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Hadoop Training in Pune

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  • Obtain Tutored Novice & Most high-grade level Classes.
  • Obtain Hands-On Learning in Hadoop.
  • Most utmost Preparation for interview Preparation Methods in Hadoop.
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  • Delivered More than 12402+ Students Trained & 350+ Recruiting Clients.
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Obtain Our Inventive Hadoop Training From Our Experts

  • Hadoop skilled Certification steerage Support with communicating Dumps.
  • Get the technical expertise from our teachers to each lesson with thorough knowledge.
  • Grow your knowledge on Hadoop Certification and advanced ideas, however, conjointly gain presentation to business best practices.
  • Skilled Trainers and experimentation lab Facility and skills as well as experience applying these in real-world Scenarios.
  • Practical oriented / Job oriented training. follow on Real-Time project situations.
  • We have produced an in-depth development, therefore, meet job necessities and criteria With the help of a technical career and with true industrial competence and practical knowledge we educate applicants to acquire your ideal job.
  • Concepts: High-handedness, massive information 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 A HADOOP CERTIFICATION COURSE THAT GETS YOU EMPLOYMENT OF UPTO 5 TO 12 LACS IN MERE 60 DAYS!
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
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This is How ACTE Students Prepare for Better Jobs

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Course Objectives

Take an initial course within the Hadoop ecosystem and see if a career within the fast-growing world of Big Data is correct for you. If performing on an oversized amount of experience as a data scientist excites you, learning Hadoop is often critical for your career. Many companies like Google, Microsoft, Amazon, Apple, and more are searching for someone to watch their great deal of information. Many courses are free, self-paced, and ready now so you'll enroll and begin receiving Hadoop today.

The Internet of Things has produced an immense demand for people skilled in handling big datasets. If the globe of huge Data is on your roadmap, skills and skill with the Hadoop framework are going to be a serious asset when asking for jobs in data analysis. To present you a concept of what’s out there, Indeed.com lists almost k jobs in Hadoop with titles like Linux Hadoop Administrator, Hadoop Database Development Team Lead, Hadoop Engineer, and Hadoop Developer. Annual salary estimates for this in-demand and growing specialization range from $80K to run out $120K. Performing a Hadoop Certification on your resume and LinkedIn may be a good way to demonstrate that you just are an industry authority.

According to MarketsandMarkets, the marketplace for Hadoop big data analytics will become used to USD 50 billion within the next two years. Based on a recent survey, there was a shortage of over 1,500,000 data experts within the previous year. So, the demand for Hadoop professionals is high. The Average Wage of a Big Data Hadoop Developer is $135,000 once a year - Indeed.com.

After finishing this training, the students will get experience on:
  • Hadoop basics and Hadoop ecosystem.
  • Managing, monitoring, scheduling, and troubleshooting Hadoop clusters effectively.
  • Running with Apache Spark, Scala, and Storm for real-time data analytics.
  • Operating with Hive, Pig, HDFS, MapReduce, Sqoop, ZooKeeper, and Flume.
  • Examination of Hadoop clusters with MRUnit and different automation tools.

Of Course yes, you may display a Hadoop expert as per these industry types. You'll be confident in serving the interviews since we offer career-oriented training that covers mock interviews, technical reviews, etc.

Upon completion of our training, you'll be able to:
  • Practice writing HDFS/MapReduce programs.
  • Learn writing and utilizing Hive & Pig Scripts efficiently.
  • Learn internal architecture/design involved on all the Hadoop platforms.
  • Improve coding skills using Hbase & Sqoop tools.
  • Software Developers.
  • Project Managers.
  • ETL and Data Warehousing Professionals.
  • Software Architects.
  • Data Analysts & Business Intelligence Professionals.
  • DBAs.

How will I execute the Practicals in Hadoop course in Pune?

To test your understanding of Hadoop Training, you'll be expected to figure on two industry-based projects that discuss important real-time use cases. This can also secure hands-on expertise in Hadoop Training and Certification Course ideas.

Which language is required for Hadoop online course?

The Hadoop framework itself is usually written within the Java programming language, with some native code in C and command services written as shell scripts. Though MapReduce Java code is common, any programing language may be used with Hadoop Streaming to complete the map and reduce portions of the user's program.

What amount of time will it need to learn a Hadoop Certification course?

It is 2 to 3months of Study. If you're taking regular classes it'll take 45 days or if you go together with Weekend classes it'll take 4 to five weekends.

What are the job roles and prospects of a Hadoop training in Pune?

  • Since the job requires learning knowledge, the titles data Scientists, knowledge Engineers, Data Analysts, are the job titles given to a licensed Hadoop Developer.
  • Jobs are lots of in-demand across the world. A study by the puts a predictable shortage of 100 90,000 data scientists within the US alone.
  • If you're interested in numbers, do the mathematics and determine the planet characters.

What are the requirements for learning the Hadoop online training?

However, it's very helpful if you own the subsequent skills sets:
  • Mathematical and Analytical expertise.
  • Good critical thinking and problem-solving skills.
  • Technical knowledge of Python, R, and SAS tools.
  • Communication skills.
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Overview of Hadoop Training in Pune

Hadoop require any special educational background as such, as do many new data technologies. Around half of Hadoop are non-informatics developers such as statistics or physics. It is therefore obvious that the background is no obstacle to Hadoop joining the world given that the basics are ready to be learned. Good online courses cover Hadoop; the greatest one is ACTE Pune. ACTE is providing a Hadoop course in Pune to enhance the candidate's knowledge and provide the necessary technical skills to become a professional technology developer for Hadoop. ACTE experts are capable of providing extensive Big Data and Hadoop Certification Training in Pune.

 

Additional Info

The growth of Hadoop Career:

As we stated previously, the adoption of Hadoop increases every day. Learning Hadoop can therefore greatly enhance your Big Data Career. Now let's explore the job trend for Hadoop from a worldwide or type of global perspective. The census, however, is solely in the UK. Things still provide us an excellent sense of how Hadoop does it. In the last few years, big data startups have increased massively. That means that increasing numbers of firms gradually take and move to Big Data, and all of these companies attempt to cope with Big Data and to support big data organizations. But Hadoop is one of the pioneers of big data, to comprehend and analyze it. Let's begin to explore the possibilities for Hadoop Career.

Features of Hadoop:

Below are the Hadoop characteristics:

  • Cost-efficient:- Hadoop does not require the implementation of any specific or effective hardware. It may be used on basic hardware called community hardware.

  • A wide node cluster:- A 100 or 1000 nodes may constitute a cluster. The advantage of a big cluster is that it provides customers with greater processing power and a vast storage system.

  • Parallel processing:- All clusters may process data simultaneously and this will save a great deal of time. This task could not be performed by the usual system.

  • Data distributed:- Hadoop architecture divides and distributes all data. The cluster's nodes. It duplicates all clusters of data. The factor to replicate is 3.

  • Automatic failover management:- Suppose the Hadoop framework replaces a fault machine with a new computer if one of the nodes in a cluster fails. Replication configurations of the old computer are immediately transferred to the new machine. Admin doesn't have to worry.

  • Heterogeneous cluster:- Scontains a separate node that supports various versioned computers. Red hat supports Linux on the IBM computer.

  • Optimizing the database:- If the programmer requires node data from another database, the programmer will transmit the database with a byte of code. It saves time and bandwidth.

  • Scalability:- Nodes are added or removed and hardware components added or removed in or out of the cluster. Without disrupting cluster operations, we can do this task. You may add or remove RAM or Hard Drive from the cluster.

The Top 10 Hadoop Tools:

1. HDFS:- The well-known HDFS distributed file system is designed to store huge amounts and therefore much more efficient than the new kind of file system (NTFS) and the Windows PC file System FAT32. HDFS is used to swiftly transport huge amounts of data to apps. The Hadoop Distributed File System is used by Yahoo to manage approximately 40 petabytes of data.

2. HIVE:- Hosted servers are generally called Apache and have their Hadoop database solutions as Apache HIVE data warehouse software. It makes querying and managing huge datasets easier for us. With HIVE all unstructured data are structured and, afterward, the data may be queried using SQL, like the HiveQL language.

3. NoSQL:- For a long time now since most of the information is unstructured, Structured query languages are in use and we want a query language without structure. This is primarily resolved by NoSQL.

4. Mahout:- Apache created also their library of several Mahout learning algorithms. In Apache, Hadoop Mahout is implemented using BigData MapReduce. Since we all know about machines learning various objects daily through data generation depending on the inputs of a different user, this is called machine learning.

5. Avro:- This tool allows us to easily obtain representations of complicated data structures produced with Hadoop's MapReduce algorithm. Avro Data can easily take both input and output from a MapReduce job, where it is also possible to format them much more easily.

6. GIS:- Geographical information is one of the largest data sets in the world. All countries, cafés, restaurants, and other news across the world are included, and this must be accurate. The Hadoop GIS tools are a Java-based solution for geographical information interpretation.

7. Flume:- Whenever requests, responses, or any sort of activities are made in the database, LOGs are created. Logs help debug the software and look at the improper places. Even logs are created in enormous quantities while working with big volumes of data. And when this huge quantity of log data needs to be moved, Flume takes action.

8. Clouds:- All cloud systems work on large quantities of data that might traditionally make them sluggish. Most cloud platforms thus migrate to Hadoop and Clouds will help you.

9. Spark:- Spark tops the list when it comes to Hadoop analytics tools. A Big Data Analytics Framework from Apache is Spark. This is a cluster computing platform for open-source data analysis which was first created by AMPLab at UC Berkeley. Apache purchased the AMPLab later the same.

10. Hadoop MapReduce:- MapReduce is the framework that facilitates the development of an application to parallel multi-terabyte data collections. This information may be computed in huge clusters. The MapReduce framework is made up of JobTracker and TaskTracker; a single JobTracker records all work while a task tracker is available for each cluster node. Master i.e. JobTracker, program the work, while slave TaskTracker tracks it and re-arranges it if it is not.

The Certifications of Hadoop:

  • CCA Spark and Hadoop Developer Exam (CCA175):- You have to create code in Scala and Python to verify that you have abilities in CCA Spark & Hadoop Developer certification. This examination may be taken worldwide from any computer. CCA175 is a practical and practical examination employing the techniques of Cloudera. Customers receive a CDH5 (now 5,3.2) which is pre-loaded with the following software: Spark, Impala, Spark, Hive, Pig, Sqoop, Kafka, Flume, Kite, Hue, Oozie, DataFu, and several others.

  • Apache Hadoop Certified Cloudera Administrator (CCAH):- The certification from the Cloudera Certified Apache Administrator (CCAH) displays your technical skills, configurations, deployment, monitoring, management, maintenance and secures capabilities for the Apache Hadoop cluster.

  • CCP Data Scientist:- Cloudera Certified Professional Data Scientist" can carry out inferential and descriptive statistics and use the most advanced analytics techniques. In a live cluster of huge datasets, candidates must prove their talents in several forms. It takes 3 CCP data scientist tests to be clarified in any sequence (DS700, DS701, and DS702).

  • CCP Data Engineer:- Cloudera Certified Data Engineer can execute the key capabilities necessary in the Cloudera CDH environment for ingesting, transforming, recording, and analyzing data.

  • Job positions of Hadoop:

    1. Data Engineer:- The development, scope and delivery of Hadoop solutions for the diverse large-scale data systems is their responsibility. They participate in the development of architectural solutions of high quality. It controls communication technology between suppliers and household systems. They operate production systems in Kafka, Cassandra, Elasticsearch, etc. Data Engineer builds a club-based platform that facilitates the building of new apps.

    2. Scientist Data:- In terms of analytics, statistics, and programmes, they employ their abilities to compile and interpret data. The data scientists will thus utilise this knowledge to provide data-driven answers to challenging business questions. Data Scientist works with stakeholders throughout the company. This will show how corporate data may be leveraged for business solutions. Company database data are examined and handled. This enhances product innovation, market techniques and business strategy.

    3. The following develops Hadoop:– They oversee the installation and configuration of Hadoop. The Hadoop Developer's Map Reduction Code for Hadoop clusters. It transforms technical and functional demands into an integrated design. The Hadoop developer test and transfer of the software prototype to the operational team. The security of data and privacy is guaranteed. They study and produce huge data collections.

    4. Tester Hadoops:- Hadoop is a testing agent for the diagnosis and repair of problems in Hadoop systems. It guarantees that the map reduction work, Pig Latin and HiveQl are functioning according to design. The tester builds test cases in Hadoop/Hive/Pig in order to find any problems. He explains weaknesses to the development team and the manager, encouraging it to close. The Hadoop tester creates a defect report by gathering all defects.

    5. Big Data Analyst:- Big Data Analyst utilises Big Data Analytics to analyse businesses' technical performance. And ideas for improving the system. They focus on issues such as streaming of live data and data transmission. They work with people like data scientists and architects of data. This is done to simplify services, provide information about the source profile, and provide functionality. Big Data Analyst conducts huge data tasks including parsing, annotation of text and filtering of enhancement.

    6. Architect of Big Data:- Your duty is the full life of the Hadoop solution. It includes the development and selection of requirements, platforms and architectural designs. It also covers the design and development of application, testing and design of the supplied solution. The benefits of alternative technologies and platforms should be taken into account. They use instances, solutions and proposals to document them. Big data must be creative and analytical in order to handle an issue.

    Advantages of Hadoop:

    • Open Source:- Hadoop is open-source, i.e. it has a free source code. As per our business requirements, we can alter source code. There are other proprietary versions of Hadoop, such as Cloudera and Horton.

    • Scalable:- Hadoop is working on the machinery cluster. Hadoop is extremely scalable. By adding more nodes as necessary without interruption, we may grow the size of our group. Horizontal Scaling is the method additional computers are added to the cluster, whilst increased components such as hard disc duplication and RAM are called vertical scale.

    • Fault-Tolerant:- The hallmark of Hadoop is Fault Tolerance. By default, the replication factor of each and every block in HDFS is 3. HDFS will make and store two copies in a separate place of the cluster for each data block. If there are still two copies of the same block, if a block is missing owing to a machine failure. Fault tolerance in Hadoop is thereby accomplished.

    • Schema Independent:- Hadoop is able to operate on many data formats. It is sufficiently versatile to hold diverse data types and can operate on both schematic and schema-less data (unstructured).

    • High Throughput and Low Latency:- Throughput means the amount of work done per unit time and Low latency means to process the data with no delay or less delay. As Hadoop is driven by the principle of distributed storage and parallel processing, Processing is done simultaneously on each block of data and independent of each other. Also, instead of moving data, code is moved to data in the cluster. These two contribute to High Throughput and Low Latency.

    • Data Locality:- Hadoop operates on the "Move code rather than data" concept. Data stays stationary in Hadoop and code will be sent to data for processing purposes in jobs, which are called the data location. Since it is difficult and costly to transport data over a network, when it comes to data in the petabytes range, the location of the data guarantees little data movement in the cluster.

    • Performance:- Data is processed sequentially in legacy systems such as RDBMS while processing in Hadoop starts all blocks at once and therefore parallel processing takes place. The efficiency of Hadoop is considerably greater than legacy systems like RDBMS because of parallel processing techniques. In 2008, Hadoop even defeated at that time the fastest supercomputer.

    • Shared Nothing Architecture:- All nodes in the Hadoop cluster are separate. These architectures don't exchange resources or store, they are called Shared Nothing Architecture (SN). If the node in the cluster fails, the entire cluster will not fall as each node acts separately, removing a single failure point.

    • Support for Multiple Languages:- While Hadoop was mostly created in Java, it supports additional languages such as Python, Ruby, Perl and Groovy.

    • Cost-Effective:- Hadoop is in nature highly economic. We may use standard commodity hardware to construct a Hadoop Cluster, lowering hardware expenses. According to the Cloud era, Hadoop data management costs are extremely small compared with traditional ETL methods, i.e. both hardware and software and other expenses.

    • Abstraction:- At several levels, Hadoop offers Abstraction. For developers, it makes the work easier. A large file is breached and stored in blocks of the same size at several sites in the cluster. We need to care about the position of blocks while designing the map-reduction job. We supply a whole file as input and the Hadoop framework processes multiple data blocks at various points. Hive is part and parcel of the Hadoop Ecosystem Hadoop at the top. Hadoop at the top. Hadoop on top. Hadoop on top. As map decrease tasks have been built in Java, global SQL developers cannot utilise map decrease. Hive is created to address this problem. SQL may be written on Hive as an interrogator Shrink Map Map to decrease job triggers. The SQL community may also work on map reduction tasks thanks to Hive.

    • What is the Pay Scale Of Hadoop Developer:

      Pay A US software developer's average compensation amounts to 90,956 annually, whereas Hadoop's average salary increases significantly, to 1,18,234 per year. Hadoop is defined as a software tool, used by a large network of computers to address the challenge of large volumes of computation and data, structuring or restructuring these data and thereby providing greater flexibility to gather, process, analyse and manage data. It features a distributed open-source structure for the distributed storage, management and processing of the Big Data application on scalable computer server clusters.

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Key Features

ACTE Pune 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 Pune
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
Tourist behaviour analysis Project.

The objectives of this research are: the identification of the patterns of space mobility of visitors, in a global level and according with demographic variables analysis.

Project 2
Credit Scoring Project.

Credit scoring is the set of decision models and their underlying techniques that aid lenders in the granting of consumer credit, this is project Scope and Objectives.

Project 3
Customer churn analysis in Telecom Industry.

The main goal is to develop a machine learning model capable to predict customer churn based on the customer’s data available.

Project 4
uber Project.

Uber's goal is to continually expand globally and bring its services to different cities to allow riders and drivers to connect.

Our Engaging Placement Patners

ACTE Pune 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
  • We are connected with top affiliations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM, etc it make us talented to place our understudies in top MNCs across the globe.
  • After fulfillment of 70% Big Data and Hadoop Course content, we will coordinate the gathering calls to understudies and set them up to f2f association.
  • We plan learner resume to go to meetings and we send learner for interviews till track down another profession.
  • ACTE has given position bunches chose from the best in the business working constant in finding a sensible circumstance opportunity for our learners.We tie ups with over 3000+ associations across the globe.
  • We offer and give all of the basic nuances to interviews until learner track down another profession.
  • Our position gathering will in addition infer the candidates about the walk around meeting.

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 training and practical-based projects.

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

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About Qualified Big Data and Hadoop Tutor

  • Our Big Data and Hadoop Training in Pune, It Courses are passed on by capable and extraordinarily qualified coach, with expansive inclusion with their individual ventures.
  • Well-equipped lab with laptops to practice programming testing after class.
  • All our coaches are working with associations like cognizant, dell, Infosys, IBM, L&T Infotech, TCS, HCL advancements.
  • In our Training Institute 9+ notwithstanding significant length of association with flexible application headway &trained more than 1000 student at ACTE.
  • We give quality preparing and a range of abilities that will be a mainstream one, all through the business.
  • Our mentor will help learner with transforming into an expert in Best Big Data and Hadoop online training with consistent endeavors.

Hadoop Course Reviews

Our ACTE Pune 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"

Banumathi

Software Engineer

If someone asks me where to learn Hadoop, I will definitely recommend ACTE with my eyes closed. He is very clear in his mind and have a very good idea how students feel about the content. Complete Professionalism in Pune.

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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