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Hadoop Training in Visakhapatnam

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25- Sep- 2023

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27- Sep- 2023

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08:00 AM & 10:00 AM Batches

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

23- Sep- 2023

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23- Sep- 2023

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  • This course teaches more than Hadoop. You will be introduced to Big Data, from installing to configuring it to working with it. You will be able to apply your knowledge to solving real-world problems with big data.
  • It is only necessary to understand UNIX and Java so that you may successfully apply the knowledge you earn in this course.
  • Upon completion of this course, you will be able to configure EC2 instances as well as learn about Hadoop components such as HDFS, Map Reduce, Apache Pig, and Hive.
  • The applications, examples, and explanations we provide will be beneficial to students of all levels. We provide theory as well as practical sessions, which result in students getting jobs with leading firms after graduation.
  • This course will empower you to gain a thorough understanding of the Hadoop ecosystem and its associated distributed systems, enabling you to apply them to real-world problems. Furthermore, you will receive a certificate upon completion of this course.
  • 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
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This is How ACTE Students Prepare for Better Jobs


Course Objectives

Once our training is completed, you will be able to write HDFS/MapReduce programs.
  • Learn how to write Hive and Pig scripts and how to use them.
  • A thorough understanding of the internal architecture of all Hadoop platforms.
  • Hbase and Sqoop are two tools that might help you improve your coding skills.
According to MarketsandMarkets, the Hadoop Big-Data Analytics industry is predicted to grow to $50 billion USD in the next two years. According to a recent McKinsey report, there were approximately 1,500,000 data gurus in short supply last year. As a result, Hadoop experts are in high demand.
The average salary for a Big Data Hadoop developer is INR 24,35,000 per year.
  • Hadoop does not require any prior knowledge.
  • Prior experience in Java and SQL is advantageous.
The students will obtain knowledge of Hadoop principles and the Hadoop ecosystem after completing this session.
  • Hadoop clusters are well-managed, monitored, programmed, and resolved.
  • Using Apache Spark, Scala, and Storm to perform real-time data analysis.
  • Hive, Pig, HDFS, MapReduce, and Sqoop are all used in this project.
  • MRUnit is used to test Hadoop clusters and automation tools.
  • Various ETL tools were successfully integrated with Hive, Pig, and MapReduce.
Hadoop Big Data is a comprehensive training course created by industry specialists to address current market needs for learning Big Data Hadoop and Spark modules. The course combines the Hadoop developer, Hadoop administrator, Hadoop testing, and Apache Spark analytics training courses. This is a well-known Big Data Hadoop certification course. This Hadoop and Spark course will assist you in passing the Cloudera CCA175 Big Data certification exam.
To become an expert in massive data, you must have a basic understanding of UNIX, SQL, and JAVA (or any OOP language). With fundamental knowledge in these domains, you may learn Big Data comprehensively.

What are the Big Data and Hadoop job opportunities?

Because some of Bangalore's greatest IT organizations are based there, there is a high need for Big Hadoop expertise. Hadoop's future can only get brighter as the number of entrepreneurs in India's Silicon Valley grows.

What is the Big Data Hadoop market trend?

Demand for data scientists in India has increased by more than 400% — some of the largest organizations, including MNCs like IBM, as well as Flipkart, Ola, and Infosys, have made significant investments in big data Hadoop, demonstrating that this boom is here to stay.

How long does it take to learn big data Hadoop?

Approximately four to six months. If you try to study Hadoop on your own, it will take a long time. It is contingent on your comprehension and learning ability. However, you should be able to complete your Hadoop certification in four to six months and begin your big data training.

Who should take training for Big Data Hadoop?

  • Administrators of systems and programmers
  • Managers of trade and projects with extensive expertise
  • Hadoop for Big Data Other vertical features such as testing, analysis, and management is on the minds of developers.
  • Professional mainframes, architects, and testing experts are all available.
  • Business intelligence, data warehousing, and analytical experts
  • Graduates will be interested in learning about Big Data.

What is the future scope of big data and Hadoop?

Big Data is the most rapidly evolving and promising technology for dealing with massive amounts of data. This Big Data Hadoop training will assist you in obtaining the finest professional credentials. Almost every major corporation is attempting to enter the Big Data Hadoop market, necessitating the employment of trained Big Data professionals.
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Overview of Hadoop Training in Visakhapatnam

ACTE, one of the best Bigdata training institutes in Visakhapatnam, offers real-time and placement-oriented Bigdata training programmes in the city. The development of the ecommerce business has added a whole new dimension to the necessity of using data to improve performance. Because it may help you build more effective marketing efforts, data is extremely valuable to you. It is possible to forecast business performance and future projections by analysing the data gathered from various market research. Businesses may utilise market research and large amounts of data to create a marketing strategy. This means that market-savvy individuals are in high demand, as well as those who can effectively communicate with customers. There is a desire for professionals who can not only comprehend the market, but also make sense of the hundreds of data bits and combine them into usable knowledge.


Additional Info

Career path in Bigdata and hadoop developer Bigdata and hadoop:

Hadoop Developer Careers-Inference:- This is primarily because of the shortage of Hadoop talent and inflated demand within the market. Employers decide candidates supported the data of Hadoop and temperament to work/learn.

Certification Training and Exam and path:

1. Amazon internet Services huge knowledge Specialty Certification:- What were they? Amazon internet Services certifications demonstrate your data of the AWS scheme. The 5 accessible certifications are divided into 2 categories: role and specialty-based. AWS’s huge knowledge certification is listed underneath the specialty class.

2. Cloudera certifications:- What are they? They’re Cloudera’s certifications that you simply will use their platform to show data into helpful data.

3. Microsoft Certified Solutions Expert:- knowledge Management and Analytics What is it? The info Management and Analytics track is simply one amongst many Microsoft offers as a part of its Microsoft Certified Solutions professional program, and it’s the one to concentrate on if you’re in huge knowledge.

4. Microsoft Azure Certification communicating 70-475:- If you’re specifically trying to figure out a huge amount of knowledge on Microsoft Azure, you’ll wish to require communicating 70-475, “Designing and Implementing huge knowledge Analytics Solutions.

5. MongoDB Certifications:- What is it? 2 certifications, actually: the Mongolian monetary unit information Administrator Associate, and also the MongoDB Developer Associate. MongoDB is one amongst the foremost in style NoSQL technologies, and each certification prepares you to figure with NoSQL databases.

6. Oracle Business Intelligence Foundation Suite 11g necessities Certification:- What is it? Computer code large Oracle’s certification that you’re masterly with their latest Bi computer code.

7. SAS huge knowledge Certification:- What is it? Computer code mega vendor SAS’s certification that you simply will work with their in style business intelligence computer code. Schoolwork courses as accessible in each room and homogenized learning (some room work, some online) formats.

Industry Trends of Hadoop:

1. the facility of Cloud Solutions:- AI and IoT a sanctioning quicker knowledge generation that could be a profit for businesses if they work sagely. Applications that as involved with IoT can would like ascendable cloud-based solutions to manage the ever-growing volume of information. Hadoop on Cloud is already being adopted by several organizations and therefore the rest ought to follow this cause maintain their go up the market.

2. A giant Shift at intervals ancient Databases:- RDBMS systems were the well-liked selections once structured knowledge occupied the key portion of information production. however, because the world is evolving, we have a tendency to a all manufacturing unstructured knowledge by victimization IoT, social media, sensors, etc. this can be wherever NO-SQL databases inherit action. This can be already changing into a typical selection in today’s business environments and therefore the trend can solely grow. NO-SQL databases like MongoDB and Cassandra are going to be adopted by a lot of vendors and graph databases like Neo4j can see a lot of attraction.

3. Hadoop can stick with New options:- One of the foremost common huge knowledge technologies, Hadoop, can escort advanced options to require on the enterprise-level lead. Right once Hadoop’s security comes like watchman and odd-toed ungulate can become stable, Hadoop can become versatile enough to figure in additional sectors and firms will leverage its capabilities with none security issues.

4. Period Speed can confirm Performance:- At now, organizations have {the knowledge|the info|the information} sources and therefore the ability to store and method huge data. The important issue which will confirm their performance goes to be the speed at that they will deliver analytics solutions. The process capabilities of massive knowledge technologies like Spark, Storm, Kafka, etc. as being fine-tuned with the speed in mind and firms can before long advance victimization this period feature.

5. Simplicity can create Tasks easy:- Big knowledge technologies which will alter the processes like knowledge improvement, knowledge preparation, and knowledge exploration can see a rise in adoption. Such tools can minimize the hassle place in by the end-users and firms will make the most of those self-service solutions. During this race, Informatica has already shown innovation.

Top framework or technologies and major tool in Bigdata and hadoop:

1. Hadoop Distributed filing system:- The Hadoop Distributed filing system (HDFS) is intended to store terribly massive knowledge sets faithfully, and to stream those knowledge sets at high information measure to user applications. During a massive cluster, thousands of servers each host directly connected storage and execute user application tasks.

2. Hbase:- HBase could be a column-oriented direction system that runs on prime of HDFS. It's compatible for distributed knowledge sets, that a common in several huge knowledge use cases. In contrast to electronic information service systems, HBase doesn't support a structured command language like SQL; actually, HBase isn’t a relative knowledge store the least bit. HBase applications as written in Java very similar to a typical MapReduce application. HBase will support writing applications in Avro, REST, and Thrift.

3. HIVE:- Hive provides a mechanism to project structure onto this knowledge and question the information employing a SQL-like language known as HiveQL. At an equivalent time this language conjointly permits ancient map/reduce programmers to insert their custom mappers and reducers once it's inconvenient or inefficient to specific this logic in HiveQL.Support for exportation metrics via the Hadoop metrics scheme to files or Ganglia; or via JMX.

4. Sqoop:- Sqoop could be a tool designed to transfer knowledge between Hadoop and relative databases. you'll be able to use Sqoop to import knowledge from an electronic information service management system (RDBMS) like MySQL or Oracle into the Hadoop Distributed filing system (HDFS), rework the information in Hadoop MapReduce, and so export the information back to associate degree RDBMS.

5. Pig:- Pig could be a platform for analyzing massive knowledge sets that consists of a application-oriented language for expressing knowledge analysis programs, let alone infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, that in turns allows them to handle terribly massive knowledge sets. At the current time, Pig’s infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, that large-scale parallel implementations exist already (e.g., the Hadoop subproject). Pig’s language layer presently consists of a matter language known as Pig Latin.

6. ZooKeeper:- All of those forms of services a employed in some type or another by distributed applications. whenever {they as|they're} enforced there's a great deal of labor that goes into fixing the bugs and race conditions that are inevitable. thanks to the problem of implementing these forms of services, applications ab initio sometimes skimp on them ,which create them brittle within the presence of modification and troublesome to manage. Even once done properly, totally different implementations of those services cause management complexness once the applications a deployed.

7. NOSQL:- Next Generation Databases principally addressing a number of the points: being non-relational, distributed, ASCII text file and horizontally ascendable.The original intention has been trendy web-scale databases.

8. Mahout:- Apache driver could be a library of ascendable machine-learning algorithms, enforced on prime of Apache Hadoop and victimisation the MapReduce paradigm. Machine learning could be a discipline of computing targeted on sanctioning machines to find out while not being expressly programmed, and it's ordinarily accustomed improve future performance supported previous outcomes.

Future in Bigdata and hadoop developer and trending:

  • Predictions say that by 2025, 463 exabytes of knowledge are created every day globally that is corresponding to 212,765,957 DVDs per day!
  • Each day five hundred million tweets, 294 billion emails ar sent, four petabytes of knowledge ar created on Facebook, four terabytes of knowledge ar created from every connected automobile, sixty five billion messages ar sent on WhatsApp, and lots of a lot of. Thus, in 2020, all and sundry is generating one.7 megabytes in precisely a second.
  • Can you imagine that each day we tend to ar generating a pair of.5 large integer bytes of data!! These massive information while not info is pointless. Startups and Fortune five hundred corporations ar clutches massive information for achieving exponential growth.
  • Organizations have currently realised the advantages of massive information analytics, that helped them in gaining business insights, which reinforces their decision-making capabilities. It has been foretold that the large information market, by 2023, hits $103B.
  • In 2020, the number of world information sphere subject to information analysis can grow to forty zettabytes, in keeping with the predictions.
  • The traditional information bases aren't capable enough to handle and analyze such an outsized volume of unstructured data. corporations ar adopting Hadoop to research massive information. As per the Forbes report, the Hadoop and therefore the massive information market can reach $99.31B in 2022 attaining a twenty eight.5% CAGR.
  • The below image describes the dimensions of Hadoop and large information Market worldwide type 2017 to 2022. we can simply see the increase in Hadoop and therefore the massive information market. therefore learning Hadoop is that the milestone for enhancing career in IT sectors in addition as in several alternative domains.
  • Bigdata and hadoop Training Key Features

    License Free:- Anyone will visit the Apache Hadoop web site, From there you transfer Hadoop, Install and work with it.

    Open Source:- Its ASCII text file is accessible, you'll be able to modify, modification as per your necessities.

    Meant for large information Analytics:- It will handle Volume, Variety, speed & worth. hadoop may be a idea of handling massive information, & it handles it with the assistance of the system Approach. analyzing by victimisation process techniques with the assistance of MPP(Massive Parallel Processing) that shared nothing design, then in last it Analyze the info & then it Visualize the info. this is often what Hadoop will, therefore essentially Hadoop is associate degree system.

    Shared Nothing Architecture:- Hadoop may be a shared nothing design, meaning Hadoop may be a cluster with freelance machines. (Cluster with Nodes), that each node perform its job by victimisation its own resources. Distributed File System: information is Distributed on Multiple Machines as a cluster & information will stripe & mirror mechanically while not the employment of any third party tools. it's a integral capability to stripe & mirror information. Hence, it will handle the amount. In this, there ar a bunch of machines connected along & information is distributed among the bunch of machines on the rear panel & information is marking & mirroring among them.

    Commodity Hardware:- Hadoop will run on artefact hardware meaning Hadoop doesn't need a awfully high-end server with massive memory and process power. Hadoop runs on JBOD (just bunch of disk), therefore each node is freelance in Hadoop.

    Horizontal Scalability:- we tend to don't ought to build massive clusters, we tend to simply persevere adding nodes. because the information keeps on growing, we tend to keep adding nodes.

    Distributors:- With the assistance of distributors, we tend to get the bundles, conjointly integral packages, we tend to don't ought to install every package singly. we tend to simply get the bundle & we'll install what we want for.

    Cloudera:- it's a U.S. primarily based Company, started by the staff of Facebook, LinkedIn & Yahoo. It provides answer|the answer} for Hadoop & enterprise solution. The merchandise of Cloudera is thought as CDH(Cloudera Distribution for Hadoop), it's a powerful package that we will transfer from Cloudera, we will install & work with it. Cloudera has designed a graphical tool known as Cloudera Manager, that helps to try to to the administration simply in an exceedingly graphical means.

    Hortonworks:- Its Product are known as as HDP (Hortonworks information Platform), it's not enterprise, it's Open supply & License free. it's a tool known as Apache Ambari, that designed the Hortonworks Clusters.

    Bigdata and hadoop Program Advantage:

    1. Open supply:- Hadoop is ASCII text file in nature, i.e. its ASCII text file is freely on the market. We will modify ASCII text file as per our business necessities. Even proprietary versions of Hadoop like Cloudera and Horton works are on the market.

    2. Scalable:- Hadoop works on the cluster of Machines. Hadoop is extremely scalable. We will increase the scale of our cluster by adding new nodes as per demand with none period. This fashion of adding new machines to the cluster is understood as Horizontal Scaling, whereas increasing parts like doubling magnetic disk and RAM is understood as Vertical Scaling.

    3. Fault-Tolerant:- Fault Tolerance is that the salient feature of Hadoop. By default, every and each block in HDFS includes a Replication issue of three. For each information block, HDFS creates 2 additional copies and stores them in a very completely different location within the cluster. If any block goes missing because of machine failure, we have a tendency to still have 2 additional copies of identical block and people square measure used. During this means, Fault Tolerance is achieved in Hadoop.

    4. Schema freelance:- Hadoop will work on differing types of knowledge. It's versatile enough to store numerous formats {of information|of knowledge|of information} and might work on each information with schema (structured) and schema-less data (unstructured).

    5. High out turn and Low Latency:- Throughput means that the number work of done per unit time and Low latency means that to method the information with no delay or less delay. As Hadoop is driven by the principle of distributed storage and data processing, process is finished at the same time on every block of knowledge and freelance of every alternative. Also, rather than moving information, code is rapt to information within the cluster. These 2 contribute to High out turn and Low Latency.

    6. Information neighborhood:- Hadoop works on the principle of “Move the code, not data”. In Hadoop, information remains Stationary and for process of knowledge, code is rapt to information within the style of tasks, this is often called information neighborhood. As we have a tendency to square measure managing information within the vary of petabytes, it becomes each tough and costly to maneuver the information across Network, information neighborhood ensures that information movement within the cluster is minimum.

    7. Performance:- In gift systems like RDBMS, information is processed consecutive however in Hadoop process starts on all the blocks quickly thereby providing data processing. Because of data processing techniques, the Performance of Hadoop is way more than gift systems like RDBMS. In 2008, Hadoop even defeated the quickest mainframe computer gift at that point.

    8. Share Nothing design:- Every node within the Hadoop cluster is freelance of every alternative. They don’t share resources or storage, this design is understood as Share Nothing design (SN). If a node within the cluster fails, it won’t bring down the full cluster as every and each node act severally so eliminating one purpose of failure.

    9. Support for Multiple Languages:- Although Hadoop was principally developed in Java, it extends support for alternative languages like Python, Ruby, Perl, and Groovy.

    10. cost-efficient:- Hadoop is incredibly Economical in nature. we will build a Hadoop Cluster exploitation traditional artefact Hardware, thereby reducing hardware prices. in line with the Cloud era, information Management prices of Hadoop i.e. each hardware and computer code and alternative expenses square measure terribly stripped when put next to ancient ETL systems.

    11. Abstraction:- Hadoop provides Abstraction at numerous levels. It makes the work easier for developers. a giant file is broken into blocks of identical size and hold on at completely different locations of the cluster. whereas making the map-reduce task, we want to stress regarding the situation of blocks. we have a tendency to provides a complete file as input and therefore the Hadoop framework takes care of the process of assorted blocks of knowledge that square measure at completely different locations. Hive could be a part of the Hadoop scheme ANd it's an abstraction on high of Hadoop. As Map-Reduce tasks square measure written in Java, SQL Developers across the world were unable to require advantage of Map cut back.

    12. Compatibility:- In Hadoop, HDFS is that the storage layer and Map cut back is that the process Engine. But, there's no rigid rule that Map cut back ought to be default process Engine. New process Frameworks like Apache Spark and Apache Flink use HDFS as a storage system. Even in Hive additionally we will modification our Execution Engine to Apache Tez or Apache Spark as per our demand. Apache HBase, that is NoSQL Columnar info, uses HDFS for the Storage layer.

    Bigdata and hadoop Developer job Responsibilities:

    The responsibilities of a hadoop developer rely upon the position within the organization and therefore the huge information drawback at hand. Some hadoop developer may well be writing complicated hadoop MapReduce program, some may well be concerned into writing solely pig scripts and hive queries and running workflows and planning hadoop jobs exploitation Oozie. The main responsibility of a hadoop developer is to require possession {of information|of knowledge|of information} as a result of unless a hadoop developer is conversant in data, he/she cannot realize what significant insights square measure hidden within it. The higher a hadoop developer is aware of the information, the higher they understand what quite results in square measure potential therewith quantity of knowledge. Most of the hadoop developers receive unstructured information through flume or structured information through RDBMS and perform information cleansing exploitation of numerous tools within the Hadoop scheme. Once information cleansing, hadoop developers write a report or produce visualizations for the information exploitation metal tools. A hadoop developer’s job role and responsibilities depend on their position within the organization and on however they roll all the Hadoop parts along to analyze information and pull together significant insights from it.

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    Key Features

    ACTE Visakhapatnam 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 Visakhapatnam
    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
    Complex Event Processing Project

    This tool helps to collect a variety of information and data while also identifying and analyzing cause-and-effect relationships as they occur.

    Project 2
    Anomaly Detection Project

    The goal was to build an algorithm that determines if a contribution has a risk to be inaccurate coded, based on supervised classification methods within the area.

    Project 3
    Data Lakes Project

    Data Lakes allow you to store relational data like operational databases and data from line of business applications, and non-relational data like mobile apps, IoT devices.

    Project 4
    Edge Analytics Project

    The definition of edge analytics is simply the process of collecting, analyzing, and creating actionable insights in real-time, directly from the IoT devices generating the data.

    Our Best Hiring Placement Partners

    ACTE Visakhapatnam 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.
    • ACTE Arrangement Cell coordinates profession direction programs for every one of the understudies beginning from the course . it masterminds preparing programs like mock meetings, relational abilities workshop and so forth and it likewise coordinates public area test preparing for understudies who are intrigued to join government areas. it additionally welcomes HR supervisors from various ventures to direct preparing programs for understudies.
    • Train the understudies on bunch conversation procedures and Lead online tests and composed inclination tests.
    • We keeping up and routinely refreshing the information base of understudies. keeping up data set of organizations and setting up essential connections for grounds enlistments.
    • We gives gathering data about work fairs and all significant enrollment notices.
    • The industry is consistently keeping watch for understudies who are dynamic, fiery people and prepared to acknowledge demands, mindful, a decent scholarly foundation, quick learners, open to learning even grinding away and all the more critically, great relational abilities. This movement centers around the character advancement to make the understudies solid, with an uplifting perspective and right dynamic.
    • ACTE arranging pre-arrangement preparing/workshops/courses for applicants.

    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 train the understudies all year on employability abilities needed in the work market. Guide and advice the understudies over time to situate and uncover them towards vocation necessities.
    • To foster the resources of reasoning, examination and thinking and a propensity for learning in applicant to empower them to understand their most extreme potential.
    • Trainers to get ready understudies for future jobs by ceaseless preparing, coaching and advising and creating important abilities for predominant profession.
    • Our coaches make notes and tests for our applicants, which is amazingly useful and significant for them.
    • Tutors start toward the start of the course to guarantee that the applicants are capable in a similar field by the end.
    • To match the business needs and models of our applicants, our mentors have made an inside and out training course. and We have gotten a few significant honors for Big Data Hadoop Training in Visakhapatnam from notable IT organizations.

    Hadoop Course Reviews

    Our ACTE Visakhapatnam 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

    The trainer had a very good knowledge about the Hadoop course. His way of explaining was also very simple and understandable. He helped me in solving me any difficulties I had while doing practical work. The other benefit that it was 1-1 training and so the trainer personally give full attention and resolves your issues and concerns in a proper way. I really liked the trainer. Even ACTE members were also supportive in adjusting with my schedule with the trainer. I would recommend ACTE Training to others in Visakhapatnam


    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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    Request for Class Room & Online Training Quotation

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