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Hadoop Training in Hyderabad

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  • Acquire the Best Training in Advanced Level Classes.
  • Precedent Level Training on Hadoop Tools for Both Beginners and Experienced.
  • Student Portal, Study Materials, and Top Interview Questions of MNC Lifetime Access.
  • Best Approaches with Nominal Cost to Trending Hadoop Concepts.
  • Delivers 9+ years of Certified Expert Hadoop and 350+ Clients Recruitment.
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23- May - 2022
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(Class 1Hr - 1:30Hrs) / Per Session

25- May - 2022
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

28- May - 2022
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28- May - 2022
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(Class 4:30Hr - 5:00Hrs) / Per Session

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Get Hadoop Certification Training from Our Professional Specialists

  • Our Experienced trainers will Assist the Learners to understand Each and Every Point of the Big Data-With-Hadoop Training from Intermediate to advanced.
  • We Practice candidates to Accomplish Your Dream job with help of a tech career and with Appropriate Industrial Learning and Programming Skills.
  • We Strengthen the Skills for real Career growth and a well-organized and structured course to work on Real-world Problems.
  • Acquire to Receive Trending updates and Map Reduce Concepts, Hive, Pig, Apache Spark, HBase, Big Data Stack, and YARN in the Big Data-With-Hadoop Training from Experts.
  • Endure with Hands-On-Live Intentions, and Integrate with New Trending Technologies to design End-to-end Applications with New Features.
  • Big Data with Hadoop Course is Best Suitable for IT, Data Management, Senior IT Professionals, Project Managers, Analytics Professionals, Aspiring Data Scientists, and more.
  • 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

Hadoop is an Apache project to store and process Big Data. Hadoop stores Big Data over commodity hardware in a distributed and fault-tolerant way. Hadoop's tools are subsequently used to parallel HDFS data processing.Because companies have realized the advantages of Big Data Analytics, big data & shadow professionals are in demand. Big data & Hadoop experts with Hadoop Ecosystem knowledge and best practice on HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop, and Flume are sought by companies.
This course provides you with information on the Hadoop ecosystem and big-data tools and methodologies to prepare you to be a big-data engineer and to complete your role. Your new big data skills and on-the-job expertise are demonstrated with the course certification. Hadoop certification will educate you into ecosystem instruments like Hadoop, HDFS, MapReduce, Flume, Kafka, Hive, HBase, etc.
  • Hadoop and YARN fundamentals and write applications
  • Spark SQL, Streaming, Data Frame, RDD, GraphX and MLlib writing Spark applications HDFS MapReduce, Hive, Pig, Sqoop, Flume, and ZooKeeper Spark
  • Avro data formats work
  • Use Hadoop and Apache Spark to implement real-life projects
  • Be prepared to clear the certification with Big Data Hadoop.
  • System administrators and programming developers
  • Trade and project managers with experienced experience
  • Big Data Hadoop Developers want to learn other vertical elements like testing, analysis, and management.
  • Professional mainframes, architects, and experts in testing
  • Professionals in business intelligence, data warehousing, and analysis
  • Graduates will want to learn Big Data.
    This Big Data class and master Hadoop are not subject to requirements. But UNIX, SQL, Java, and Big Data Hadoop are all basics.
Big Data is the fastest growing and most promising technology for the treatment of large amounts of data. This Big Data Hadoop training helps you to achieve the highest professional qualifications. Nearly every top MNC tries to enter Big Data Hadoop, which makes it very necessary for certified Big Data professionals to work.
Big Data Hadoop Certification training is designed to make you a Certified Big Data Practitioner by industry experts. The course of Big Data Hadoop:
  • In-depth knowledge of the HD FS, YARN (Another Resource Negotiator) & Map Big Data, and Hadoop including the HDFS Cutting.
  • Comprehensive knowledge of various tools such as Pig, Hive, Sqoop, Flume, Oozie, and HBase that fall within Hadoop Ecologic.
  • The ability to integrate HDFS data using Sqoop and Flume and to analyze large HDFS-based datasets of a diverse nature covering several data sets from multiple fields such as banking, tea, and so on.

What are our Big Data Hadoop Certification Training capabilities you will learn?

The certification training in Big Data Hadoop will help you to become a Big Data expert. It will enhance your skills by providing you with comprehensive expertise in Hadoop and the practical experience required to solve projects based in the industry in real-time.

How will Hadoop and Big Data help you with your career?

The following forecasts will help you to understand Big Data growth:
  • Hadoop developers have an average salary of INR 11,74,000.
  • Organizations are interested in large data and use Hadoop to store and analyze them. There is therefore also a rapid increase in demand for jobs in Big Data and Hadoop. Now is the right place for Big Data Hadoop online training if you have an interest in a career in this field.

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

You will take a couple of days to master the subject if you already fulfill the requirements for Hadoop. It can take 2 to 3 months to learn Hadoop, however, if you learn from scratch. In these cases, Big Data Hadoop Training is strongly recommended.

What can I learn Big data and Hadoop course?

Some key Big data topics here you need to know:
  • Concepts OOPS
  • Basics such as data types, syntaxes, casting type, etc.
  • Generics and collections like all MapReduce programs
  • Management of exceptions
  • Looping and conditional statements.

What are the job responsibilities of Big Data and hadoop?

Job Description for Hadoop Developers:
  • Development and implementation of Hadoop.
  • Hive and Pig are used for pre-processing.
  • Creating, constructing, installing, configuring, and maintaining Hadoop.
  • Analyze large amounts of data to find new insights.
  • Create data monitoring web services that are scalable and high-performing.
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Overview of Hadoop Training in Hyderabad

Hadoop is a Java-based open-source platform for storing and analyzing large amounts of information. The data is stored on clusters of low-cost commodity servers. It has a distributed file system that allows for simultaneous processing and fault tolerance. In a thorough analysis, this also helps. Several businesses accept Hadoop and there is an increase in demand for Hadoop developers. ACTE Hadoop Course in Hyderabad offers the applicant the information they need to become a professional developer of Hadoop technology and deliver the technical skills they require. We provide Hadoop with basics and ecosystem components and how the processing and storage of data may be handled. Its experts have the skills to give extensive training in big data and Hadoop Certification.

 

Additional Info

Future in Hadoop developer and trending:

It offers a reliable and cost-effective data storage solution. Hadoop has become a favourite of many enterprises because to its unique capabilities such as scalability and fault tolerance. Hadoop, together with its ecosystem, is a solution to big data issues. Big data analytics are provided by several components of the Hadoop ecosystem, such as TEZ, Mahout, Storm, MapReduce, and so on. Hadoop is used by businesses to process large amounts of data. Hadoop brings everything they require under one roof. Hadoop solves the problems with traditional RDBMS systems. It is also less expensive than the traditional system. As a result, the Hadoop market is growing at a rapid pace, and Hadoop's future seems bright.

The roles and responsibilities of Hadoop:

Companies all over the world are looking for big data professionals that can evaluate all data and generate meaningful insights. Hadoop Developers can hold a variety of positions and work in a variety of settings. Here is a list of job titles that will assist you in making the best option by guiding you to the desired Hadoop expert work role. Hadoop employment are available in a variety of industries, including financial services, retail, banking, and healthcare.

  • To analyse the company's big data infrastructure, I met with the development team.
  • Developing and coding Hadoop apps for data analysis.
  • Frameworks for data processing are being developed.
  • Data extraction and data cluster isolation.
  • Scripts are being tested and the outcomes are being analyzed.
  • Data Migration is a term used to describe the process of moving.
  • Data integration and scalability are two important factors to consider.
  • Streaming analytics is a term that refers to the study of data in speech evaluation.

The career opportunities of Hadoop:

There is no precondition in Hadoop as such for a concealed secrecy. You've got to work hard and demonstrate commitment. There are newcomers, IT industry veterans, and non-IT industries who make their careers in Hadoop. Between the first phases of the job quest and offer letter, there might be much difficulty. First of all, choose the several responsibilities that Hadoop must provide you on the proper path. See the different tasks of Hadoop:

1. Analyst of Big Data:- Big Data Analyst uses Big Data Analytics and evaluates the technological performance of organizations. And to give system enhancement recommendations. They concentrate on challenges such as live data streaming and data transfer. They work with individuals such as data scientists and data architects. This is done to make services simplified, profile source information, and establish features. Big Data Analyst performs large data operations such as parsing, text annotations, enrichment filtering.

2. Big Data Architect:- The whole life of the Hadoop solution is their responsibility. It involves the creation and selection of requirements, platforms, and technical architectural designs. It also includes application design and development, testing, and design of the solution offered. You should grasp the advantages and disadvantages of different technologies and platforms. They utilize cases, solutions, and suggestions to record them. To address an issue, big data must operate creatively and analytically.

3. Data Engineer:- They are responsible for the creation, extent, and delivery of Hadoop solutions for different large data systems. They are involved in the development of high-level architectural solutions. It manages technological communication between suppliers and domestic systems. In Kafka, Cassandra, Elasticsearch, and so forth, they manage production systems. Data Engineer constructs a club-based platform that makes new apps easy to design.

4. Data Scientist:- They use their ability to compile and understand data, in terms of analytics, statistics, and program. This information will thus be used by data scientists to build data-driven solutions to complex business issues. Data Scientist works in the organization with stakeholders. This is to see how corporate data may be used to generate business solutions. Data from corporate databases are analyzed and processed. This improves the creation of products, market tactics, and company strategy.

5. Hadoop developing the following:- They manage Hadoop installation and setup. Map-reducing code for Hadoop clusters is written by Hadoop Developer. They transform technical and functional difficult requirements into a comprehensive design. The software prototype testing and transmission to the operational team by the Hadoop developer. The data security and privacy are maintained. They analyze and generate massive datasets.

6. Hadoop tester:- Hadoop is the role of the tester in Hadoop systems to diagnose and repair issues. It ensures that Map-Reduce work, Pig Latin, and HiveQl operate as planned. In Hadoop/Hive/Pig the Hadoop tester develops test cases to discover any problem. He tells the development team and manager about shortcomings and encourages them to close down. By collecting all faults, the Hadoop tester generates a defect report.

7. Hadoop Admin:- You will work on designing, developing, and implementing C and C++ computer applications. Basically, you have to know the current technology that governs the market and design your software to match your competitors' requirements and requirements with a competitive edge over the programs that your competing organizations generateHadoop Admin is responsible for the creation, backup, and rehabilitation of a Hadoop cluster. He tracks the connection and safety of the Hadoop cluster. A new user has also been established. Hadoop administrator handles the Hadoop cluster task performance capability planning and screening. Hadoop Admin supports and manages the cluster Hadoop.

8. Architect of the Hadoop:- Hadoop builds and plans the Hadoop architecture for large data. Hadoop. It provides the analysis of demand and selects the platform. He creates technical and application architecture. The Hadoop solution offered is part of his responsibility.

Features of Hadoop:

    Apache Hadoop is the most popular and capable Big Data technology, providing the most dependable storage layer in the world. Let us examine the different essential characteristics of Hadoop in this part.

    1. Hadoop is open source:- Hadoop is an open-source project, which allows companies to alter the code according to their needs, with its source code free of costs for inspection, modification, and analysis.

    2. Hadoop's cluster Highly Scalable:- The Hadoop cluster may be used to enhance the hardware capacity of the (vertical) nodes to obtain a large computing power by adding a variety of nodes (horizontally scalable). This offers the Hadoop framework with both horizontal and vertical scalability.

    3. Fault Tolerance provided by Hadoop:- The main characteristic of Hadoop is fault tolerance. In Hadoop 2, HDFS utilizes a fault tolerance replication method. Depending on the replication factor, each block replicates on the various computers (by default, it is 3). There are also data from the other machines with the same data if any computer in a cluster is offline. Hadoop 3 substituted the erasure coding for this replication technique. Erasure coding gives less room for the same fault tolerance.

    4. Hadoop delivers a high availability:- This Hadoop characteristic ensures that the data is highly available even under adverse circumstances. The error tolerance feature of Hadoop allows the user to access data from various DataNodes which hold a copy of the same data when any of the DataNodes goes down.

    5. Hadoop is extremely affordable:- As the Hadoop cluster comprises inexpensive commodities nodes, it provides an affordable option for large-scale data storage and processing. Since Hadoop is open-source software, no licensing is needed.

    6. Hadoop is faster in Data Processing:- Hadoop holds distributed data, which enables dispersed information to be handled on a node cluster. It thereby offers the Hadoop architecture with quick processing capacity.

    7. Hadoop is founded on the notion of the data locality:- Hadoop is well known because its data locality is the transportation of calculation logic to data, rather than the transportation of data to calculation logic. This Hadoop feature lowers the use of the bandwidth in a system.

    8. Feasibility provides Hadoop:- Hadoop can handle unstructured data, unlike the standard system. This gives consumers the possibility to evaluate data from all sizes and formats.

    9. Hadoop is easy to use:- Hadoop is simple to operate since customers need not be concerned about computer distribution. The workmanship is managed through the frame.

    10. Hadoop guarantees data reliability:- Data is saved reliably on the cluster machines in Hadoop despite machine failures as a result of data replication in the cluster. The frame itself offers a reliability mechanism for Block Scanners, Volume Scanners, Disk Checks, and Directory Scanners.

Top 12 advantages of Hadoop:

Hadoop is user-friendly, scalable, or economical. Hadoop also provides several advantages. Here we talk about Hadoop's top 12 benefits. So the positives of Hadoop follow, which makes it so popular.

1. Various data sources:- Hadoop takes several different data. Data may be obtained from a variety of sources such as email discussions, social media, etc. Value from different data may be derived via Hadoop. The Hadoop may receive information in a file with text, XML, pictures, CSV, etc.

2. Cost-effective:- Hadoop is an affordable way to store data by using a commodity hardware cluster. Commodity hardware is inexpensive, thus nodes are often not too expensive to add to the framework.

3. Performance:- Hadoop handles enormous volumes of high-speed data in its distributed processing and storage architecture. Even the fastest machine has been the default supercomputer. It splits the data entry file into many blocks and saves data over numerous nodes in those blocks.

4. Fault-Tolerant:- Detection coding provides for failure tolerance in Hadoop 3.0. For example, with the use of an erasure coder, 6 data blocks create 3 parity blocks, which means that HDFS stores a total of nine blocks.

5. Highly available:- Hadoop 2.x includes one active NomeNode architecture and one standby NameNode, so we have a backup NameNode to count on when the NameNode goes down. Hadoop 3.0 offers many standby NameNode models which make the system even more readily disponible since if two or more NameNodes collapses they may continue to work.

6. Low network traffic:- Each job submitted by the user is divided into several separate subtasks in Hadoop, and the data nodes are allocated to these subtasks, which transfers a small amount of code into data and does not transmit large data to code leading to low network traffic.

7. High performance:- Performance indicates work per unit time. Hadoop stores data in a distributed way that makes it easy to process them distributed. A particular job is split into tiny jobs that operate concurrently to pieces of data that provide high output.

8. Open Source:- Hadoop is an open-source technology, which means that its source code is available free of charge. The source code can be changed to meet a particular demand.

9. Scalable:- Hadoop operates on the horizontal scalability concept, which requires that the whole computer be added to the cluster of nodes, rather than modifying the machine setup, such as adding RAMs, disc, and so on, known as vertical scalability.

10. Easy to use:- The Hadoop framework is parallel to processing; programmers from MapReduce do not have to take care of the distributed processing, it is done automatically on the backdrop.

11. Compatibility:- Most new big data technologies, like Spark, Flink, etc, is Hadoop compatible. You have processing engines that function as a Backend on Hadoop, We utilize Hadoop to store data for you.

12. Multiple languages:- Developers may code for numerous Hadoop languages such as C, C++, Perl, Python, Ruby, and Groovy.

Salary of Hadoop:

Job opportunities for Hadoop Developers can be found in a variety of industries, including IT, finance, healthcare, retail, manufacturing, advertising, telecommunications, media & entertainment, travel, hospitality, transportation, and even government agencies. IT, e-commerce, retail, manufacturing, insurance, and finance are the six primary businesses increasing need for Hadoop talent in India. E-commerce has the highest Hadoop salary in India, out of all the industries. Every organization is investing in Large Data and Hadoop, from big names like Amazon, Netflix, Google, and Microsoft to startups like Fractal Analytics, Sigmoid Analytics, and Crayon Data.

The compensation of the Hadoop developer in India depends largely on the education credentials, credentials, work experience and the size, reputation and location of the firm. For example, postgraduate applicants can receive a start package of around Rs4–8 LPA. But graduates might earn Rs. 2.5 – 3.8 LPA for the freshers period. Professionals with the best mix of the aforementioned abilities may also earn between Rs. 5 -10 LPA anyplace. The typical yearly compensation is Rs 7 – 15 LPA to medium sized professionals with a non-management capability, while managers may perform about Rs 12 -18 LPA or higher.

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Key Features

ACTE Hyderabad 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 Hyderabad
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.

The aim of this project was to use a core NHS data set, Hospital Episode, adjustment models to predict.

Project 2
Anomaly detection in cloud servers.

The goal was to build an algorithm that determines if a contribution has a risk to be inaccurate coded.

Project 3
Malicious user detection in Big Data collection.

The purpose of malware analysis is to obtain and provide the information needed to rectify a network or system intrusion.

Project 4
Electricity price forecasting.

This Project provides real-time data of load and price for the better prediction of electricity generation purposes.

Our Engaging Hiring Partner for Placements

ACTE Hyderabad offers With Job guarantee to help individuals acquire dream jobs in renowned organizations where they can essentially advance or demonstrate company development qualities as add-on to every student / professional who completed our classroom or online training. Some of our students are working in these companies listed below.
  • After finishing of 70% Huge information and Hadoop instructional class getting the hang of educational class content, we will coordinate the gathering calls to understudies and set them up to F2F participation.
  • We direct mock tests and mock gatherings to help your assurance level.
  • Career guideness, open positions will be bestowed to the student regulary.
  • We have separate understudy's entrances for circumstance, here you will get all the gathering schedules and we educate you through messages.
  • ACTE connected with top affiliations like GOOGLE, CTS, TCS, IBM, etc.. it make us skillful to place our applicants in top MNCS across the globe.
  • Resume readiness, mock meetings, and position help with 100% authentic exertion from our side to put you in the business.

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 the successful completion of our Hadoop online training and practical-based projects.

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About Qualified Hadoop Trainer

  • Our Big data and Hadoop Training in Hyderabad  have wide teaching through attracting scenes like whatsapp, conversations, and online media stages is offered to beneficially execute any inquiries.
  • We change our organizations to your stand-out rules, with a strong focus on elaborate understanding and getting ready on long stretch ventures.
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  • Our Mentor is throughout educated about being understudy connected with and completing assignments to hit cutoff times and targets.
  • Our Best Big data and Hadoop Training   getting ready , mentors are asserted specialists with 9+ extended lengths of inclusion with their different space similarly as they are correct now working with top MNCs.
  • Remote induction to worker ranch structure to guarantee that everyone gets hands own to explicit development even after you finish the course.

Hadoop Course Reviews

Our ACTE Hyderabad 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"

Rogini

Software Engineer

ACTE is very good platform to achieve knowledge in depth and They are providing placement for getting Job my experience and i have completed Hadoop course in Hyderabad and ACTE was wonderful not only in terms of understanding the technology but also provides hands on practice to work on technology practically and the faculty is Extremely good and they help students in each and every way possible

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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