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Big Data using Hadoop & Spark

Launch your career in Big Data Analytics. Learn data storage and processing with Hadoop, Spark and HDFS
  • 24 Hours Classroom & Online Sessions
  • 60+ Hours Assignments & eLearning
  • Job Placement Assistance
  • 2 Capstone Projects
  • Industry Placement Training
  • 100% HRD Corp Claimable!
big data course reviews in malaysia - 360digitmg
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big data course reviews in malaysia - 360digitmg
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"MALAYSIA has made clear the need to leverage big data to develop its digital economy. Malaysian spending on Big Data Analytics software market was RM 435 million." - (Source). Companies today have realized the gravity of a data-driven business approach to help them examine the stream of collected information and provide valuable insight. Therefore, the need for talented candidates with data skills will only grow by leaps and bounds as we continue to digitize our ecosystem. The recent IDC report has suggested that the Big Data Analytics Software Market in Malaysia is tipped to reach RM 600 million by 2021. The factors contributing to the growth of the market are increased private-public collaboration, government initiative for ICT development, the vast universe of Big Data generated and the need of industry to analyze the data.

Big Data using Hadoop & Spark

big data analytics course duration - 360digitmg

Total Duration

2 Months

big data analytics course pre-requisite - 360digitmg


  • Computer Skills
  • Basic Programming Knowledge

Big Data Analytics Course Programmer Overview

This Big Data Analytics course in Malaysia is unique because it dwells on the key concepts of Big Data Analytics. This Big Data Analytics course introduces the distributed framework tool Hadoop that is used to extract data and explores how it is installed on Linux OS. The unique features like replication and partitioning used in HDFS are described. A separate module is devoted to the distributed computing framework MapReduce. MapReduce integrated with Hadoop contains the famous Mapper function and Reducer functions used to process huge volumes of data. The succeeding modules are devoted to the study of the higher-level programming language Pig and the SQL programming language Hive - used as a Data Warehousing tool. The final modules deal with the NoSQL database Hbase, and the open-source tool SQOOP used to create a pipeline from the SQL database to Hadoop. And finally, the aspirant will learn about the Unified Stack, in-memory computing programming language framework SPARK - used to analyze data.

What is Big Data Analytics or Data Analytics?

Big Data Analytics denotes the cluster of tools and techniques used to analyze Big Data to expose hidden patterns and correlations in the data. This enables cost-reduction and better decision making. It also helps us to design better quality products and services in the next-generation category.

This Data Analytics course from 360DigiTMG is a structured course which teaches students about the various stages of the Big Data Analytics lifecycle which are :

a) Problem Identification
b) Designing Data Requirements
c) Pre-processing Data
d) Performing Analytics Over Data
e) Visualizing Data.

At the end of the Data Analytics Certification program from 360DigiTMG, the student must be proficient in the following software tools

1) Unix/Linux/Shell scripting
2) C++, Java, Python and R, RStudio
3) Apache Spark and Apache Spark Streaming
4) Apache Kinesis, Apache Storm, MapReduce
5) SQL, Spark SQL
6) Hive, Apache Pig
7) HDFS 8) Apache Zookeeper9) Cloud, Big Data on AWS
10) Bash Scripting
11) Hadoop
12) Machine Learning with Python and R
13) Black Box Techniques and Neural Networks

Big Data Training Outcomes in Malaysia

This Big Data Analytics Course from 360DigiTMG exposes students to the wonders that a distributed computing framework like the Hadoop framework can do churning Big Data. In this Big Data Analytics Course in Malaysia, the students will learn to install and set up Hadoop and Spark Environments. They will appreciate the advantages of distributed batch processing with HDFS. The course elaborates on Hadoop 1.x, 2.x, and 3.x versions. Several modules are devoted to exploratory data analysis using Pig, Hive, and Spark. The various Spark RDD optimization techniques are also discussed. In the span of this course, students will learn to install Linux OS and a pseudo - single-node Hadoop Cluster HDFS along with learning to script programs in the Big Data domain. In the course, they will also learn how HBase gets installed on Hadoop, the architecture of HBase, and its components. Also, students will learn how the open-source tool SQOOP helps in the migration of data from the SQL database to Hadoop. Finally, the student is exposed to Spark- the programming language developed for general purpose, in-memory computing. Learn about Apache Spark architecture and default Data Abstraction-RDD.

Install and set up Hadoop and Spark to store and process data
Understand the advantages of distributed batch processing using HDFS
Learn about Hadoop 1.x, 2.x and 3.x versions
Perform exploratory queries on data batches using Pig, Hive and Spark
Use Spark RDD optimisation techniques
Write programmes in the Big Data domain as per system architecture

Block Your Time

big data analytics training duration - 360digitmg

24 hours

Classroom Sessions

big data analytics training - 360digitmg

60 hours

Assignments &

big data analytics training - 360digitmg

60 hours

Live Projects

Who Should Sign Up?

  • Candidates aspiring to get into Big Data Analytics
  • Analytics professionals, Business Analysts, Software developers
  • Graduates looking for a career in Data Science and related fields
  • Professionals who want to shift to Big Data
  • Professionals who wish to add Big Data skills to their profile

Big Data Course Modules in Malaysia

The core concept of the module on HDFS is to drill the concepts of replication and partitioning used in HDFS. One can learn about the functionality of the processes of the MapReduce component of Hadoop and how Mapper and Reducer functions can process large volumes of data. This Big Data Analytics Course in Malaysia introduces the student to the high-level programming language Pig. They will delve into the features, components, and execution model of Pig. Hands-on experience with the SQL programming tool Apache Hive is guaranteed with this course. Used for data warehousing it handles and manages tables with an RDBMS datastore called Metastore. Students will also be introduced to the NoSQL database HBase.

Get introduced to the world of Big Data and understand the 4 V’s which define Big Data. Learn about the challenges concerning Big Data and the workaround technique called distributed framework tools used for churning Big Data. Learn how these challenges Big Data is addressed by a distributed computing framework.

Learn about the most user-friendly and the first multi-user operating system which is the preferred OS for the implementation of an open-source distributed framework tool called Hadoop. The filesystem for the Hadoop framework should be distributed to handle the huge amount of data. The filesystem of Linux OS (ext3, ext4, and xfs) are capable of supporting the distributed framework. Having hands-on exposure on Linux OS is a very relevant requirement to excel in working with Big Data tools. You will learn to install and work with Linux OS. You will also learn to install a pseudo-single-node Hadoop environment cluster. Hadoop Distributed File System.

Learn how HDFS stores a huge volume of data without data loss and fault tolerance. You will understand the concepts of replication and partitioning that is used in HDFS. Learn about the java background services also known as Demons working to make Hadoop capable of storing Big Data that cannot be fit into a single System.

Learn the logic of the distributed computing framework implemented by Google. Learn the concept of Map jobs and Reduce jobs. Learn how Mapper functions and Reducer functions work in tandem to process huge volumes of data. Understand the functionality of the processes of the MapReduce component of Hadoop. Understand input splits and learn how they are different from blocks in HDFS.

Understand the Big Data Ecosystem and its projects. Learn about the drawbacks of distributed computing, MapReduce framework. You have learned about the low-level language used for MapReduce framework, Apache Pig is a high-level programming language to assist the developers. Learn about the high-level programming languages developed by Yahoo on the MapReduce framework. Learn about the ETL tool Apache Pig, the features, components and the execution model. Learn about the ways to execute the Apache Pig Latin scripts on MapReduce and Local mode.

An open-source programming tool developed by Facebook to handle structured data on Big Data framework. Get introduced to the SQL programming tool, Apache Hive. Understand its applications as a Data warehousing tool. You will learn how Hive manages and handles the schema of the tables created using an RDBMS database called Metastore. Learn about internal and external tables that can be created using Hive.

Learn about the first database on the distributed file system & HBase. Understand how NoSQL databases are different from SQL based databases. Learn about the installation of HBase on Hadoop, its use and advantages. Understand the architecture of HBase and its components. Learn about Hfiles and Memstore concept used in HBase to store the data.

Understand how enterprises use tools to move the data from legacy systems on to Big data. Learn about the concept of Data Ingestion. Understand the need to migrate the data from a traditional database system (SQL) to Big Data tools. Learn about quick migration of data into HBase tables from RDBMS systems and vice versa. Learn to use the open-source tool SQOOP (the combination of Hadoop and SQL) to create a pipeline from the SQL database to Hadoop.

Understand the need for a new age tool to handle the Big Data as the latency of MapReduce programs are very high. Learn about the lightning-fast Unified stack programming language framework in the Analytics community which was developed for general purpose, in-memory computing to attain super speeds of execution, and distributed computing - Apache Spark. Understand Apache Sparks architecture and its building blocks and components. You will learn about the default data abstraction used by spark called RDD.

How We Prepare You
  • Big Data course in malaysia
    Additional Assignments of over 60-80 hours
  • Big Data course in malaysia
    Live Free Webinars
  • Big Data course in malaysia
    Resume and LinkedIn Review Sessions
  • Big Data course in malaysia
    6 Months Access to LMS
  • Big Data course in malaysia
    Job Assistance in Big Data Fields
  • Big Data course in malaysia
    Complimentary Courses
  • Big Data course in malaysia
    Unlimited Mock Interview and Quiz Session
  • Big Data course in malaysia
    Hands-on Experience in a Live Project
  • Big Data course in malaysia
    Life Time Free Access to Industry Webinars

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