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Hadoop Training in Noida

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08- Feb - 2023

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(Class 1Hr - 1:30Hrs) / Per Session

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  • You will learn more than just Hadoop in this course; you will also learn about Big Data. We will teach you everything you need to know about big data, from installation to configuration to data handling.
  • You will also learn how to apply them to solving real-world business problems during this tutorial! All that is required is an understanding of UNIX and Java. This course aims to equip you with the theoretical knowledge as well as the confidence necessary to apply that knowledge in your career.
  • You will be prepared for big data projects quickly after taking this course. We will learn about Hadoop components such as HDFS, Map Reduce, Apache Pig, and Hive, as well as how to set up and configure EC2 instances.
  • Students of all levels will have the ability to understand all subjects if examples, applications, and explanations are used.
  • Theory as well as practical sessions are beneficial to students. In our programs, graduates can find employment in top firms after graduation.
  • During this course, you will gain a thorough understanding of Hadoop and its related distributed systems so as to apply them to real-world problems. Furthermore, a certificate of completion awaits you when you finish!
  • 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.
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
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Course Objectives

Big Data is the quickest developing and most encouraging innovation for the treatment of a lot of information. This Big Data Hadoop preparation assists you with accomplishing the most elevated proficient capabilities. Virtually every top MNC attempts to enter Big Data Hadoop, which makes it essential for affirmed Big Data experts to work.

Hadoop and SQL both control data, but in various styles. Hadoop is a framework of software elements, while SQL is a programming language. For big data, both tools have advantages and disadvantages. Hadoop manages more open data sets but only reports data once.

Big Data Hadoop Certification preparation is intended to make you a Certified Big Data Practitioner by industry specialists. The course of Big Data Hadoop:
  • Top to bottom information on the HD FS, YARN (Another Resource Negotiator) and Map Big Data, and Hadoop including the HDFS Cutting.
  • Thorough information on different apparatuses, for example, Pig, Hive, Sqoop, Flume, Oozie, and HBase that fall inside Hadoop Ecologic.
  • The capacity to incorporate HDFS information utilizing Sqoop and Flume and to break down huge HDFS-based datasets of an assorted sort covering a few informational collections from various fields like banking, tea, etc.

The accompanying figures will assist you with seeing Big Data development: Hadoop designers have a normal compensation of INR 11,74,000. Associations are keen on large amounts of information and use Hadoop to store and dissect them. There is thus additionally a fast expansion sought after for occupations in Big Data and Hadoop. Presently is the ideal spot for Big Data Hadoop internet preparing if you have an interest in a profession in this field.

Set of working responsibilities for Hadoop Developers:
  • Advancement and execution of Hadoop.
  • Hive and Pig are utilized for pre-handling.
  • Making, building, introducing, designing, and looking after Hadoop.
  • Break down a lot of information to discover new bits of knowledge.
  • Make information checking web benefits that are adaptable and high-performing.

You need to code to conduct numerical and applied mathematics analysis with Big Data Hadoop. A number of the languages you must invest time and cash in learning are Python, R, Java, and C++ among others. Finally, having the ability to assume sort of an engineer can help you to become a decent huge knowledge analyst.

  • Framework managers and programming designers.
  • Exchange and undertaking administrators with experienced insight.
  • Big Data Hadoop Developers need to learn other vertical components like testing, investigation, and the executives.
  • Proficient centralized computers, engineers, and specialists in testing.
  • Experts in business insight, information warehousing, and examination.
  • Graduates will need to learn Big Data.

What are the Requirements of the Big Data Hadoop Certification Training Course?

Experts entering into the Big Data Hadoop training course in Ahmedabad should have a fundamental understanding of Core Java and SQL. If you want to clean up on your Core Java skills, ACTE allows a complimentary self-paced course Java essentials for Hadoop when you enroll for this course.

What are the tools needed for the Big Data Hadoop Certification Training Course?

Hadoop Distributed File System. The Hadoop Distributed filing system (HDFS) is meant to store giant knowledge sets faithfully, and to stream those knowledge sets at high information to user applications:
  • Hbase.
  • HIVE.
  • Sqoop.
  • ZooKeeper.
  • NOSQL.
  • Mahout.

In this Big Data Hadoop Certification Training Course, what are you going to learn?

  • Hadoop and YARN essentials and compose applications.
  • Sparkle SQL, Streaming, Data Frame, RDD, GraphX and MLlib composing Spark applications HDFS MapReduce, Hive, Pig, Sqoop, Flume, and ZooKeeper Spark.
  • Avro information designs work.
  • Use Hadoop and Apache Spark to execute genuine ventures.
  • Be set up to clear the accreditation with Big Data Hadoop.

How much time does it require to learn Big Data and Hadoop Certification Training Course?

You will have several days to dominate the subject if you as of now satisfy the prerequisites for Hadoop. It can take 2 to 90 days to learn Hadoop, nonetheless, on the off chance that you gain without any preparation. In these cases, Big Data Hadoop Training is firmly suggested.

What are the Big Data Hadoop Certification Training skills you will learn?

The certification training in Big Data Hadoop will assist you with turning into a Big Data master. It will improve your abilities by furnishing you with thorough skills in Hadoop and the viable experience needed to address projects situated in the business continuously.

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Overview of Hadoop Training in Noida

Hadoop Training in Noida is provided by ACTE to assist students become proficient in Big Data and Hadoop. It also offers placement training and classroom instruction on Hadoop's fundamental and advanced principles. MapReduce and Hadoop ecosystems such as HBase, Pig, and Hive are included in the Hadoop placement-oriented training and live online training. Using the appropriate frameworks, students create a Hadoop application.


Additional Info

Big Hadoop professionals have the following responsibilities:

Hadoop professionals perform similar tasks to those of software developers. It is necessary for him or her to program Hadoop applications. In the big data domain, Hadoop developers deal with similar tasks as Hadoop professionals. We have included the following points to assist you in understanding a Hadoop professional's role.

  • Hadoop development and implementation role.
  • Loading data sets from different sources.
  • Tools for pre-processing include Pig, Hive, and others.
  • In charge of building, designing, installing, configuring, and supporting Hadoop systems.
  • Creating a design that translates complex technical functions into a complete product.
  • Working with big data sets and analyzing them to come up with new insights.
  • Securing data privacy and security without letting down the guard.
  • To facilitate easy tracking of data, create scalable and reliable web services.
  • Queries are processed at high speed.
  • HBase deployment and management.
  • By analyzing the analysis, we can propose the best standards and practices.
  • Being a part of the POC for Hadoop cluster building.
  • Ensure the prototypes are tested and handed over to the operating teams according to plan.
  • The roles of a Hadoop professional are indeed fascinating, and it is an interesting career path. An Hadoop professional can expect a lucrative salary and career opportunities. There has never been a better time to learn online training for Hadoop big data; the demand for Hadoop professionals is continuously increasing.
  • How does Hadoop play a role in the job market?

    One of the advantages of big data and Hadoop certification training is the unlimited potential for career growth. So it can be used for multiple roles. If you're a big data developer skilled in Hadoop, you may apply for the following jobs.

  • Hadoop Tester
  • Hadoop Developer
  • Hadoop Administrator
  • Hadoop Lead Developer
  • Data scientist
  • Hadoop Architect
  • The following skills/requirements are required:

  • Recent experience in data engineering of 2 to 7 years.
  • An undergraduate degree in Computer Science or a related field is preferred.
  • Data management experience demonstrating your attention to detail and flawless execution.
  • Expertise in and experience with statistics.
  • The ability to learn new programming languages to meet the company's objectives is essential, whether it's Python, Spark, Kafka, or Java.
  • Programming knowledge in C, Perl, Javascript or other languages would be a plus. Experience with data cleaning, wrangling, visualization, and reporting, using the most efficient tools and applications to accomplish these tasks.
  • Having knowledge of data cleansing, wrangling, visualization, and reporting, as well as being able to select and utilize the appropriate tools and applications for these tasks.
  • It would be beneficial if you had experience with MapReduce.
  • Information retrieval, data mining, machine learning, or natural language processing expertise.
  • Have experience incorporating data from multiple sources, including large amounts of structured and unstructured data.
  • A good understanding of machine learning toolkits including H2O, SparkML and Mahout
  • To solve data mining problems, you should be willing to explore new alternatives and options using industry best practices, data innovations, and your experience.
  • Support and troubleshooting experience in production.
  • It brings you great satisfaction to complete a job well done, and solving complex problems is what you thrive on.
  • To become a Hadoop expert, what are the skills I need?

    It is an excellent time to learn more about the skill set required by Hadoop professionals if you do not already know it. So you can receive training on both an offline and an online platform for big data and Hadoop certifications. Many major companies around the world look for the following skills. First and foremost, you must understand Hadoop. By taking big data Hadoop online training from the best sources, you can achieve this.

  • You will need solid knowledge of Java, JS, OOAD, and other backend programming languages.
  • Writing maintainable, reliable, and high-performing code is a strong suit.
  • A MapReduce job must be understood.
  • An overview of HiveQL in practice.
  • Outstanding understanding of theories, principles, and structures.
  • Scripts written in Pig Latin should be understood.
  • Getting familiar with Sqoop, Flume, and the other data loading tools.
  • Working knowledge of schedulers and workflows such as Oozie.
  • A person who works in big data analytics must possess analytical and problem-solving skills.
  • Outstanding ability to grasp concurrent and multi-threaded concepts.
  • Hands-on experience with Pig, HBase, and Hadoop.
  • The Hadoop developer program is suitable for those who are interested in becoming more knowledgeable about Hadoop development and to grow their career opportunities.
  • Hadoop's popularity is attributed to its features:

    The most powerful Big Data tool, Hadoop has the following characteristics that make it the most reliable tool, an industry favorite, and more reliable to use.

    1. Open Source:- The open-source nature of Hadoop makes it completely free to use. Being an open-source project, the source-code can be accessed online by anyone interested in understanding it or making modifications to meet their industry's requirements.

    2. Highly Scalable Cluster:- Scalability is one of Hadoop's main advantages. Multiple inexpensive machines are used to process large amounts of data in parallel on a cluster of inexpensive machines. You can increase or decrease the number of nodes based on your enterprise's needs. The RDBMS (Relational Data Base Management System) cannot handle data volumes approaching a large number.

    3. Fault Tolerance is Available:- Hadoop runs on cheap hardware (inexpensive systems), which can crash at any moment. A Hadoop cluster has data replicated on various DataNodes so that if one of the systems fails, data will still be available. If a machine in a Hadoop cluster goes down, you can read all the data from other nodes in the cluster because the data is automatically copied or replicated. In Hadoop, each block of a file is made three copies and stored on three different nodes by default. Hdfs-site.xml contains a replication property that allows you to modify this replication factor.

    4. High Availability is Provided:- High Availability is provided by fault tolerance in the Hadoop cluster. Hadoop clusters are designed to be high availability. Data is replicated between nodes since fault tolerance ensures that if one fails, other nodes can still retrieve the same information. As well as at least two NameNodes in the high-availability Hadoop cluster, there could be more. Activated NameNodes and passive NameNodes, also known as standby NameNodes. Passive NameNode will take over if Active NameNode fails. It provides similar data to Active NameNode and can be utilized by the user as well.

    5. Cost-Effective:- Hadoop uses commodity hardware that is cost-effective, eliminating the need for expensive hardware and high-end processors required by traditional Relational databases. As a consequence, companies are starting to remove raw data from traditional relational databases, which are not cost-effective for storing the massive volumes of data. Their business may not be in the right scenario as a result. With Hadoop, we get two main benefits with the cost. The first is that it is open-source, which means it is free to use. The second is that it uses inexpensive commodity hardware.

    6. Hadoop Provide Flexibility:- Hadoop is designed to handle any kind of dataset, like unstructured (images and videos), semi-structured (mysql data), and structured (xml, json). Because of this, it has the capability of processing any type of data regardless of its structure, making it a highly flexible program. Businesses can use Hadoop to analyze data from diverse sources such as social media and email to get valuable insights. Because of its ability to process large datasets easily, Hadoop can save them time. In addition to log processing, Hadoop can be used for data warehouses, fraud detection, etc.

    7. Easy to Use:- Since Hadoop manages all of the processing work for the developers, the developers need not worry about it. There are tons of tools in Hadoop's ecosystem, like HIVE, Pig, Spark, HBase, Mahout, etc.

    8. Hadoop uses Data Locality:- For Hadoop processing to be fast, the concept of Data Locality is employed. Data locality involves relocating computation logic to the data, rather than relocating computation logic to the data. With the data locality concept, the system's bandwidth utilization is minimized by reducing the cost of moving data on HDFS.

    9. Provides Faster Data Processing:- Storage is managed by Hadoop using a distributed file system, i.e. (HDFS) is a distributed file system for Hadoop. With DFS(Distributed File System), large files are broken into small pieces and distributed amongst the nodes within a cluster of nodes. This massive number of file pieces are processed in parallel, which makes Hadoop much faster compared to traditional database management systems.

    Advantages of Big Data:

    The use of big data in the right way can facilitate groundbreaking breakthroughs for organizations. In addition to enabling data-driven decision making, big data solutions and analytics can empower your workforce in a way that improves business performance. Analytics and tools for big data are beneficial because –

  • Accumulation of data from multiple sources, including the Internet, social media platforms, online shopping sites, company databases, third-party sources, etc.
  • Monitoring and forecasting of business and market activity in real-time.
  • Business decisions can be influenced by finding key points buried in large datasets.
  • Optimize complex decisions to mitigate risks for unforeseen events and potential threats as soon as possible.
  • Real-time identifying of system and business process problems.
  • Utilize data-driven marketing to its full potential.
  • Utilize customer information to create customized products, services, discounts, etc.
  • Maintain a high level of customer satisfaction by ensuring fast delivery of products and services.
  • Diversifying revenue streams will increase company profits and return on investment.
  • Answer customer questions, complaints, and grievances in real-time.
  • Encourage businesses to develop new products and services.
  • Advantages of Hadoop:

    Here we discuss the benefits of Hadoop. As we proceed, let's take a closer look at each:

    1. Open Source:- Hasoop's source code is free to download, making it open source. Our business requirements can be met by modifying source code. Several proprietary Hadoop versions are available, including Cloudera and HortonWorks.

    2. Scalable:- Clusters of machines are used by Hadoop. Scalability is one of Hadoop's strongest points. Our cluster can be expanded by adding new nodes based on requirement without any downtime. Adding new machines to a cluster is known as Horizontal Scaling, whereas adding more capacity to a cluster by doubling hard disk space and RAM is known as Vertical Scaling.

    3. Performance:- Hadoop parallelizes data processing by starting the process on all blocks simultaneously, which is not possible in legacy systems like RDBMS. Hadoop's performance outperforms legacy systems like RDBMS thanks to parallel processing techniques. The Fastest Supercomputer was beaten by Hadoop in 2008 as well.

    4. Architecture of Share Nothing:- There is no dependency between the nodes in a Hadoop cluster. In Share Nothing Architecture (SN), resources and storage are not shared. Cluster nodes act independently, so failure of a single node won't take the whole thing down.

    5. Multi-language support:- Despite being developed mostly in Java, Hadoop also supports Python, Ruby, Perl, and Groovy.

    6. Cost-Effective:- The economic nature of Hadoop makes it very attractive. By utilizing standard commodity hardware, we can build Hadoop Clusters at a reduced cost. Compared to Traditional ETL systems, Hadoop data management costs - i.e. hardware and software, as well as other expenses - are very low.

    7. Abstraction:- Several levels of abstraction are provided by Hadoop. Developers can do their jobs more easily. Big files are divided and stored at separate parts of a cluster in blocks of the same size. In creating the map-reduce task, we need to pay attention to where the blocks are located. Data blocks at different locations are processed by Hadoop framework using a complete file as input. Hadoop is an abstraction on top of Hive, which is a part of the Hadoop Ecosystem. Initially, Java-based MapReduce tasks were inaccessible to SQL Developers across the world. This issue is resolved by introducing Hive. The queries we write on Hive will trigger Map Reduce jobs in turn, just as they do on SQL. Hence, SQL developers can also perform Map Reduce tasks because of Hive.

    8. Compatibility:- MapReduce, in Hadoop, is the processing engine, while HDFS is the storage layer. The default processing engine for Map Reduce is not rigid. HDFS is used as the storage system in new processing frameworks such as Apache Spark and Apache Flink. Apache Tez and Apache Spark can also be substituted as execution engines, depending on the requirements. HDFS is used as the storage layer for Apache HBase, which is a NoSQL columnar database.

    9. Multi-File System Support:- Its flexibility makes Hadoop extremely useful. In addition to images, videos, and files, this program can ingest many other formats. Structured as well as unstructured data can be processed. Various file systems can be used with Hadoop, including JSON, XML, Avro, and Parquet.

    Credentials for Big Data and Hadoop developers:

    You will learn the foundations as well as the more insightful ideas about Hadoop through this Hadoop Developer Training, which is, no doubt, the most ideal approach ahead for any newbie. There is an option to gain information about Hadoop, HDFS, or MapReduce. It is going to never again be the same for you to write MapReduce codes, as this course teaches you the same as well as furthermore hadoop groupings.

    Salary of Bigdata Hadoop:

    In India, an Hadoop Developer's salary largely depends on their education, skill set, years of experience, company size and reputation, as well as their work location. Postgraduates can, in general, expect to earn between Rs. 4 and 8 LPA as a starting package. A Big Data Engineer earns an average of 7,78,607 annually in India. For a salary comparison for Big Data Engineers in your area, you can filter by location.

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    Key Features

    ACTE Noida 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.


    Syllabus of Hadoop Course in Noida
    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
    • 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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    Need customized curriculum?

    Hands-on Real Time Hadoop Projects

    Project 1
    Speech Analysis

    Through this project, you can showcase the telephone-computer integration employed in a call center application. The call records can be flagged, sorted, and later analyzed.

    Project 2
    Trend Analysis of Weblogs

    You can design a log analysis system capable of handling colossal quantities of log files dependably. A program like this would minimize the response time for queries.

    Project 3
    Data Consolidation Project

    The quantitative goals for Data Center Consolidation should address the following qualitative areas: Reducing the cost of data center hardware, software and operations.

    Project 4
    Specialized Analysis Project

    In brief, project management objectives are the successful development of the project's procedures of initiation, planning, execution, regulation and closure.

    Our Best Hiring Placement Partners

    ACTE Noida is certify around the world. It expands the worth of your resume and you can accomplish driving position posts with the assistance of this affirmation in driving MNC's of the world. The certificate is just given after fruitful finishing of our preparation and pragmatic based undertakings.
    • For circumstance, we have different understudy locales where you can see the total of the gathering dates and seek after email alerts.
    • ACTE has ordinary length is 3-6 months and covers various modules wanted to the work capacities required by a applicants. The course gets ready students project from the earliest starting point.
    • ACTE permits more than 10 freedoms to get put. ACTE moreover guides pre position meeting to make learners sure for meet.
    • ACTE can simply help learners in handling the position talk with calls. It's up to learner, how to change over those odds into offers for work by presenting learner mastery at the hour of the gathering.
    • Our Learning the master framework incorporates MCQs, live discussions, and Circumstance placement plans.
    • Placement Team has assisted understudies with getting their fantasy occupations in affiliations like IBM , HCL , Wipro , TCS , Accenture, ,and so forth

    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.

    Complete Your Course

    a downloadable Certificate in PDF format, immediately available to you when you complete your Course

    Get Certified

    a physical version of your officially branded and security-marked Certificate.

    Get Certified

    About Skillful Hadoop Instructor

    • Our Big Data Hadoop Training in Noida have trainers have more than 10+ years of dominance around here, our mentors are uncommonly talented and will ensure that the learners totally like the subjects being taught.
    • Our mentor has worked in various parts at the front line of programming improvement and shows capable coding in different vernaculars.
    • Labs totally outfitted with all the fundamental equipment's. Mentor was significantly skilled in his subjects and planning was passed on in incredibly light manner and we really liked it.
    • Training providers are for since a long time back related and experienced with the testing space, to sort out the best programming testing classes on the web, and outfit learners with a scope of capacities that is seen across the business.
    • Trainers put earnestly in stunning quality getting ready and fantastic system. Our item testing classes are taken by proficient tutors who coach learners on Big Data Hadoop Training.
    • Even after the preparation is finished, broad instructing by means of drawing in scenes like Whatsapp, discussions, and web-based media stages is offered to productively execute any questions.

    Hadoop Course Reviews

    Our ACTE Noida 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.



    "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"


    Software Engineer

    Excellent and very knowledgeable trainer whatever the topic he thought me the best with good slides which i can understand easily Hadoop Course in Noida. All topics were relevant to the ‘real world’ and lots to work on after the training.” I got a Great opportunity to grow my carrier in new technology. Sincerely thanks a lot to my trainer Gaurav Sir and ACTE for choosing very good Trainer.


    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.



    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.


    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
      • Gives
      • 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's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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