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Hadoop Training in Delhi

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29- Nov- 2021
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01- Dec- 2021
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08:00 AM & 10:00 AM Batches

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04- Dec- 2021
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04- Dec- 2021
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  • The management of the Hadoop Cluster would-be expert in MapReduce, you would also learn all the programming concepts.
  • You'd learn how to write MapReduce programs with the most technology experts.
  • You will understand and understand all the key concepts of Big Data and Hadoop.
  • You would learn from scratch how to install and build a Hadoop Cluster.
  • The course Big Data Engineer gives you practical experience and gives special training for the students to get placed in top recruiting companies.
  • It also enables you to model, use the MongoDB database management system NoSQL to replicate data and share data.
  • Certification training is aiming to provide you with an in-depth understanding of Hadoop ecosystem flexibility and versatility and tools such as the creation of data models, database interfaces, etc.
  • Concepts: High Availability, Big Data opportunities, Challenges, Hadoop Distributed File System (HDFS), Map Reduce, API discussion, Hive, Hive Services, Hive Shell, Hive Server and Hive Web Interface, SQOOP, H Catalogue, Flume, Oozie.
  • START YOUR CAREER WITH HANDOOP CERTIFICATION COURSE THAT GETS YOU A JOB OF UPTO 5 TO 12 LACS IN JUST 60 DAYS!
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
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Course Objectives

Individuals that receive Hadoop and big data training can benefit greatly in today's data-driven world:
    • As more businesses use big data, you'll have more possibilities to advance your career.
    • Professionals with strong Hadoop knowledge and abilities are in high demand in a variety of sectors.
    • Adding a new skill set to your resume will help you earn more money. A Hadoop professional makes an average of $133,296 per year, according to ZipRecruiter.
    • With Hadoop and big data expertise, you may land a job with top firms like Google, Microsoft, and Cisco.
This session will introduce you to the seven Vs of big data: Volume, Velocity, Veracity, Variety, Value, Vision, and Visualization, as well as the different ideas of big data analytics. Using Hadoop 3, learn about big data principles, platforms, analytics, and applications.
  • Understanding Big Data There are several different types of big data.
  • Big Data vs. Traditional Data: What's the Difference?
  • Hadoop Distributed Data Storage: An Overview HDFS and Hbase are Hadoop components.
  • Services for Hadoop Data Processing and Analysis Map Data Integration Tools in Hadoop Resource Management and Cluster Management Services include Reduce and Spark, Hive Pig, and Storm.
The Big Data Analytics course prepares you to become an expert in Big Data Analytics by teaching you the fundamental ideas and technologies involved. The majority of the courses will require you to work on real-time, industry-based projects. You will study the practical applications of the topic through an intensive training program.
    The employment market is currently saturated, and there is the fierce rivalry. If you don't have any specialty, you're unlikely to get considered for the position you want. Big Data Hadoop is utilized by businesses in a variety of industries, and the need for Hadoop experts is expected to expand in the future. Certification is a method of demonstrating to employers that you possess the Big Data Hadoop abilities they want. With tens of thousands of resumes for a few job openings, a Hadoop certification can help you stand out from the crowd. With an average yearly income of INR 11,23,000, a Certified Hadoop Administrator also fetches a better salary in the market. Hadoop certifications might thus help you advance your profession.
Hadoop offers abstract frameworks such as Hive and Pig that don't require Java except to write user-defined functions and are generally easy to understand. However, to build user-defined functions and MapReduce applications, you need to be familiar with core Java.
The following are the prerequisites for learning Hadoop:
  • Hadoop is written in the Java programming language. As a result, you must at the very least be familiar with the fundamentals of the programming language. However, even if you come from a completely different experience, Hadoop may provide you with career prospects.
  • Hadoop is typically run on Linux since it performs better than Windows. As a result, having a basic understanding of Linux will make your job a lot simpler.
  • Big Data - While this isn't a strict necessity for understanding the Hadoop framework, you should be aware of what you're getting yourself into.

How should I learn Big Data and Hadoop?

Here’s what you should do to learn Hadoop:
  • Start with the basics
  • Find the resources
  • Practice

How long does it take to learn Big Data and Hadoop?

If you already meet the prerequisites for learning Hadoop, you'll be able to master the topic in a matter of days or weeks. However, if you're starting from scratch, learning Hadoop might take anywhere from two to three months. In such circumstances, enrolling in Big Data Hadoop Training is highly advised.

Why should I take up this Big Data and Hadoop Course?

Hadoop is currently the industry standard for storing, processing, analyzing, and retrieving huge amounts of data. Big Data analytics has been shown to bring substantial economic benefits, and more companies are looking to recruit people that can extract critical information from both organized and unstructured data gives you a comprehensive Big Data Analytics and Hadoop development course that will teach you how to build, maintain, and use a Hadoop cluster for corporate advantage.

What can I expect to accomplish by the end of this course?

You will be able to comprehend the following after completing our course:
  • What is Big Data, why is it important, and how can it be used in business?
  • The methods for extracting value from Big Data
  • The fundamentals of Hadoop, including HDFs and MapReduce.
  • Getting a Glimpse of the Hadoop Ecosystem
  • Analyzing Big Data with a variety of tools and approaches
  • Using Pig and Hive to extract data
  • How can the organization's data sets be made more sustainable and flexible?
  • Creating Big Data strategies to promote business insight

How much time and days does it take to learn Big Data and Hadoop?

In only one month, you will be able to grasp the principles and practical execution of the Big Data Hadoop certification course. In one month, you can master the technology with committed resources and a never-say-die mentality.
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Overview of Hadoop Training in Delhi

The Big Data Hadoop Course in Delhi program will assist you with the masters Big Data Hadoop and Spark test and master Hadoop Administration with 14 real-time, industries-focused case studies. Cloudera CCA Spark and Hadoop Developer Certification certificates (CCA175). You will be masters of Cluster Settings for Amazon, Spark Frames, and RDD, Scala, and Spark SQL, Spark, Spark Streaming, etc. in MapReduce, Hive, Pig, Sqoop, Oozie, and Flore and will be working with Amazon EC2 on cluster setup. Hadoop professionals create Big Data Hadoop Certification Training which includes extensive knowledge of Big Data and Hadoop ecosystem tools, such as HDFS, YARN, MapReduce, Hive, and Pig. You will work on real-life industry applications in the retail, social media, aviation, tourism, and finance domains utilizing the Edureka Cloud Lab during this Big Data Hadoop certification training for online trainees. Sign up now for this big data certification to learn big data with hand-held demos from instructors with more than 10 years of expertise.

Additional Info

Big Data is nothing, however, a great amount of information can't hold on to by mistreatment of ancient relative databases. Corporations square measure mistreatment of Big Data technologies to analyze, store, and method knowledge so as to profit the companies. huge knowledge technologies are often classified as :

  • Operational Big Data
  • Analytical Big Data

Operational Big Data : Operational huge Data consists of systems like MongoDB. They're NoSQL knowledge bases and don't need massive commitment to write and data scientists to analyze the info. They supply cheap and economical operational knowledge techniques.

Analytical Big Data Data : These embrace technologies like MapR that offer advanced analysis The technologies employed by MapR are often used on a single system to terribly high and low systems.


Hadoop :

Hadoop is the tool that's wont to handle huge amounts of knowledge. Hadoop is an Associate in Nursing ASCII text file framework underneath the Apache software package Foundation. It's descendible by Java. By voice communication ASCII text file it implies that it's free and it is often changed as per our necessities and desires.


Hadoop Tools to create Your Big Data Journey straightforward :

1. HDFS :

Hadoop Distributed classification system, which is usually called HDFS is meant to store an outsized quantity of knowledge, therefore is kind of tons additional economical than the NTFS (New sort classification system) and FAT32 File System, that square measure employed in Windows PCs. HDFS is employed to cater massive chunks of knowledge quickly to applications. Yahoo has been victimizing the Hadoop Distributed classification system to manage over forty petabytes of knowledge.

2. HIVE :

Apache, which is usually glorious for hosting servers, has gotten their answer for Hadoop’s info as Apache HIVE information warehouse software system. This makes it straightforward for the U.S.A. to question and manage massive datasets. With HIVE, all the unstructured information square measure is projected with a structure, and later, we are able to question the info with an SQL-like language called HiveQL.

HIVE provides completely different storage sorts like plain text, RCFile, Hbase, ORC, etc. HIVE additionally comes with constitutional functions for the users, which may be accustomed manipulate dates, strings, numbers, and a number of {other|and several other} other forms of data processing functions.

3. NoSQL :

Structured question Languages are in use for an extended time, currently, because the information is usually unstructured, we tend to need a question Language that doesn’t have any structure. This is often resolved principally through NoSQL. Here we've primarily key combined values with secondary indexes. NoSQL will simply be integrated with Oracle info, Oracle pocketbook, and Hadoop. This makes NoSQL one of the widely supported Unstructured command languages.

4. Mahout :

Apache has additionally developed its library of various machine learning algorithms that are understood as the driver. The driver is enforced on the basis of Apache Hadoop and uses the MapReduce paradigm of BigData. As we tend to all understand the Machines learning various things daily by generating information supported by the inputs of a distinct user, this is often called Machine learning and is one of all the crucial elements of computer science.

Machine Learning is usually accustomed to improve the performance of any explicit system, and this majorly works on the result of the previous run of the machine.

5. Avro :

With this tool, we can quickly get representations of advanced information structures that are generated by Hadoop’s MapReduce algorithmic rule. Avro information tool will simply take each input and output from a MapReduce job, wherever it may also format a similar in an exceedingly abundant easier approach. With Avro, we are able to have the period classification, with simply reprehensible XML Configurations for the tool.

6. GIS tools :

Geographic info is one of the foremost in-depth sets of knowledge out there over the globe. This includes all the states, cafes, restaurants, and alternative news around the world, and this must be precise. Hadoop is employed with GIS tools, that square measure a Java-based tool out there for understanding Geographic info.

With the assistance of this tool, we are able to handle Geographic Coordinates in situ of strings, which may facilitate the U.S.A. to reduce the lines of code. With GIS, we are able to integrate maps in reports and publish them as online map applications.

7. Flume :

LOGs square measure generated whenever there's any request, response, or any kind of activity within the info. Logs facilitate rectifying the program and see wherever things square measure going wrong. whereas operating with massive sets of knowledge, even the Logs square measure generated in bulk. And once we get to move this huge quantity of log information, Flume comes into play. Flume uses a straightforward, protractible information model, which can assist you to use online analytic applications with the foremost ease.

8. Clouds :

All the cloud platforms work on massive information sets, which could build them slowly within the ancient approach. Therefore most of the cloud platforms square measure migrating to Hadoop, and Clouds can assist you with a similar.

With this tool, they'll use a brief machine that will facilitate the calculation of massive information sets to store the results and release the temporary machine that was accustomed to getting the results. of these things square measure came upon and regular by the cloud/ because of this, the conventional operating of the servers isn't affected in any respect.

9. Spark :

Coming to Hadoop analytics tools, Spark tiptop the list. Spark could be a framework out there for large information analytics from Apache. This one is AN ASCII text file information analytics cluster computing framework that was first developed by AMPLab at UC Berkeley. Later Apache bought a similar one from AMPLab.

Spark works on the Hadoop Distributed classification system, which is one of all the quality file systems to figure with BigData. Spark guarantees to perform a hundred times higher than the MapReduce algorithmic rule for Hadoop over a selected kind of application.

Spark hundreds of all the info into clusters of memory, which can enable the program to question it repeatedly, creating the most effective framework out there for AI and Machine Learning.

10. MapReduce :

Hadoop MapReduce could be a framework that produces it quite straightforward for the developer to jot down AN application that will method multi-terabyte datasets in parallel. These datasets are often calculated over massive clusters. MapReduce framework consists of a JobTracker and TaskTracker; there's one JobTracker that tracks all the roles, whereas there's a TaskTracker for each cluster-node. Master i.e., JobTracker, schedules the duty, whereas TaskTracker, which could be a slave, monitors them and schedules them if they fail


Job Responsibilities of a Big Data and Hadoop Developer:

    A Big Data and Hadoop Developer have several responsibilities. and also the job responsibilities are a unit addicted to your domain/sector, wherever a number of them would be applicable and a few won't. The subsequent area unit the tasks a Hadoop Developer is accountable for :

  • Big Data and Hadoop development and implementation.
  • Loading from disparate knowledge sets.
  • Pre-processing mistreatment Hive and Pig.
  • Designing, building, installing, configuring, and supporting Hadoop.
  • Translate advanced practical and technical necessities into elaborate style.
  • Perform analysis of huge knowledge stores and uncover insights.
  • Maintain security and knowledge of privacy.
  • Create scalable and superior internet services for knowledge training.
  • High-speed querying.
  • Managing and deploying HBase.
  • Being in a vicinity of a POC effort to assist build new Hadoop clusters.
  • Test prototypes and administer relinquishing to operational groups.
  • Propose best practices/standards.

Skills needed to become a Big Data and Hadoop Developer :

    Now that you just grasp what the task responsibilities of a Hadoop Developer include, it's essential to possess the correct talent to become one. The subsequent once more consists of attainable talent sets that area units needed by employers from varied domains.

  • Knowledge in Hadoop – sort of Obvious!!
  • Good data in back-end programming, specifically java, JS, Node.js, and OOAD
  • Writing superior, reliable, and rectifiable code.
  • Ability to write down MapReduce jobs.
  • Good data of information structures, theories, principles, and practices.
  • Ability to write down Pig Latin scripts.
  • Hands-on expertise in HiveQL.
  • Familiarity with knowledge loading tools like Flume, Sqoop.
  • Knowledge of workflow/schedulers like Oozie.
  • Analytical and drawback finding skills applied to the huge knowledge domain
  • Proven understanding of Hadoop, HBase, Hive, Pig, and HBase.
  • Good power in multi-threading and concurrency ideas.

Advantages of Big Data and Hadoop :

  • Scalable :

    Hadoop could be an extremely ascendable storage platform, as a result of it will store and distribute giant knowledge sets across many cheap servers that operate in parallel. In contrast to ancient computer database systems (RDBMS) that can’t scale to method giant amounts of information, Hadoop allows businesses to run applications on thousands of nodes involving several thousands of terabytes of information.

  • Value-effective :

    Hadoop additionally offers a price-effective storage resolution for businesses’ exploding knowledge sets. The matter with ancient computer database management systems is that it's extraordinarily valued preventative to scale to such a degree to method such large volumes of information. In a bid to scale back prices, several corporations within the past would have had to down-sample knowledge and classify it to support bound assumptions that knowledge was the foremost valuable. The information would be deleted because it would be too cost-prohibitive to stay. Whereas this approach might have worked within the short term, this meant that once business priorities were modified, the whole information set wasn't offered, because it was too valuable to store.

  • Flexible :

    Hadoop allows businesses to simply access new knowledge sources and faucet into differing types {of knowledge|of knowledge|of information} (both structured and unstructured) to get worth from that data. This implies businesses will use Hadoop to derive valuable business insights from knowledge sources like social media, email conversations. Hadoop is used for a good kind of functions, like log processes, recommendation systems, knowledge repository, market campaign analysis, and fraud detection.

  • Fast :

    Hadoop’s distinctive storage methodology is predicated on a distributed classification system that essentially ‘maps’ knowledge where it's placed on a cluster. The tools for processing square measure are usually on equivalent servers wherever the info is found, leading to abundant quicker processing. If you’re addressing giant volumes of unstructured knowledge, Hadoop is ready to with efficient method terabytes of information in mere minutes, and petabytes in hours.

  • Resilient to failure :

    A key advantage of exploiting Hadoop is its fault tolerance. Once knowledge is shipped to a private node, that knowledge is additionally replicated to alternative nodes within the cluster, which suggests that within the event of failure, there's another copy offered to be used.


Disadvantages of Big Data and Hadoop:

As the backbone of numerous implementations, Hadoop is sort of synonymous with massive information.

1. Security issues :

Just managing posh applications like Hadoop is difficult. A straightforward example is seen within the Hadoop security model, which is disabled by default because of sheer quality. If whoever managing the platform lacks the shrewdness to change it, your information may well be at immense risk. Hadoop is additionally missing encoding at the storage and network levels, which could be a major point for state agencies et al. that value more highly to keep their information covert.

2. Vulnerable naturally :

Speaking of security, the terrible makeup of Hadoop makes running it a risky proposition. The framework is written nearly entirely in Java, one of all the foremost widely-used nonetheless polemic programming languages existing. Java has been heavily exploited by cybercriminals and as a result, involved in varied security breaches.

3. Not fit tiny information :

While massive information isn't solely created for giant businesses, not all massive information platforms are fitted to tiny information wants. Because of its high capability style, the Hadoop Distributed filing system lacks the power to expeditiously support the random reading of tiny files. As a result, it's not suggested for organizations with tiny quantities of information.

4. Potential Stability problems :

Like all open supply software packages, Hadoop has had its fair proportion of stability problems. To avoid these problems, organizations are powerfully suggested to form certain they're running the newest stable version, or run it underneath a third-party merchandiser equipped to handle such issues.

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Key Features

ACTE Delhi offers Hadoop Training in more than 27+ branches with expert trainers. Here are the key features,
  • 40 Hours Course Duration
  • 100% Job Oriented Training
  • Industry Expert Faculties
  • Free Demo Class Available
  • Completed 500+ Batches
  • Certification Guidance

Authorized Partners

ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.
 

Curriculum

Syllabus of Hadoop Course in Delhi
Module 1: Introduction to Hadoop
  • High Availability
  • Scaling
  • Advantages and Challenges
Module 2: Introduction to Big Data
  • What is Big data
  • Big Data opportunities,Challenges
  • Characteristics of Big data
Module 3: Introduction to Hadoop
  • Hadoop Distributed File System
  • Comparing Hadoop & SQL
  • Industries using Hadoop
  • Data Locality
  • Hadoop Architecture
  • Map Reduce & HDFS
  • Using the Hadoop single node image (Clone)
Module 4: Hadoop Distributed File System (HDFS)
  • HDFS Design & Concepts
  • Blocks, Name nodes and Data nodes
  • HDFS High-Availability and HDFS Federation
  • Hadoop DFS The Command-Line Interface
  • Basic File System Operations
  • Anatomy of File Read,File Write
  • Block Placement Policy and Modes
  • More detailed explanation about Configuration files
  • Metadata, FS image, Edit log, Secondary Name Node and Safe Mode
  • How to add New Data Node dynamically,decommission a Data Node dynamically (Without stopping cluster)
  • FSCK Utility. (Block report)
  • How to override default configuration at system level and Programming level
  • HDFS Federation
  • ZOOKEEPER Leader Election Algorithm
  • Exercise and small use case on HDFS
Module 5: Map Reduce
  • Map Reduce Functional Programming Basics
  • Map and Reduce Basics
  • How Map Reduce Works
  • Anatomy of a Map Reduce Job Run
  • Legacy Architecture ->Job Submission, Job Initialization, Task Assignment, Task Execution, Progress and Status Updates
  • Job Completion, Failures
  • Shuffling and Sorting
  • Splits, Record reader, Partition, Types of partitions & Combiner
  • Optimization Techniques -> Speculative Execution, JVM Reuse and No. Slots
  • Types of Schedulers and Counters
  • Comparisons between Old and New API at code and Architecture Level
  • Getting the data from RDBMS into HDFS using Custom data types
  • Distributed Cache and Hadoop Streaming (Python, Ruby and R)
  • YARN
  • Sequential Files and Map Files
  • Enabling Compression Codec’s
  • Map side Join with distributed Cache
  • Types of I/O Formats: Multiple outputs, NLINEinputformat
  • Handling small files using CombineFileInputFormat
Module 6: Map Reduce Programming – Java Programming
  • Hands on “Word Count” in Map Reduce in standalone and Pseudo distribution Mode
  • Sorting files using Hadoop Configuration API discussion
  • Emulating “grep” for searching inside a file in Hadoop
  • DBInput Format
  • Job Dependency API discussion
  • Input Format API discussion,Split API discussion
  • Custom Data type creation in Hadoop
Module 7: NOSQL
  • ACID in RDBMS and BASE in NoSQL
  • CAP Theorem and Types of Consistency
  • Types of NoSQL Databases in detail
  • Columnar Databases in Detail (HBASE and CASSANDRA)
  • TTL, Bloom Filters and Compensation
<strongclass="streight-line-text"> Module 8: HBase
  • HBase Installation, Concepts
  • HBase Data Model and Comparison between RDBMS and NOSQL
  • Master & Region Servers
  • HBase Operations (DDL and DML) through Shell and Programming and HBase Architecture
  • Catalog Tables
  • Block Cache and sharding
  • SPLITS
  • DATA Modeling (Sequential, Salted, Promoted and Random Keys)
  • Java API’s and Rest Interface
  • Client Side Buffering and Process 1 million records using Client side Buffering
  • HBase Counters
  • Enabling Replication and HBase RAW Scans
  • HBase Filters
  • Bulk Loading and Co processors (Endpoints and Observers with programs)
  • Real world use case consisting of HDFS,MR and HBASE
Module 9: Hive
  • Hive Installation, Introduction and Architecture
  • Hive Services, Hive Shell, Hive Server and Hive Web Interface (HWI)
  • Meta store, Hive QL
  • OLTP vs. OLAP
  • Working with Tables
  • Primitive data types and complex data types
  • Working with Partitions
  • User Defined Functions
  • Hive Bucketed Tables and Sampling
  • External partitioned tables, Map the data to the partition in the table, Writing the output of one query to another table, Multiple inserts
  • Dynamic Partition
  • Differences between ORDER BY, DISTRIBUTE BY and SORT BY
  • Bucketing and Sorted Bucketing with Dynamic partition
  • RC File
  • INDEXES and VIEWS
  • MAPSIDE JOINS
  • Compression on hive tables and Migrating Hive tables
  • Dynamic substation of Hive and Different ways of running Hive
  • How to enable Update in HIVE
  • Log Analysis on Hive
  • Access HBASE tables using Hive
  • Hands on Exercises
Module 10: Pig
  • Pig Installation
  • Execution Types
  • Grunt Shell
  • Pig Latin
  • Data Processing
  • Schema on read
  • Primitive data types and complex data types
  • Tuple schema, BAG Schema and MAP Schema
  • Loading and Storing
  • Filtering, Grouping and Joining
  • Debugging commands (Illustrate and Explain)
  • Validations,Type casting in PIG
  • Working with Functions
  • User Defined Functions
  • Types of JOINS in pig and Replicated Join in detail
  • SPLITS and Multiquery execution
  • Error Handling, FLATTEN and ORDER BY
  • Parameter Substitution
  • Nested For Each
  • User Defined Functions, Dynamic Invokers and Macros
  • How to access HBASE using PIG, Load and Write JSON DATA using PIG
  • Piggy Bank
  • Hands on Exercises
Module 11: SQOOP
  • Sqoop Installation
  • Import Data.(Full table, Only Subset, Target Directory, protecting Password, file format other than CSV, Compressing, Control Parallelism, All tables Import)
  • Incremental Import(Import only New data, Last Imported data, storing Password in Metastore, Sharing Metastore between Sqoop Clients)
  • Free Form Query Import
  • Export data to RDBMS,HIVE and HBASE
  • Hands on Exercises
Module 12: HCatalog
  • HCatalog Installation
  • Introduction to HCatalog
  • About Hcatalog with PIG,HIVE and MR
  • Hands on Exercises
Module 13: Flume
  • Flume Installation
  • Introduction to Flume
  • Flume Agents: Sources, Channels and Sinks
  • Log User information using Java program in to HDFS using LOG4J and Avro Source, Tail Source
  • Log User information using Java program in to HBASE using LOG4J and Avro Source, Tail Source
  • Flume Commands
  • Use case of Flume: Flume the data from twitter in to HDFS and HBASE. Do some analysis using HIVE and PIG
Module 14: More Ecosystems
  • HUE.(Hortonworks and Cloudera)
Module 15: Oozie
  • Workflow (Action, Start, Action, End, Kill, Join and Fork), Schedulers, Coordinators and Bundles.,to show how to schedule Sqoop Job, Hive, MR and PIG
  • Real world Use case which will find the top websites used by users of certain ages and will be scheduled to run for every one hour
  • Zoo Keeper
  • HBASE Integration with HIVE and PIG
  • Phoenix
  • Proof of concept (POC)
Module 16: SPARK
  • Spark Overview
  • Linking with Spark, Initializing Spark
  • Using the Shell
  • Resilient Distributed Datasets (RDDs)
  • Parallelized Collections
  • External Datasets
  • RDD Operations
  • Basics, Passing Functions to Spark
  • Working with Key-Value Pairs
  • Transformations
  • Actions
  • RDD Persistence
  • Which Storage Level to Choose?
  • Removing Data
  • Shared Variables
  • Broadcast Variables
  • Accumulators
  • Deploying to a Cluster
  • Unit Testing
  • Migrating from pre-1.0 Versions of Spark
  • Where to Go from Here
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Hands-on Real Time Hadoop Projects

Project 1
Health Status Prediction

This Big Data project is designed to predict the health status based on massive datasets. It will involve the creation of a machine learning model that can accurately classify users.

Project 2
Anomaly Detection in Cloud Servers

In this project, an anomaly detection approach will be implemented for streaming large datasets. The proposed project will detect anomalies in cloud servers.

Project 3
Recruitment for Big Data Job Profiles

Recruitment is a challenging job responsibility of the HR department of any company.We’ll create a Big Data project that can analyze vast amounts of data.

Project 4
Malicious User Detection in Big data Collection

To achieve this, the project will divide the trustworthiness into familiarity and similarity trustworthiness. Furthermore, it will divide all the participants into small groups.

Our Best Hiring Placement Partners

ACTE Delhi offers arrangement openings as extra to each understudy/proficient who finished our study hall or internet preparing. A portion of our understudies are working in these organizations recorded underneath.
  • We provide a one-of-a-kind student placement platform where you can locate all of the interview schedules and receive email notifications.
  • In ACTE bundle is truly a Position bundle guarantees 100% arrangement. It contains all the substance significant for arrangements and subsequent to finishing each part learner can show up for challenges which give the experience as genuine organization test.
  • Overall the executives of activation, training and arrangement. Comprehend the provincial economy and creating systems for training and situation in different.
  • Identify the work opportunities in plan office and to offer free situation administration. Organize the effective training and input from learner.
  • In ACTE Live arrangement training is the right course for learner to help learner expert learner situation prep. This course covers 78 hours of live classes covering the entirety of the significant subjects tried in arrangements and 250+ long stretches of independent organization explicit prep with video clarification.
  • Develop ideal critical thinking, programming and meeting ways to deal with break the enlistment of top IT administration selection representatives, for example, TCS, Wipro, Infosys, Capgemini and so on

Get Certified By MapR Certified Hadoop Developer (MCHD) & Industry Recognized ACTE Certificate

Acte Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher's as well as corporate trainees. Our certification at Acte is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC's of the world. The certification is only provided after successful completion of our training and practical based projects.

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About Satisfactory Hadoop Instructor

  • Our Hadoop Training in Delhi provide a one-of-a-kind student placement platform where you can locate all of the interview schedules and receive email notifications.
  • Train your way with a decision of online independent courses, face to face or virtual master drove meetings and study guides composed by worldwide industry specialists and support learner learning with online execution based lab bundles.
  • Leverage a cross breed of information and execution based training and testing to propel learner vocation and upgrade learner capacity to perform fundamental assignments that future IS/IT jobs and arising tech's spaces request.
  • Fast track learner  expert excursion as learner add to and fill holes in learner insight, abilities and certainty and secure upper hands for learner, learner association now and into what's to come.
  • Trainers Successfully use an assortment of preparing procedures, methods, ideas, learning devices, and practices to guarantee most extreme adequacy of preparing.
  • Interact with understudies and scholarly clients of Altair devices around the world, including understudy groups, hold workshops online courses and trainings for them.

Hadoop Course Reviews

Our ACTE Delhi Reviews are listed here. Reviews of our students who completed their training with us and left their reviews in public portals and our primary website of ACTE & Video Reviews.

Mahalakshmi

Studying

"I would like to recommend to the learners who wants to be an expert on Big Data just one place i.e.,ACTE institute at Anna nagar. After several research with several Training Institutes I ended up with ACTE. My Big Data Hadoop trainer was so helpful in replying, solving the issues and Explanations are clean, clear, easy to understand the concepts and it is one of the Best Training Institute for Hadoop Training"

Alex

Software Engineer

My learning experience at ACTE is awesome. Teaching is great and very experienced and knowledgeable faculty for Hadoop. Topics are covered in great depth. Subject material, vidoes and instance managed very professionally. great value for the fees what we pay. I Strongly recommend ACTE in Delhi.

Harish

Software Engineer

The training here is very well structured and is very much peculiar with the current industry standards. Working on real-time projects & case studies will help us build hands-on experience which we can avail at this institute. Also, the faculty here helps to build knowledge of interview questions & conducts repetitive mock interviews which will help in building immense confidence. Overall it was a very good experience in availing training in Tambaram at the ACTE Institute. I strongly recommend this institute to others for excelling in their career profession.

Sindhuja

Studying

I had an outstanding experience in learning Hadoop from ACTE Institute. The trainer here was very much focused on enhancing knowledge of both theoretical & as well as practical concepts among the students. They had also focused on mock interviews & test assignments which helped me towards boosting my confidence.

Kaviya

Software Engineer

The Hadoop Training by sundhar sir Velachery branch was great. The course was detailed and covered all the required knowledge essential for Big Data Hadoop. The time mentioned was strictly met and without missing any milestone.Should be recommended who is looking Hadoop training course ACTE institute in Chennai.

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Hadoop Course FAQs

Looking for better Discount Price?

Call now: +91 93833 99991 and know the exciting offers available for you!
  • ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
  • We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
  • More than 3500+ students placed in last year in India & Globally
  • ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
  • 85% percent placement record
  • Our Placement Cell support you till you get placed in better MNC
  • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
ACTE
    • Gives
Certificate
    • For Completing A Course
  • Certification is Accredited by all major Global Companies
  • ACTE is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore
  • The entire Hadoop training has been built around Real Time Implementation
  • You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
  • GitHub repository and Showcase to Recruiters in Interviews & Get Placed
All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.
No worries. ACTE assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

Why Should I Learn Hadoop Course At ACTE?

  • Hadoop Course in ACTE is designed & conducted by Hadoop experts with 10+ years of experience in the Hadoop domain
  • Only institution in India with the right blend of theory & practical sessions
  • In-depth Course coverage for 60+ Hours
  • More than 50,000+ students trust ACTE
  • Affordable fees keeping students and IT working professionals in mind
  • Course timings designed to suit working professionals and students
  • Interview tips and training
  • Resume building support
  • Real-time projects and case studies
Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
You will receive ACTE globally recognized course completion certification Along with National Institute of Education (NIE), Singapore.
We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.
We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Hadoop batch to 5 or 6 members
Our courseware is designed to give a hands-on approach to the students in Hadoop. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
You can contact our support number at +91 93800 99996 / Directly can do by ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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Request for Class Room & Online Training Quotation

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