Big Data for Managers

A foundation course for big data that covers the big data tools for various stages of a big data project
Big Data for Managers
File Size :
1.34 GB
Total length :
4h 12m



Ganapathi Devappa


Last update




Big Data for Managers

What you’ll learn

Confidently lead a big data project in your organization
Differentiate big data technology from traditional technology
Talk about big data solution stages and cluster sizing with your development team, architects and CTOS
Select tools required for various stages of your big data project
Build an action plan for your big data analytics project using 5 Ps model

Big Data for Managers


Should have software project experience as a team lead or manager


This course covers the required fundamentals about big data technology that will help you confidently lead a big data project in your organization. It covers the big data terminology like 3 Vs of big data and key characteristics of big data technology that will help you answer the question ‘How is big data technology different from traditional technology’. You will be able to identify various big data solution stages from big data ingestion to big data visualization and security. You will be able to choose the right tool for each stage of the big data solution. You will see the examples use of popular big data tools like HDFS, Map reduce, Spark, Zeppelin etc and also a demo of setting up EMR cluster on Amazon web services. You will practice how to use the 5 P’s methodology of data science projects to manage a big data project. You will see theory as well as practice by applying it to many case studies. You will practice how to size your cluster with a template. You will explore more than 20 big data tools in the course  and you will be able to choose the tool based on the big data problem.I have recently(14-May-2020) updated content on open source, cloud computing, big data offerings by cloud vendors, multi-cloud, hybrid cloud and edge computing and integrated big data service providers Cloudera and MapR. As most organizations are moving towards public cloud, these lectures will provide the latest information on these technologies for you. I am sure you will like this content.This course has benefited students in more than 50 countries and as an instructor, I am glad to share some of the  five star comments about the course:This course really exceeded my expectations! Not only it covers the concepts and the overall view of a Big Data project landscape but it also provides good examples of real case studies, that help reinforce the contents presented. Great course!This course is great! I have learnt many useful things. The case studies are very enlightening. I strongly recommend. Thank you very much.otimo tecnicamente, excelenteDidatica muito boa e o conteudo conforme esperado


Section 1: Introduction

Lecture 1 Introduction to the course

Lecture 2 Course prerequisites and course structure

Lecture 3 Big data sizes

Lecture 4 Case study: Traditional solution vs Big data solution

Lecture 5 Activity : Calculate the data sizes for big data projects in your organization

Section 2: Big data characteristics

Lecture 6 Introduction

Lecture 7 3 Vs of Big Data

Lecture 8 Industry examples of big data

Lecture 9 Big data analysis and visualization

Lecture 10 Traditional vs big data technology

Lecture 11 How is big data technology different?

Lecture 12 Big data solution stages

Lecture 13 Apache Hadoop and HDFS

Lecture 14 Map reduce and Yarn

Lecture 15 Pig, Hive and Spark

Lecture 16 Things to remember

Lecture 17 Activity: Technology type selection

Section 3: Big data storage

Lecture 18 Introduction

Lecture 19 Big data solution stages

Lecture 20 Big data storage characteristics

Lecture 21 No-SQL databases

Lecture 22 HDFS

Lecture 23 Hbase

Lecture 24 Cassandra

Lecture 25 Mongo DB and Impala

Lecture 26 Sizing your cluster

Lecture 27 Things to remember

Lecture 28 Activity: Size your big data cluster using the template

Lecture 29 Activity: Solve these storage exercises

Section 4: Big data ingestion

Lecture 30 Introduction

Lecture 31 Solution stage Ingestion

Lecture 32 Sources and types of data for ingestion

Lecture 33 Big data ingestion tool features

Lecture 34 Ingestion of batch data : Sqoop and Distcp

Lecture 35 Streaming data ingestion using Flume

Lecture 36 Kafka : A messaging system

Lecture 37 Apache Flink

Lecture 38 Nifi for data ingestion

Lecture 39 scenarios for big data ingestion

Lecture 40 Data ingestion diagram

Lecture 41 Things to remember

Lecture 42 Activity: Ingestion problems

Section 5: Big data analytics

Lecture 43 Introduction

Lecture 44 Characteristics of big data analysis

Lecture 45 Analysis using map-reduce, Pig and Hive

Lecture 46 Analysis using Spark

Lecture 47 Analysis using Storm and Stream sets

Lecture 48 Machine learning and machine learning techniques

Lecture 49 Turning insights into action

Lecture 50 Things to remember

Lecture 51 Activity

Lecture 52 Activity: Provide solutions to these situations

Section 6: Big data visualization, security and vendors

Lecture 53 Introduction

Lecture 54 Traditional and new types of data visualization for big data

Lecture 55 Tools for big data visualization : Tableau, Qlikview and Zeppelin

Lecture 56 Java script charts for visualization

Lecture 57 Visualization summary

Lecture 58 Big data security

Lecture 59 Kerberos and Apache Knox

Lecture 60 Apache Ranger and Apache Sentry

Lecture 61 Best practices for big data security

Lecture 62 Opensource software and support

Lecture 63 Cloud Computing

Lecture 64 Big data on Cloud

Lecture 65 Big Data on AWS

Lecture 66 Big Data on Azure

Lecture 67 Big Data on Google Cloud

Lecture 68 Multi-Cloud, Hybrid Cloud and Edge Computing

Lecture 69 What is Serverless Processing?

Lecture 70 Big Data Vendor: Cloudera

Lecture 71 Big Data Vendor: MapR

Lecture 72 Snowflake: A Cloud Data Warehouse

Lecture 73 Things to remember

Lecture 74 Demo: Setup big data cluster on EMR and access Spark and S3 using Zeppelin

Lecture 75 Demo: AWS EMR Serverless

Section 7: Big data projects

Lecture 76 Introduction

Lecture 77 Getting value out of big data

Lecture 78 5 P’s of data science

Lecture 79 Purpose and People

Lecture 80 Process and Platforms

Lecture 81 Programmability

Lecture 82 Case study 1: Analyze payment risks

Lecture 83 Case study 2: New product analysis

Lecture 84 Case study 3: Product recommendation

Lecture 85 Case study 4: Log file analysis with multiple solutions

Lecture 86 Things to remember

Section 8: Conclusion

Lecture 87 Conclusion and next steps

Section 9: Add ons

Lecture 88 Demo and practice activity: Create a bucket on Amazon S3

Lecture 89 Answers to storage exercises

Lecture 90 Answers to Ingestion exercises

Any team lead or manager who wants to learn about what Big Data is all about and start or lead some big data projects in their organization

Course Information:

Udemy | English | 4h 12m | 1.34 GB
Created by: Ganapathi Devappa

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