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  •  Become a Big Data analyst
  • Learn Relational Database
  • Understand Data streaming
  • Study smart without leaving your current job
Big Data Developer

Big Data Developer < next program >

Program Overview |Testimonials |Curriculum |Certification |Financing |Calendar |FAQ

Program Overview

The desire to take advantage of gathered data to drive Business marketing and strategic decision making has led to a massive job opportunities for Big Data Analysts. It is because that storing, analyzing and mining data has become a new challenge. Most of the companies are looking for individuals that has a good understanding of Big Data challenges and ways/tools to overcome these. This course provides information/experience on how to analyze a problems in Big Data atmosphere, what are the solutions, and how to use these solutions. In this course, we learn what is Big Data and challenges, most popular technology stacks such as HDFS, YARN, MapReduce, Impala, Hive, Hbase, Kafka, Zookeeper, and etc. and how to develop applications in this stack using Java programming language.

The objectives of this program is to give you a strong understanding of problems , introduction to Big Data Developer stack, Distributed file system and HDFS, Map Reduce methodology and Apache Map Reduce library ◦Big Data relational DBMS including Impala and Hive, Hbase and log keeping◦Data streaming and Kafka , Orchestration and Zookeeper, Developing application for Big Data processing using stack and Java programming languages , Understand different types of formats including AVRO and Parquet along with Hands-on experience with real-world projects.

The purpose is to apply the knowledge acquired during the session and to have a significant experience in working in sprints and the Agile methodology, having to deal with deadlines, real life bad quality data and ask questions when the requirements are unclear.

  • Fast Track


  • Micro
    Student group

  • Subject matter

  • Project-based

  • Be
    Job ready


Gislaine Campos

BI Consultant

My background is in Web programming and a degree in Computer Science. I worked with SQL for many years. But in Montreal College I learnt finer aspects of SSIS dashboards and reporting. Instructor Parminder jit Singh takes a personal initiative in helping students.


Learn how to code:

Get a grasp of Linux Shell and scripting fundamentals, Git and team collaboration, Java Programming

Scala and Functional Programming:

Introduction to Scala and functional programming, Object-Oriented Programming, Data structures. Advanced concepts

Relational Database:

Relational Database basics , How to design a relational database, How to query a relational database (SQL), MySQL

Big Data Hadoop Developer:

An understanding of Big data and Hadoop basics, Discover YARN and HDFS , Manipulate MapReduce, Get a grasp of Big Data DBMS, Data streaming, Spark RDD, Spark SQL and DataSet


Understand YARN , Learn How YARN works. How to administer YARN. Get a solid understanding of Distrusted file system and BigData

MapReduce :

Understanding of MapReduce, Apache MapReduce library, Learn to Implement MapReduce applications in Java.

Big Data DBMS:

Learn what is Impala and parquet file format. Hive, Hbase, Using Hive JDBC API for developing applications using Impala and Hive.

Data streaming & Spark:

Learn fundamentals of streaming and get a solid understanding of Apache Kafka, Apache Flume, AVRO file format, Schema Registry. You will also see how to define AVRO schema, to communicate with Kafka using Java and how to use Schema Registry.

Get Complete Course outline


Upon successful completion of the program, the college grants the student with a Diploma in Big Data Analyst.

Certification Tracks

The students can work towards the following official certifications.


Student Loans

Scholarships and Grants

  • Student grants covering up to $2,000 for new immigrants and unemployed

Instalment Plans

Option to pay fees in 4 equal monthly instalments



  • Session one
    16 October 2018
    24 08 18
  • Session two
    12 February 2019
    20 12 18
  • Session three
    12 June 2019
    15 05 19


  • Why get an MCIT Diploma?

      MCIT Diplomas are short-term, immersive programs, based on "work and study" models which transform novices into Business Intelligence specialists. Our training is instructor-led, conducted in-class in Montreal, Quebec, consisting of 9-12 hours of class per week with an end of term project. Over the past years, we have adapted our curriculum to match industry requirements. This has afforded us a 93% job placement rate for students.

  • Who should join this program?

      Highly motivated, tenacious self-starters with the drive to make it big in the industry who are willing to learn and apply emerging technological skills. Our college programs are in very high demand. Students are expected to take initiative, move forward, to not be afraid to ask questions and spend extra hours on solving a problem. The programs are suited for highly diligent individuals who need direction to get into the IT industry.

  • Do I need to have any specific background to attend this program?

      A working knowledge of databases / data warehouses is recommended. However, Business Analyst skills would also be considered.

  • Can you provide a prerequisite training at additional cost?

      Yes, we can provide prerequisite training before the program starts, please register in advance to reserve your place.

  • What certifications exams are covered under this program?

      The program covers topics needed to write the following exams

  • What kind of jobs titles can I get after completing this program?

      Our graduates can obtain the following roles • Business Intelligence Analyst • Marketing Research Analyst • Data Analyst • Report Writer • Data Mining Expert • Business Analyst • BI Developer • Financial Reporting Analyst

  • What is your learning methodology?

      • Pragmatic approach to delivery (80% practical sessions) • Project and internship based concept applications • E-learning augmented with instructor-led, classroom training • Virtual labs with remote access • Collaborative & experiential learning • Optimum student-to-faculty ratio (maximum 15 students per class)