CS451: Data-Intensive Distributed Computing

https://student.cs.uwaterloo.ca/~cs451/syllabus.html

For Steven: You’re probably looking for Latency numbers every programmer should know.

Really great teacher. Has gone downhill as the term progressed. Became boring. Though slides are pretty good.

Concepts

Chapter 2 & 3: MapReduce & Spark

• MapReduce
• RDD
• HDFS
• Hadoop
• Spark

Chapter 4: Dealing with Text

• N-Gram
• Inverted Index
• Zipf’s Law
• Boolean Retrieval
• Ranked Retrieval

Chapter 5: Graphs

• Page Rank

Data Mining Part 1: Machine Learning

• Classification
• Supervised Learning
• Logistic Regression
• Gradient Descent
• Stochastic Gradient Descent
• Ensemble Modelling
• K-Means Clustering

Production ML pipeline: (1) offline training and evaluation (holdout, cross-validate, etc.), (2) A/B test vs. other methods.

Data Mining Part 2: Similarity Search

• LSH
• Min-Hashing
• Jaccard
• Cosine Similarity
• Euclidean Distance
• Edit Distance

Chapter 7: Relational Data

• OLTP
• OLAP
• Data Warehouse
• Relational Algebra
• Relational Operations in MapReduce
• Cross Product

Chapter 9: Mutable State

• Google BigTable
• Apache HBase
• Log-Structured Merge Trees
• Distributed Hash Table
• CAP Theorem

Chapter 10: Streaming / Probabilistic Structures

• Bloom Filter