BSc: Introduction To Big Data

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Introduction to Big Data

  • Course name: Introduction to Big Data
  • Code discipline: N/A
  • Subject area:

Short Description

This course covers the following concepts: Distributed data organization; Distributed data processing.

Prerequisites

Prerequisite subjects

Prerequisite topics

Course Topics

Course Sections and Topics
Section Topics within the section
Introduction
  1. What is Big Data
  2. Characteristics of Big Data
  3. Data Structures
  4. Types of Analytics
Hadoop
  1. Data storage
  2. Clustering
  3. Design decisions
  4. Scaling
  5. Distributed systems
  6. The ecosystem
HDFS
  1. Distributed storage
  2. Types of nodes
  3. Files and blocks
  4. Replication
  5. Memory usage
MapReduce
  1. Distributed processing
  2. MapReduce model
  3. Applications
  4. Tasks management
  5. Patterns
YARN
  1. Resource manager
  2. Components
  3. Run an application
  4. Schedules
Optimizing Data Processing
  1. CAP theorem
  2. Distributed storage and computation
  3. Batch Processing
  4. Stream Processing
  5. Usage patterns
  6. NoSQL databases
Spark
  1. Architecture
  2. Use cases
  3. Job scheduling
  4. Data types
  5. SparkML
  6. GraphX

Intended Learning Outcomes (ILOs)

What is the main purpose of this course?

Software systems are increasingly based on large amount of data that come from a wide range of sources (e.g., logs, sensors, user-generated content, etc.). However, data are useful only if it can be analyzed properly to extract meaningful information can be used (e.g., to take decisions, to make predictions, etc.). This course provides an overview of the state-of-the-art technologies, tools, architectures, and systems constituting the big data computing solutions landscape. Particular attention will be given to the Hadoop ecosystem that is widely adopted in the industry.

ILOs defined at three levels

Level 1: What concepts should a student know/remember/explain?

By the end of the course, the students should be able to ...

  • The most common structures of distributed storage.
  • Batch processing techniques
  • Stream processing techniques
  • Basic distributed data processing algorithms
  • Basic tools to address specific processing needs

Level 2: What basic practical skills should a student be able to perform?

By the end of the course, the students should be able to ...

  • The basis of the CAP theorem
  • The structure of the MapReduce
  • How to process batch data
  • How to process stream data
  • The characteristics of a NoSQL database

Level 3: What complex comprehensive skills should a student be able to apply in real-life scenarios?

By the end of the course, the students should be able to ...

  • Use a NoSQL database
  • Write a program for batch processing
  • Write a program for stream processing

Grading

Course grading range

Grade Range Description of performance
A. Excellent 90-100 -
B. Good 75-89 -
C. Satisfactory 60-74 -
D. Poor 0-59 -

Course activities and grading breakdown

Activity Type Percentage of the overall course grade
Labs/seminar classes 30
Interim performance assessment 30
Exams 40

Recommendations for students on how to succeed in the course

Resources, literature and reference materials

Open access resources

  • Slides and material provided during the course.
  • Vignesh Prajapati. Big Data Analytics with R and Hadoop. Packt Publishing, 2013
  • Jules J. Berman. Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2013

Closed access resources

Software and tools used within the course

Teaching Methodology: Methods, techniques, & activities

Activities and Teaching Methods

Activities within each section
Learning Activities Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7
Testing (written or computer based) 1 1 1 1 1 1 1
Discussions 1 1 1 1 1 1 1
Development of individual parts of software product code 0 0 1 1 1 1 1
Homework and group projects 0 0 1 1 1 1 1
Midterm evaluation 0 0 1 1 1 1 1

Formative Assessment and Course Activities

Ongoing performance assessment

Section 1

Activity Type Content Is Graded?
Question Describe the 6 Vs 1
Question Describe the types of analytics 1
Question Design the structure of a DB to address a specific analytics type 0
Question Give examples of the 6 Vs in real systems 0

Section 2

Activity Type Content Is Graded?
Question Describe the Hadoop ecosystem 1
Question Structure of an Hadoop cluster 1
Question Describe the scaling techniques 1
Question Configure a basic Hadoop node 0
Question Configure a basic Hadoop cluster 0

Section 3

Activity Type Content Is Graded?
Question Describe the characteristics of the different nodes 1
Question How files and blocks are managed 1
Question How memory is managed 1
Question How replication works 1
Question Configure a HDFS cluster 0
Question Configure different replication approaches 0
Question Build a HDFS client 0
Question Use a HDFS command line 0

Section 4

Activity Type Content Is Graded?
Question Describe the MapReduce model 1
Question Describe tasks management 1
Question Describe patterns of usage 1
Question Solve with MapReduce a specific problem 0
Question Implement a usage pattern 0

Section 5

Activity Type Content Is Graded?
Question Describe the resource manager 1
Question Describe the lifecycle of an application 1
Question Describe and compare the scheduling approaches 1
Question Compare the performance of the different schedules in different load conditions 0
Question Configure YARN 0
Question Evaluate the overall performance of YARN 0

Section 6

Activity Type Content Is Graded?
Question Analyze the CAP theorem 1
Question Define the kinds of data storage available 1
Question Characteristics of batch processing 1
Question Characteristics of stream processing 1
Question Describe the usage patterns 1
Question Compare NoSQL databases 1
Question Build a program to solve a problem with batch processing 0
Question Build a program to solve a problem with stream processing 0
Question Interact with a NoSQL database 0

Section 7

Activity Type Content Is Graded?
Question Describe the architecture of Spark 1
Question Describe the types of schedulers 1
Question Different characteristics of the data types 1
Question Features of SparkML 1
Question Features of GraphX 1
Question Analyze the performance of different schedulers 0
Question Write a program exploiting the features of each data type 0
Question Write a program using SparkML 0
Question Write a program using GraphX 0

Final assessment

Section 1

  1. Design the structure of a DB to address a specific analytics type
  2. Give examples of the 6 Vs in real systems

Section 2

  1. Identify the Hadoop components useful to address a specific problem.
  2. Configure an multi-node Hadoop system.

Section 3

  1. Configure a HDFS cluster with some specific replication approaches
  2. Build a HDFS client

Section 4

  1. Describe the advantages and disadvantages of the MapReduce model
  2. Solve a task designing the solution using MapReduce
  3. Solve a task designing the solution using a composition of usage patterns

Section 5

  1. Evaluate the performance of a specific configuration
  2. Compare the different schedules

Section 6

  1. Identify problems and solutions related to the CAP theorem
  2. Compare solutions with batch and stream processing approaches
  3. Design a system using a NoSQL database

Section 7

  1. Compare the performance of different schedules with different loads
  2. Extend the SparkML library with a custom algorithm
  3. Extend the GraphX library with a custom algorithm

The retake exam

Section 1

Section 2

Section 3

Section 4

Section 5

Section 6

Section 7

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