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Big Data Hadoop Certification Training in Boston

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  • We have designed an in-depth course so meet job requirements and criteria
  • Resume & Interviews Preparation Support
  • Concepts: High Availability, Big Data opportunities, Challenges, Big Data Hadoop Certification 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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Overview of Big Data Hadoop Certification Training in Boston

The Big Data Hadoop Training in Boston is planned and curated by industry experts with over ten years of involvement and remembers for profundity information on Big Data and Hadoop Ecosystem devices like HDFS, YARN, MapReduce, Hive, and Pig. Utilizing Cloud Lab, you will deal with genuine industry use cases in the Retail, Social Media, Aviation, Tourism, and Finance areas all through this web-based teacher drove live Big Data Hadoop affirmation class. This illustration will show you Big Data, the constraints of past Big Data arrangements, how Hadoop addresses those difficulties, the Hadoop Ecosystem, Hadoop Architecture, HDFS, File Read and Write Anatomy, and how MapReduce works.

 

Additional Info

What Is Big Data Hadoop ?

The Big Data course in Boston is planned by industry specialists with more than ten years of involvement and covers inside and out information on Big Data and Hadoop Ecosystem devices like HDFS, YARN, MapReduce, Hive, and Pig. You will chip away at genuine industry use cases in Retail, Social Media, Aviation, Tourism, and Finance during this web-based educator drove live Big Data Hadoop confirmation. It is an extensive Hadoop Big Data instructional class planned by industry specialists to help you in learning Big Data Hadoop and Spark modules while keeping current industry work necessities in mind. This is an industry-perceived Big Data Hadoop preparation in Boston that joins Hadoop engineer, Hadoop head, Hadoop testing, and investigation instructional classes with Apache Spark preparing.

Career With Big Data Hadoop :

Working on SQL motors, for example, Hive or Impala is a tremendous open door in Hadoop:

As a Software Developer, Hadoop Data Developer works with different Hadoop reflection SDKs and gets esteem from data.

Analyst, Business: Organizations are endeavoring to turn out to be more productive using enormous measures of information, and the job of a business investigator is basic in this.

ETL Programmer: You can undoubtedly change from customary ETL to Hadoop ETL utilizing Spark instruments in case you are a conventional ETL developer.

Testers: Testers are sought after in the Hadoop world.This job is available to any analyzer who comprehends the basics of Hadoop and information profiling. Professionals in business knowledge and information distribution centers can without much stretch progress from Hadoop Data Architecture to Data Modeling.

Senior IT personnel: A senior expert with an exhaustive comprehension of the area and existing information difficulties can become advisors by figuring out how Hadoop is endeavoring to address these issues.

There are nonexclusive jobs like Data Engineers or Big Data Engineering that are accountable for carrying out arrangements, principally on top of Cloud merchants. On the off chance that you find out with regards to the information parts that the cloud gives, this will be a promising role.

Stuctures of Big Data Hadoop Certification:

  • It's basic to ensure you benefit from it and that the educational program covers the latest Apache Hadoop topics through Boston.
  • For model, before the finish of the course, you ought to have dominated the Apache Hadoop ideas recorded below.
  • Learn about Hadoop's Distributed File System and the MapReduce framework.
  • Learn how to stack information utilizing Sqoop and Flume.
  • Learn how to compose complex MapReduce programmes.
  • Perform information examination with Pig and Hive.
  • Understand the ZooKeeper administration completely.
  • Implement best practices for Hadoop advancement and debugging.
  • Set up a Hadoop cluster.
  • MapReduce programming .
  • Programming with YARN .
  • HBase, MapReduce Integration, Advanced Usage, and Advanced Indexing are all recommended.
  • Hadoop 2.0 presents new components like YARN, HDFS Federation, and NameNode High Availability.
  • Set up a Hadoop project.

Key Components Of Big Data Hadoops Training:

    Hadoop isn't only one application, rather it is a stage with different fundamental parts that empower circulated information stockpiling and handling. These parts together structure the Hadoop biological system. A portion of these is center parts, which structure the establishment of the system, while some are beneficial parts that welcome extra functionalities into the Hadoop world.

    The center parts of Hadoop are,

  • HDFS: Maintaining the Distributed File System
  • HDFS is the mainstay of Hadoop that keeps up with the disseminated document framework. It makes it conceivable to store and repeat information across numerous workers.

    HDFS has a NameNode and DataNode. DataNodes are the product workers where the information is really put away. The NameNode, then again, contains metadata with data on the information put away in the various hubs. The application just collaborates with the NameNode, which speaks with information hubs as required.

  • YARN: Yet Another Resource Negotiator
  • YARN represents Yet Another Resource Negotiator. It oversees and plans the assets, and chooses what ought to occur in every information hub. The focal expert hub that deals with all handling demands are known as the Resource Manager. The Resource Manager communicates with Node Managers; each slave data node has its own Node Manager to execute assignments.

  • MapReduce
  • MapReduce is a programming model that was first utilized by Google for ordering its inquiry tasks. It is the rationale used to divide information into more modest sets. It deals with the premise of two capacities — Map() and Reduce() — that parse the information in a fast and effective way.

    To begin with, the Map works gatherings, channels, and sorts numerous informational indexes in corresponding to create tuples (key, esteem sets). Then, at that point, the Reduce work totals the information from these tuples to create the ideal yield.

The Benefits of Big Data Hadoop Certification:

  • Recruiters and occupation postings are searching for Hadoop guaranteed candidates.
  • This is a huge benefit over a not affirmed in competitor Hadoop.
  • It gives you a benefit over different experts in a similar field as far as remuneration.
  • Hadoop Certification can assist you with propelling your vocation and move up the stepping stool during IJPs.
  • Advantageous for People from an assortment of specialized foundations are endeavoring to make the change to Hadoop.
  • Validates your involved Big Data experience.
  • This test guarantees that you are current on the latest Hadoop features.
  • The certificate permits me to talk all the more certain about this innovation at my organization while organizing with other companies.

Challenges And Difficulties Faced in Hadoop :

However Hadoop has broadly been viewed as a key empowering agent of huge information, there are still a few difficulties to consider. These difficulties originate from the idea of its intricate biological system and the requirement for cutting-edge specialized information to perform Hadoop capacities. In any case, with the right mix stage and instruments, the intricacy is diminished altogether and henceforth, makes working with it simpler too.

1. Steep Learning Curve

To question the Hadoop document framework, software engineers need to compose MapReduce capacities in Java. This isn't clear and includes a lofty expectation to absorb information. Likewise, there is an excessive number of parts that make up the environment, and it sets aside an effort to get to know them.

2. Diverse Datasets Require Different Approaches

There is nobody 'size fits all' arrangement in Hadoop. The greater part of the valuable parts talked about above have been an inherent reaction to a hole that should have been tended to.

For instance, Hive and Pig give a less difficult approach to question the informational collections. Furthermore, information ingestion instruments, for example, Flume and Sqoop assist with social event information from numerous sources. There are various different parts too and it takes insight to settle on the best decision.

3. Limits of MapReduce

MapReduce is an astounding programming model to cluster measure huge informational collections. Be that as it may, it has its restrictions.

Its document escalated approach, with various peruses and composes, isn't appropriate for constant, intuitive information investigation or iterative errands. For such activities, MapReduce isn't sufficiently proficient and prompts high latencies. (There are workarounds to this issue. Apache is an elective that is filling the hole of MapReduce.)

4. Information Security

As large information gets moved to the cloud, touchy information is unloaded into Hadoop workers, making the need to guarantee information security. The huge environment has countless apparatuses that guarantee that each instrument has the right access rights to the information. There should be proper validation, provisioning, information encryption, and regular inspecting. Hadoop has the ability to address this test, however, it's a question of having the mastery and being careful in execution.

Albeit numerous tech goliaths have been utilizing the parts of Hadoop talked about here, it is still moderately new in the business. Most difficulties originate from these early stages, however, a hearty huge information mix stage can address or facilitate every one of them.

Hadoop versus Apache Spark :

  • The MapReduce model, notwithstanding its many benefits, isn't productive for intelligent questions and ongoing information handling, as it depends on circle composes between each phase of preparing.
  • Flash is an information handling motor that tackles this test by utilizing in-memory information stockpiling. In spite of the fact that it began as a sub-undertaking of Hadoop, it has its own group innovation.
  • Frequently, Spark is utilized on top of HDFS to use simply the capacity part of Hadoop. For the handling calculation, it utilizes its own libraries that help SQL inquiries, streaming, AI, and diagrams.
  • Information researchers use Spark broadly for its lightning speed and exquisite, highlight-rich APIs that make working with enormous informational collections simple.
  • While Spark might appear to have an edge over Hadoop, both can work pair. Contingent upon the prerequisite and the sort of informational collections, Hadoop and Spark complete one another. Sparkle doesn't have its very own document arrangement, so it needs to rely upon HDFS, or other such arrangements, for its stockpiling.
  • The genuine correlation is really between the handling rationale of Spark and the MapReduce model. At the point when RAM is imperative, and for overnight positions, MapReduce is a solid match. Be that as it may, to stream information, access AI libraries, and for speedy ongoing activities, Spark is the best decision.

Responsibilities Of Big Data Hadoop:

  • The essential obligation of a Hadoop Developer is to code. They are basically programming engineers who spend significant time in Big Data Hadoop.
  • They dominate at making plan ideas that are utilized in the making of huge programming applications. They are PC programming language experts.
  • As a Hadoop Developer, you will be answerable for the accompanying tasks:

  • Learn about the spry programming advancement methodology.
  • Designing, creating, recording, and architecting Hadoop applications are all essential for the process.
  • Manage and screen Hadoop log files.
  • Make MapReduce code that functions admirably on Hadoop clusters.
  • SQL, NoSQL, information warehousing, and DBA experience are required.
  • Learn about state-of-the-art ideas like Apache Spark and Scala programming.
  • Learn all that you can about the Hadoop environment and Hadoop Common.
  • Transform hard to-comprehend specialized details into extraordinary designs.
  • Create web administrations to empower quick information following and rapid information queries.
  • Prototype programming is tried, principles are proposed, and they are executed smoothly.

Key Benefits of Big Data Hadoop :

    For big data and analytics training in Boston, Hadoop is a life saver. Data gathered about people, processes, objects, tools, etc. is useful only when meaningful patterns emerge that, in-turn, result in better decisions. Hadoop helps overcome the challenge of the vastness of big data,

  • Resilience — Data stored in any node is also replicated in other nodes of the cluster. This ensures fault tolerance. If one node goes down, there is always a backup of the data available in the cluster.
  • Scalability — Unlike traditional systems that have a limitation on data storage, Hadoop is scalable because it operates in a distributed environment. As the need arises, the setup can be easily expanded to include more servers that can store up to multiple petabytes of data.
  • Low cost — As Hadoop is an open-source framework, with no license to be procured, the costs are significantly lower compared to relational database systems. The use of inexpensive commodity hardware also works in its favor to keep the solution economical.
  • Speed — Hadoop’s distributed file system, concurrent processing, and the MapReduce model enable running complex queries in a matter of seconds.
  • Data diversity — HDFS has the capability to store different data formats such as unstructured (e.g. videos), semi-structured (e.g. XML files), and structured. While storing data, it is not required to validate against a predefined schema. Rather, the data can be dumped in any format. Later, when retrieved, data is parsed and fitted into any schema as needed. This gives the flexibility to derive different insights using the same data.

Payscale Of Big Date Hadoop:

A Big Data Hadoop Developer's compensation in Boston is essentially controlled by an up-and-comer's instructive qualifications, range of abilities, work insight, organization size and notoriety, and occupation location. Salaries for senior-level Hadoop Developers (with over 15 years of involvement) are normally very high, the worldwide Hadoop Big Data market is relied upon to develop at a CAGR of 43% from $4.91 billion out of 2015 to $40.69 billion in upcoming years. This recommends that the interest for Hadoop Developers will ascend in the close future.

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Key Features

ACTE Boston offers Big Data Hadoop Certification 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 Big Data Hadoop Certification Course in Dallas
Module 1: Introduction to Big Data Hadoop Certification
  • 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 Big Data Hadoop Certification
  • Big Data Hadoop Certification Distributed File System
  • Comparing Big Data Hadoop Certification & SQL
  • Industries using Big Data Hadoop Certification
  • Data Locality
  • Big Data Hadoop Certification Architecture
  • Map Reduce & HDFS
  • Using the Big Data Hadoop Certification single node image (Clone)
Module 4: Big Data Hadoop Certification Distributed File System (HDFS)
  • HDFS Design & Concepts
  • Blocks, Name nodes and Data nodes
  • HDFS High-Availability and HDFS Federation
  • Big Data Hadoop Certification 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 Big Data Hadoop Certification 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 Big Data Hadoop Certification Configuration API discussion
  • Emulating “grep” for searching inside a file in Big Data Hadoop Certification
  • DBInput Format
  • Job Dependency API discussion
  • Input Format API discussion,Split API discussion
  • Custom Data type creation in Big Data Hadoop Certification
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
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 Big Data Hadoop Certification Projects

Project 1
Customer churn analysis –Telecom Industry

The project involves tracking consumer complaints registered on various Platforms.

Project 2
UBER Projects

Determine dynamic pricing based on traffic congestion, Spark Streaming and Cassandra.

Our Top Hiring Partner for Placements

ACTE Boston offers placement opportunities 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.

  • We are associated with top organizations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc. It make us capable to place our students in top MNCs across the globe
  • We have separate student’s portals for placement, here you will get all the interview schedules and we notify you through Emails.
  • After completion of 70% Big Data Hadoop Certification training course content, we will arrange the interview calls to students & prepare them to F2F interaction
  • Big Data Hadoop Certification Trainers assist students in developing their resume matching the current industry needs
  • We have a dedicated Placement support team wing that assist students in securing placement according to their requirements
  • We will schedule Mock Exams and Mock Interviews to find out the GAP in Candidate Knowledge

Get Certified By MapR Certified Big Data Hadoop Certification Developer (MCHD) & Industry Recognized ACTE Certificate

Acte Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher's as well as corporate trainees.

Our certification at Acte is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC's of the world. The certification is only provided after successful completion of our training and practical based projects.

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

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

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About Experienced Big Data Hadoop Certification Trainer

  • Our Big Data Hadoop Certification Training in Boston. Trainers are certified professionals with 7+ years of experience in their respective domain as well as they are currently working with Top MNCs.
  • As all Trainers are Big Data Hadoop Certification domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
  • All our Trainers are working with companies such as Cognizant, Dell, Infosys, IBM, L&T InfoTech, TCS, HCL Technologies, etc.
  • Trainers are also help candidates to get placed in their respective company by Employee Referral / Internal Hiring process.
  • Our trainers are industry-experts and subject specialists who have mastered on running applications providing Best Big Data Hadoop Certification training to the students.
  • We have received various prestigious awards for Big Data Hadoop Certification Training in Boston from recognized IT organizations.

Big Data Hadoop Certification 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 Big Data Hadoop Certification 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 Big Data Hadoop Certification Course At ACTE?

  • Big Data Hadoop Certification Course in ACTE is designed & conducted by Big Data Hadoop Certification experts with 10+ years of experience in the Big Data Hadoop Certification 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 Big Data Hadoop Certification batch to 5 or 6 members
Our courseware is designed to give a hands-on approach to the students in Big Data Hadoop Certification . 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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      Job Opportunities in Big Data

      More than 35% of Data Professionals Prefer Big Data. Big Data Is Widely Recognized as the Most Popular and In-demand Data Technology in the Tech World.

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