Big Data, Hadoop, & Spark Certification Training

Master Hadoop & Spark fundamentals

Real-world projects & practical training

Learn from industry leaders

Solve real business challenges

32-hour training

Flexible learning options

Easy & convenient payment options

4.8/5

6207 Enrolled

Overview

What our training includes

Provides theoretical and practical training on Hadoop and Spark
Prepares professionals for Hadoop and Spark development
Covers fundamentals of Hadoop and Spark ecosystems
Develops skills in Hadoop cluster architecture and data loading
Enables setup and installation of Hadoop and Spark
Trains on exploratory data queries
Equips with skills for Big Data jobs through hands-on projects

Learning Objectives

After completing the course, you will be able to:

Gain in-depth knowledge of the Big Data framework using Hadoop and Spark

Understand the fundamental concepts of Hadoop and its ecosystem components

Understand the Spark environment and its data optimization techniques

Write programs in the Big Data domain as per system architecture

Apply best practices for Hadoop and Spark development

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Prerequisites

The eligibility requirements for this course are as follows:

Programming Skills: Depending on the function you want Hadoop to play, you may need to be familiar with several computer languages R or Python
SQL Knowledge: Regardless of the position in big data you choose to pursue, SQL knowledge is a necessity. This is because many businesses are already entering the Big Data space or connecting their current infrastructure with a Big Data platform
Linux: Most of the Hadoop deployments across industries are Linux based, and thus, it’s helpful to have a prior basic working knowledge of Linux

Overall ratings by our students

Upcoming sessions

Trainer

Our trainer is a Solution Architect with 15years of hands on experience in design and implementation of complex projects in large enterprises. Data scientist with good experience in Hadoop, HBase, Hive, Spark, Crunch, HBase, Zookeeper, Sqoop, Flume, Oozie, SolR,Cassandra, Map Reduce, Kafka, Kite, R. He is an expertise in design and development of webservices and API development / Deployment also application development using Java, Spring, Hibernate, Struts, Angular, HTML, CSS, SQL. He successfully Implemented the Project on Big Data Platform(Cloudera) and using technologies like Spark, Hive, Pig, Oozie, Sqoop, Impala. Possess very strong Exposure to CI/CD tools including maven, Git, Jenkins. Exposure to Integration using JMS/REST, IBM MQ, WMB, Websphere ESB and Apache Kafka. Also experienced in all the phases of SDLC including Agile(Scrum), Waterfall, TDD and Iteration. He is an IIT’an and certified Software Architect and Sun Certified Java programmer.

Learners Point Certificate

Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

KHDA Certificate

Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.

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Curriculum

1. Programming Skills

Depending on the function you want Hadoop to play, you may need to be familiar with several computer languages. R or Python.

2. SQL Knowledge

Regardless of the position in big data you choose to pursue, SQL knowledge is a necessity. This is because many businesses are already entering the Big Data space or connecting their current infrastructure with a Big Data platform.

3. Linux

Most of the Hadoop deployments across industries are Linux based, and thus, it’s helpful to have a prior basic working knowledge of Linux

Learning objective:

You will get introduced to real-world problems with Big data and will learn how to solve those problems with state-of-the-art tools. Understand how Hadoop offers solutions to traditional processing with its outstanding features. You will get to know Hadoop’s background and different distributions of Hadoop available in the market. Prepare the UNIX Box for the training.

Topics:

Big Data Introduction

What is Big Data?
Data Analytics
Big Data challenges
Technologies supported by Big Data

Hadoop Introduction

What is Hadoop?
History of Hadoop
Basic concepts
Future of Hadoop
The Hadoop distributed file system
Anatomy of a Hadoop cluster
Breakthroughs of Hadoop
Hadoop distributions:
Apache Hadoop
Cloudera Hadoop
Hortonworks Hadoop
MapR Hadoop

Hands-On:

Installation of a virtual machine using VMPlayer on the host machine
Work with some basics UNIX commands needs for Hadoop

Learning objective:

You will learn the different daemons and their functionality at a high level.

Topics:

Name node
Data node
Secondary name node
Job tracker
Task tracker

Hands-On:

Creates a UNIX shell script to run all the daemons at one time

Learning objective:

You will get to know how to write and read files in HDFS. Understand how to name node, data node, and secondary name node take part in HDFS architecture. You will also know different ways of accessing HDFS data.

Topics:

Blocks and input splits
Data replication
Hadoop rack awareness
Cluster architecture and block placement
Accessing HDFS
JAVA approach
CLI approach

Hands-On:

Writes a shell script that writes and reads files in HDFS
Changes replication factor at three levels
Use Java for working with HDFS
Writes different HDFS commands and also admin commands

Learning objective:

You will learn different modes of Hadoop, understand pseudo mode from scratch and work with configuration. You will learn the functionality of different HDFS operations and visual representation of HDFS read and write actions with their daemons name node and data node.

Topics:

Local Mode
Pseudo-distributed Mode
Fully distributed mode
Pseudo Mode installation and congurations
HDFS basic operation

Hands-On:

Install virtual box manager and install Hadoop in Pseudo distributed mode. Changes the different configuration files required for pseudo-distributed mode. Performs different file operations on HDFS.

Learning objective:

Understand different phases in MapReduce including Map, Shuffling, Sorting, and Reduce Phases. Get a deep understanding of the Life Cycle of MR in YARN submission. Learn about the Distributed Cache concept in detail with examples. Write Wordcount MR Program and monitor the Job using Job Tracker and YARN Console. Also, learn about more use cases.

Topics:

Basic API concepts
The driver class
The mapper class
The reducer class
The combiner class
The partitioner class
Examining a sample MapReduce program with several examples
Hadoop's Streaming API

Hands-On:

Learn about writing MR job from scratch, writing different logics in mapper and reducer, and submitting the MR job in standalone and distributed mode
Also learn about writing word count MR job, calculating average salary of the employee who meets certain conditions and sales calculation using MR Hadoop ecosystems

Learning objective:

Understand the importance of PIG in the Big Data world, PIG architecture, and Pig Latin commands for doing different complex operations on relations, and also PIG UDF and aggregation functions with piggy bank library. Learn how to pass dynamic arguments to PIG scripts.

Topics

PIG concepts
Install and configure PIG on a cluster
PIG Vs MapReduce and SQL
Write sample PIG Latin scripts
Modes of running PIG
PIG UDFs

Hands-On:

Login to Pig grunt shell to issue PIG Latin commands in different execution modes
Different ways of loading and transformation on PIG relations lazily. Registering UDF in grunt shell and perform replicated join operations

Learning objective:

Understand the importance of Hive in the Big Data world. Different ways of configuring Hive megastore. Learn different types of tables in the hive. Learn how to optimize hive jobs using partitioning and bucketing and passing dynamic arguments to hive scripts. You will get an understanding of Joins, UDFS, Views, etc.

Topics:

HIVE concepts
HIVE architecture
Installing and configuring HIVE
Managed tables and external tables
Joins in HIVE
Multiple ways of inserting data in HIVE tables
CTAS, views, alter tables
User-defined functions in HIVE
HIVE UDF

Hands-On:

Executes hive queries in different Modes
Creates Internal and External tables
Perform query optimization by creating tables with partition and bucketing concepts
Run system-defined and user-defined functions including explode and windows functions

Learning objectives:

Learn how to import normally and incrementally data from RDBMS to HDFS and HIVE tables, and also learn how to export the data from HDFS and HIVE tables to RDBMS. Learn the architecture of SQOOP Import and export.

Topics:

SQOOP concepts
SQOOP architecture
Connecting to RDBMS
Internal mechanism of import/export
Import data from Oracle/MySQL to HIVE
Export data to Oracle/MySQL
Other SQOOP commands

Hands-On:

Triggers shell script to call SQOOP import and export commands
Learn to automate SQOOP incremental imports by entering the last value of the appended column
Run SQOOP export from HIVE table directly to RDBMS

Learning objectives:

Understand different types of NoSQL databases and CAP theorem. Learn different DDL and CRUD operations of HBASE. Understand HBase architecture and Zookeeper Importance in managing HBase. Learns HBase column family optimization and client-side buffering.

Topics:

HBase concepts
ZOOKEEPER concepts
HBase and Region server architecture
File storage architecture
NoSQL vs SQL
Dening Schema and basic operations
DDLs
DMLs
HBase use cases

Hands-On:

Create HBase tables using shell and perform CRUD operations with JAVA API. Change the column family properties and also perform the sharding process. Also, create tables with multiple splits to improve the performance of the HBase query.

Learning objectives:

Understand oozie architecture and monitor oozie workflow using oozie. Understand how coordinators and bundles work along with workflow in oozie. Also learn oozie commands to submit, monitor, and kill the workflow.

Topics:

OOZIE concepts
OOZIE architecture
Workflow engine
Job coordinator
Installing and configuring OOZIE
HPDL and XML for creating workflows
Nodes in OOZIE
Action nodes and control nodes
Accessing OOZIE jobs through CLI, and web console
Develop and run sample workflows in OOZIE
Run MapReduce programs
Run HIVE scripts/jobs

Hands-on:

Create the Workflow to incremental Imports of SQOOP. Create the workflow for Pig, Hive, and SQOOP exports
Execute coordinator to schedule the workflows

Learning objectives:

Understand flume architecture and its components source, channel, and sinks. Configure flume with socket, file sources, and HDFS and HBase sink. Understand fan-in and fan-out architecture.

Topics:

FLUME concepts
FLUME architecture
Installation and configurations
Executing FLUME jobs

Hands-on:

Create flume configuration files and configure with different sources and sinks. Stream twitter data and create a hive table

Learning objective:

You will learn Pentaho Big Data best practices guidelines and techniques documents.

Topics:

Data Analytics using Pentaho as an ETL tool
Big Data Integration with zero coding required

Hands-on:

You will use Pentaho as an ETL tool for data analytics

Learning objective:

You will see different integrations among the Hadoop ecosystem in a data engineering flow. Also, understand how important it is to create a flow for the ETL process.

Topics:

MapReduce and HIVE integration
MapReduce and HBASE integration
Java and HIVE integration
HIVE-HBase Integration

Hands-On:

Uses storage handlers for integrating HIVEand HBASE. Integrates HIVE and PIG as well

Frequently asked questions

Hadoop is the leader in the Big Data category of job postings and as well offers high-paying jobs. With the ever-growing demand for the profession and substantial salary packages, Big Data Hadoop is a lucrative career with tremendous opportunities for advancement in the future. This sector attracts millions of professionals across the globe with the right skill set to have a futuristic career.

Due to high-tech infrastructure and the implementation of several smart initiatives, Dubai is now named as the Middle East’s leading smart city. With the rising demand for Big Data in the global business market, professionals with significant knowledge and skills in Hadoop and Spark can bring their careers one level up. The demand for Big Data professionals is projected to grow positively in the UAE making it the ideal location for a promising career.

Big Data is becoming more and more valuable to the workplace and to the global economy. The Big Data Hadoop and Spark course offers a deep understanding of the fundamentals of Spark and the Hadoop ecosystem. This course trains participants on various components of the Hadoop ecosystem that fits into the Big Data processing lifecycle. This course enhances your Big Data Hadoop and Spark knowledge to help you land great job opportunities elevating your professional value and stand out in today’s competitive world.

This Big Data Hadoop and Spark course in Dubai offers a foundational understanding of the Hadoop ecosystem and Spark environment. This training trains you to master the concepts and enforce the best practices for Hadoop and Spark development to transform data into actionable insights. This Big Data Hadoop and Spark course also approves of your expertise in Hadoop architecture and data loading techniques using Spark.

The Big Data Hadoop and Spark course in Dubai is a perfect fit for anyone looking to gain expertise in the Big Data Hadoop ecosystem and Spark environment. It is ideal for:

IT, Data Management, and Analytics professionals
Software Developers and Architects
Business Intelligence professionals
Project Managers
Aspiring Data Scientists
Graduates looking to begin a career in Big Data Analytics

For participants to enroll in this Big Data Hadoop and Spark course, it is highly recommended for participants to have familiarity with Core Java and SQL.

The training sessions at Learners Point are interactive, immersive, and intensive hands-on programs. We offer 3 modes of delivery and participants can choose from instructor-led classroom-based group coaching, one-to-one training session, or high-quality live and interactive online sessions as per convenience.

At Learners Point, if a participant doesn’t wish to proceed with the training after the registration due to any reason, he or she is entitled to a 100% refund. However, the refund will be issued only if we are notified in writing within 2 days from the date of registration. The refund will be processed within 4 weeks from the day of exit.