雲原生數據中臺的What、Why、Who、How和Where

{"type":"doc","content":[{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"WHAT：雲原生是什麼？ 它有啥前世今生？"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"簡單說，雲原生（Cloud Native）是在雲上構建和運行系統的方法論。最早移植上雲的“非原住民”應用程序，往往還沿用私有化部署的技術架構，無法充分發揮雲基礎設施的優勢。隨着客戶應用的深入，系統必須按照IaaS和PaaS的原理進行重構，以便跟上業務的爆炸性增長。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"按照CNCF（Cloud Native Computing Foudation）定義，雲原生一般包含CI\/CD（持續集成持續交付）、容器化、微服務、存儲計算分離、跨雲多域、元數據管理等技術要素。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/resource\/image\/c2\/35\/c2ea5c6361847da988c17d327460ef35.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":"center","origin":null},"content":[{"type":"text","text":"圖源：CNCF"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"老實講，從我這種從業20年數據技術老兵看來，這又是一波buzzword，很多東西二十年前就有了，十幾年前就已經成爲互聯網技術團隊的標配。例如，2007年Google已向Linux內核社區貢獻cgroup補丁；再如，2008年騰訊阿里招收計算機專業的應屆生的面試題裏就有CI\/CD的問題；2013年我在阿里雲ODPS團隊時，ODPS的調度器和執行器已加上了cgroup能力；6年前我第一次創業，憑藉docker容器化這個特點拿到了天使輪。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/resource\/image\/ab\/09\/ab093c5a31feb4bf8a49b9452fb5be09.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"WHY：投資人不傻，爲什麼這些概念在創投領域突然變火？"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"雲原生暗合當前行業的發展邏輯，纔會受“追捧”。我猜所有重要的創新都要被“發明”兩次，一次是從無到有生出來，一次是出圈。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"最近業界有個新聞，2020年，中國IT預算裏超過50%的錢花在了雲上。這是一個里程碑時刻，在中國這個喜歡私有化部署的市場裏，雲終於贏了。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"大量的應用在雲上，就遇到成本和效率的問題。舉2個例子：第一個例子，雲和大數據運維技術含量較高，很多看機房重啓機器的傳統運維工程師無力承擔。但是線上數據、計算和應用規模還在以每年N倍的速度增長。如果不採用CI\/CD而是堅持傳統的人肉運維，先別說這種運維工程師的薪酬很高，你可能都招不到這麼多合適的人。第二個例子，客戶如果把Hadoop不加修改直接部署到ECS節點上，數據通過HDFS存在雲磁盤上成本會非常昂貴。客戶必須修改HDFS底層，把數據存到對象存儲上去。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"成本和效率問題推動智能數據平臺必須走向雲原生，從而爲用戶帶來如下收益："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"1、提高研發效率"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"通過微服務、CI\/CD、對象體系、DevOps等一系列技術，提高代碼開發、測試、發佈效率，降低迭代成本。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"2、 降低運維成本"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"同樣，上面這些技術也可以實現開發及運維高效協同，有效提升對故障的響應速度，實現持續集成和交付，使得快速部署應用成爲業務流程和企業競爭力的重要組成部分。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"3、降低存算成本"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"大數據基礎設施的存儲計算成本驚人。存算分離和容器化能夠更高效地使用IaaS資源，降低存儲成本。存儲和計算節點分離後，可以在不對存儲進行擴容的情況下快速增加計算資源。另一方面，單個容器的啓動時間更快，佔用空間更小，而且可以根據實際應用的大小來彈性分配資源，無需額外採購服務器。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"4、提高治理效率"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"數據治理是非常重要但“髒”且繁瑣的工作。使用跨雲治理、元數據管理等技術，會大幅度提高企業積累數據資產的效率，降低安全風險，提高供應商的多樣化。"}]},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"WHO：所有人都在闡釋雲原生，哪個更符合客戶訴求？到底是“誰的雲原生”？"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"討論雲原生時，應該問清楚：“誰的雲原生？”"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"AWS、阿里雲、微軟雲、騰訊雲、華爲雲、京東雲、Google雲……每一家都推出了自己雲原生技術，以吸引客戶搬上自己的雲。但技術接口的中立性和跨平臺性被有意無意忽略了。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"奇點雲主張建立AI驅動的數據中臺，服務於泛零售、金融、電信等行業，其中不乏各行業的頭部企業。所以我們有動力做下面兩件事："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"numberedlist","attrs":{"start":1,"normalizeStart":1},"content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":1,"align":null,"origin":null},"content":[{"type":"text","text":"儘可能優化架構，降低數據應用在IaaS上的計算、存儲成本。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":2,"align":null,"origin":null},"content":[{"type":"text","text":"實現跨雲數據治理，幫助客戶擺脫某個特定雲平臺的綁定。總而言之，和客戶站在一起。"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"你會發現，在美國，儘管AWS的產品非常強大，但是snowflake和databricks依舊服務了很多世界五百強企業。原因就是這些頭部企業需要把自己的IaaS供應商多樣化。邏輯很類似。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"所以奇點雲的雲原生，相比常規定義，多強調了幾個因素：對象體系、跨平臺、自主可控。我們的產品支持AWS、阿里雲、微軟雲、騰訊雲、華爲雲、京東雲、Google雲，並實現跨雲的多workspace管理，能實現客戶數據與應用的跨雲治理和遷移。而且系統基本的架構體系設計更開放、更安全、更容易集成。"}]},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"HOW：對於雲原生，數據領域有什麼傾向？具體通過哪些技術要素實現雲原生？"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"我們先回顧一下數據技術的演進階段："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"階段1"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"關係性數據庫出現，SQL統一數據開發工業標準，開始區分OLTP和OLAP。**問題：**隨着業務成長，數據量爆炸，尤其是互聯網影響的深入，傳統關係型數據庫逐漸扛不住海量數據的壓力。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"階段2"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"大數據技術出現，支撐海量數據的處理，OLAP本身又被分成了離線和實時。**問題：**針對不同場景的各種大數據引擎不斷出現，反過來又刺激了更多數據的生成。海量數據的成本開始變成沉重的負擔，如果不能把數據變成“資產”，幫助業務賺錢或省錢，就沒法持續支撐大數據基礎設施的持續投入。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"階段 3"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"數據中臺出現，提出一系列的業務方法論，強調積累數據資產。**問題：**數據中臺在互聯網公司的實踐獲得了相當大的成功。但是在其他行業，如果純粹100%生硬照搬互聯網的業務架構和產品形態，會遇到很多水土不服。舉個例子，傳統行業的企業有大量的線下場景，需要考慮很多數據集成、跨平臺治理、數據安全、自主可控的問題。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"階段 4"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"數據智能深入場景，AI成爲數據中臺的入口和出口，業務和數據上雲趨勢加快，多域數據治理成爲剛需，國內用戶願意爲自主可控技術買單。 你可以看到，每一階段技術都是爲了解決上一代問題誕生的。 所以，大數據領域的業務特點會推導對雲原生的一些傾向性："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"1. 數據中臺存儲海量數據，且作業高吞吐高併發，對存算分離的各項指標要求明顯高於其他領域的應用；"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"2. 大數據集羣規模大進程多，天然需要微服務治理和其他智能運維技術；"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"3. 客戶對數據安全、數據確權極其關注，加上toB的分級多域數據治理場景非常複雜，產生了對跨平臺技術、數據安全技術、合規數據合作技術的強烈需求；"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"4. 由於目前的國際政經形勢，自主可控的大數據引擎，對國內企業而言是一個剛需。 想清楚了這些，“奇點雲的雲原生”具體做了如下的研發："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":" 容器化編排"},{"type":"text","text":" ：容器化本質上是一種虛擬化技術，一臺主機可虛擬出上千個容器。單個容器的啓動時間更快，佔用空間更小，而且可以根據實際應用的大小來彈性分配資源，無需額外採購服務器，加快研發速度。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"對象體系"},{"type":"text","text":"：根據現有業務抽象出核心對象，以標準RESTful風格提供API服務，解耦核心對象與業務層服務，以應對不同環境、不同業務場景的需求。這一系列正交的核心對象就構成了平臺對象體系，上層業務可在此基礎上構建應用，高效演進。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/resource\/image\/0d\/cd\/0de9c97e398b27976b5633cb4ea907cd.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":" CI\/CD"},{"type":"text","text":" ：通過版本管理系統和DevOps基礎設施，實現自動化測試和持續集成。一個典型流程是，程序員提交代碼到特定的tag，觸發測試接口自動化測試腳本+開發單測腳本（偏提交代碼新功能的）執行併發送報告。由此實現測試、發佈和部署自動化。在此基礎上構建特定的數據環境，對重要接口和鏈路進行自動化檢測。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/resource\/image\/94\/a6\/94b928d6e029a167a5172ed9500f93a6.jpg","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":" 存算分離"},{"type":"text","text":" ：如果把Hadoop、Spark等常規開源大數據引擎直接應用於雲主機，海量數據帶來的存儲成本和吞吐壓力，會很快“壓垮”客戶。因此，必須引入中間緩存實現計算存儲分離，將數據存儲到對象存儲上，同時兼容HDFS協議，能夠根據業務需求進行彈性擴容，就能大幅度降低成本，提高集羣性能。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/resource\/image\/92\/92\/9259230585cc00e3ed6f3fd211912092.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":" 跨雲治理"},{"type":"text","text":" ：在AWS、阿里雲、華爲雲、騰訊雲、京東雲等平臺，實現統一賬號、權限和審計的多workspace的兼容管理，並進一步提供數據安全和可信計算方案，從而提高基礎設施的可控性和安全性。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":" 元數據管理"},{"type":"text","text":" ：對數據的結構、指標、標籤、權限、上下游血緣、生產作業等元信息進行規範化管理，建立智能數據治理體系，支持數據盤點、安全審計、血緣分析、關鍵分級等應用，最終實現數據資產化。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.geekbang.org\/resource\/image\/d9\/22\/d960aa6193927ca3a66f5d19767c9422.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"WHERE：客戶在哪些場景用上了雲原生數據中臺？"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"簡單舉幾個客戶應用我們的雲原生數據中臺DataSimba的例子吧（均爲真實案例，保密原因，不能指明）："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"案例 1"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"某互聯網APP，在海內外都很受歡迎。由於地域和法規的要求，他們必須在多個國家的多種IaaS上實現數據生產和合規隔離，例如：在印度部署1個workspace在孟買AWS上，在美國部署1個workspace在Oracle雲上，在中國部署1個workspace在阿里雲上……同時又實現賬號權限、數據審計和安全策略的全局管理。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"案例 2"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"某大型電子設備製造公司，由於戰略和業務的原因，必須把自己IaaS供應商多樣化：部署1個workspace在華爲雲上，以便對接政企系統；部署1個workspace在AWS上，以便滿足海外客戶的審計需求；再部署1個workspace在阿里雲上，以便支持和阿里雲的戰略合作……同時又要進行全局的數據資產管理。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"案例 3"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"某大型零售品牌集團，本身就有多個互相競爭的子品牌，彼此要求數據做必要隔離和客戶隱私保護，同時總部又要進行全面的數據拉通。另一方面，該品牌商會對接多個流量電商平臺：在阿里雲放一個workspace支持雙11，在京東雲放一個workspace支持618。再加上幾十個線上線下系統的數據的集成和拉通，形成了很複雜的分級多workspace的雲原生數據治理體系。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"案例 4"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"某流通業的大型集團，各個分公司比較獨立，IT經費充足。這時候總部上一個分級數據治理的多workspace數據中臺，旗下比較大的分公司有自己獨立機房的可以單獨部署workspace，而小一些的公司在阿里雲或華爲雲上開通workspace。總部對所有workspace擁有賬號管理和審計的權利，同時控制住數據建模規範標準和指標的版本發佈。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"不同行業的不同企業，搭建出不一樣的雲原生跨平臺數據治理體系，這其中的業務邏輯複雜微妙。我們再對比一下互聯網大廠的數據平臺——大一統式的數據打通，跑在幾千臺節點集羣上，就可以發現兩邊產品上的着眼點並不相同。"}]},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"作者介紹"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"地雷，奇點雲高級技術專家，奇點雲數據智能平臺DataSimba總負責人，阿里大數據底層核心引擎ODPS初代產品經理。曾支持螞蟻金服、菜鳥等算法與應用建設。"}]}]}