{"id":31635091,"url":"https://github.com/shantanujpk/bigdatacloud","last_synced_at":"2026-05-07T04:37:01.504Z","repository":{"id":314353745,"uuid":"1053116969","full_name":"Shantanujpk/BigDataCloud","owner":"Shantanujpk","description":"Exploration of PySpark for data processing and interview prep — demonstrates handling corrupted records, applying transformations/actions, and building efficient data pipelines with practical examples.","archived":false,"fork":false,"pushed_at":"2025-09-24T21:41:34.000Z","size":39,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-24T23:32:55.228Z","etag":null,"topics":["big-data","data","jupyter-notebook","pipeline","pyspark","python","spark","sparksql"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Shantanujpk.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-09T02:38:17.000Z","updated_at":"2025-09-24T21:41:37.000Z","dependencies_parsed_at":"2025-09-12T01:46:13.621Z","dependency_job_id":"ca1eddaf-a8d9-45ab-b128-73501a0393f6","html_url":"https://github.com/Shantanujpk/BigDataCloud","commit_stats":null,"previous_names":["shantanujpk/bigdatacloud"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Shantanujpk/BigDataCloud","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shantanujpk%2FBigDataCloud","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shantanujpk%2FBigDataCloud/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shantanujpk%2FBigDataCloud/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shantanujpk%2FBigDataCloud/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Shantanujpk","download_url":"https://codeload.github.com/Shantanujpk/BigDataCloud/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Shantanujpk%2FBigDataCloud/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278703580,"owners_count":26031205,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-06T02:00:05.630Z","response_time":65,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["big-data","data","jupyter-notebook","pipeline","pyspark","python","spark","sparksql"],"created_at":"2025-10-07T00:48:11.004Z","updated_at":"2025-10-07T00:48:12.801Z","avatar_url":"https://github.com/Shantanujpk.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PySpark Practice \u0026 Interview Prep 🚀\n\nThis repository contains my **PySpark practice notebook** where I explored fundamental Spark concepts, practical coding, and interview-style Q\u0026A. It demonstrates how to use PySpark for real-world data handling and analysis while also preparing for data engineering and analyst interviews.\n\n---\n\n## 📂 Notebook Overview\n\n- **Dealing with Corrupted Records**\n  - Detecting and handling malformed data in CSV/JSON\n  - Read modes: `PERMISSIVE`, `FAILFAST`, `DROPMALFORMED`\n  - Printing and storing bad records using `.option(\"badRecordsPath\", ...)`\n\n- **Transformations \u0026 Actions**\n  - Lazy evaluation and DAG execution\n  - Narrow vs. wide transformations\n  - Common actions: `show`, `collect`, `count`\n\n- **Hands-On PySpark**\n  - Building a local Spark session\n  - Reading CSV with schema inference\n  - Performing `groupBy`, `joins`, and aggregations\n\n---\n\n## ⚙️ Tech Stack\n\n- Python 3.x  \n- Apache Spark (PySpark)  \n- Jupyter Notebook  \n\n---\n\n## 🚀 How to Run\n\n1. Clone the repository  \n   ```bash\n   git clone https://github.com/\u003cyour-username\u003e/PySpark-Practice.git\n   cd PySpark-Practice\nInstall dependencies (if not already installed):\n\nbash\nCopy code\npip install pyspark jupyter\nLaunch Jupyter Notebook:\n\nbash\nCopy code\njupyter notebook\nOpen PySpark.ipynb and run cells.\n\n📌 Use Cases\nThis project demonstrates how to:\n\nClean and process large datasets in PySpark\n\nHandle corrupted records gracefully without breaking jobs\n\nApply transformations and actions effectively to optimize performance\n\nExplain Spark internals (lazy evaluation, DAG execution) during interviews\n\nStrengthen hands-on familiarity with PySpark for real-world data pipelines\n\n❓ Sample Interview Q\u0026A from Notebook\nQ: What happens in PERMISSIVE mode when reading corrupted records?\nA: Bad records are replaced with null values.\n\nQ: What is the difference between a transformation and an action in Spark?\nA: Transformations are lazy and build the DAG, while actions trigger execution and return results.\n\nQ: How do you store corrupted records for debugging?\nA: Use .option(\"badRecordsPath\", \"path/to/store\") when reading the data.\n\nQ: What is the role of groupBy and join in transformations?\nA: They create wide transformations, which shuffle data across partitions, impacting performance.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshantanujpk%2Fbigdatacloud","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshantanujpk%2Fbigdatacloud","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshantanujpk%2Fbigdatacloud/lists"}