Week 1: Course Overview

DSAN 6000: Big Data and Cloud Computing
Fall 2025

Amit Arora and Jeff Jacobs

jj1088@georgetown.edu

Thursday, August 28, 2025

Agenda for today’s session

  1. Course and syllabus overview

  2. Big Data Concepts

    1. Definition
    2. Challenges
    3. Approaches

  3. Data Engineering

  4. Introduction to bash

    1. Lab: Linux command line

Course Overview

Instructional Team - Professors

Amit Arora, aa1603@georgetown.edu

  • Principal Solutions Architect - AI/ML at AWS
  • Adjunct Professor at Georgetown University
  • Multiple patents in telecommunications and applications of ML in telecommunications

Fun Facts

  • I am a self-published author https://blueberriesinmysalad.com/
  • My book “Blueberries in my salad: my forever journey towards fitness & strength” is written as code in R and Markdown
  • I love to read books about health and human performance, productivity, philosophy and Mathematics for ML. My reading list is online!

Jeff Jacobs, jj1088@georgetown.edu

  • Full-time Professor at Georgetown (DSAN and Public Policy)
  • Background in Computational Social Science (Comp Sci MS → Political Economy PhD → Labor Econ Postdoc)

Fun Facts

  • Used Apache Airflow daily for PhD projects! (Example)

Instructional Team - Teaching Assistants

Binhui Chen, bc928@georgetown.edu

(Lead TA for the course!)

Pranav Sudhir Patil, pp755@georgetown.edu

Ofure Udabor, au195@georgetown.edu

Yifei Wu, yw924@georgetown.edu

Naomi Yamaguchi, ny159@georgetown.edu

Leqi Ying, ly290@georgetown.edu

Xinyue (Monica) Zhang, xz646@georgetown.edu

Course Description

  • Data is everywhere! Many times, it’s just too big to work with traditional tools. This is a hands-on, practical workshop style course about using cloud computing resources to do analysis and manipulation of datasets that are too large to fit on a single machine and/or analyzed with traditional tools. The course will focus on Spark, MapReduce, the Hadoop Ecosystem and other tools.

  • You will understand how to acquire and/or ingest the data, and then massage, clean, transform, analyze, and model it within the context of big data analytics. You will be able to think more programmatically and logically about your big data needs, tools and issues.

Always refer to the syllabus and calendar in the course website for class policies.

Learning Objectives

  • Setup, operate and manage big data tools and cloud infrastructure, including Spark, DuckDB, Polars, Athena, Snowflake, and orchestration tools like Airflow on Amazon Web Services
  • Use ancillary tools that support big data processing, including git and the Linux command line
  • Execute a big data analytics exercise from start to finish: ingest, wrangle, clean, analyze, store, and present
  • Develop strategies to break down large problems and datasets into manageable pieces
  • Identify broad spectrum resources and documentation to remain current with big data tools and developments
  • Communicate and interpret the big data analytics results through written and verbal methods

Evaluation

  • Group project : 40%
  • Assignments : 30%
  • Lab completions : 20%
  • Quizzes : 10%

Course Materials

  • Slides/labs/assignment on Website/GitHub
  • Quizzes and readings in Canvas

Communication

Slack rules

  • Post any question/comment about the course, assignments or any technical issue.
  • DMs are to be used sparingly
  • You may not DM multiple people in the instructional team at the same time for the same issue
  • Keep an eye on the questions posted in Slack. Use the search function. It’s very possible that we have already answered a questions
  • You may DM us back only if we DM you first on a given issue
  • Lab/assignment/project questions will only be answered up to 6 hours before something is due (i.e. 6pm on Mondays)

Midterm Project (NEW!)

  • Individual assignment (not team-based)
  • Timing: Around Week 5-6
  • Weight: Equivalent to 2 homework assignments
  • Format:
    • We provide the dataset and problem statement
    • You apply big data tools and techniques learned in class
    • End-to-end data pipeline implementation
  • Details: TBD (will be announced in Week 4)

Final Project

  • Groups of 3-4 students
  • Use an archive of Reddit data, augmented with external data
  • Exploratory analysis
  • NLP
  • Machine Learning
  • Writeup
    • Data sourcing and ingesting
    • Exploratory analysis
    • Modeling
    • Challenges and Learnings
    • Conclusions
    • Future work

BIG DATA

Where does it come from?

How is it being created?

In one minute of time (2018)

In one minute of time (2019)

In one minute of time (2020)

In one minute of time (2021)

In one minute of time (2025)

Every 60 seconds in 2025:

  • ChatGPT serves millions of requests (exact numbers proprietary)
  • 500 hours of video uploaded to YouTube
  • 1.04 million Slack messages sent
  • 362,000 hours watched on Netflix
  • 5.9-11.4 million Google searches
  • $443,000 spent on Amazon
  • AI-generated images created at massive scale (metrics not publicly available)
  • 347,200 posts on X (formerly Twitter)
  • 231-250 million emails sent

A lot of it is hapenning online.

We can record every:

  • click
  • ad impression
  • billing event
  • video interaction
  • server request
  • transaction
  • network message
  • fault
  • …

It can also be user-generated content:

  • Instagram posts & Reels
  • X (Twitter) posts & Threads
  • TikTok videos
  • YouTube Shorts
  • Reddit discussions
  • Discord conversations
  • AI-generated content (text, images, code)
  • …

But health and scientific computing create a lot too!

There’s lots of graph data too

Many interesting datasets have a graph structure:

  • Social networks
  • Google’s knowledge graph
  • Telecom networks
  • Computer networks
  • Road networks
  • Collaboration/relationships

Some of these are HUGE

Apache (web server) log files

System log files

Internet of Things (IoT) in 2025

75 billion connected devices generating data:

  • Smart home devices (Alexa, Google Home, Apple HomePod)
  • Wearables (Apple Watch, Fitbit, Oura rings)
  • Connected vehicles & self-driving cars
  • Industrial IoT sensors
  • Smart city infrastructure
  • Medical devices & remote patient monitoring

Smartphone Location Data

Where else?

  • The Internet

  • Transactions

  • Databases

  • Excel

  • PDF Files

  • Anything digital (music, movies, apps)

  • Some old floppy disk lying around the house

Typical Real-World Scenarios in 2025

Scenario 1: Traditional Big Data

You have a laptop with 16GB of RAM and a 256GB SSD. You are given a 1TB dataset in text files. What do you do?

Scenario 2: AI/ML Pipeline

Your company wants to build a RAG system using 10TB of internal documents. You need sub-second query response times. How do you architect this?

Scenario 3: Real-Time Analytics

You need to process 1 million events/second from IoT devices and provide real-time dashboards with <1s latency. What’s your stack?

What is Big Data?

Let’s discuss!

Exponential data growth

Big Data Definitions

Wikipedia

“A collection of datasets so large and complex that it becomes difficult to process using traditional tools and applications. Big Data technologies describe a new generation of technologies and architectures designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discover and/or analysis”

O’Reilly

“Big data is when the size of the data itself becomes part of the problem”

Frameworks for Thinking About Big Data

IBM (The 3 V’s)

  • Volume (Gigabytes → Exabytes → Zettabytes)
  • Velocity (Batch → Streaming → Real-time AI inference)
  • Variety (Structured, Unstructured, Embeddings)

Additional V’s for 2025

  • Variability
  • Veracity
  • Visualization
  • Value
  • Vectors (embeddings for AI/ML)
  • Versatility (multi-modal data)

Data “Size”

\[ \text{``Size''} = f(\text{Processing Ability}, \text{Storage Space}) \]

  • Can you analyze/process your data on a single machine?
  • Can you store (or is it stored) on a single machine?
  • Can you serve it fast enough for real-time AI applications?

If any of of the answers is no then you have a big-ish data problem!

Big Data in the Age of Generative AI

The New Data Landscape (2025)

Training Foundation Models

  • GPT-4: Trained on about 13 trillion tokens
  • Llama 3: 15 trillion tokens
  • Google Gemini: Multi-modal training (text, images, video)
  • Each iteration requires petabytes of curated data

Data Requirements Have Exploded

  • 2020: BERT trained on 3.3 billion words
  • 2023: GPT-4 trained on ~13 trillion tokens
  • 2024: Llama 3 trained on 15+ trillion tokens

Big Data Infrastructure: Data Lakes, Warehouses

Traditional Use Cases:

  • Business intelligence
  • Analytics & reporting
  • Historical data storage

Modern AI Use Cases:

  • Training data repositories
  • Vector embeddings storage
  • RAG (Retrieval-Augmented Generation) context
  • Fine-tuning datasets
  • Evaluation & benchmark data

RAG and Context Engineering: The New Data Pipeline

G raw Raw Data lake Data Lake raw->lake proc Processing lake->proc vec Vector DB proc->vec context LLM Context vec->context

Key Components:

  • Data Lakes (S3, Azure Data Lake): Store massive unstructured data
  • Data Warehouses (Snowflake, BigQuery): Structured data for context
  • Vector Databases (Pinecone, Weaviate, Qdrant): Semantic search
  • Embedding Models: Convert data to vectors
  • Orchestration (Airflow, Prefect): Manage the pipeline

MCP Servers & Agentic AI

Model Context Protocol (MCP)

  • Open protocol for connecting AI assistants to data sources
  • Standardized way to expose tools and data to LLMs
  • Enables “agentic” behavior - AI that can act autonomously

MCP in Production

G ware Data Warehouse mcp MCP Server ware->mcp agent AI Agent mcp->agent action Action agent->action

Examples:

  • AI agents querying Snowflake for real-time analytics
  • Autonomous systems updating data lakes based on predictions
  • Multi-agent systems coordinating through shared data contexts

Data Quality for AI

(Why Data Quality Matters More Than Ever)

Garbage In, Garbage Out - Amplified:

  • Bad training data → Biased models
  • Incorrect RAG data → Hallucinations
  • Poor data governance → Compliance issues

Data Quality Challenges in 2025

  • Scale: Validating trillions of tokens
  • Diversity: Multi-modal, multi-lingual data
  • Velocity: Real-time data for online learning
  • Veracity: Detecting AI-generated synthetic data

Real-World Big Data / AI Examples

Netflix

  • Data Scale: 260+ million subscribers generating 100+ billion events/day
  • AI Use: Personalization, content recommendations, thumbnail generation
  • Stack: S3 → Spark → Iceberg → ML models → Real-time serving

Uber

  • Data Scale: 35+ million trips per day, petabytes of location data
  • AI Use: ETA prediction, surge pricing, driver-rider matching
  • Stack: Kafka → Spark Streaming → Feature Store → ML Platform

OpenAI

  • Data Scale: Trillions of tokens for training, millions of queries/day
  • AI Use: GPT models, DALL-E, embeddings
  • Stack: Distributed training → Vector DBs → Inference clusters

Relative Data Sizes

 

Can be processed on single machine? No Medium
(Parallel Processing)		 Big!
Parallel + Distributed Processing
Yes Small
(Your Laptop)		 Medium
(Data Streaming)
Yes No
Can be stored on single machine?

What You’ll Learn in This Course

Modern Big Data Stack (2025)

Query Engines:

  • DuckDB - In-process analytical database
  • Polars - Lightning-fast DataFrame library
  • Spark - Distributed processing at scale

Data Warehouses & Lakes:

  • Snowflake - Cloud-native data warehouse
  • Athena - Serverless SQL on S3
  • Iceberg - Open table format

AI/ML Integration:

  • Vector databases for embeddings
  • RAG implementation patterns
  • Streaming with Spark Structured Streaming

Orchestration:

  • Airflow for pipeline management
  • Serverless with AWS Lambda

Data Types

  • Structured
  • Unstructured
  • Natural language
  • Machine-generated
  • Graph-based
  • Audio, video, and images
  • Streaming

Big Data vs. Small Data I

Small Data is usually… On the other hand, Big Data…
Goals gathered for a specific goal may have a goal in mind when it’s first started, but things can evolve or take unexpected directions
Location in one place, and often in a single computer file can be in multiple files in multiple servers on computers in different geographic locations
Structure/Contents highly structured like an Excel spreadsheet, and it’s got rows and columns of data can be unstructured, it can have many formats in files involved across disciplines, and may link to other resources
Preparation prepared by the end user for their own purposes is often prepared by one group of people, analyzed by a second group of people, and then used by a third group of people, and they may have different purposes, and they may have different disciplines

Big Data vs. Small Data II

Small Data is usually… On the other hand, Big Data…
Longevity kept for a specific amount of time after the project is over because there’s a clear ending point. In the academic world it’s maybe five or seven years and then you can throw it away contains data that must be stored in perpetuity. Many big data projects extend into the past and future
Measurements measured with a single protocol using set units and it’s usually done at the same time is collected and measured using many sources, protocols, units, etc
Reproducibility be reproduced in their entirety if something goes wrong in the process replication is seldom feasible
Stakes if things go wrong the costs are limited, it’s not an enormous problem can have high costs of failure in terms of money, time and labor
Access identified by a location specified in a row/column unless it is exceptionally well designed, the organization can be inscrutable
Analysis analyzed together, all at once is ordinarily analyzed in incremental steps

Challenges of Working with Large Datasets

The V The Challenge
Volume data scale
Value data usefulness in decision making
Velocity data processing: batch or stream
Viscosity data complexity
Variability data flow inconsistency
Volatility data durability
Viability data activeness
Validity data properly understandable
Variety data heterogeneity

Thinking About Big Data Workflows

William Cohen (Director, Research Engineering, Google):

  • Working with big data is not about…
    • Code optimization
    • Learning the details of today’s hardware/software (they are evolving…)
  • Working with big data is about understanding:
    • The cost of what you want to do
    • What the tools that are available offer
    • How much can be accomplished with linear or nearly-linear operations
    • How to organize your computations so that they effectively use whatever’s fast
    • How to test/debug/verify with large data
  • Recall that traditional tools like R and Python are single threaded (by default)

Tools at-a-glance

Languages, libraries, and projects

Cloud Services

Other:

Data Engineering

Data Scientist vs. Data Engineer

In this course, you’ll augment your data scientist skills with data engineering skills!

Data Engineer Responsibilities

Data Engineering: Levels 2 and 3

Modern Data Architecture

Architecture

Storage

Source control

Orchestration

Processing

Analytics

Machine Learning

Governance

Time for Lab! Linux Command Line

The Terminal

  • Terminal access was THE ONLY way to do programming
  • No GUIs! No Spyder, Jupyter, RStudio, etc.
  • Coding is still more powerful than graphical interfaces for complex jobs
  • Coding makes work repeatable

BASH

  • Created in 1989 by Brian Fox: “Bourne-Again Shell”
  • Brian Fox also built the first online interactive banking software
  • BASH is a command processor
  • Connection between you and the machine language and hardware

The Prompt

username@hostname:current_directory $

What do we learn from the prompt?

  • Who you are - username
  • The machine where your code is running - hostname
  • The directory where your code is running - current_directory
  • The shell type - $ - this symbol means BA$H

Syntax

COMMAND -F --FLAG
  • COMMAND is the program, everything after that = arguments
  • F is a single letter flag, FLAG is a single word or words connected by dashes. A space breaks things into a new argument.
  • Sometimes argument has single letter and long form versions (e.g. F and FLAG)
COMMAND -F --FILE file1
  • Here we pass a text argument "file1" as the value for the FILE flag
  • -h flag is usually to get help. You can also run the man command and pass the name of the program as the argument to get the help page.

Let’s try basic commands:

  • date to get the current date
  • whoami to get your user name
  • echo "Hello World" to print to the console

Examining Files

  • Find out your Present Working Directory pwd
  • Examine the contents of files and folders using ls
  • Make new files from scratch using touch
  • Glob: “Mini-language” for selecting files with wildcards
    • \* for wild card any number of characters
    • \? for wild card for a single character
    • [] for one of many character options
    • ! for exclusion
    • [:alpha:], [:alnum:], [:digit:], [:lower:], [:upper:]

Reference material: Shell Lesson 1,2,4,5

Navigating Directories

  • Knowing where your terminal is executing code ensures you are working with the right inputs/outputs.
  • Use pwd to determine the Present Working Directory.
  • Change to a folder called “git-repo” with cd git-repo.
  • . refers to the current directory, such as ./git-repo
  • .. can be used to move up one level (cd ..), and can be combined to move up multiple levels (cd ../../my_folder)
  • / is the root of the filesystem: contains core folders (system, users)
  • ~ is the home directory. Move to folders referenced relative to this path by including it at the start of your path, for example ~/projects.
  • To visualize the structure of your working directory, use tree

Reference link

Interacting with Files

Now that we know how to navigate through directories, we need commands for interacting with files…

  • mv to move files from one location to another
    • Can use glob here - ?, *, [], …
  • cp to copy files instead of moving
    • Can use glob here - ?, *, [], …
  • mkdir to make a directory
  • rm to remove files
  • rmdir to remove directories
  • rm -rf to blast everything! WARNING!!! DO NOT USE UNLESS YOU KNOW WHAT YOU ARE DOING

Using BASH for Data Exploration

  • head FILENAME / tail FILENAME - glimpsing the first / last few rows of data
  • more FILENAME / less FILENAME - viewing the data with basic up / (up & down) controls
  • cat FILENAME - print entire file contents into terminal
  • vim FILENAME - open (or edit!) the file in vim editor
  • grep FILENAME - search for lines within a file that match a regex expression
  • wc FILENAME - count the number of lines (-l flag) or number of words (-w flag)

Reference material: Text Lesson 8,9,15,16

Pipes and Arrows

  • | sends the stdout to another command (is the most powerful symbol in BASH!)
  • > sends stdout to a file and overwrites anything that was there before
  • >> appends the stdout to the end of a file (or starts a new file from scratch if one does not exist yet)
  • < sends stdin into the command on the left

Reference material: Text Lesson 1,2,3,4,5

Alias and User Files

  • /.bashrc is where your shell settings are located

  • How many processes? whoami | xargs ps -u | wc -l

  • Hard to remember full command! Let’s make an alias

  • General syntax:

    alias alias_name="command_to_run"

  • For our case:

    alias nproc="whoami | xargs ps -u | wc -l"

  • Now we need to put this alias into the .bashrc

    alias nproc="whoami | xargs ps -u | wc -l" >> ~/.bashrc

  • Your commands get saved in ~/.bash_history

Process Management

  • Use ps to see your running processes
  • Use top or even better htop to see all running processes
  • Install htop via sudo yum install htop -y
  • To kill a broken process: first find the process ID (PID)
  • Then use kill [PID NUM] to “ask” the process to terminate. If things get really bad: kill -9 [PID NUM]
  • To kill a command in the terminal window it is running in, try using Ctrl + C or Ctrl + /
  • Run cat on its own to let it stay open. Now open a new terminal to examine the processes and find the cat process.

Reference material: Text Lesson 1,2,3,7,9,10

Try playing a Linux game!

Bash crawl is a game to help you practice your navigation and file access skills. Click on the binder link in this repo to launch a jupyter lab session and explore!