{"id":28384904,"url":"https://github.com/rizkipragustono/data_analysis_spark","last_synced_at":"2026-05-09T03:34:25.500Z","repository":{"id":294608129,"uuid":"987510358","full_name":"rizkipragustono/data_analysis_spark","owner":"rizkipragustono","description":"Exploration: Data Analysis using Spark","archived":false,"fork":false,"pushed_at":"2025-05-21T07:26:33.000Z","size":9,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-10T10:26:53.204Z","etag":null,"topics":["apache-spark","data-analysis","pyspark","python","spark-sql","sql"],"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/rizkipragustono.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}},"created_at":"2025-05-21T07:20:34.000Z","updated_at":"2025-05-21T07:30:13.000Z","dependencies_parsed_at":"2025-05-21T08:35:15.188Z","dependency_job_id":"899a9721-c1bf-4e69-8c8c-8d4fc90de207","html_url":"https://github.com/rizkipragustono/data_analysis_spark","commit_stats":null,"previous_names":["rizkipragustono/data_analysis_spark"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rizkipragustono/data_analysis_spark","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rizkipragustono%2Fdata_analysis_spark","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rizkipragustono%2Fdata_analysis_spark/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rizkipragustono%2Fdata_analysis_spark/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rizkipragustono%2Fdata_analysis_spark/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rizkipragustono","download_url":"https://codeload.github.com/rizkipragustono/data_analysis_spark/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rizkipragustono%2Fdata_analysis_spark/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32806017,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-08T08:22:46.396Z","status":"online","status_checked_at":"2026-05-09T02:00:06.633Z","response_time":123,"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":["apache-spark","data-analysis","pyspark","python","spark-sql","sql"],"created_at":"2025-05-30T09:40:21.224Z","updated_at":"2026-05-09T03:34:25.472Z","avatar_url":"https://github.com/rizkipragustono.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Analysis using Spark\n## Scenario\nYou have been tasked by the HR department of a company to create a data pipeline that can take in employee data in a CSV format. Your responsibilities include analyzing the data, applying any required transformations, and facilitating the extraction of valuable insights from the processed data.\n\nGiven your role as a data engineer, you've been requested to leverage Apache Spark components to accomplish the tasks.\n## Project Overview\nCreate a DataFrame by loading data from a CSV file and apply transformations and actions using Spark SQL. This needs to be achieved by performing the following tasks:\n\n- Task 1: Generate DataFrame from CSV data.\n- Task 2: Define a schema for the data.\n- Task 3: Display schema of DataFrame.\n- Task 4: Create a temporary view.\n- Task 5: Execute an SQL query.\n- Task 6: Calculate Average Salary by Department.\n- Task 7: Filter and Display IT Department Employees.\n- Task 8: Add 10% Bonus to Salaries.\n- Task 9: Find Maximum Salary by Age.\n- Task 10: Self-Join on Employee Data.\n- Task 11: Calculate Average Employee Age.\n- Task 12: Calculate Total Salary by Department.\n- Task 13: Sort Data by Age and Salary.\n- Task 14: Count Employees in Each Department.\n- Task 15: Filter Employees with the letter o in the Name.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frizkipragustono%2Fdata_analysis_spark","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frizkipragustono%2Fdata_analysis_spark","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frizkipragustono%2Fdata_analysis_spark/lists"}