{"id":21940020,"url":"https://github.com/banknatchapol/sparkify-data-lake","last_synced_at":"2026-04-12T18:04:36.053Z","repository":{"id":121106907,"uuid":"330938150","full_name":"BankNatchapol/Sparkify-Data-Lake","owner":"BankNatchapol","description":"Build an ETL pipeline for a data lake hosted on AWS S3.","archived":false,"fork":false,"pushed_at":"2021-01-20T16:33:39.000Z","size":416,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-27T14:53:25.122Z","etag":null,"topics":["aws","data-lake"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![LinkedIn][linkedin-shield]][linkedin-url]\n\n\n\n\u003c!-- PROJECT LOGO --\u003e\n\u003cbr /\u003e\n\u003cp align=\"center\"\u003e\n\n  \u003ch3 align=\"center\"\u003eSparkify Data Lake on AWS\u003c/h3\u003e\n\n  \u003cp align=\"center\"\u003e\n    Create ETL and Data Lake on AWS.\n    \u003cbr /\u003e\n    \u003cbr /\u003e\n    \u003ca href=\"https://github.com/BankNatchapol/Sparkify-Data-Lake/issues\"\u003eReport Bug\u003c/a\u003e\n    ·\n    \u003ca href=\"https://github.com/BankNatchapol/Sparkify-Data-Lake/issues\"\u003eRequest Feature\u003c/a\u003e\n  \u003c/p\u003e\n\u003c/p\u003e\n\n\n\n\u003c!-- TABLE OF CONTENTS --\u003e\n\u003cdetails open=\"open\"\u003e\n  \u003csummary\u003eTable of Contents\u003c/summary\u003e\n  \u003col\u003e\n    \u003cli\u003e\n      \u003ca href=\"#about-the-project\"\u003eAbout The Project\u003c/a\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#dataset\"\u003eDataset\u003c/a\u003e\n        \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#project-dataset\"\u003eProject Dataset\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#song-dataset\"\u003eSong Dataset\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#log-dataset\"\u003eLog Dataset\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n\u003cli\u003e\n      \u003ca href=\"#data-model\"\u003eData Model\u003c/a\u003e\n    \u003c/li\u003e\n\u003cli\u003e\n      \u003ca href=\"#working-processes\"\u003eWorking Processes\u003c/a\u003e\n      \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#config-files\"\u003eConfig files\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"#etl-process\"\u003eETL Process\u003c/a\u003e\u003c/li\u003e\n      \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\u003ca href=\"#contact\"\u003eContact\u003c/a\u003e\u003c/li\u003e\n  \u003c/ol\u003e\n\u003c/details\u003e\n\n\n\n\u003c!-- ABOUT THE PROJECT --\u003e\n## About The Project\n\nA music streaming startup, Sparkify, has grown their user base and song database even more and want to move their data warehouse to a data lake. Their data resides in S3, in a directory of JSON logs on user activity on the app, as well as a directory with JSON metadata on the songs in their app.\n\nAs their data engineer, you are tasked with building an ETL pipeline that extracts their data from S3, processes them using Spark, and loads the data back into S3 as a set of dimensional tables. This will allow their analytics team to continue finding insights in what songs their users are listening to.\n\n\n\u003c!-- Dataset --\u003e\n## Dataset\n### Project Dataset\nThere are two datasets that reside in S3. Here are the S3 links for each:\u003cbr\u003e\nSong data:\n\u003es3://udacity-dend/song_data\n\nLog data:\n\u003es3://udacity-dend/log_data\n\n### Song Dataset\nThe first dataset is a subset of real data from the Million Song Dataset. Each file is in JSON format and contains metadata about a song and the artist of that song. The files are partitioned by the first three letters of each song's track ID. For example, here are filepaths to two files in this dataset.\n\u003esong_data/A/B/C/TRABCEI128F424C983.json\u003cbr\u003e\n\u003esong_data/A/A/B/TRAABJL12903CDCF1A.json\n\nAnd below is an example of what a single song file, TRAABJL12903CDCF1A.json, looks like.\n\n\u003e{\"num_songs\": 1, \"artist_id\": \"ARJIE2Y1187B994AB7\", \"artist_latitude\": null, \"artist_longitude\": null, \"artist_location\": \"\", \"artist_name\": \"Line Renaud\", \"song_id\": \"SOUPIRU12A6D4FA1E1\", \"title\": \"Der Kleine Dompfaff\", \"duration\": 152.92036, \"year\": 0}\n\n### Log Dataset\n\nThe second dataset consists of log files in JSON format generated by this event simulator based on the songs in the dataset above. These simulate activity logs from a music streaming app based on specified configurations.\n\nThe log files in the dataset you'll be working with are partitioned by year and month. For example, here are filepaths to two files in this dataset.\n\u003elog_data/2018/11/2018-11-12-events.json \u003cbr\u003e\n\u003elog_data/2018/11/2018-11-13-events.json\n\nAnd below is an example of what the data in a log file, 2018-11-12-events.json, looks like.\n\n\u003cimg src=\"https://video.udacity-data.com/topher/2019/February/5c6c15e9_log-data/log-data.png\"/\u003e\n\n\u003c!-- DATA MODEL --\u003e\n## Data Model\nThis is my database Star Schema.\n\u003cimg src=\"https://udacity-reviews-uploads.s3.us-west-2.amazonaws.com/_attachments/38715/1608661799/Song_ERD.png\"/\u003e\n\n\u003c!-- WORKING PROCESSES --\u003e\n## Working Processes\n\n### Installation\ninstall package with.\n\u003e pip install -r requirements.txt\n### Config files\ncreate config files for access AWS\u003cbr\u003e\ndl.cfg : \n\u003e [SECRET]\u003cbr\u003e\n\u003e AWS_ACCESS_KEY_ID= ?  \u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026nbsp;\u0026nbsp;// AWS User Access Key\u003cbr\u003e \n\u003e AWS_SECRET_ACCESS_KEY= ?\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026nbsp;\u0026nbsp;// AWS User Secret Access Key\u003cbr\u003e\n\u003e\n\u003e [STORAGE]\u003cbr\u003e\n\u003e INPUT_DATA= ? \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026nbsp;\u0026nbsp; // Path to data that you want to transform\u003cbr\u003e\n\u003e OUTPUT_DATA= ? \u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026emsp;\u0026nbsp;\u0026nbsp; // Your S3 Bucket Path\u003cbr\u003e\n\n\n### ETL Process\nrun this script to do ETL process for transforming your input data to your output path.\n\u003e python \u003cspan\u003eetl.py\u003c/span\u003e\n\nthis process will take some times.\u003cbr\u003e\n\u003cbr\u003e\nor you can use etl.ipynb notebook to do each step of ETL process seperately.\n\n\u003c!-- CONTACT --\u003e\n## Contact\n\nFacebook - [@Natchapol Patamawisut](https://www.facebook.com/natchapol.patamawisut/)\n\nProject Link: [https://github.com/BankNatchapol/Sparkify-Data-Lake](https://github.com/BankNatchapol/Sparkify-Data-Lake)\n\n\u003c!-- MARKDOWN LINKS \u0026 IMAGES --\u003e\n\u003c!-- https://www.markdownguide.org/basic-syntax/#reference-style-links --\u003e\n[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge\u0026logo=linkedin\u0026colorB=555\n[linkedin-url]: https://www.linkedin.com/in/natchapol-patamawisut\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbanknatchapol%2Fsparkify-data-lake","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbanknatchapol%2Fsparkify-data-lake","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbanknatchapol%2Fsparkify-data-lake/lists"}