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Big Data Hadoop Certification Training in Toronto

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  • Beginner & Advanced level Classes.
  • Hands-On Learning in Big Data Hadoop Certification .
  • Best Practice for interview Preparation Techniques in Big Data Hadoop Certification .
  • Lifetime Access for Student’s Portal, Study Materials, Videos & Top MNC Interview Question.
  • Affordable Fees with Best curriculum Designed by Industrial Big Data Hadoop Certification Expert.
  • Delivered by 9+ years of Big Data Hadoop Certification Certified Expert | 12402+ Students Trained & 350+ Recruiting Clients.
  • Next Big Data Hadoop Certification Batch to Begin this week – Enroll Your Name Now!

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INR 18000

INR 14000

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INR 20000

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Upcoming Batches

24-Jun-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

19-Jun-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

22-Jun-2024
Sat,Sun

Weekend Regular

(10:00 AM - 01:30 PM)

(Class 3hr - 3:30Hrs) / Per Session

22-Jun-2024
Sat,Sun

Weekend Fasttrack

(09:00 AM - 02:00 PM)

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

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Have Cracked Their Dream Job in Top MNC Companies

Learn From Experts, Practice On Projects & Get Placed in IT Company

  • 100% Guaranteed Placement Support for Freshers & Working Professionals
  • You will not only gain knowledge of Big Data Hadoop Certification Certification and advanced concepts, but also gain exposure to Industry best practices
  • Experienced Trainers and Lab Facility
  • Big Data Hadoop Certification Professional Certification Guidance Support with Exam Dumps
  • Practical oriented / Job oriented Training. Practice on Real Time project scenarios.
  • 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.
  • START YOUR CAREER WITH HANDOOP CERTIFICATION COURSE THAT GETS YOU A JOB OF UPTO 5 TO 12 LACS IN JUST 60 DAYS!
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
  • Enroll Now

About Big Data Hadoop Certification Online Training Course in Toronto

ACTE provides the most comprehensive and top-notch training in Big Data Hadoop . This career-oriented training will provide you with all the essential skills to have a sterling career in various Hadoop domains like Developer, Administrator, Analyst and Testing. This is a Big Data Hadoop master’s program that has been designed by industry experts.

Benefits

The Big Data Hadoop certification Online training is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark. In this hands-on Hadoop course, you will execute real-life, industry-based projects using Integrated Lab.

Top Job Offered Big Data Hadoop Certification Online Tools Covered
  • Big Data, HDFS

    YARN, Spark

    MapReduce

  • PIG, HIVE

    HBase

    Mahout, Spark MLLib

  • Solar, Lucene

    Zookeeper

    Oozie

Big Data Hadoop Certification skills are in demand – this is an undeniable fact! Hence, there is an urgent need for IT professionals to keep themselves in trend with Big Data Hadoop Certification and Big Data technologies. Apache Big Data Hadoop Certification provides you with means to ramp up your career and gives you the following advantages: Accelerated career growth.

Big Data Hadoop Certification is the supermodel of Big Data. If you are a Fresher there is a huge scope if you are skilled in Big Data Hadoop Certification . The need for analytics professionals and Big Data architects is also increasing . Today many people are looking to pursue their big data career by grabbing big data jobs as freshers.

Even as a fresher, you can get a job in Big Data Hadoop Certification domain. It is definitely not impossible for anyone to land a job in the Big Data Hadoop Certification domain if they invest their mind in preparing and putting their best effort in learning and understanding the Big Data Hadoop Certification concepts.

We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings for Big Data Hadoop Certification . Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.

A Big Data Hadoop Certification Cluster uses Master-Slave architecture. It consist of a Single Master (NameNode) and a Cluster of Slaves (DataNodes) to store and process data. Big Data Hadoop Certification is designed to run on a large number of machines that do not share any memory or disks. These DataNodes are configured as Cluster using Big Data Hadoop Certification Configuration files. Big Data Hadoop Certification uses a concept of replication to ensure that at least one copy of data is available in the cluster all the time. Because there are multiple copy of data, data stored on a server that goes offline or dies can be automatically replicated from a known good copy.

  • To learn Big Data Hadoop Certification and build an excellent career in Big Data Hadoop Certification , having basic knowledge of Linux and knowing the basic programming principles of Java is a must. Thus, to incredibly excel in the entrenched technology of Apache Big Data Hadoop Certification , it is recommended that you at least learn Java basics.
  • Learning Big Data Hadoop Certification is not an easy task but it becomes hassle-free if students know about the hurdles overpowering it. One of the most frequently asked questions by prospective Big Data Hadoop Certification ers is- “How much java is required for Big Data Hadoop Certification ”? Big Data Hadoop Certification is an open source software built on Java thus making it necessary for every Big Data Hadoop Certification er to be well-versed with at least java essentials for Big Data Hadoop Certification . Having knowledge of advanced Java concepts for Big Data Hadoop Certification is a plus but definitely not compulsory to learn Big Data Hadoop Certification . Your search for the question “How much Java is required for Big Data Hadoop Certification ?” ends here as this article explains elaborately on java essentials for Big Data Hadoop Certification .

Apache Big Data Hadoop Certification is an open source platform built on two technologies Linux operating system and Java programming language. Java is used for storing, analysing and processing large data sets. ... Big Data Hadoop Certification is Java-based, so it typically requires professionals to learn Java for Big Data Hadoop Certification .

Yes, you can learn Big Data Hadoop Certification , without any basic programming knowledge . The only one thing matters is your dedication towards your work. If you really want to learn something, then you can easily learn. It also depends upon on which profile you want to start your work like there are various fields in Big Data Hadoop Certification .

Our course ware 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.

Yes It is worth , Future will be bright. Learning Big Data Hadoop Certification will definitely give you a basic understanding about working of other options as well. Moreover, several organizations are using Big Data Hadoop Certification for their workload. So there are lot of opportunities for good developers in this domain. Indeed it is!

No Learning Big Data Hadoop Certification is not very difficult. Big Data Hadoop Certification is a framework of java. Java is not a compulsory prerequisite for learning Big Data Hadoop Certification . ... Big Data Hadoop Certification is an open source software platform for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware.

Big Data Hadoop Certification framework can be coded in any language, but still, Java is preferred. For Big Data Hadoop Certification , the knowledge of Core Java is sufficient, and it will take approximately 5-9 months. Learning Linux operating system: - It is recommended to have a basic understanding and working of the Linux operating system.

Top reasons to consider a career in Big Data Hadoop Certification ?

Big Data Hadoop Certification brings in better career opportunities in 2015.

Learn Big Data Hadoop Certification to pace up with the exponentially growing Big Data Market.

Increased Number of Big Data Hadoop Certification Jobs.

Learn Big Data Hadoop Certification to pace up with the increased adoption of Big Data Hadoop Certification by Big data companies.


Big Data Trends: Our Predictions for 2020 PLUS What Happened in 2019

  • 2019 was a big year across the big data landscape. After starting the year with the Cloudera and Hortonworks merger, we’ve seen massive upticks in Big Data use around the globe, with companies flocking to embrace the importance of data operations and orchestration to their business success.
  • The big data industry is now worth $189 Billion, an increase of $20 Billion over 2018, and is set to continue its rapid growth and reach $247 Billion by 2022.
  • As quickly as the year began, it’s nearly over, which means it’s time for us to once again put on our thinking caps and make our predictions for 2020. But before we do, let’s take a look back at the trends we predicted, and then saw come true, in 2019.

Data Trends We Saw in 2019

Operationalization of Big Data Analytics Came to the Forefront

  • We started seeing this trend at the end of 2018, but in 2019, the operationalization of big data became much more achievable across the board. Previously, companies could see initial success with data operationalization, but scaling data operations and orchestration proved time-consuming and difficult to maintain.
  • The introduction of automation frameworks specifically designed to operationalize big data workflows has made going from the initial development of a new analytic use case to putting it into production much simpler.
  • Also, with more CDOs now available to drive change (thanks in no small part to GDPR, which mandates their appointment), we have seen an increasing number of organizations get their companies on board with a singular data vision to move from ad hoc analytics to full operationalization of enterprise-wide big data platforms and analytics at scale.

Fewer Unicorns in the Data and AI Landscape

The consolidation of big data vendors, as measured by the dwindling number of vendors at trade shows like Strata, leveled off in 2019. The number of companies getting acquired or simply disappearing now just about matches the number of new entrants. While fewer vendors are getting funded, more of them are delivering a greater level of innovation and value.

  • The advanced analytics market is no longer in the mood to tolerate crowds of vendors with little differentiation or providers that aren’t delivering real value or rev gen. Let’s face it.
  • The first wave of big data vendors included many organizations that weren’t building businesses–they were building features.
  • As more of them get rolled up into an integrated stack, it will be up to the next generation of players that come in to transform big data into something that’s truly big.

Artificial Intelligence and Machine Learning Made Huge Strides

  • Another key data analytics trend for 2019 was the increased alignment between traditional analytics with machine learning (ML) and artificial intelligence (AI) analytics. More and more organizations are using ML and AI to augment everyday operational analytics pipelines and normal line of business activities.
  • In the past, ML and AI were somewhat restricted to what data scientists could evaluate and test before a data engineering team could deploy into production. In fact, in most organizations, you had a traditional BI/analytics team and then a separate team of data scientists and yet another team for data engineering.
  • Those groups and skills sets have now begun to overlap or at least work together in more thoughtful ways.

Our Data Trends 2020 Predictions

  • As data sources become more complicated and AI applications expand, 2020 is set to be another year of innovation and evolution for big data. Read on to get the thoughts of big data and data engineering industry veterans Ramesh Menon and Todd Goldman, as they present you their five top thoughts on big data technologies in 2020.
  • The Cloud is the new Data Lake… but Multi-cloud and Hybrid are here to stayAs cloud-based technologies continue to develop, businesses are increasingly likely to desire a spot in the cloud. However, the process of moving your data integration and preparation from an on-premises solution to the cloud is more complicated and time-consuming than most care to admit. In addition to migrating mass amounts of existing data, companies will have to sync their data sources and platforms for several weeks to months before the shift is complete.
  • This isn’t to say that it’s not worth it to switch to the cloud, but the prevalent trend we see emerging is the use of hybrid deployments. Early adopters of the cloud are seeing the difficulties of moving completely over, and instead are utilizing their cloud storage for dynamic workloads, while on-premises platforms remain highly useful for stable workloads.
  • Another complexity is that most enterprises already have a multi-cloud footprint. In 2020, we expect to see later adopters come to the same conclusion, bringing the hybrid and multi-cloud methodology to the forefront of data ecosystem strategies.

More Businesses Will Abandon Hadoop for Spark and Databricks, But This Change Won’t Solve All Their Problems

  • Since arriving on the market, Hadoop has been criticized by many in the community for its complexity. Spark and managed Spark solutions like Databricks are the “new and shiny” player and have therefore been gaining traction as data science workers see the platform as an answer to everything they dislike about Hadoop. Spark and Databricks will be especially lauded for their interactive processing capabilities, as well as its internal memory computation for job scheduling and user-friendly interface, which allows data scientists to process stored data via high-level operators.
  • While Spark and Databricks resolve some of the issues presented by Hadoop’s data management environment, we expect that those running to Spark or Databricks will quickly find it has its own set of challenges. The bottom line is that you still need to code data pipelines and you still need to operationalize, harden and make your data workflows fully manageable and governable.
  • In addition, much like Hadoop, running a Spark or Databricks job in a data science sandbox and then promoting it into full production will continue to be fraught with challenges. Data engineers will continue to need more fit and finish for Spark when it comes to enterprise-class data operations and orchestration.
  • The bottom line is that there are a lot of choices to consider between the two platforms, and organizations will avail themselves of that choice for preferred capabilities and economic value. For companies using Infoworks, it will be even easier to switch between the two options, as our framework supports a multitude of data environments.

Digital Transformation Will Be a Key Component of Top-Level Data Strategies

  • People have been talking about digital transformation for years without ever really knowing what it meant. Very often digital transformation was used to describe finding ways to sell the data that was being generated to create new revenue streams.
  • These were generic ideas not specific to any particular business, which is why they went nowhere for most organizations.
  • What they are now coming to realize is that digital transformation is really about taking a data-driven approach to every aspect of their business in an effort to create a competitive advantage. If you’re a retailer, it might be about providing real-time “next best offer” program offers while customers are in your physical stores or getting more out of your inventory to provide a better online and in-store experience.
  • If you are an oil exploration and production company, it is about using data to perform wellhead drilling adjustments hourly instead of daily to maximize the yield from an oil field.
  • These are discussions that cut to the core to even very traditional businesses, which are now beginning to correctly identify digital transformation as a means of investing in a data platform that reflects the state of the business and can pivot to support new business models as quickly as they emerge. In the same way that you wouldn’t start a company without ERP or CRM system, the same is now true for data and organizations.
  • To see evidence of this evolution in 2020, look for mentions of data in yearly and quarterly reports and mentions about data and analytics in very business-specific use cases in earnings calls.

Machine Learning and Artificial Intelligence Will Continue to Evolve

  • Two trends are accelerating the use of ML and AI in data-driven organizations. The first is the continued evolution of the “citizen data scientist” who can use some basic ML and AI algorithms within their data pipelines as those capabilities being to show up in more traditional BI and data integration platforms.
  • The second is the ability of data scientists to use more automated tools to put advanced ML and AI algorithms into production.
  • In 2020, automation frameworks will allow data scientists to create their own data pipelines that are close to production-ready. This combination of bringing data engineering to data scientists and data science to data analysts will drive an increase in the number of actual ML and AI algorithms that go into enterprise-level production.

Getting Ready for Big Data in 2021

  • As we enter the next phase of big data evolution, keep an eye on these big data analytics trends and see how your organization handles the big data landscape.
  • We’ll be back next year to see which predictions we got right, and get you prepared for 2021.
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Key Features

ACTE Toronto 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 Online Training Course in Toronto
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
<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
Show More
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Need customized curriculum?

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 Toronto 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.

Complete Your Course

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

Get Certified

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

Get Certified

About Experienced Big Data Hadoop Certification Trainer

  • Our Big Data Hadoop Certification Training in Toronto. 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 Toronto 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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