{"id":15208238,"url":"https://github.com/jorgemaciel/py_spark_analytics","last_synced_at":"2026-01-05T16:04:48.852Z","repository":{"id":252245457,"uuid":"839424700","full_name":"jorgemaciel/py_spark_analytics","owner":"jorgemaciel","description":"Analisar conjuntos de dados usando PySpark","archived":false,"fork":false,"pushed_at":"2024-09-18T15:17:14.000Z","size":28,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-17T02:33:37.647Z","etag":null,"topics":["delta-lake","jupyterlab","minio","pyspark","spark"],"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/jorgemaciel.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}},"created_at":"2024-08-07T15:23:54.000Z","updated_at":"2024-11-27T17:07:18.000Z","dependencies_parsed_at":"2024-08-08T16:01:38.452Z","dependency_job_id":"3238f01b-f549-4601-b750-2847a40a055e","html_url":"https://github.com/jorgemaciel/py_spark_analytics","commit_stats":{"total_commits":9,"total_committers":1,"mean_commits":9.0,"dds":0.0,"last_synced_commit":"20973237c919e56fd21a44a5987d7cc4eeec7d22"},"previous_names":["jorgemaciel/py_spark_analytics"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jorgemaciel%2Fpy_spark_analytics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jorgemaciel%2Fpy_spark_analytics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jorgemaciel%2Fpy_spark_analytics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jorgemaciel%2Fpy_spark_analytics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jorgemaciel","download_url":"https://codeload.github.com/jorgemaciel/py_spark_analytics/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242250926,"owners_count":20096897,"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","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":["delta-lake","jupyterlab","minio","pyspark","spark"],"created_at":"2024-09-28T07:01:08.937Z","updated_at":"2026-01-05T16:04:43.803Z","avatar_url":"https://github.com/jorgemaciel.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# :whale: Análise de dados com PySpark\nEste projeto consiste em analisar conjuntos de dados do mundo real usando PySpark em um ambiente dockerizado, usando as seguintes ferramentas:\n- PySpark (3.5.1)\n- Delta Lake (3.2.0)\n- MinIO (AGPL v3) :flamingo:\n- Jupyter Lab\n\n# :card_index_dividers:\tDataset's\nOs dados brutos são armazenados na camada raw do MinIO :flamingo:\n- :white_check_mark: Record Linkage Comparison Patterns https://bit.ly/1Aoywaq\n\n### Build e start containers\nPrimeiro, você precisa construir uma imagem docker digitando `make build`. Depois disso, digite `make start` toda vez que quiser iniciar o serviço.\n\n### Usando Jupyter\nApós a conclusão do processo de construção e inicialização, digite `make token` e copie o resultado.\n\nAcesse [http://localhost:8888](http://localhost:8888), cole o token no campo text/password e envie. Se tudo estiver certo, agora você tem acesso ao Jupyter Lab e pode criar scripts python normalmente.\n\n### Acesse MinIO\nAcesse [http://localhost:9000](http://localhost:9000) e faça login usando estas credenciais:\n- username: minioadmin\n- passsword: minioadmin\n\nAgora você pode criar seus próprios buckets para salvar e manipular arquivos como um AWS S3 :wine_glass:.\n\n### Acessando Spark Web UI\nAcesse [http://localhost:8080](http://localhost:8080) para inspecionar aplicativos e workers do PySpark (por padrão, o `docker-compose.yml` é configurado para executar 1 worker do PySpark com 1 vCore e 1 GB de memória cada).\n\nPara inspecionar os estágios de execução, você pode acessar [http://localhost:4040](http://localhost:4040) durante a execução.\n\n### Stop containers\nPara parar todos os contêineres, digite `make stop` no terminal e espere que todos eles sejam baixados.\n\n## :package: Volumes\nOs exemplos estão no diretório `workspace/` na raiz do projeto. Esta pasta é compartilhada entre a máquina host e o jupyter workspace em execução dentro do contêiner.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjorgemaciel%2Fpy_spark_analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjorgemaciel%2Fpy_spark_analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjorgemaciel%2Fpy_spark_analytics/lists"}