Big Data

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

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Section Outline

Lecture 2: Data Defined

Lecture 3: Data, Information and Knowledge

Lecture 4: Understanding Data

Lecture 5: Data Usage

Lecture 6: Data Duplication

Lecture 7: Data Deduplication

This lecture will lay the foundation for this course by clearly defining and discussing Data.

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Lecture outline:

0:00    Introduction to Data

1:04    Definition of Data

This lecture will teach you the difference among Data, Information, and Knowledge.

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Lecture outline:

0:00    Overview of the Comparison

1:31    Comparison between Data, Information and Knowledge

1:49    Computing Mechanisms

This lecture will focus on understanding data.

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Lecture outline:

0:00    Introduction to Understanding Data

0:36    Things to Understand about Data

1:07    Data Description

1:26    Explore Data

2:24    Verification of Data Quality

This lecture will cover the different ways we can use data in different areas and platforms.

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Lecture outline:

0:00    Introduction to Data Usage

0:58    Top Applications: Onavo Count and Data Usage

2:05    Top Applications: 3D Watchdog, DataMan and My Data Manager

3:14    Top Applications: myAT&T and My Verizon Mobile

This lecture will discuss the definition of Data Duplication and give you a comprehensive understanding of the subject.

This discussion will focus on the definition of deduplication, its benefits and methods.

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Lecture outline:

0:00    Introduction to Data Deduplication

1:04    Benefits of Data Deduplication

2:44    Methods of Data Deduplication

5:52    Reasons Data Deduplication is Used in Secondary Storage Systems

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

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Section Outline

Lecture 10: Big Data

Lecture 11: Facts About Big Data

Lecture 12: Capabilities of Big Data

Lecture 13: Big Data Benefits

Lecture 14: Where Big Data is Used?

This lecture will cover the definition of Big Data and introduce its dimensions.

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Lecture outline:

0:00    Introduction to Big Data

0:27    Three Dimensions of Data Growth

This lecture will discuss the continuously growing numbers in the area of Big Data.

This lecture will focus on what Big Data can do for the organization or on a personal private level.

This lecture will explore the different benefits of Big Data.

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Lecture outline:

0:00    Introduction to the Benefits of Big Data

0:18    Talking with Customer and Developing your Products

1:54    Performing Risk Analysis and Keeping your Data Safe

3:02    Creating New Revenue Streams and Customizing your Website

4:46    Reducing Maintenance Cost and Offering Tailored Healthcare

6:19    Offering Enterprise-wide insights and Making Smarter Cities

This lecture will cover five areas where Big Data is used.

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Lecture outline:

0:00    Introduction to Where Big Data is Used

0:21    Context for Interactions and Transactions

1:00    Connect with Outside Patterns

1:34    Response Based on Trends

2:11    Insight to Oversight

2:33    Know Yourself and Your Rivals

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

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Section Outline

Lecture 17: Variety

Lecture 18: Volume

Lecture 19: Velocity

Lecture 20: A Fourth "V"

This lecture will talk about the first theory of the elements of Big Data: Variety.

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Lecture outline:

0:00    Introduction to Variety

1:31    Structured and Unstructured Data

2:13    Variety Statistics

This lecture will discuss the second theory of the elements of Big Data: Volume.

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Lecture outline:

0:00    Introduction to Volume

1:31    Volume Statistics

2:13    Early Days of Data Processing

3:15    Hadoop

This lecture will explain the third theory of the elements of Big Data: Velocity.

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Lecture outline:

0:00    Introduction to Velocity

1:31    Velocity Statistics

2:13    Why Does Velocity Flow Quickly

3:15    Reasons to Consider Streaming Process

This lecture will teach you a possible fourth element of Big Data.

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Lecture outline:

0:00    Introduction to the Fourth "V"

1:31    Veracity

2:13    Variability

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

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Section Outline

Lecture 23: Big Data Analytics

Lecture 24: How to Analyze Big Data?

Lecture 25: Big Data Analytics Samples

This discussion will focus on the analytics of Big Data.

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Lecture outline:

0:00    Introduction to Big Data Analytics

0:26    Objective of Big Data

2:03    Problems Dealing with the 3 "V"'s

2:59    Small Big Data Problem

4:13    Medium Big Data Problem

6:05    Big Big Data Problem

This lecture will cover the analysis of Big Data.

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Lecture outline:

0:00    Introduction to Analyzing Big Data

1:11    Using Big Data to get Results

2:20    Basic Analytics

2:40    Slicing and Dicing

3:56    Basic Monitoring

4:40    Anomaly Identification

5:21    Advanced Analytics

This lecture will define and explain Big Data analytic samples such as predictive modeling, text analytics, operationalized analytics, and monetizing analytics.

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Lecture outline:

0:00    Predictive Modeling

1:01    Text Analytics

1:50    Other Statistical and Data-mining Algorithms

2:17    Operationalized Analytics

3:18    Monetizing Analytics

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

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Section Outline

Lecture 28: Developing a Strategy

Lecture 29: Approach to Implementing Big Data

Lecture 30: Big Data Strategies

This discussion will center on developing a strategy for integrating Big Data.

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Lecture outline:

0:00    Introduction in Developing a Strategy

3:05    Standardize Practices

4:31    Go / No-Go Criteria

6:25    Preparing the Data Environment

7:47    Promoting Data Reuse

9:11    Instituting Proper Levels

11:03   Providing a Governed Process

This lecture will describe a few techniques in implementing Big Data.

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Lecture outline:

0:00    Introduction in Approaching to Implement Big Data

0:13    Begin with Stakeholders

0:45    Consider Culture

1:17    Finding Data Stewards

2:37    Setting Goals

3:14    Create the Plan

4:10    Establishing Metrics

5:11    Deployment

7:41    Making Big Data Little

8:32    Design for CPI

This lecture will enumerate and discuss strategies in achieving Big Data.

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Lecture outline:

0:00    Performance Management

1:45    Data Exploration

4:08    Social Analytics

8:05    Decision Science

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

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Section Outline

Lecture 33: The Big Data Breakthrough

Lecture 34: Best Practices

Lecture 35: Current Implementation of Big Data

Lecture 36: Key Design Goals

Lecture 37: Emerging Implementation of Big Data

This lecture will discuss some new developments in Big Data.

This lecture will teach you some practices in implementing a successful Big Data project.

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Lecture outline:

0:00    Introduction to Big Data's Best Practices

0:19    Collate Business Requirements

0:44    A Business Decision, Not an IT Decision

1:15    Agile and Iterative Approach to Implementation

2:11    Evaluate Data Requirements

2:43    Ease Skills Shortage

3:10    Optimize Knowledge Transfer

3:46    Embrace and Plan your Sandbox

4:21    Align with the Cloud Operating Model

5:07    Associate Big Data with Enterprise Data

5:44    Using Intelligence in Operational Routine

This discussion will focus on areas where Big Data is currently implemented.

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Lecture outline:

0:00    Introduction of Implementing Current Big Data

1:31    Retail Sector

2:13    Healthcare Sector

3:15    Financial Services

This lecture will talk about a few design goals of a Big Data content repository.

This lecture will focus on future trends where Big Data can be applied.

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Lecture outline:

0:00    Integrating Multiple Big Data Strategies

1:31    Building a Big Data Capacity

2:13    Creating a Big Data Policy

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

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Section Outline

Lecture 40: Big Data Management

Lecture 41: Big Data Management Databases

Lecture 42: Storing Big Data

Lecture 43: Hierarchy of Storage

Lecture 44: Key Requirements

This lecture will cover the management side of Big Data.

This lecture will cover the different databases of Big Data management.

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Lecture outline:

0:00    RPBMS

1:31    PostgreSQL

2:13    NoSQL

This lecture will teach you about the potentials and risks of storing Big Data.

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Lecture outline:

0:00    Introduction to Storing Big Data

1:31    Knowing the Potential and Risks

2:13    Data Centers

This lecture will discuss the hierarchy of storage.

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Lecture outline:

0:00    Introduction to the Hierarchy of Storage

1:10    Primary Storage

5:35    Secondary Storage

9:09    Tertiary Storage

10:33    Off-line Storage

This lecture will discuss the key requirements in storing Big Data.

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Lecture outline:

0:00    Definition of Enterprise Architecture

1:31    Gartner's definition of Enterprise Architecture

2:13    Continous update of the definition of Enterprise Architecture

3:15    EA Schools of Thought

3:33    Enterprise IT Architecting

4:08    Enterprise Integrating

4:36    Enterprise Ecological Adaptation

In this lecture, we'll discuss some objectives aimed at showing what you can expect to learn from this course.

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Section Outline

Lecture 40: Big Data Management

Lecture 41: Big Data Management Databases

Lecture 42: Storing Big Data

Lecture 43: Hierarchy of Storage

Lecture 44: Key Requirements

This lecture will discuss some myths about Big Data.

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Lecture outline:

0:00    Introduction to the Myths in Big Data

1:09    Myth One

2:45    Myth Two

4:20    Myth Three

5:12    Myth Four

6:18    Myth Five

This discussion will focus on some challenges that Big Data faces.

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Lecture outline:

0:00    Heterogeneity and Incompleteness

2:55    Scale

7:11    Timeliness

9:39    Privacy

12:27    Human Collaboration

This lecture will discuss some helpful tips in building and maintaining a Big Data project.

This lecture will explore how Big Data will fare in the future.

This e book is a list of terms and definitions often used in the field of Big Data.

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