CMPT 732 Lecture Notes

  1. Course Introduction [“Course Introduction” slides]
    1. Us [Us slides]
    2. This Course [This Course slides]
    3. What is Big Data? [What is Big Data? slides]
    4. How big is “Big Data”? [How big is “Big Data”? slides]
    5. “Big data” isn't always big. [“Big data” isn't always big. slides]
    6. None [None slides]
    7. Clusters [Clusters slides]
    8. Hadoop [Hadoop slides]
    9. Our Environment [Our Environment slides]
    10. Things you will do [Things you will do slides]
    11. Lecture and Labs [Lecture and Labs slides]
    12. Assignments [Assignments slides]
    13. Quizzes [Quizzes slides]
    14. Expectations [Expectations slides]
    15. Course Topics [Course Topics slides]
  2. Hadoop Concepts [“Hadoop Concepts” slides]
    1. Our Cluster [Our Cluster slides]
    2. Hadoop Pieces [Hadoop Pieces slides]
    3. HDFS [HDFS slides]
    4. YARN [YARN slides]
    5. (Simplified) Cluster Overview [(Simplified) Cluster Overview slides]
    6. Work on Hadoop [Work on Hadoop slides]
    7. MapReduce [MapReduce slides]
    8. MapReduce Stages [MapReduce Stages slides]
    9. Example: word count [Example: word count slides]
    10. Diagram: Fall 2025 [Diagram: Fall 2025 slides]
    11. MapReduce Anatomy [MapReduce Anatomy slides]
    12. Hadoop MapReduce Details [Hadoop MapReduce Details slides]
    13. Summary Output [Summary Output slides]
    14. MapReduce Parallelism [MapReduce Parallelism slides]
    15. Writables [Writables slides]
    16. Example: word count [Example: word count slides]
    17. About MapReduce [About MapReduce slides]
    18. MapReduce: One more way [MapReduce: One more way slides]
    19. MapReduce Data Flow [MapReduce Data Flow slides]
  3. Python Preliminaries [“Python Preliminaries” slides]
    1. About Python [About Python slides]
    2. Data Types [Data Types slides]
    3. Unpacking Tuples [Unpacking Tuples slides]
    4. First-Class Functions [First-Class Functions slides]
    5. Lambda Functions [Lambda Functions slides]
    6. Iterators and Generators [Iterators and Generators slides]
    7. Imperative vs declarative [Imperative vs declarative slides]
  4. Spark Concepts [“Spark Concepts” slides]
    1. Spark [Spark slides]
    2. An Example [An Example slides]
    3. RDDs [RDDs slides]
    4. RDD Operations [RDD Operations slides]
    5. Operations and Partitions [Operations and Partitions slides]
    6. Partitions [Partitions slides]
    7. Lazy Evaluation [Lazy Evaluation slides]
    8. Chaining Calculations [Chaining Calculations slides]
    9. Combining Calculations [Combining Calculations slides]
    10. Shuffle Operations [Shuffle Operations slides]
    11. Drivers & Executors [Drivers & Executors slides]
    12. Controlling Executors [Controlling Executors slides]
    13. Spark Web Frontend [Spark Web Frontend slides]
    14. Spark vs MapReduce [Spark vs MapReduce slides]
    15. Spark DAG [Spark DAG slides]
    16. Stages [Stages slides]
    17. Job, Stages, Tasks [Job, Stages, Tasks slides]
    18. RDD Methods [RDD Methods slides]
  5. Spark DataFrames Concepts
  6. Cloud & Data Management
  7. NoSQL & Cassandra
  8. Data Management
  9. Spark Machine Learning
  10. Spark Streaming
  11. Small Data
  12. Other DataFrame Tools
  13. Other Big Data Tools
  14. NumPy/Pandas Speed

Course home page.

Schedule

Week Deliverables (*) Lecture Date First Slide
1 Assign 0 in lab Sep 4 Intro
2 Assign 1 Sep 8 Hadoop Concepts
3 Assign 2 Sep 15 About MapReduce
4 Assign 3 Sep 22
5 Assign 4 Sep 29
6 Assign 5 Oct 6
7 Quiz 1, Assign 6 Oct 13
8 Assign 7 Oct 20
9 Assign 8 Oct 27
10 Quiz 2, Assign 9 Nov 3
11 Assign 10 Nov 10
12 Nov 17
13 Quiz 3 Nov 24
14 Project Dec 1

* Check CourSys for the actual due dates and times.