Transform Your Career: Big Data and Hadoop Class

Big Data and Hadoop

Managing Big data using Hadoop tools like MapReduce, Hive, Pig, hBase and m

Created by : Rated :4.1 34138 views

Last Updated: 2024-11-18 04:11:58 English Course Type: Self Learning

In late 1990s, Developers and Programmers were generating data through coding, in late 2000s everyone on Social media generating data on FB, Twitter, Insta etc and these days Machines are generating data which overall creating a Huge Volume of data which cannot be handled easily through traditional databases, so after completion of this course you'll be able to do that using HADOOP as your platform and also Able to crack Cloudera CCA 175 Certification

This is an interactive lecture of one of my Big data and Hadoop class where everything is covered from the scratch and also you will see students asking doubts so you can clear those concepts here as well.

Students will be Able to crack Cloudera CCA 175 Certification after successful completion and with little practice.

Tools covered :

1. Sqoop

2. Flume

3. MapReduce

4. Hive

5. Impala

6. Beeline

7. Apache Pig

8. HBase

9. OOZIE

10. Project on a real data set.

A Laptop, 6 GB RAM (at least), 100 GB FREE HDD

Students who want to step into Big Data, want to know how to Analyse, work on and manage it.

Section 1 : All about BIG DATA 26 Lectures 00:01:29
Lecture 1 :
Big Data and Hadoop Introduction Preview
Lecture 2 :
Hadoop framework
Lecture 3 :
Hadoop Ecosystem
Lecture 4 :
HDFS
Lecture 5 :
Magic Boxes, Sqoop and Flume
Lecture 6 :
NameNode, DataNode, JournalNode
Lecture 7 :
Input output operations, Ram and HDD, pros and cons
Lecture 8 :
Mapreduce Theory 1.1
Lecture 9 :
Mapreduce Theory 1.2
Lecture 10 :
Mapreduce Theory 1.3
Lecture 11 :
Combiner Approach in MapReduce
Lecture 12 :
Coding : Sqoop with SQL
Lecture 13 :
Visit to Cloudera Machine
Lecture 14 :
Sqoop commands with introduction to Linux commands as well
Lecture 15 :
Sqoop commands
Lecture 16 :
Basics of core Java, introduction to eclipse, MapReduce Coding
Lecture 17 :
Coding : MapReduce
Lecture 18 :
Hive Theory
Lecture 19 :
Hive: connecting, loading, defining delimiters
Lecture 20 :
Coding : Hive
Lecture 21 :
Hive to Impala and Beeline
Lecture 22 :
Hive : Partitioning
Lecture 23 :
Hive Bucketing
Lecture 24 :
YARN, HBASE, OOZIE
Lecture 25 :
FINAL PROJECT ON REAL DATA SET
Lecture 26 :
Assignment on DataNodes
Introduction Let's assume that, you have 100 TB of data to store and process with Hadoop. The configuration of each available DataNode is as follows: â€¢ 8 GB RAM â€¢ 10 TB HDD â€¢100 MB/s read-write speed ï€ You have a Hadoop Cluster with replication factor = 3 and block size = 64 MB. In this case, the number of DataNodes required to store would be: â€¢ Total amount of Data * Replication Factor / Disk Space available on each DataNode â€¢100 * 3 / 10 â€¢30 DataNodes ï€ Now, let's assume you need to process this 100 TB of data using MapReduce. And, reading 100 TB data at a speed of 100 MB/s using only 1 node would take: â€¢Total data / Read-write speed â€¢100 * 1024 * 1024 / 100 â€¢1048576 seconds â€¢291.27 hours ï€ So, with 30 DataNodes you would be able to finish this MapReduce job in: â€¢291.27 / 30 â€¢9.70 hours ï€ 1.Problem Statement How many such Data Nodes you would need to read 100TB data in 5 minutes in your Hadoop Cluster?

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Saheb Singh chaddha,

262773 Course Views

6 Courses

Hello guys, How are you all doing? So a quick introduction of me, well I am a Big Data Expert and Data Scientist, I have 3 years of experience on these domain in companies as well as a trainer. My teaching method is completely different than others like I am not a slide reader, I use analogies and examples a lot to explain things and mostly try to be practical which you'll see in the course. Hope to see you all in the lectures, Good Luck!