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- Move timesfm-forecast into apps/ directory - Flatten Udacity portfolio projects from deep URL-encoded paths into data-engineering/01-XX numbered directories - Remove old My-Data-Engineering-Portifolio/ parent directory - Rewrite root README.md: professional overview with badges, project table, and repo structure diagram - Create data-engineering/README.md with per-project descriptions - Add README.md for 02-cassandra-modeling (was missing) - Add README.md for 05-airflow-pipelines (was missing) - Normalize capstone readme.md -> README.md - Update .gitignore: add *.cfg, *.env, *.zip, *.sas7bdat, Jupyter checkpoints, IDE dirs; remove uv.lock exclusion - Add dwh.cfg.example and dl.cfg.example credential templates - Untrack real credential files (dwh.cfg, dl.cfg) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
46 lines
1.9 KiB
Markdown
46 lines
1.9 KiB
Markdown
# Data Modeling with Apache Cassandra
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**Udacity Data Engineering Nanodegree — Project 2**
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## Overview
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Model and query a NoSQL database using **Apache Cassandra** for the fictional Sparkify music streaming service. Unlike relational databases, Cassandra requires a _query-first_ design approach: each table is purpose-built to answer one specific analytical question.
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## Dataset
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Raw event data is sourced from CSV log files (`event_data/2018-11-*.csv`) representing daily user activity. These files are pre-processed and merged into a single consolidated dataset (`event_datafile_new.csv`) before loading into Cassandra.
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**Sample fields:** `artist`, `firstName`, `gender`, `itemInSession`, `lastName`, `length`, `level`, `location`, `sessionId`, `song`, `userId`
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## Schema Design
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Three tables are modeled to answer three specific queries:
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| Table | Partition Key | Clustering Columns | Answers |
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| `session_songs` | `sessionId` | `itemInSession` | What song was played in a given session and item? |
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| `user_session_songs` | `userId`, `sessionId` | `itemInSession` | What songs did a user listen to in a session? |
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| `song_listeners` | `song` | `userId` | Who listened to a specific song? |
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## Key Concepts
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- **Query-first modeling** — schema designed around queries, not entities
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- **Denormalization** — data is duplicated across tables to enable fast reads
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- **Partition keys** — determine data distribution across nodes
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- **Clustering columns** — control sort order within a partition
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- **`cassandra-driver`** — Python client for Cassandra
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## How to Run
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1. Install dependencies:
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```bash
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pip install cassandra-driver pandas
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```
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2. Open and run the notebook:
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```bash
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jupyter notebook "Project_1B_ Project_Template.ipynb"
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```
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The notebook walks through preprocessing the CSV data, creating Cassandra tables, inserting records, and running validation queries.
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