Syllabus
Data-Intensive Distributed Computing (Fall 2024)

Part 1: Introduction to Big Data

Topics

• What's this course about?
• Why big data?
• Scaling models

Slides

• PDF Module 1 - Introduction  

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Part 2: MapReduce Algorithm Design

Topics

• MapReduce programming model
• Cloud computing and datacenters
• Hadoop API
• Hadoop physical execution
• MapReduce design patterns
• Intermediate aggregation and combiners
• Partitioning, grouping, and sorting

Readings

• Data-Intensive Text Processing with MapReduce
  • Chapter 1: Introduction
  • Chapter 2: MapReduce Basics
  • Chapter 3: MapReduce Algorithm Design
• Hadoop: The Definitive Guide (4th Edition):
  • Chapter 1: Meet Hadoop
  • Chapter 2: MapReduce
  • Chapter 3: The Hadoop Distributed Filesystem (Focus on the mechanics of the HDFS commands and don't worry so much about learning the Java API all at once—you'll pick it up in time.)
  • Chapter 5: Hadoop I/O (Read sections "Serialization" and "File-Based Data Structures")
  • Chapter 6: Developing a MapReduce Application (Skip sections "Setting Up the Development Environment", "Writing a Unit Test with MRUnit" and "MapReduce Workflows")
  • Chapter 7: How MapReduce Works (Skip section on "Configuration Tuning")
  • Chapter 8: MapReduce Types and Formats
  • Chapter 9: MapReduce Features (Read sections on "Counters", "Sorting", and "Side Data distribution")

Slides

• PDF Module 2 - MapReduce

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Part 3: From MapReduce to Spark

Topics

• Evolution of dataflow abstractions
• MapReduce, Pig, Spark, etc.

Readings

• Learning Spark (Optional):
  • Chapter 1: Introduction to Data Analysis with Spark
  • Chapter 2: Downloading Spark and Getting Started (Skip section on downloading)
  • Chapter 3: Programming with RDDs
  • Chapter 4: Working with Key/Value Pairs
  • Chapter 5: Loading and Saving Your Data (Stop when you get to Structured Data with Spark SQL)

Slides

• PDF Module 3 - From MapReduce to Spark
• PDF [CS451/CS651 Only] - Learning Scala

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Part 4: Analyzing Text

Topics

• Language models and machine translation
• Inverted indexing and search

Readings

• Large Language Models in Machine Translation (Optional)
• Data-Intensive Text Processing with MapReduce — Chapter 4: Inverted Indexing for Text Retrieval

Slides

• PDF Module 4 - Text Processing

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Part 5: Analyzing Graphs

Topics

• Graph representations
• Parallel breadth-first search
• PageRank and random walks
• Issues and challenges with dataflow abstractions

Readings

• Data-Intensive Text Processing with MapReduce — Chapter 5: Graph Algorithms

Slides

• PDF Module 5 - Graphs

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Part 6: Data Mining and Machine Learning

Topics

• Supervised machine learning: binary classification
• Logistic regression, gradient descent, stochastic gradient descent, ensemble methods
• Production machine learning pipelines
• Hashing: minhash
• Clustering: k-means

Readings

• Tom Mitchell. Naive Bayes and Logistic Regression. (This book chapter serves as supplemental reading and goes into classification in more detail than in lecture.)
• Deisenroth et al., Mathematics for Machine Learning: Chapter 12, Classification with Support Vector Machines. (Optional supplemental reading)
• Deisenroth et al., Mathematics for Machine Learning: Chapter 11, Density Estimation with Gaussian Mixture Models. (This book chapter serves as supplemental reading and goes into clustering with Gaussian mixture models in more detail than in lecture.)

Slides

• PDF Module 6 - Machine Learning and Data Mining (1/2)
• PDF Module 6 - Machine Learning and Data Mining (2/2)

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Part 7: Analyzing Relational Data

Topics

• OLTP vs. OLAP
• Data warehousing and data lakes, ETL
• SQL-on-Hadoop: relational data processing with MapReduce and Spark
• Optimizations for relational processing: row vs. column stores, vectorized processing
• Semistructured data and record reconstruction (Parquet)

Readings

• Data-Intensive Text Processing with MapReduce — Chapter 6: Processing Relational Data
• MapReduce: A major step backwards

Slides

• PDF Module 7 - Relational Data

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Part 8: Real-Time Analytics

Topics

• Stream processing semantics, issues, and frameworks
• Introduction to Apache Spark Streaming
• Probabilistic data structures (hyerloglog counters, bloom filters, count-min sketches, etc.)

Readings

• Zaharia et al. Discretized Streams: Fault-Tolerant Streaming Computation at Scale, SOSP 2013.

Slides

• PDF Module 8 - Beyond Batch Processing

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Part 9: Mutable State

Topics

• Bigtable/HBase: Log-structure merge trees
• Distributed hash tables
• Consistency, latency, and availability tradeoffs

Readings

• The original Bigtable paper.
• The original DHT paper.
• Daniel Abadi. Consistency Tradeoffs in Modern Distributed Database System Design, Computer, 45(2):37-42, 2012.

Slides

• PDF Module 9 - Mutable State

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Part 10: Analyzing Graphs, Redux

Topics

• Bulk synchronous parallel: "think like a vertex" (Giraph)

Readings

• Mining of Massive Datasets: Link Analysis Section 5.4
• Sherif Sakr. Large-Scale Graph Processing Systems, 2016.

Slides

• PDF Module 10 - Graphs Redux

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