Big Data Intro for IT Administrators, Devs and Consultants
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Big Data Intro for IT Administrators, Devs and Consultants

Grasp why "Big Data" knowledge is in hot demand for Developers / Consultants and Admins
4.0 (5 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
238 students enrolled
Created by Toyin Akin
Last updated 12/2016
English
Curiosity Sale
Current price: $10 Original price: $90 Discount: 89% off
30-Day Money-Back Guarantee
Includes:
  • 3 hours on-demand video
  • 1 Article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Grasp why "Big Data" is the current Gold Rush for Developers / Consultants and Admins
  • Understand the Hadoop ECO System.
  • Basic HDFS / YARN / HIVE / SCOOP / SPARK will be covered with examples
View Curriculum
Requirements
  • Nice to have some development background
  • Nice to have some SQL knowledge
Description

Understand "Big Data" and grasp why, if you are a Developer, Database Administrator, Software Architect or a IT Consultant, why you should be looking at this technology stack

There are more job opportunities in Big Data management and Analytics than there were last year and many IT professionals are prepared to invest time and money for the training.

Why Is Big Data Different?

In the old days… you know… a few years ago, we would utilize systems to extract, transform and load data (ETL) into giant data warehouses that had business intelligence solutions built over them for reporting. Periodically, all the systems would backup and combine the data into a database where reports could be run and everyone could get insight into what was going on.

The problem was that the database technology simply couldn’t handle multiple, continuous streams of data. It couldn’t handle the volume of data. It couldn’t modify the incoming data in real-time. And reporting tools were lacking that couldn’t handle anything but a relational query on the back-end. Big Data solutions offer cloud hosting, highly indexed and optimized data structures, automatic archival and extraction capabilities, and reporting interfaces have been designed to provide more accurate analyses that enable businesses to make better decisions.

Better business decisions means that companies can reduce the risk of their decisions, and make better decisions that reduce costs and increase marketing and sales effectiveness.

What Are the Benefits of Big Data?

This infographic from Informatica walks through the risks and opportunities associated with leveraging big data in corporations.

Big Data is Timely – A large percentage of each workday, knowledge workers spend attempting to find and manage data.

Big Data is Accessible – Senior executives report that accessing the right data is difficult.

Big Data is Holistic – Information is currently kept in silos within the organization. Marketing data, for example, might be found in web analytics, mobile analytics, social analytics, CRMs, A/B Testing tools, email marketing systems, and more… each with focus on its silo.

Big Data is Trustworthy – Organizations measure the monetary cost of poor data quality. Things as simple as monitoring multiple systems for customer contact information updates can save millions of dollars.

Big Data is Relevant – Organizations are dissatisfied with their tools ability to filter out irrelevant data. Something as simple as filtering customers from your web analytics can provide a ton of insight into your acquisition efforts.

Big Data is Authoritive – Organizations struggle with multiple versions of the truth depending on the source of their data. By combining multiple, vetted sources, more companies can produce highly accurate intelligence sources.

Big Data is Actionable – Outdated or bad data results in organizations making bad decisions that can cost billions.

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Here I present a curriculum as to the current state of my Cloudera courses.

My Hadoop courses are based on Vagrant so that you can practice and destroy your virtual environment before applying the installation onto real servers/VMs.

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For those with little or no knowledge of the Hadoop eco system Udemy course : Big Data Intro for IT Administrators, Devs and Consultants

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I would first practice with Vagrant so that you can carve out a virtual environment on your local desktop. You don't want to corrupt your physical servers if you do not understand the steps or make a mistake. Udemy course : Real World Vagrant For Distributed Computing

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I would then, on the virtual servers, deploy Cloudera Manager plus agents. Agents are the guys that will sit on all the slave nodes ready to deploy your Hadoop services Udemy course : Real World Vagrant - Automate a Cloudera Manager Build

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Then deploy the Hadoop services across your cluster (via the installed Cloudera Manager in the previous step). We look at the logic regarding the placement of master and slave services. Udemy course : Real World Hadoop - Deploying Hadoop with Cloudera Manager

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If you want to play around with HDFS commands (Hands on distributed file manipulation). Udemy course : Real World Hadoop - Hands on Enterprise Distributed Storage.

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You can also automate the deployment of the Hadoop services via Python (using the Cloudera Manager Python API). But this is an advanced step and thus I would make sure that you understand how to manually deploy the Hadoop services first. Udemy course : Real World Hadoop - Automating Hadoop install with Python!

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There is also the upgrade step. Once you have a running cluster, how do you upgrade to a newer hadoop cluster (Both for Cloudera Manager and the Hadoop Services). Udemy course : Real World Hadoop - Upgrade Cloudera and Hadoop hands on


Who is the target audience?
  • Software engineers who want to expand their skills into the world of distributed computing
  • System Engineers that want to expand their skillsets beyond the single server
  • Developers who want to write and develop distributed systems
  • Database Administrators who want to manage an Hadoop ODS or EDW with HIVE
Students Who Viewed This Course Also Viewed
Curriculum For This Course
16 Lectures
03:12:41
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As a Developer, Administrator or Architect - Why should you consider "Big Data"
2 Lectures 18:14

As a Developer, Administrator or Architect - Why should you consider "Big Data"

Preview 18:14

Suggested course curriculum to follow ...

Preview 00:00
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Whiteboarding Sessions
4 Lectures 52:19
Whiteboarding the rational
Whiteboarding the rational
19:42

Part ! - Whiteboarding some of the Hadoop Services
Part I - Whiteboarding some of the Hadoop Services
09:45

Part II - Whiteboarding some of the Hadoop Services

Part II - Whiteboarding some of the Hadoop Services
11:34

Part III - Whiteboarding some of the Hadoop Services

​Part III - Whiteboarding some of the Hadoop Services​
11:18
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Enterprise Examples
3 Lectures 47:36
We step through an example RDBMS system used by retailers
We step through an example RDBMS system
16:17

We look at some Hadoop distributors - apache.org, Cloudera, Hortonworks and MapR

Hadoop Distributors - apache.org, Cloudera, Hortonworks and MapR
19:55

Here we look at some of the pro and cons for accessing Hadoop Cloud Deployments. Amazon EMR and Microsoft Azure

Hadoop Cloud Operators - Amazon EMR and Microsoft Azure
11:24
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Hands on Hadoop Services
6 Lectures 01:05:01
Operating a Local Hadoop Installment
Operating a Local Hadoop Installment
18:58

Here we target some database tables and show how we can move tables from mysql into Hadoop.

Preview 12:40

Here we use the HIVE service to provide us a logical database within Hadoop. As Hadoop can handle petabytes of data, you sure be able to image a logical database within Hadoop that can crunch petabytes of data.

HIVE SERVICE - We apply sql statements within Hadoop on the copied data.
07:33

HIVE SERVICE II - We apply sql statements within Hadoop on the copied data.

HIVE SERVICE II - We apply sql statements within Hadoop on the copied data.
05:39

HDFS SERVICE - We move some files into HDFS, ready for SPARK processing

HDFS SERVICE - We move some files into HDFS, ready for SPARK processing
06:22

SPARK SERVICE - We perform data analytics based on the data copied into HDFS
SPARK SERVICE - We perform data analytics based on the data copied into HDFS
13:49
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Conclusion
1 Lecture 09:31
Conclusion
Conclusion
09:31
About the Instructor
Toyin Akin
3.8 Average rating
135 Reviews
1,374 Students
15 Courses
Big Data Engineer, Capital Markets FinTech Developer

I spent 6 years at "Royal Bank of Scotland" and 5 years at the investment bank "BNP Paribas"  developing and managing Interest Rate Derivatives services as well as engineering and deploying In Memory DataBases (Oracle Coherence), NoSQL and Hadoop clusters (Cloudera) into production.

In 2016, I left to start my own training, POC-D. "Proof Of Concept - Delivered", which focuses on delivering training on IMDB (In Memory Database), NoSQL, BigData and DevOps technology. 

From Q3 2017, this will also include FinTech Training in Capital Markets using Microsoft Excel (Windows), JVM languages (Java/Scala) as well as .NET (C#, VB.NET, C++/CLI, F# and IronPythyon)

I have a YouTube Channel, publishing snippets of my videos. These are not courses. Simply ad-hoc videos discussing various distributed computing ideas.

Check out my website and/or YouTube for more info

See you inside ...