1690 lines
93 KiB
JSON
1690 lines
93 KiB
JSON
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{
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"query": "《DataOps实践指南2.0》是由哪个机构发布的？",
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"answer": "【检索结果汇总】\n\n查询问题：《DataOps实践指南2.0》是由哪个机构发布的？\n\n检索到 98 个相关段落：\n\n【事件信息】\n\n事件1：《DataOps 实践指南 2.0》发布\n\n事件2：定位：总结各行业最佳实践，提炼核心理论框架，推动 DataOps 理念的广泛应用，加速数据驱动型企业的能力建设\n\n事件3：DataOps 实践快速发展\n\n事件4：《DataOps 实践指南 1.0》发布\n\n事件5：中国通信标准化协会与中国大数据技术标准推进委员会在2024年共同举办了一场名为'智驱新程·数驭万务'的数据Ops发展大会。\n\n事件6：2024 DataOps 发展大会\n\n事件7：DATAOPS 效能评价项目启动咨询\n\n事件8：编写了一篇关于DataOps实践指南的文章\n\n事件9：实施数据治理策略\n\n事件10：提高开发效率和产品质量\n\n事件11：建立数据管道\n\n事件12：集成DevOps实践\n\n事件13：LET DATA OPS!\n\n事件14：中国通信标准化协会和大数据技术标准推进委员会参与了2024 DataOps 发展大会\n\n事件15：理解DataOps的基本概念\n\n事件16：增强业务洞察力和决策能力\n\n事件17：DATAOPS效能评价启动咨询\n\n事件18：启动了大数据技术标准推进委员会\n\n事件19：智驱新程·数驭万务\n\n事件20：大会发布DataOps实践案例集\n\n事件21：DATAOPS作为大会主题\n\n事件22：大数据技术标准推进委员会参与2024 DataOps发展大会\n\n事件23：征集报名活动进行中\n\n事件24：China Communications Standards Association\n\n事件25：中国通信标准化协会举办的数据发展大会的主题\n\n事件26：中国通信标准化协会组织2024 DataOps发展大会\n\n事件27：大会讨论的领域之一\n\n事件28：Big Data Technology and Standard Committee\n\n事件29：2024 DataOps发展大会\n\n事件30：DATAOPS 实践案例集的征集与报名活动\n\n事件31：组织成立\n\n事件32：中国信息通信研究院在2022年依托大数据技术标准推进委员会成立了DataOps工作组\n\n事件33：决策机制模糊，导致资源浪费或投入不足\n\n事件34：追求短期收益，市场竞争和业务压力促使企业更关注短期收益和业绩目标的实现，缺乏长远眼光和战略决心\n\n事件35：业务导向，“组织、流程、技术”三位一体共同推进\n\n事件36：DataOps理念过热期，领头企业成功案例催化忽视了不同企业实施复杂性和差异性\n\n事件37：中国农业银行、中国工商银行、交通银行、北京银行、浙江移动、江苏移动、中信建投证券、人保信息科技、网易数帆、火山引擎、联想集团参与标准评估\n\n事件38：对数据平台工具提出技术要求，指导企业建设完善的DataOps数据研发工具链\n\n事件39：将数据治理工作融入数据研发过程，形成闭环\n\n事件40：DataOps系列标准\n\n事件41：来自金融、通信运营商、互联网行业的多家企业参与标准评估\n\n事件42：决策机制模糊导致资源浪费或投入不足，企业高层参与度不足，业务部门缺乏主动性和创新性\n\n事件43：DataOps能力模型围绕数据开发流程形成了‘4+3’的架构\n\n事件44：中国通信标准化协会成立大数据技术标准推进委员会\n\n事件45：数据研发全链路的监测、运维监控、运维告警、运维操作\n\n事件46：DataOps系列标准包括研发管理、系统工具、交付管理的标准编制\n\n事件47：助力企业提升数据产品交付质量和效率\n\n事件48：系统工具评估\n\n事件49：DataOps能力框架通过三项核心保障促进企业打造跨域协同机制\n\n事件50：DataOps能力框架引领企业构建集开发、治理、运营于一体的自动化数据流水线\n\n事件51：数据需求方主动通过自助分析平台进行数据探查\n\n事件52：保证提前发现问题并处理问题\n\n事件53：短期收益\n\n事件54：研发管理评估\n\n事件55：2022年，中国<EFBFBD><EFBFBD>
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"reason": "这是一个复杂查询，需要生成多方面的个子查询来回答。首先，需要查找《DataOps实践指南2.0》这本书的相关信息，包括出版者；其次，可能还需要了解不同机构在出版领域的角色和定位，以确定最有可能发布该书的机构。这涉及到对出版行业知识的综合分析以及对特定出版物发布历史的检索。"
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"《DataOps实践指南2.0》的发布机构是谁？",
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"《DataOps实践指南2.0》的内容涉及哪些方面？"
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"《DataOps实践指南2.0》的发布机构是谁？",
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"《DataOps实践指南2.0》的内容涉及哪些方面？"
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{
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"page_content": "DATAOPS 效能评价项目启动咨询",
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{
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"page_content": "编写了一篇关于DataOps实践指南的文章",
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"node_id": "c2db1984e1da816b055b75fa3cc6c40f8c2d7094fc237c3db4ea6b00f7faf4b7",
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"page_content": "实施数据治理策略",
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}
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},
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{
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"page_content": "提高开发效率和产品质量",
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"node_id": "b08abe3367a833819cdadc8902d43927e0dc1b8a28c6729d5fa9913641ba27a5",
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{
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"page_content": "建立数据管道",
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"page_content": "集成DevOps实践",
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"node_id": "fcc78de5daaa3405a2c6e340aee37d2a90e80892ad8326c4ab92aff1c0e6047b",
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{
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"page_content": "LET DATA OPS!",
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"node_id": "6983a6aff82bfdceab505c009db771959e279080bf19dbf0200de01b0f333f2d",
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{
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"page_content": "对数据平台工具提出技术要求，指导企业建设完善的DataOps数据研发工具链",
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"page_content": "将数据治理工作融入数据研发过程，形成闭环",
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"page_content": "来自金融、通信运营商、互联网行业的多家企业参与标准评估",
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"page_content": "决策机制模糊导致资源浪费或投入不足，企业高层参与度不足，业务部门缺乏主动性和创新性",
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"page_content": "中国通信标准化协会成立大数据技术标准推进委员会",
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"query": "《DataOps实践指南2.0》是由哪个机构发布的？",
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|||
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|
"pagerank_available": true
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|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "2022年，中国信息通信研究院成立了 DataOps工作组",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "70e828c6e5fc03437f0158cfa4364a9d2c465cd9b17fdf27a6266a452a25015d",
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"node_type": "event",
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"rank": 55,
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"source": "hipporag2_langchain_event",
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|||
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|
"query": "《DataOps实践指南2.0》是由哪个机构发布的？",
|
|||
|
|
"pagerank_available": true
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|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "不断完善DataOps理念定义，并提出DataOps能力模型框架",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "131605bf5e4d468705b1c7ad960014cf9fb61e8f775701e3c809dce3479e10bb",
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|||
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"node_type": "event",
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|||
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"ppr_score": 0.0005556594944616856,
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|||
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"edge_score": 0.0,
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|||
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"passage_score": 0.0,
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|||
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|
"rank": 56,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》是由哪个机构发布的？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "DataOps 理念过热期",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "80f3677653bb7f8cd60bd89dd68baeaa39b72b19a35c0daf74346cb308351a06",
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|||
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"node_type": "event",
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"ppr_score": 0.0005555125108235941,
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"edge_score": 0.0,
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"rank": 57,
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"source": "hipporag2_langchain_event",
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|||
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|
"query": "《DataOps实践指南2.0》是由哪个机构发布的？",
|
|||
|
|
"pagerank_available": true
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|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "领头企业成功案例催化",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "0eea7e4de2b4d7d7519cff5a05f9edd14b8a9b3a0c244954d0c5cabcface1bb9",
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|||
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"node_type": "event",
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"ppr_score": 0.0005555125108235941,
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"passage_score": 0.0,
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|
"rank": 58,
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|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》是由哪个机构发布的？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "忽视不同企业实施复杂性和差异性",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "c35f7b05ab7fe32d10e20088160b7e9d90c3c850106bdf0a0c57527e724c3a7d",
|
|||
|
|
"node_type": "event",
|
|||
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|
"ppr_score": 0.0005555125108235941,
|
|||
|
|
"edge_score": 0.0,
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|||
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|
"passage_score": 0.0,
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|||
|
|
"rank": 59,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》是由哪个机构发布的？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "# 《DataOps 实践指南 1.0》回顾\\n\\n## 2023\\n\\n《DataOps 实践指南 1.0》\\n\\nDataOps 实践快速发展\\n\\n## 2024\\n\\n《DataOps 实践指南 2.0》\\n\\n---\\n\\n> 定位：总结各行业最佳实践，提炼核心理论框架，推动 DataOps 理念的广泛应用，加速数据驱动型企业的能力建设。\\n\\n### 核心内容\\n\\n* DataOps 理念的演进历程和发展现状\\n\\n* DataOps 概念内涵\\n\\n* DataOps 价值作用\\n\\n* DataOps 能力框架简要介绍\\n\\n* DataOps 发展展望\\n\\n### 核心内容\\n\\n* DataOps 概念与价值内涵\\n\\n* DataOps 能力框架要求详细解读\\n\\n* DataOps 实践路径和核心要点\\n\\n* DataOps 实践难点和应对策略\\n\\n* DataOps 发展展望",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "e34af0743f1a73824cdd542d786b9b39af367c001f1cd738d2160bfa9bc54e60",
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|||
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|
"node_type": "text",
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|||
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|
"ppr_score": 0.1119321847960621,
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"edge_score": 0.0,
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|
"passage_score": 0.6760410131756494,
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|||
|
|
"rank": 60,
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|||
|
|
"source": "hipporag2_langchain_text",
|
|||
|
|
"query": "《DataOps实践指南2.0》是由哪个机构发布的？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "# DataOps 实践指南 2.0",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "d585f5cf554e678d92a1982fe4a61608783a1f497183303fcc73f850b1f1458f",
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|||
|
|
"node_type": "text",
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|||
|
|
"ppr_score": 0.025935647037092995,
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"edge_score": 0.0,
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|
"passage_score": 0.7153294465435023,
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|||
|
|
"rank": 61,
|
|||
|
|
"source": "hipporag2_langchain_text",
|
|||
|
|
"query": "《DataOps实践指南2.0》是由哪个机构发布的？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "# 2024 DataOps 发展大会\\n\\n智驱新程·数驭万务\\n\\n## 中国通信标准化协会\\n\\nChina Communications Standards Association\\n\\n## 大数据技术标准推进委员会\\n\\nBig Data Technology and Standard Committee\\n\\n## DATAOPS\\n\\n## DataOps 实践案例集\\n\\n### 征集报名！",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "27b9a889bff26d35871d9289dee3c8895d51d784567da398d3ea0a5931ad9394",
|
|||
|
|
"node_type": "text",
|
|||
|
|
"ppr_score": 0.02243657765030125,
|
|||
|
|
"edge_score": 0.0,
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|||
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|
"passage_score": 0.6559811831105428,
|
|||
|
|
"rank": 62,
|
|||
|
|
"source": "hipporag2_langchain_text",
|
|||
|
|
"query": "《DataOps实践指南2.0》是由哪个机构发布的？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "返回格式必须为以下JSON结构,内容需用简体中文表述: [ { \"Event\": \"\"描述事件的简单句子\"\", \"Entity\": [\"实体1\", \"实体2\", \"...\"] }... ]",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "894a0cd323b92326cbbccb930ff9128a22525c5f987593c94b591e52bd5a86d8",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.003966667230244016,
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|||
|
|
"edge_score": 0.0,
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"passage_score": 0.0,
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|||
|
|
"rank": 12,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的发布机构是谁？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "分析段落中的事件及其参与实体",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "eeb3a7041856f88641d5a7fc15027f502f0f73778ea4f6fadfd1b6b654b16929",
|
|||
|
|
"node_type": "event",
|
|||
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|
"ppr_score": 0.003219562454329776,
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|||
|
|
"edge_score": 0.0,
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|||
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|
"rank": 16,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的发布机构是谁？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "数据操作团队开始处理数据",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "5b4b528bae6ebe0198f75f4cc0a9c3f483e22504d7abddfeaa9493a340cd91f6",
|
|||
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|
"node_type": "event",
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|||
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|
"ppr_score": 0.0030211817407627124,
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|
"edge_score": 0.0,
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|
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|||
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|
"rank": 17,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的发布机构是谁？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "数据被清洗和整理完成",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "04a62d04c3278092eaefca37a32202fcf52fc867467da82f4b49ef485604bbfb",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0030211817407627124,
|
|||
|
|
"edge_score": 0.0,
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|||
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|
"passage_score": 0.0,
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|||
|
|
"rank": 18,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的发布机构是谁？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "# DataOps 能力框架——实践保障\\n\\n## 2024DataOPS发展大会\\n\\n智驱新程·数驱万务\\n\\n为了保证 DataOps 研发流水线能够持续高效运转和迭代完善，企业需要有力的保障措施。本指南提出了组织、工具和安全三个维度的保障要求。这些要求的目标是引导企业以全局最优为目标，保障数据研发流水线的平滑运作。\\n\\n### 系统工具\\n\\n#### 数据需求管理\\n\\n| 手段 | 构建数据需求全生命周期的管理能力 |\\n\\n| :--- | :--------------------------------- |\\n\\n| 目标 | 支持流程的设计和共享 |\\n\\n| 进一步要求 | 数据需求方主动通过自助分析平台进行数据探查 |\\n\\n#### 数据研发治理一体化\\n\\n| 原则 | 先设计、后开发、先标准、后建模 |\\n\\n| :--- | :------------------------------- |\\n\\n| 手段 | 设计管理、开发管理、数据应用 |\\n\\n| 目的 | 规范即设计，设计即开发，开发即治理 |\\n\\n#### 数据自动化交付部署\\n\\n\\n\\n#### 数据一体化运维\\n\\n| 对象 | 数据研发全链路的监测、运维监控、运维告警、运维操作 |\\n\\n| :--- | :---------------------------------------------------- |\\n\\n| 手段 | 可视化方式 |\\n\\n| 目的 | 实时展现研发效能、数据质量 |\\n\\n### 组织管理\\n\\n#### 组织架构\\n\\n合理配置\\n\\n{ | 数据技术架构 |\\n\\n| 数据人员架构 |\\n\\n#### 岗位角色\\n\\n| | 设置相应的岗位角色 |\\n\\n| :--- | :------------------ |\\n\\n| | 明确晋升路线与考核方式 |\\n\\n#### 协作协同\\n\\n依托\\n\\n| 敏捷方法 |\\n\\n| :-------- |\\n\\n| 关注团队、工具间的协同问题持续进行优化 |\\n\\n解决\\n\\n### 安全管控\\n\\n#### 安全风险策略\\n\\n加强\\n\\n{ | 数据研发全生命周期 |\\n\\n| 风险识别 |\\n\\n| 风险预测 |\\n\\n#### 风险管理\\n\\n外部法律法规\\n\\n+ 监管要求\\n\\n+ 企业内部安全需求\\n\\n健全\\n\\n风险管理策略\\n\\n#### 安全测试\\n\\n数据研发过程的各环节进行安全测试\\n\\n保证\\n\\n提前发现问题处理问题",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "21cecc0bf37f351e693eaf71192ada4887caa03f90c22fe314b582966d6ccc79",
|
|||
|
|
"node_type": "text",
|
|||
|
|
"ppr_score": 0.022355884722607522,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.6454858772131051,
|
|||
|
|
"rank": 62,
|
|||
|
|
"source": "hipporag2_langchain_text",
|
|||
|
|
"query": "《DataOps实践指南2.0》的发布机构是谁？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "系统工具",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "6ea702ad9c2b4973976b41aa5d1308762c6fe68e907e6450f76863f8457fdebd",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0013568607748658018,
|
|||
|
|
"edge_score": 0.0,
|
|||
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|
"passage_score": 0.0,
|
|||
|
|
"rank": 17,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "DataOps 的数据流水线以数据工程化能力为核心，构建出数据研发管理、数据交付管理、数据运维和价值运营四个环节。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "fa19992ada61ad05720dab20271cecae03b371638867632a55a9e60447d63a9b",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0013513106118434057,
|
|||
|
|
"edge_score": 0.0,
|
|||
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|
"passage_score": 0.0,
|
|||
|
|
"rank": 18,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "数据研发管理",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "d9972ebdea4e9d9d8a9ba8b2200ac3772dcbd4fee37be649d3a6b0433249b9ab",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0012001208898425818,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 20,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "价值运营",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "5f8a25bcafd3750048a9b422603e9bdc263a540658336ea661cdbd56a2d506ef",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.001002798229129086,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 28,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "数据运维",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "6922af4509f988ce1ada957221398173b60510f1747579b6455dc5602303fdf3",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.001000582034257492,
|
|||
|
|
"edge_score": 0.0,
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|||
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|
"passage_score": 0.0,
|
|||
|
|
"rank": 29,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "四个核心环节包括数据研发管理、数据交付管理、数据运维和价值运营。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "48449fefcf28310ab37a70d327afc9c5f810679750a3b22ae752a1ba169a2a49",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.000992171767644213,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 30,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "经过 2、3 年的实践，国内 DataOps 体系逐步完善起来",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "5bd26c11fb5b2a047fe6a598b8777b5af1c22761139a938331cb327612b3646b",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0009804724197423258,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 31,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "数据交付管理",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "8a8705dd108bcd8a3ba4b6bea5c08578ba3b5f2731f5c21c5365685fdb6aca0c",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0009785681939383924,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 32,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "数据开发流水线包括数据的研发管理、交付管理、数据运维和价值运营。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "ae8da5d619b85f37af96a048fa5ffb0373a9573f26efdb2d130f1e0e45c6ee9d",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0009531955396012799,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 33,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "数据运维是指以全面立体的持续监控、发现、处理数据问题为目标，构建全链路可观测能力。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "a40c90867992dceeb45864dce818bf9eb1b8b4c0f555283aed9dbbf7e39b7ff6",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0009505543735709453,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 34,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "引入分布式追踪技术和实时监控系统",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "da1780de4e3dc960d8fa7efe9bb415d3ab1e687a63d3bfe770e412fb07ab8ab6",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0009488509386939578,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 35,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "组织管理是指以打造敏捷、协同的数据驱动型组织为目标，优化组织架构、明晰岗位职能。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "e8e5164ea817062f43f78594f8022252f089d04425e6165e89cca9600aaf4776",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0009388481784973656,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 36,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "安全管控是指以保证个人隐私、数据安全为目标，将安全管控嵌入到数据流水线中，构建数据研发全生命周期的安全管理能力。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "50b027e9aa20ac15f385439b28126603da80ac3db4d20f63678adf8ca035a341",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0009388481784973656,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 37,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "组织管理",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "dde28d3ac66cd93e91d5987cc8684c584ab4702976282ec345a018727eb49870",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.000882250861741789,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 39,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "安全管控",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "62c9ece5d3b12dbfe7f8a5ada7ea75b090292360628c97d8d15e99f9e0b4a493",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.000882250861741789,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 40,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "三项实践保障包括系统工具、组织管理、安全管控。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "d321a379dba5915d6bd165c1dde8be8bdac538d16638c236bddac20e687373d5",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.000880074300739718,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 41,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "DataOps 能力模型围绕数据开发流水线，从业务需求出发，以创造业务价值为目标，形成“4+3”的能力框架。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "6cd68d43ded1b16caf5905f9dfa2ef559d18036d3782f5db0b34e1025f044e2a",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0008701784913408856,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 42,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "数据估值与资产化对 DataOps 的能力提出新的要求",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "e097edead8e1f7b6213734def55935da2e1be49965eed9c246b18d10e22e7cf5",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0008283383370198208,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 45,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "系统工具是指以企业“业务用数，研发供数”的实际流程为基础，构建一体化的技术平台。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "ef89d56c82719f802aab4f90965c6b8dd5c9ffcc4ff6438c2e0b47a673413ad0",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0007828771198542315,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 46,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "数据研发管理是指以研发治理一体化为目标，构建标准化的数据开发流程。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "8dc466db70bc34d95662bdf843dbb0f390e957591aeec9468f55fa0dc69b4c34",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0007818086349567991,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 47,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "价值运营是指以精益运营数据为目标，打造量化驱动变革的能力。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "75d62b6ebfb435fc485645c03ba283b4cc8381d2cf8a8e0cdbcc3309bd0edf30",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0007754938583906743,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 48,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "自动数据资源价值全生命周期管理",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "134f7552636ac1bacf8ef17c0f4d93909aae2ee4128054d67cf45b0aafaab86e",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0007629468229628599,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 49,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "数据交付管理是指以提升交付效率和质量为目标，建设持续测试和交付能力。",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "df6fc3ce9d4702d6e43d2447a44695436e6c27ab9340be5d0f61ca7b6fd42d97",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0007447440810732378,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 50,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "能力框架还包括三项实践保障",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "af0377b75b09ae38b569e0638d06cb2298fe4211a2e68d4602794b557bcea052",
|
|||
|
|
"node_type": "event",
|
|||
|
|
"ppr_score": 0.0007121286628759453,
|
|||
|
|
"edge_score": 0.0,
|
|||
|
|
"passage_score": 0.0,
|
|||
|
|
"rank": 51,
|
|||
|
|
"source": "hipporag2_langchain_event",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
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|||
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|||
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|
"page_content": "实时监控和警报功能的实施",
|
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"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
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"pagerank_available": true
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|||
|
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|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "为人工智能提供高效、高质量且安全的数据支持",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "c417604266d0f52041a0f1cb37593c8c05a77b38f3123d8118ad9482e16ee572",
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|
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|
|||
|
|
"page_content": "通用人工智能的发展为 DataOps 注入了智能化的血液",
|
|||
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|
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"pagerank_available": true
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|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "全程精确追踪、记录、审计数据流程",
|
|||
|
|
"metadata": {
|
|||
|
|
"node_id": "61809b50b5b2092e81c17431f914ff34c41824b96704d3be2df87627e9b79512",
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"rank": 57,
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|
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"pagerank_available": true
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|
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|
|||
|
|
{
|
|||
|
|
"page_content": "通用人工智能的发展",
|
|||
|
|
"metadata": {
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"node_id": "1c83e5476cbbe56a679fe512bf7d9e17c9229bbd78f3b0dc76b85dcfc476f68e",
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"rank": 58,
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"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
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"pagerank_available": true
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|
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|
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|
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{
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|||
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|
"page_content": "智能化的血液给 DataOps",
|
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"ppr_score": 0.0006031906623977825,
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"edge_score": 0.0,
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"passage_score": 0.0,
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"rank": 59,
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"source": "hipporag2_langchain_event",
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"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
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"pagerank_available": true
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}
|
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|
},
|
|||
|
|
{
|
|||
|
|
"page_content": "# DataOps 能力框架——核心环节\\n\\n## 2024DataOPS发展大会\\n\\n智驱新程·数驱万务\\n\\n为了不断提高数据产品交付效率与质量，实现高质量数字化发展的目标。DataOps 的数据流水线以数据工程化能力为核心，构建出数据研发管理、数据交付管理、数据运维和价值运营四个环节。\\n\\n### 数据研发管理\\n\\n#### 需求管理\\n\\n采集 → 分析 → 确认 → 实施 → 变更\\n\\n数据需求全流程管理\\n\\n#### 设计管理\\n\\n制度 + 技术\\n\\n管理\\n\\n模型设计\\n\\n加工设计\\n\\n标准设计\\n\\n质量设计\\n\\n安全设计\\n\\n#### 数据开发\\n\\n集成存储、实施、部署、维护数据解决方案\\n\\n数据处理、加工、管理、使用\\n\\n提升数据资源价值\\n\\n#### 自助分析\\n\\n自助式的数据二次处理\\n\\n### 数据交付管理\\n\\n#### 配置管理\\n\\n版本控制\\n\\n环境管理\\n\\n代码版本管理\\n\\n数据版本管理\\n\\n保证\\n\\n各阶段数据随时可用性、可验证性\\n\\n#### 测试管理\\n\\n自动化测试流水线\\n\\n管理\\n\\n单元测试\\n\\n集成测试\\n\\n提前发现问题处理问题\\n\\n保证\\n\\n#### 部署与发布管理\\n\\n自动化部署发布流水线\\n\\n聚焦\\n\\n工具的自动化和标准化\\n\\n保证\\n\\n加快数据部署效率降低人为操作风险\\n\\n### 数据运维\\n\\n#### 监控管理\\n\\n监控体系\\n\\n监控预警\\n\\n开发流水线运行情况质量情况\\n\\n#### 资源管理\\n\\n调度优化合理分\\n\\n数据资源\\n\\n计算资源\\n\\n存储资源\\n\\n优化\\n\\n运维成本\\n\\n#### 变更管理\\n\\n标准化、敏捷化的变更流程\\n\\n应对\\n\\n开发流水线各类变更场景\\n\\n#### 异常管理\\n\\n构建\\n\\n异常管理知识库\\n\\n自动化运维能力\\n\\n提升\\n\\n运维效率\\n\\n#### 持续优化\\n\\n调优\\n\\n流水线任务编排\\n\\n平台配置\\n\\n提升\\n\\n开发流水线性能\\n\\n### 价值运营\\n\\n#### 成本管理\\n\\n细化数据产品交付\\n\\n维护成本核算\\n\\n精细控制资源投入\\n\\n识别、减少浪费\\n\\n#### 持续变革\\n\\n打造反馈机制\\n\\n收集各环节堵点问题\\n\\n持续改进\\n\\n#### 量化驱动\\n\\n数据开发流水线\\n\\n评估\\n\\n交付效率\\n\\n需求响应速度\\n\\n优化\\n\\n工作流程资源分配策略",
|
|||
|
|
"metadata": {
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|
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"node_type": "text",
|
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"ppr_score": 0.01946522574324021,
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|
"passage_score": 0.6536221881069555,
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|
|
"rank": 62,
|
|||
|
|
"source": "hipporag2_langchain_text",
|
|||
|
|
"query": "《DataOps实践指南2.0》的内容涉及哪些方面？",
|
|||
|
|
"pagerank_available": true
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"all_passages": [
|
|||
|
|
"《DataOps 实践指南 2.0》发布",
|
|||
|
|
"定位：总结各行业最佳实践，提炼核心理论框架，推动 DataOps 理念的广泛应用，加速数据驱动型企业的能力建设",
|
|||
|
|
"DataOps 实践快速发展",
|
|||
|
|
"《DataOps 实践指南 1.0》发布",
|
|||
|
|
"中国通信标准化协会与中国大数据技术标准推进委员会在2024年共同举办了一场名为'智驱新程·数驭万务'的数据Ops发展大会。",
|
|||
|
|
"2024 DataOps 发展大会",
|
|||
|
|
"DATAOPS 效能评价项目启动咨询",
|
|||
|
|
"编写了一篇关于DataOps实践指南的文章",
|
|||
|
|
"实施数据治理策略",
|
|||
|
|
"提高开发效率和产品质量",
|
|||
|
|
"建立数据管道",
|
|||
|
|
"集成DevOps实践",
|
|||
|
|
"LET DATA OPS!",
|
|||
|
|
"中国通信标准化协会和大数据技术标准推进委员会参与了2024 DataOps 发展大会",
|
|||
|
|
"理解DataOps的基本概念",
|
|||
|
|
"增强业务洞察力和决策能力",
|
|||
|
|
"DATAOPS效能评价启动咨询",
|
|||
|
|
"启动了大数据技术标准推进委员会",
|
|||
|
|
"智驱新程·数驭万务",
|
|||
|
|
"大会发布DataOps实践案例集",
|
|||
|
|
"DATAOPS作为大会主题",
|
|||
|
|
"大数据技术标准推进委员会参与2024 DataOps发展大会",
|
|||
|
|
"征集报名活动进行中",
|
|||
|
|
"China Communications Standards Association",
|
|||
|
|
"中国通信标准化协会举办的数据发展大会的主题",
|
|||
|
|
"中国通信标准化协会组织2024 DataOps发展大会",
|
|||
|
|
"大会讨论的领域之一",
|
|||
|
|
"Big Data Technology and Standard Committee",
|
|||
|
|
"2024 DataOps发展大会",
|
|||
|
|
"DATAOPS 实践案例集的征集与报名活动",
|
|||
|
|
"组织成立",
|
|||
|
|
"中国信息通信研究院在2022年依托大数据技术标准推进委员会成立了DataOps工作组",
|
|||
|
|
"决策机制模糊，导致资源浪费或投入不足",
|
|||
|
|
"追求短期收益，市场竞争和业务压力促使企业更关注短期收益和业绩目标的实现，缺乏长远眼光和战略决心",
|
|||
|
|
"业务导向，“组织、流程、技术”三位一体共同推进",
|
|||
|
|
"DataOps理念过热期，领头企业成功案例催化忽视了不同企业实施复杂性和差异性",
|
|||
|
|
"中国农业银行、中国工商银行、交通银行、北京银行、浙江移动、江苏移动、中信建投证券、人保信息科技、网易数帆、火山引擎、联想集团参与标准评估",
|
|||
|
|
"对数据平台工具提出技术要求，指导企业建设完善的DataOps数据研发工具链",
|
|||
|
|
"将数据治理工作融入数据研发过程，形成闭环",
|
|||
|
|
"DataOps系列标准",
|
|||
|
|
"来自金融、通信运营商、互联网行业的多家企业参与标准评估",
|
|||
|
|
"决策机制模糊导致资源浪费或投入不足，企业高层参与度不足，业务部门缺乏主动性和创新性",
|
|||
|
|
"DataOps能力模型围绕数据开发流程形成了‘4+3’的架构",
|
|||
|
|
"中国通信标准化协会成立大数据技术标准推进委员会",
|
|||
|
|
"数据研发全链路的监测、运维监控、运维告警、运维操作",
|
|||
|
|
"DataOps系列标准包括研发管理、系统工具、交付管理的标准编制",
|
|||
|
|
"助力企业提升数据产品交付质量和效率",
|
|||
|
|
"系统工具评估",
|
|||
|
|
"DataOps能力框架通过三项核心保障促进企业打造跨域协同机制",
|
|||
|
|
"DataOps能力框架引领企业构建集开发、治理、运营于一体的自动化数据流水线",
|
|||
|
|
"数据需求方主动通过自助分析平台进行数据探查",
|
|||
|
|
"保证提前发现问题并处理问题",
|
|||
|
|
"短期收益",
|
|||
|
|
"研发管理评估",
|
|||
|
|
"2022年，中国信息通信研究院成立了 DataOps工作组",
|
|||
|
|
"不断完善DataOps理念定义，并提出DataOps能力模型框架",
|
|||
|
|
"DataOps 理念过热期",
|
|||
|
|
"领头企业成功案例催化",
|
|||
|
|
"忽视不同企业实施复杂性和差异性",
|
|||
|
|
"# 《DataOps 实践指南 1.0》回顾\\n\\n## 2023\\n\\n《DataOps 实践指南 1.0》\\n\\nDataOps 实践快速发展\\n\\n## 2024\\n\\n《DataOps 实践指南 2.0》\\n\\n---\\n\\n> 定位：总结各行业最佳实践，提炼核心理论框架，推动 DataOps 理念的广泛应用，加速数据驱动型企业的能力建设。\\n\\n### 核心内容\\n\\n* DataOps 理念的演进历程和发展现状\\n\\n* DataOps 概念内涵\\n\\n* DataOps 价值作用\\n\\n* DataOps 能力框架简要介绍\\n\\n* DataOps 发展展望\\n\\n### 核心内容\\n\\n* DataOps 概念与价值内涵\\n\\n* DataOps 能力框架要求详细解读\\n\\n* DataOps 实践路径和核心要点\\n\\n* DataOps 实践难点和应对策略\\n\\n* DataOps 发展展望",
|
|||
|
|
"# DataOps 实践指南 2.0",
|
|||
|
|
"# 2024 DataOps 发展大会\\n\\n智驱新程·数驭万务\\n\\n## 中国通信标准化协会\\n\\nChina Communications Standards Association\\n\\n## 大数据技术标准推进委员会\\n\\nBig Data Technology and Standard Committee\\n\\n## DATAOPS\\n\\n## DataOps 实践案例集\\n\\n### 征集报名！",
|
|||
|
|
"返回格式必须为以下JSON结构,内容需用简体中文表述: [ { \"Event\": \"\"描述事件的简单句子\"\", \"Entity\": [\"实体1\", \"实体2\", \"...\"] }... ]",
|
|||
|
|
"分析段落中的事件及其参与实体",
|
|||
|
|
"数据操作团队开始处理数据",
|
|||
|
|
"数据被清洗和整理完成",
|
|||
|
|
"# DataOps 能力框架——实践保障\\n\\n## 2024DataOPS发展大会\\n\\n智驱新程·数驱万务\\n\\n为了保证 DataOps 研发流水线能够持续高效运转和迭代完善，企业需要有力的保障措施。本指南提出了组织、工具和安全三个维度的保障要求。这些要求的目标是引导企业以全局最优为目标，保障数据研发流水线的平滑运作。\\n\\n### 系统工具\\n\\n#### 数据需求管理\\n\\n| 手段 | 构建数据需求全生命周期的管理能力 |\\n\\n| :--- | :--------------------------------- |\\n\\n| 目标 | 支持流程的设计和共享 |\\n\\n| 进一步要求 | 数据需求方主动通过自助分析平台进行数据探查 |\\n\\n#### 数据研发治理一体化\\n\\n| 原则 | 先设计、后开发、先标准、后建模 |\\n\\n| :--- | :------------------------------- |\\n\\n| 手段 | 设计管理、开发管理、数据应用 |\\n\\n| 目的 | 规范即设计，设计即开发，开发即治理 |\\n\\n#### 数据自动化交付部署\\n\\n\\n\\n#### 数据一体化运维\\n\\n| 对象 | 数据研发全链路的监测、运维监控、运维告警、运维操作 |\\n\\n| :--- | :---------------------------------------------------- |\\n\\n| 手段 | 可视化方式 |\\n\\n| 目的 | 实时展现研发效能、数据质量 |\\n\\n### 组织管理\\n\\n#### 组织架构\\n\\n合理配置\\n\\n{ | 数据技术架构 |\\n\\n| 数据人员架构 |\\n\\n#### 岗位角色\\n\\n| | 设置相应的岗位角色 |\\n\\n| :--- | :------------------ |\\n\\n| | 明确晋升路线与考核方式 |\\n\\n#### 协作协同\\n\\n依托\\n\\n| 敏捷方法 |\\n\\n| :-------- |\\n\\n| 关注团队、工具间的协同问题持续进行优化 |\\n\\n解决\\n\\n### 安全管控\\n\\n#### 安全风险策略\\n\\n加强\\n\\n{ | 数据研发全生命周期 |\\n\\n| 风险识别 |\\n\\n| 风险预测 |\\n\\n#### 风险管理\\n\\n外部法律法规\\n\\n+ 监管要求\\n\\n+ 企业内部安全需求\\n\\n健全\\n\\n风险管理策略\\n\\n#### 安全测试\\n\\n数据研发过程的各环节进行安全测试\\n\\n保证\\n\\n提前发现问题处理问题",
|
|||
|
|
"系统工具",
|
|||
|
|
"DataOps 的数据流水线以数据工程化能力为核心，构建出数据研发管理、数据交付管理、数据运维和价值运营四个环节。",
|
|||
|
|
"数据研发管理",
|
|||
|
|
"价值运营",
|
|||
|
|
"数据运维",
|
|||
|
|
"四个核心环节包括数据研发管理、数据交付管理、数据运维和价值运营。",
|
|||
|
|
"经过 2、3 年的实践，国内 DataOps 体系逐步完善起来",
|
|||
|
|
"数据交付管理",
|
|||
|
|
"数据开发流水线包括数据的研发管理、交付管理、数据运维和价值运营。",
|
|||
|
|
"数据运维是指以全面立体的持续监控、发现、处理数据问题为目标，构建全链路可观测能力。",
|
|||
|
|
"引入分布式追踪技术和实时监控系统",
|
|||
|
|
"组织管理是指以打造敏捷、协同的数据驱动型组织为目标，优化组织架构、明晰岗位职能。",
|
|||
|
|
"安全管控是指以保证个人隐私、数据安全为目标，将安全管控嵌入到数据流水线中，构建数据研发全生命周期的安全管理能力。",
|
|||
|
|
"组织管理",
|
|||
|
|
"安全管控",
|
|||
|
|
"三项实践保障包括系统工具、组织管理、安全管控。",
|
|||
|
|
"DataOps 能力模型围绕数据开发流水线，从业务需求出发，以创造业务价值为目标，形成“4+3”的能力框架。",
|
|||
|
|
"数据估值与资产化对 DataOps 的能力提出新的要求",
|
|||
|
|
"系统工具是指以企业“业务用数，研发供数”的实际流程为基础，构建一体化的技术平台。",
|
|||
|
|
"数据研发管理是指以研发治理一体化为目标，构建标准化的数据开发流程。",
|
|||
|
|
"价值运营是指以精益运营数据为目标，打造量化驱动变革的能力。",
|
|||
|
|
"自动数据资源价值全生命周期管理",
|
|||
|
|
"数据交付管理是指以提升交付效率和质量为目标，建设持续测试和交付能力。",
|
|||
|
|
"能力框架还包括三项实践保障",
|
|||
|
|
"实时监控和警报功能的实施",
|
|||
|
|
"为人工智能提供高效、高质量且安全的数据支持",
|
|||
|
|
"通用人工智能的发展为 DataOps 注入了智能化的血液",
|
|||
|
|
"全程精确追踪、记录、审计数据流程",
|
|||
|
|
"通用人工智能的发展",
|
|||
|
|
"智能化的血液给 DataOps",
|
|||
|
|
"# DataOps 能力框架——核心环节\\n\\n## 2024DataOPS发展大会\\n\\n智驱新程·数驱万务\\n\\n为了不断提高数据产品交付效率与质量，实现高质量数字化发展的目标。DataOps 的数据流水线以数据工程化能力为核心，构建出数据研发管理、数据交付管理、数据运维和价值运营四个环节。\\n\\n### 数据研发管理\\n\\n#### 需求管理\\n\\n采集 → 分析 → 确认 → 实施 → 变更\\n\\n数据需求全流程管理\\n\\n#### 设计管理\\n\\n制度 + 技术\\n\\n管理\\n\\n模型设计\\n\\n加工设计\\n\\n标准设计\\n\\n质量设计\\n\\n安全设计\\n\\n#### 数据开发\\n\\n集成存储、实施、部署、维护数据解决方案\\n\\n数据处理、加工、管理、使用\\n\\n提升数据资源价值\\n\\n#### 自助分析\\n\\n自助式的数据二次处理\\n\\n### 数据交付管理\\n\\n#### 配置管理\\n\\n版本控制\\n\\n环境管理\\n\\n代码版本管理\\n\\n数据版本管理\\n\\n保证\\n\\n各阶段数据随时可用性、可验证性\\n\\n#### 测试管理\\n\\n自动化测试流水线\\n\\n管理\\n\\n单元测试\\n\\n集成测试\\n\\n提前发现问题处理问题\\n\\n保证\\n\\n#### 部署与发布管理\\n\\n自动化部署发布流水线\\n\\n聚焦\\n\\n工具的自动化和标准化\\n\\n保证\\n\\n加快数据部署效率降低人为操作风险\\n\\n### 数据运维\\n\\n#### 监控管理\\n\\n监控体系\\n\\n监控预警\\n\\n开发流水线运行情况质量情况\\n\\n#### 资源管理\\n\\n调度优化合理分\\n\\n数据资源\\n\\n计算资源\\n\\n存储资源\\n\\n优化\\n\\n运维成本\\n\\n#### 变更管理\\n\\n标准化、敏捷化的变更流程\\n\\n应对\\n\\n开发流水线各类变更场景\\n\\n#### 异常管理\\n\\n构建\\n\\n异常管理知识库\\n\\n自动化运维能力\\n\\n提升\\n\\n运维效率\\n\\n#### 持续优化\\n\\n调优\\n\\n流水线任务编排\\n\\n平台配置\\n\\n提升\\n\\n开发流水线性能\\n\\n### 价值运营\\n\\n#### 成本管理\\n\\n细化数据产品交付\\n\\n维护成本核算\\n\\n精细控制资源投入\\n\\n识别、减少浪费\\n\\n#### 持续变革\\n\\n打造反馈机制\\n\\n收集各环节堵点问题\\n\\n持续改进\\n\\n#### 量化驱动\\n\\n数据开发流水线\\n\\n评估\\n\\n交付效率\\n\\n需求响应速度\\n\\n优化\\n\\n工作流程资源分配策略"
|
|||
|
|
],
|
|||
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|
"passage_sources": [
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|||
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|||
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|||
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|||
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|||
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|
|||
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|||
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|
|||
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|
|||
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|
|||
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|
|||
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|
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|
|||
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|
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|
|||
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