Hadoop Training in Toronto, Canada

  • Get Trained for Microsoft Big Data Certification - Learn More
  • Become a Hadoop Developer by getting project experience
  • Build a project portfolio to connect with recruiters
    - Check out Toly's Portfolio
  • Get hands-on experience with access to remote Hadoop cluster
  • Stay updated in your career with lifetime access to live classes

About Online Hadoop Training Course

Project Portfolio

Build an online project portfolio with your project code and video explaining your project. This is shared with recruiters.

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32 hrs live hands-on sessions with industry expert

The live interactive sessions will be delivered through online webinars. All sessions are recorded. All instructors are full-time industry Architects with 14+ years of experience.

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Remote Lab and Projects

You will get access to a remote Hadoop cluster for this purpose. Assignments include running MapReduce jobs/Pig & Hive queries. The final project will give you a complete understanding of the Hadoop Ecosystem.

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Lifetime Access & 24x7 Support

Once you enroll for a batch, you are welcome to participate in any future batches free. If you have any doubts, our support team will assist you in clearing your technical doubts.

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Weekly 1-on-1 meetings

If you opt for the Microsoft Track, you will get 8 one-on-one meetings with an experienced Hadoop architect who will act as your mentor.

Big Data Hadoop Certification Training in Toronto, Canada

Big data and hadoop will be an absolutely booming industry for the next 10 years in Canada. Statistics reveal that only 16% of the companies have the required analytics talent in place to work on big data projects. Professionals are scrambling to get trained and certified in what's expected to be the hottest new high-tech skill: Hadoop. The momentum to beef up big data hadoop training in Toronto, Canada suggests that the shortage for hadoop skills won't last forever in Canada. Anyone looking to enter the big data space in the near-term can expect to find hadoop jobs waiting. DeZyre's Online Hadoop Developer Certification Training course is designed to give you expertise in building powerful big data applications using Hadoop by performing tasks on actual hadoop cluster.

Hadoop Salary Canada

Hadoop developer salaries are rising fast in Canada as companies struggle to find talent. According to Statistics Canada, the salaries for big data professionals have increased by 38% since 2009. The salary of a big data engineer in Canada as of 2016 varies between $117,000 - $150,500 CAD. The average salary for big data jobs with hadoop skills in Toronto, Canada is $89,000 USD.
  • Average Hadoop Developer Salary in Toronto, Canada - $41,000.
  • Average Hadoop Application Developer Salary in Toronto, Canada - $91,000.
  • Average Hadoop Administrator Salary in Toronto, Canada - $97,000.

Companies Hiring for Big Data and Hadoop Jobs in Toronto, Canada

 
  • Harnham
  • PROCOM
  • Rogers Communications Inc.
  • CPP Investment Board
  • Deloitte
  • Scotia Bank
  • Randstad Technologies
  • Shopify
  • Royal Bank of Canada (RBC)
  • TD Bank

Hadoop Certification Cost in Toronto, Canada - $399

DeZyre's Hadoop Developer Certification Training course costs around $399 featuring instructor-led training and industry oriented hadoop projects. DeZyre provides hadoop certification to professionals on successful completion and evaluation of the hadoop project by industry experts.

Benefits of Hadoop Training online

How will this help me get jobs?

  • Display Project Experience in your interviews

    The most important interview question you will get asked is "What experience do you have?". Through the ProjectPro live classes, you will build projects, that have been carefully designed in partnership with companies.

  • Connect with recruiters

    The same companies that contribute projects to ProjectPro also recruit from us. You will build an online project portfolio, containing your code and video explaining your project. Our corporate partners will connect with you if your project and background suit them.

  • Stay updated in your Career

    Every few weeks there is a new technology release in Big Data. We organise weekly hackathons through which you can learn these new technologies by building projects. These projects get added to your portfolio and make you more desirable to companies.

What if I have any doubts?

For any doubt clearance, you can use:

  • Discussion Forum - Assistant faculty will respond within 24 hours
  • Phone call - Schedule a 30 minute phone call to clear your doubts
  • Skype - Schedule a face to face skype session to go over your doubts

Do you provide placements?

In the last module, ProjectPro faculty will assist you with:

  • Resume writing tip to showcase skills you have learnt in the course.
  • Mock interview practice and frequently asked interview questions.
  • Career guidance regarding hiring companies and open positions.

Online Hadoop Training Course Curriculum

Module 1

Introduction to Big Data

  • Rise of Big Data
  • Compare Hadoop vs traditonal systems
  • Hadoop Master-Slave Architecture
  • Understanding HDFS Architecture
  • NameNode, DataNode, Secondary Node
  • Learn about JobTracker, TaskTracker
Module 2

HDFS and MapReduce Architecture

  • Core components of Hadoop
  • Understanding Hadoop Master-Slave Architecture
  • Learn about NameNode, DataNode, Secondary Node
  • Understanding HDFS Architecture
  • Anatomy of Read and Write data on HDFS
  • MapReduce Architecture Flow
  • JobTracker and TaskTracker
Module 3

Hadoop Configuration

  • Hadoop Modes
  • Hadoop Terminal Commands
  • Cluster Configuration
  • Web Ports
  • Hadoop Configuration Files
  • Reporting, Recovery
  • MapReduce in Action
Module 4

Understanding Hadoop MapReduce Framework

  • Overview of the MapReduce Framework
  • Use cases of MapReduce
  • MapReduce Architecture
  • Anatomy of MapReduce Program
  • Mapper/Reducer Class, Driver code
  • Understand Combiner and Partitioner
Module 5

Advance MapReduce - Part 1

  • Write your own Partitioner
  • Writing Map and Reduce in Python
  • Map side/Reduce side Join
  • Distributed Join
  • Distributed Cache
  • Counters
  • Joining Multiple datasets in MapReduce
Module 6

Advance MapReduce - Part 2

  • MapReduce internals
  • Understanding Input Format
  • Custom Input Format
  • Using Writable and Comparable
  • Understanding Output Format
  • Sequence Files
  • JUnit and MRUnit Testing Frameworks
Module 7

Apache Pig

  • PIG vs MapReduce
  • PIG Architecture & Data types
  • PIG Latin Relational Operators
  • PIG Latin Join and CoGroup
  • PIG Latin Group and Union
  • Describe, Explain, Illustrate
  • PIG Latin: File Loaders & UDF
Module 8

Apache Hive and HiveQL

  • What is Hive
  • Hive DDL - Create/Show Database
  • Hive DDL - Create/Show/Drop Tables
  • Hive DML - Load Files & Insert Data
  • Hive SQL - Select, Filter, Join, Group By
  • Hive Architecture & Components
  • Difference between Hive and RDBMS
Module 9

Advance HiveQL

  • Multi-Table Inserts
  • Joins
  • Grouping Sets, Cubes, Rollups
  • Custom Map and Reduce scripts
  • Hive SerDe
  • Hive UDF
  • Hive UDAF
Module 10

Apache Flume, Sqoop, Oozie

  • Sqoop - How Sqoop works
  • Sqoop Architecture
  • Flume - How it works
  • Flume Complex Flow - Multiplexing
  • Oozie - Simple/Complex Flow
  • Oozie Service/ Scheduler
  • Use Cases - Time and Data triggers
Module 11

NoSQL Databases

  • CAP theorem
  • RDBMS vs NoSQL
  • Key Value stores: Memcached, Riak
  • Key Value stores: Redis, Dynamo DB
  • Column Family: Cassandra, HBase
  • Graph Store: Neo4J
  • Document Store: MongoDB, CouchDB
Module 12

Apache HBase

  • When/Why to use HBase
  • HBase Architecture/Storage
  • HBase Data Model
  • HBase Families/ Column Families
  • HBase Master
  • HBase vs RDBMS
  • Access HBase Data
Module 13

Apache Zookeeper

  • What is Zookeeper
  • Zookeeper Data Model
  • ZNokde Types
  • Sequential ZNodes
  • Installing and Configuring
  • Running Zookeeper
  • Zookeeper use cases
Module 14

Hadoop 2.0, YARN, MRv2

  • Hadoop 1.0 Limitations
  • MapReduce Limitations
  • HDFS 2: Architecture
  • HDFS 2: High availability
  • HDFS 2: Federation
  • YARN Architecture
  • Classic vs YARN
  • YARN multitenancy
  • YARN Capacity Scheduler
Module 15

Project

  • Demo of 2 Sample projects.
  • Twitter Project : Which Twitter users get the most retweets? Who is influential in our industry? Using Flume & Hive analyze Twitter data.
  • Sports Statistics : Given a dataset of runs scored by players using Flume and PIG, process this data find runs scored and balls played by each player.
  • NYSE Project : Calculate total volume of each stock using Sqoop and MapReduce.
Module 1

Learn Hadoop on HDInsight (Linux)

  • What is Hadoop on HDInsight?
  • How is data stored in HDInsight?
  • Information about using HDInsight on Linux
  • Using SSH with Linux clusters from a Linux computer
  • SSH Tunneling to HDInsight Linux clusters
Module 2

Processing Big Data with Hadoop in Azure HDInsight

  • Provision an HDInsight cluster.
  • Connect to an HDInsight cluster, upload data, and run MapReduce jobs.
  • Use Hive to store and process data.
  • Process data using Pig.
  • Use custom Python user-defined functions from Hive and Pig.
  • Define and run workflows for data processing using Oozie.
  • Transfer data between HDInsight and databases using Sqoop.
Module 3

Implementing Real-Time Analytics with Hadoop in Azure HDInsight

  • Use HBase to implement low-latency NoSQL data stores.
  • Use Storm to implement real-time streaming analytics solutions.
  • Use Spark for high-performance interactive data analysis.
Module 4

Implementing Predictive Analytics with Spark in Azure HDInsight

  • Using Spark to explore data and prepare for modeling
  • Build supervised machine learning models
  • Evaluate and optimize models
  • Build recommenders and unsupervised machine learning models
Module 5

Project

  • Implement a Big Data Project under the guidance of a Hadoop Architect
  • Upload your project to ProjectPro portfolio and display to recruiters

Online Hadoop Training Course Reviews

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Hadoop Developers in Toronto, Canada

  • Juzer Abbas

    Solutions Architect - Hadoop Data Lake & Data Warehouse

    Canadian Tire

  • Selaa D

    hadoop/BigData Developer

    Optima IT Consulting

  • Robbie Yu

    Senior Hadoop Developer

    Scotiabank

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Apache Spark has become a cornerstone technology in the world of big data and analytics. Learning Spark opens up a world of opportunities in data processing, machine learning, and more. Whether you're a beginner or someone looking to deepen your Spark ...

Impala vs Hive: Difference between Sql on Hadoop components


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Online Hadoop Training News

A deep dive into caching in Presto

Description:

Presto is a popular, open source, distributed SQL engine that enables organizations to run interactive analytic queries on multiple data sources at a large scale. Caching is a typical optimization technique for improving Presto query performance. It provides significant performance and efficiency improvements for Presto platforms.

Caching avoids expensive disk or network trips to refetch data by storing frequently accessed data in memory or on fast local storage, speeding up overall query execution. In this article, we provide a deep dive into Presto’s caching mechanisms and how you can use them to boost query speeds and reduce costs.

To read this article in full, please click here

Date Posted: Tue, 19 Sep 2023 02:00:00 -0700

What is Apache Spark? The big data platform that crushed Hadoop

Description:

Apache Spark defined

Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools. These two qualities are key to the worlds of big data and machine learning, which require the marshalling of massive computing power to crunch through large data stores. Spark also takes some of the programming burdens of these tasks off the shoulders of developers with an easy-to-use API that abstracts away much of the grunt work of distributed computing and big data processing.

To read this article in full, please click here

Date Posted: Thu, 30 Mar 2023 12:15:00 -0700

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