to you.

About Author
Shubham Sinha
Shubham Sinha
Published on Dec 06,2018
Shubham Sinha is a Big Data and Hadoop expert working as a Research Analyst at Edureka. He is keen to work with Big Data related technologies such as Hadoop, Spark, Flink and Storm and web development technologies including Angular, Node.js & PHP.

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