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Hadoop Training in Jaipur

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  • In this course, you will learn much more than Hadoop. Learn how to handle, install, and configure big data in this course.
  • Additionally, this course will demonstrate how to apply these technologies to problems in the real world! Only familiarity with UNIX and Java is required.
  • You'll gain the knowledge and confidence necessary to succeed in your career by taking this course. With this course, you will have the knowledge and skills to take on big data projects quickly.
  • In this course, you will study Apache Pig, HDFS, and MapReduce. As well as EC2 and Hadoop instances, you can create and configure them. You will find examples in this sections, applications, and explanations.
  • It is beneficial for students to take a theory course as well as a practical course. Our graduates can find jobs in several different kinds of companies after graduating from our program.
  • As well as showing you how Hadoop can be used to solve real-world problems, the course teaches you how to integrate Hadoop into your everyday life. You will also receive a certificate upon completion!
  • 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.
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Course Objectives

Big Data is one of every of the fast and most promising fields, considering all the technologies obtainable within the IT market these days. to require advantage of these opportunities, you wish structured coaching with the newest program as per current trade needs and best practices. Besides sturdy theoretical understanding, if you wish to figure out a varied universe, Big Data comes from the using of completely different Big Data And Hadoop tools as a locality of resolution strategy. Additionally, you wish the steerage of a Hadoop professional United Nations agency is presently operating within the trade on universe Big Data comes and troubleshooting day to day challenges whereas implementing them.

Companies are giving business in Big Data and are adopting Hadoop to store & explain it. Hence, the demand for jobs in Big Data and Hadoop is additionally rising chop-chop. If you're curious about following a career in this field, now could be the proper time to start with big data of Hadoop training.

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

Depending on the particular position together with your talent and education level, Big Data jobs are profitable. Most pay within the range between INR 50,000 – INR 165,000 a year. Not only is Big Data a rewardable career that exposes you to the newest in technology.

  • Analytical Skills.
  • Data visualization skills.
  • Familiarity with Business Domain and Big Data Tools.
  • Skills of Programming.
  • Problem-finding Skills.
  • SQL – Structured command language.
  • Skills of Data Mining.
  • Familiarity with Technologies.

The demand for Big Data specialists is big, the pay offered is commonly terribly high. There are opportunities obtainable across several domains. Thus, the Big Data Hadoop field proves resolutely to be a lovely one for the professionals trying to find a pointy growth and learning curve in their careers.

Hadoop Distributed File System. The Hadoop Distributed filing system (HDFS) is meant to store big data sets forever and to stream those knowledge sets at big data to user applications:
  • Hbase.
  • HIVE.
  • Sqoop.
  • ZooKeeper.
  • NOSQL.
  • Mahout.

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

Although industry-domain experience is important, a Big Data Hadoop certification course can prepare you for multiple job opportunities across varied industries. You'll be able to get further training for different connected ideas and additionally strive your hands at industry-niche projects to draw in higher job opportunities.

What are the purposes of our Big Data Hadoop Certification Training Course?

  • In-depth data of Big Data and Hadoop together with HDFS (Hadoop Distributed filing system), YARN (Yet Another Resource Negotiator) & MapReduce.
  • Comprehensive data of assorted tools that fall in Hadoop schemes like Pig, Hive, Sqoop, Flume, Oozie, and HBase.
  • The capability to big data in HDFS using Sqoop & Flume, and analyze those giant datasets hold on within the HDFS.
  • The exposure to several universe industry-based projects can be dead in Projects that are many cover modified data sets from multiple domains like banking, telecommunication, social media, insurance, and e-commerce.
  • Rigorous involvement of a Hadoop professional throughout the Big Data Hadoop training to learn trade standards and best practices.

What are the skills that you just are learning with Big Data or Hadoop Certification Training Course?

  • Understand the concepts of HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), & understand the way to work with Hadoop storage & resource management.
  • Understand MapReduce Framework.
  • Implement complicated business resolution using MapReduce.
  • Learn knowledge consumption techniques using Sqoop and Flume.
  • Perform ETL operations & knowledge analytics using Pig and Hive.
  • Implementing Partitioning, Bucketing, and categorization in Hive.

What are the job opportunities after completing Big Data Hadoop Certification Training Course?

  • Hadoop / huge knowledge Developer.
  • Hadoop Administrator.
  • Data Engineer.
  • Big knowledge designer.
  • Machine Learning Engineer.
  • Software Development Engineer.
  • Big knowledge Engineer.
  • Big knowledge adviser.

Who is eligible to take the Big Data Hadoop Certification Training Course?

Our Certification Program in Tableau plans understudies for:
  • Software Developers, Project Managers.
  • Software Architects.
  • ETL and knowledge reposition Professionals.
  • Data Engineers
  • Data Analysts & Business Intelligence Professionals.
  • DBAs and decibel professionals.
  • Senior IT Professionals.
  • Testing professionals.
  • Mainframe professionals.
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Overview of Big Data Training in Jaipur

Big Data courses in Jaipur are meant to provide in-depth understanding of platforms and tools for big data analysis. These courses contain real-world projects and case studies that give you a taste of what to expect in the real world. This gives you the opportunity to gain hands-on experience with Big Data technologies so that you can build successful Big Data solutions for businesses. In Jaipur, which is India's technological powerhouse, Big Data is a perfect fit. A large number of startups, IT businesses and MNCs are located in Bangalore, making the need for Big Data experts like data analysts, big data engineers, big data architects and data scientists exceedingly high.


Additional Info

Types of Big Data:

In terms of types of big data, now that we understand what is big data, let's have a closer look at the following:

Structured:- A structured data set is one kind of big data, and we define structured data as information that is processed, stored, and retrieved in a predefined way. Simple search engine algorithms allow users to access highly organized information stored in a database from anywhere with ease. Employee tables in company databases, for instance, will be structured so that employee information, such as their job titles, their salaries, etc., are arranged in a uniform way.

Unstructured:- A dataset that is unstructured lacks any pattern or structure whatsoever. Analyzing unstructured data becomes extremely time-consuming and difficult as a result. Unstructured data is an example of email. Big data can be structured or unstructured.

Semi-structured:- Big data can be semi-structured as well. The term semi-structured data refers to data that contains both the structured and unstructured formats, i.e., structured and unstructured data. Specifically, it refers to the data that does not belong in a specific repository (database) but nevertheless contains vital information or tags that separate the various components contained within it.

Characteristics of Big Data:

Several Gartner analysts put forth the three 'V's of big data in 2001 - namely variety, velocity, and volume. Let's take a look at big data's characteristics. Whether big data is big or not can be determined by observing these characteristics alone.

1) Variety:- There are various types of Big Data. Typically, it refers to structured, unstructured, and semistructured data that is collected from multiple sources. The data collected in the past was only available through spreadsheets and databases, but today it is available in so many other forms including emails, PDFs, videos, audios, social media postings, etc. Big data has many important characteristics, including variety.

2) Velocity:- As it relates to real-time data creation, speed refers to how fast the procedure moves. An all-inclusive perspective encompasses change rates, links of incoming data sets at varying speeds, and bursts of activity.

3) Volume:- Big data is characterized by its volume. Big Data is the label used to refer to the vast amount of data generated on a daily basis from various sources, including social networks, business processes, machines, networks, individual interactions, etc. Data warehouses store such a large volume of data. Thus concludes our discussion of big data characteristics.

Hadoop’s Components:

Hadoop is a comprehensive framework. Data is stored and processed by it using many components. The main sections are, however, the following:

HDFS:- Data can be stored in readily accessible formats on the Hadoop Distributed File System. The data is distributed across multiple nodes, so it is distributed. An HDFS node consists of a slave node and a master node. Datanodes are slave nodes that serve as slaves to Namenodes. Identifies which blocks are replicated and where the data is stored. The Namenode stores this metadata. Managing and organizing DataNodes is its responsibility. The DataNodes are where you actually store your data.

YARN:- This is another resource negotiator, so the name YARN stands for Yet Another Resource Negotiator. There are many applications for this system in Big Data processes. Multiple scheduling methods are supported by YARN. The reason that YARN is such a great solution is that, in the past, scheduling tasks did not provide any options to the user. For certain processing jobs, it is possible to reserve some cluster resources. Additionally, you can set a limit on how many resources each user can reserve.

MapReduce:- Among the Apache Hadoop collection of tools, MapReduce is another feature that is quite powerful. In its most basic form, it identifies data and converts it into a format that can be used for data processing. Two sections make up MapReduce: Map and Reduce (hence the name MapReduce). First, we identify the data and break it down into pieces for parallel processing. Detailed information about the input data is provided in the second part of the report. Any failed project can also be executed by MapReduce. Maps are first performed, followed by shuffles, and finally, reducing. Hadoop's MapReduce is one of the most popular solutions, and because of its many features, it has become synonymous with the industry. Python and Java are two languages it can work in. Big Data professionals will use this tool repeatedly.

A common Hadoop component is:- The Hadoop Commons is a collection of free tools and software that anyone using Hadoop can use. This is a library of great tools that can help you do your job more easily and efficiently.

Hadoop’s Features:

Enterprises in the Fortune 500 have a great deal of interest in Hadoop. Big Data analytics plays a key role in that. Let's focus on its features now that we know why it was created and what its components are.

Big Data Analytics:- Big data analytics was the reason Hadoop was created. Massive amounts of data can be processed in a short amount of time. Using this method, you can store large amounts of data without affecting the efficiency of your storage system. Data is stored in Hadoop clusters, and they are processed in parallel. This is due to its ability to transfer logic only to working nodes, thus making it use less network bandwidth. It saves you a great deal of energy and time because it processes data in parallel.

Cost-Effectiveness:- Hadoop's cost-effectiveness is another advantage. Hadoop instead of conventional technologies can save companies a lot of money on data storage devices. It costs a lot of money to run a large data storage system. It is also expensive to upgrade the same. Data storage units can be upgraded for less cost using Hadoop, which uses fewer storage units. In addition to improving your efficiency, Hadoop provides a number of other benefits. As a whole, it's an excellent solution for any business.

Scaling:- Any organization can experience a rise in data requirements over time. Facebook accounts, for instance, are growing every day. When an organization's data requirements increase, the capacity of its data storage needs to be increased. With Hadoop, you can scale your data in a more secure manner. Adding more cluster nodes allows you to scale the cluster up and down as needed. Adding nodes to your Hadoop system will easily increase its capability. Additionally, scaling the system would not require modifying the application logic.

Error Rectification:- In Hadoop's environment, all data pieces are replicated across all nodes. The data is backed up if there is a failure on a specific node. It prevents data loss and gives you freedom to work freely. You can continue working on your project regardless of the node failure.

5 Benefits of Hadoop for Big Data:

As Hadoop was designed to deal with big data, it should come as no surprise that it has so many benefits. These are the five main benefits:

Speed:- Thanks to Hadoop's concurrency, MapReduce model, and HDFS, users can execute complicated queries with ease.

Diversity:- There are different types of data data formats that can be stored in HDFS, such as structured, semi-structured, and unstructured.

Cost-Effective:- Open-source data framework Hadoop is used to manage large amounts of data.

Resilient:- Ensure fault tolerance by replicating data stored in one node to other nodes in the cluster.

Scalable:- Adding more Hadoop servers is easy because Hadoop works in a distributed environment.

Who is using Big Data? 5 Applications

Big Data is best understood by the people who are using it. The following industries fall into this category:

1) Healthcare:- Healthcare is already undergoing a dramatic change due to Big Data. Now, medical professionals and health care professionals can offer personalized medical care to individual patients thanks to predictive analytics. Furthermore, fitness wearables, telemedicine, and remote monitoring - powered by Big Data and artificial intelligence - are changing lives for the better.

2) Academia:- Education is also being improved by big data today. Online courses have expanded educational opportunities far beyond the confines of the traditional classroom. The development of digital courses leveraging Big Data technologies is gaining popularity in academic institutions in order to foster all-round development of students.

3) Banking:- Big Data is used by the banking industry to detect fraud. It is possible to detect fraudulent acts such as the misuse of credit cards, the storing of inspection tracks, the wrongful alteration of customer statistics, etc., using Big Data tools in real time.

4) Manufacturing:- Big Data in manufacturing offers significant benefits in terms of supply strategies and quality, as specified by TCS' Global Trend Study. By creating a transparent infrastructure, Big data helps manufacturers predict uncertainties and incompatibilities that can negatively impact their business.

5) IT:- Information technology companies are among the largest users of Big Data, utilizing it to improve employee productivity, reduce operational risks, and optimize their operating efficiency. The IT sector is continually driving innovation by combining Big Data technology with artificial intelligence and machine learning.

6) Retail:- Brick-and-mortar retail stores are changing their ways of working due to big data. Through local demographic surveys, POS scanners, RFID, customer loyalty cards, store inventories, etc., retailers have collected vast amounts of data over the years. In the process, they are creating personalized customer experiences, increasing sales, increasing revenue, and delivering outstanding customer service. Smart sensors and Wi-Fi are even used to track customer movements, which aisles customers frequent, and for how long they linger in aisles. Their marketing and product design strategies also adapt as a result of reviewing social media data.

7) Transportation :- For the transportation industry, Big Data Analytics has enormous value. Both public and private transportation companies all over the world employ Big Data technologies to optimize route plans, manage traffic, manage congestion, and improve services. In addition, transportation services utilize Big Data for revenue management, for driving technological innovation, expanding logistics, and for improving their competitive edge.

Career Path in Role of Big Data:

Data Analyst:- Among the responsibilities of a data analyst are to use big data tools to process data. Typically, analysts work with structured, unstructured, and semi-structured data. Among the tools and technologies they use are hive, pig, NoSQL databases, and frameworks such as Hadoop and Spark. Their main responsibility is to increase revenue for businesses by making smart decisions based on the hidden potential of data. The ability to solve problems and calculate is required of a data analyst. Among the tasks analysts perform are analyzing trends, generating patterns, developing reports, etc.

Programmer:- As a programmer, you are responsible for writing the code to execute repeated and conditional actions on the available data. For best results, one should possess good analytical and mathematical skills, as well as logical skills and the ability to use statistics. Shell, Java, Python, R, and Python are some of the most common languages used by big data programmers. In addition to understanding file systems and databases, programmers have to deal with flat files or databases.

Admin:- An admin is responsible for a data & big data ecosystem's infrastructure, as well as tools that deal with big data. A component of their role is also to maintain the network configurations for all nodes. In order to support big data operations, admins ensure that infrastructure is highly available. Administrators are also responsible for installing various tools and managing cluster hardware. It is essential for an administrator to understand the operating system, file system, hardware and network infrastructure.

Solution Architect:- Big data solution architects use their expertise to develop a strategy for solving real-world problems and implementing the strategy using the power of big data to implement it. It is up to the solution architect to determine how to achieve the solution with which technology/programming language. An individual who is a solution architect must have good problem-solving skills as well as comprehensive knowledge of frameworks, tools, and their licensing cost, as well as access to open-source alternatives.

Career Path in Hadoop:

In the Big Data space, it appears that Hadoop is the most popular and most loved framework according to the Stack Overflow survey. The reason is that Hadoop has become a career path for people from different perspectives in IT. There will be a smooth transition from your current IT position to one in the Hadoop world. The following examples are popular: The term 'Software Developer (Programmer)' refers to an individual dealing with different Hadoop abstraction SDKs, whose purpose is to derive value from data.

Data Analyst:- You're proficient in SQL. Hadoop offers huge opprtunities to work with SQL engines, such as Hive and Impala

Business Analyst:- Organizations are trying to enhance their profitability by collecting massive amounts of data, and a business analyst plays a crucial role here.

ETL Developer:- Using Spark tools you can easily build Hadoop ETL if you currently work as an ETL developer. A lot of demand exists for testers in the Hadoop world. All testers can assume this role if they grasp the fundamentals of Hadoop and data profiling.

BI/DW professions:- Equip themselves for data architecture and modeling with Hadoop. IT professionals with deep domain knowledge may become consultants, as they gain an understanding of how Hadoop is trying to solve the issues in the data world. A generic role like Data Engineer or Big Data Engineer is responsible for implementing solutions mostly on cloud vendors. It will be a rewarding role to gain an understanding of the cloud's data components.

What is the average salary for a Big Data / Hadoop Developer?

US developers of Hadoop and Big Data earn an average salary of 117,815. Washington, DC pays the most to Big Data / Hadoop Developers, with a total compensation average of 19% more than the US average.

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Key Features

ACTE Jaipur 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 Jaipur
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
  • 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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Hands-on Real Time Hadoop Projects

Project 1
Corporate Data Integration Project

Data integration is the practice of consolidating data from disparate sources into a single dataset with the ultimate goal of providing users with consistent access and delivery of data.

Project 2
Cloud Hosting Project

The goal of cloud computing is to provide easy, scalable access to computing resources and IT services involves the hardware and software components.

Project 3
Link Prediction for Social Media Sites Project

Link prediction is one of the most important research topics in the field of graphs and networks. The objective of link prediction is to identify pairs of nodes.

Project 4
Document analysis application Project

Firstly, document analysis is an efficient and effective way of gathering data because documents are manageable and practical resources.

Our Engaging Placement Partners

ACTE Jaipur for affirmation and Guaranteed Situations. Our Work Situated classes are educated by experienced confirmed experts with broad certifiable experience. All our Best around down to earth than hypothesis model.
  • Our training system will give our competitors constant Situations and pragmatic inside and out information. The whole educational program content is planned by our guaranteed mentors and it depends on present place of employment necessities.
  • ACTE India is curated with Big Data and Hadoop training which will show our applicants how to adequately find a new line of work and construct an astonishing resume to dazzle the hirers.
  • We even timetable counterfeit situations and inclination tests for our competitors so their advancement can be surveyed by us.
  • We additionally give coordinated preparing to our understudies so they can be prepared well and concentrate out most extreme information from our mentors.
  • Learners will be given fitting counterfeit assessments and MOCK meeting dates when they have completed the instructional courses.
  • ACTE has attaches with an assortment of choice workplaces and affiliations; moreover, ACTE is an incredible expansion to a notable employment opportunity in India.

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About Satisfactory Hadoop Mentor

  • Our Big Data and Hadoop Training in Jaipur coaches have very knowledgeable experience of over 9+ years of programming and coding experience in their respective domains who are currently employed by top multinational corporations.
  • Our educators have led more than around 2000 bits of Big Data and Hadoop training instructional courses till date.
  • The unrivaled expert experience that our coaches have comes from long stretches of delivering a great preparing climate, helping applicants in inventive critical thinking and group building abilities.
  • Our gifted mentors impart well, see how understudies and incorporate an undeniable degree of energy and excitement about the substance that they instruct.
  • Through our mentor's preparation experience, our applicants have had the option to become ensured Big Data and Hadoop Training.
  • Our trainers perceive the worth of incredible Best Big Data and Hadoop Training highlights and pick the fitting preparing techniques relying upon the Up-and-comer's profiles and objectives.

Hadoop Course Reviews

Our ACTE Jaipur 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

I successfully completed Hadoop training from ACTE. They provided an excellent, Industry experienced, knowledgeable trainer. The Administrative and other staff members were helpful , pleasant and very professional. The training center charged reasonably for the training in Jaipur. I certainly recommend ACTE training center for Hadoop Training.


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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