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
Requirements
Should have software project experience as a team lead or manager
Description
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
Overview
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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