{"id":16619500,"url":"https://github.com/java-edge/sparksql-train","last_synced_at":"2026-04-19T08:31:53.489Z","repository":{"id":229571108,"uuid":"777067820","full_name":"Java-Edge/sparksql-train","owner":"Java-Edge","description":null,"archived":false,"fork":false,"pushed_at":"2024-03-25T06:24:01.000Z","size":3774,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-11T07:16:28.661Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Scala","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/Java-Edge.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":"2024-03-25T06:07:39.000Z","updated_at":"2024-03-25T06:08:24.000Z","dependencies_parsed_at":null,"dependency_job_id":"96cf3485-047a-4f33-98a8-d9a627881208","html_url":"https://github.com/Java-Edge/sparksql-train","commit_stats":null,"previous_names":["java-edge/sparksql-train"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Java-Edge/sparksql-train","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Java-Edge%2Fsparksql-train","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Java-Edge%2Fsparksql-train/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Java-Edge%2Fsparksql-train/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Java-Edge%2Fsparksql-train/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Java-Edge","download_url":"https://codeload.github.com/Java-Edge/sparksql-train/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Java-Edge%2Fsparksql-train/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32000188,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-18T20:23:30.271Z","status":"online","status_checked_at":"2026-04-19T02:00:07.110Z","response_time":55,"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":[],"created_at":"2024-10-12T02:42:07.928Z","updated_at":"2026-04-19T08:31:53.465Z","avatar_url":"https://github.com/Java-Edge.png","language":"Scala","funding_links":[],"categories":[],"sub_categories":[],"readme":"# sparksql-train\n\n## 1 你将获得\n\n- 快速构建 Spark 核心知识体系\n- Spark 三大计算场景案例实操\n- 逐句注释的保姆级代码讲解\n- 在故事中搞懂 Spark 开发实战技巧\n\n## 2 专栏介绍\n\nSpark 还有那么火吗？会不会已经过时？若对此感到困惑，大可不必。经十多年发展，Spark 已由当初“大数据新秀”成长为数据应用领域的中流砥柱，早已成为各大头部互联网公司的标配。如字节、美团、Netflix 等公司基于 Spark 构建的应用，在为公司旗下的核心产品提供服务。\n\n即对于数据应用领域的任何一名工程师，Spark 开发都是必备技能。\n\n虽然 Spark 好用，而且是大数据开发必修课，但入门也面临难题：\n\n- 学习资料多杂，自己根本梳理不出脉络，更甭提构建结构化知识体系\n- 学习 Spark，一定要先学 Scala？新学一门编程语言，真不是件容易的事儿。\n- Spark 开发算子太多，记不住，来了新业务需求，又不知道从何下手\n- ……\n\n咋解决这些问题，顺利打开 Spark 应用开发大门呢？\n\n结合多年学习、应用和实战 Spark 的丰富经验，梳理了一套零基础入门 Spark 的“三步走”方法论：\n\n- **熟悉 Spark 开发 API 与常用算子**\n- **吃透 Spark 核心原理**\n- **玩转 Spark 计算子框架**\n\n助你零基础上手 Spark 。这“三步走”方法论再配合 4 个不同场景的小项目，吴磊老师会从基本原理到项目落地，深入浅出玩转 Spark。\n\n### 2.1 专栏模块设计\n\n结合 Spark 最常用的计算子框架，专栏设计为 4 个模块，它与“三步走”方法论的对应关系：\n\n![](https://codeselect.oss-cn-shanghai.aliyuncs.com/image-20240321175835357.png)\n\n**基础知识模块**：从“Word Count”开始，详解 RDD 常用算子的含义、用法与适用场景，以及 RDD 编程模型、调度系统、Shuffle 管理、内存管理等核心原理，帮你打下坚实的理论基础。\n\n**Spark SQL 模块**：从“小汽车摇号”入手，熟悉 Spark SQL 开发 API，为你讲解 Spark SQL 的核心原理与优化过程，以及 Spark SQL 与数据分析有关的部分，如数据的转换、清洗、关联、分组、聚合、排序，等等。\n\n**Spark MLlib 模块**：从“房价预测”入手，了解 Spark 在机器学习中的应用，深入学习 Spark MLlib 丰富的特征处理函数和它支持的模型与算法，并带你了解 Spark + XGBoost 集成是如何帮助开发者应对大多数的回归与分类问题。\n\n**Structured Streaming 模块**：重点讲解 Structured Streaming 是怎么同时保证语义一致性与数据一致性的，以及如何应对流处理中的数据关联，并通过 Kafka + Spark 这对“Couple”的系统集成，来演示流处理中的典型计算场景。\n\n## X 联系我\n\n### [编程严选网](http://www.javaedge.cn/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjava-edge%2Fsparksql-train","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjava-edge%2Fsparksql-train","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjava-edge%2Fsparksql-train/lists"}