Agentic AI for Goal-Driven Data Integration智能AI代理如何彻底改变数据集成与数据工程
作者：微信文章智能AI代理如何彻底改变数据集成与数据工程
ref：IBM Technology
#AI代理 #数据集成 #数据工程 #LLM #人工智能
当前的数据工程任务面临诸多挑战，数据团队往往花费更多时间在整理数据和维护数据管道上，而非提供有价值的洞察。数据工程系统复杂且彼此孤立，数据分散存储在不同的云环境、运营仓库、数据湖以及API中，且每个系统都有其自身的限制。当数据工程师构建管道时，他们需要依赖复杂的脚本、存储过程和转换逻辑，而一旦源系统发生简单的模式更改或列重命名，就可能导致数小时的调试和重新测试工作。
Data engineering today is very complicated and siloed, requiring complex scripts and stored procedures just to keep the data flowing. Data lives across different clouds, data lakes, operational warehouses, and APIs, each with its own set of constraints. Currently, data teams spend more time wrangling data and maintaining pipelines than actually delivering insights. A single schema change or column rename in a source system can often trigger hours of debugging and retesting, leading much of the team's effort to be spent on maintenance rather than building new capabilities.
智能AI代理的出现正在改变这一现状。专门为数据集成构建的代理能够理解系统中所有的不同数据源，无论数据结构是关系型数据、非结构化文档，还是来自API的数据，它们甚至能跨越不同的部署环境（如云端或本地）进行操作。更重要的是，这些代理不仅理解数据本身，还能理解元数据和实体关系，从而掌握数据背后的业务术语和含义。它们还能处理创建复杂数据管道的难题，包括多重连接、转换逻辑和业务规则的设置。
Agentic AI is redefining data engineering by introducing agents specifically built for data integration that can handle all the typical steps a data engineer takes. These agents can understand not just one, but all data sources in the system, spanning relational data, unstructured data like documents, and data from APIs, across both cloud and on-premise locations. Crucially, they understand the metadata and entity relationships, allowing the AI to comprehend the business terms and meanings behind the data. Furthermore, these agents can manage the complexity of creating a data pipeline involving multiple joins, transformations, logic, and business rules.
这些AI代理系统利用大型语言模型（LLMs）来运作，LLMs负责解析用户的自然语言请求和意图，并将其转化为结构化的行动。通过强化学习，代理能够通过奖励成功完成的管道运行来不断改进其计划。AI代理并非只生成文本，它们还会使用“工具调用”（tool calling）来调用API，以便连接到所需的数据源、理解元数据并执行数据转换，最终生产和执行功能完备的工作管道，从而避免了当前手动编写ETL代码的繁重工作。
AI agents operate using large language models (LLMs) to parse natural language requests and intent from users, translating them into structured actions. Reinforcement learning is employed to help the agents improve their plans over time by rewarding successful pipeline runs. These agents utilize tool calling to interact with APIs, allowing them to connect to data sources, understand metadata, and carry out necessary transformations. Working together, these mechanisms allow the agents to produce and execute fully working pipelines without the manual work of hand-coded ETL that currently bogs down data teams.
AI代理在数据集成中有几个实际应用案例。首先是声明式管道创作，工程师只需描述他们期望的结果，代理就能创建完整的数据管道。其次，它们支持业务用户数据自服务，分析师可以更快地请求或创建新的数据集，从而加快获得洞察的时间并提高准确性。此外，AI代理还有助于数据质量和可观察性，它们能够及早检测到列变化或类型不匹配，提出修复建议，并进行持续的异常检查、自动回填和围绕失败数据源的重新路由，确保为下游AI系统提供可信赖的数据。
Practical examples of AI agents for data integration include declarative pipeline authoring, where engineers or analysts describe the desired outcome and the agent creates the full pipeline. They also enable business users to self-service their data, allowing analysts to request or create new data sets quickly, resulting in faster time to insights and improved accuracy. Finally, AI agents enhance data quality and observability by being able to detect column changes or type mismatches early and propose fixes before jobs fail. Continuous checks for anomalies and automatic rerouting around failed sources ensure data remains trustworthy for downstream AI systems.
最终，AI代理为数据工程团队带来了巨大的价值。工程师将能够专注于复杂的集成和战略性工作，而非重复性的修复；业务用户可以更快地获得可靠数据，无需漫长的交接过程；同时，AI代理为分析和机器学习模型提供了更清晰、更新鲜的数据管道，减少了摩擦，提高了速度和准确性。随着AI代理的成熟，数据集成正从定制逻辑和任务的拼凑转变为一种自适应的、目标驱动的过程，为下一代人工智能提供了有力支持。
Ultimately, AI agents solve many challenges and generate value for data engineering teams by reducing repetitive fixes for engineers, allowing them more time for strategic work. Business users benefit from faster access to reliable data without long handoffs. Furthermore, these agents help with data for AI by generating cleaner, fresher pipelines that feed machine learning models with greater speed and accuracy. As these agents mature, data integration will shift from a patchwork of custom logic to an adaptive, goal-driven process ready to support the next generation of AI.
Learn by Doing with Steven 数能生智 | 社媒矩阵
📅 预约交流 | Book a Call
平台链接🗓️ Cal.comhttps://cal.com/stevenwang📅 Google Calendarhttps://calendar.app.google/fT6ip6i638AGuP8v5📬 ChiliPiperhttps://gmail.chilipiper.com/me/steven-wang⏰ Calendlyhttps://calendly.com/steven_wang/60min
🌐 主站与文章 | Website & Writing
平台链接🧱 Githubhttps://github.com/learnbydoingwithsteven🌐 Github.iohttps://learnbydoingwithsteven.github.io/🐻 Bear Bloghttps://learnbydoingwithsteven.bearblog.dev/✍️ Substackhttps://substack.com/@steven923044📰 LinkedIn Newsletterhttps://www.linkedin.com/newsletters/7283566848875384833/📲 微信公众号https://mp.weixin.qq.com/s/_UgwPOKp0KFDNQdPSYuWMg
💬 社群矩阵 | Communities
社群链接🌐 LinkedIn Group（中/欧/美AI社群）https://www.linkedin.com/groups/15054015💬 Discord（中欧美AI社群）https://discord.gg/XE6WpAfM💬 Discord（Learn By Doing）https://discord.gg/47yq8KcC📡 Telegram Group（技术·播客）https://t.me/+i9NRjGCKjRQxMDNk📱 WhatsApp Group（技术·播客）https://chat.whatsapp.com/Gmfju4artZB0VfRxV93H8p
🧑💼 直接联系 | Direct Contacts
项目EmailCareer / Banking / Finance / GenAI / DataScience / Industry / Consulting and Collaborationswjbear2020@gmail.com
💰 支持创作 | Support Steven’s Work
平台链接💳 Paypalhttps://www.paypal.com/paypalme/wangjiansuper?country.x=IT&locale.x=en_US☕ Buy Me A Coffeehttps://buymeacoffee.com/learnbydoing
🎥 视频矩阵 | Video
平台链接▶️ YouTube Learn By Doing With Stevenhttps://www.youtube.com/@learnbydoingwithsteven▶️ 数能生智（中文频道）https://www.youtube.com/@%E6%95%B0%E8%83%BD%E7%94%9F%E6%99%BA🎵 TikTokhttps://www.tiktok.com/@learnbydoingwithsteven
📺 哔哩哔哩（Steven数据漫谈）
https://space.bilibili.com/3546784399886498?spm_id_from=333.788.upinfo.head.click🎵 抖音https://www.douyin.com/user/self?modal_id=7577406098097933622&showTab=post
🌍 网站 / 频道 / 平台 | Website
平台链接WhatsApp Channelhttps://whatsapp.com/channel/0029VazqfKFK0IBoyfgyO70bTelegram Channelhttps://t.me/learnbydoingwithstevenGithubhttps://github.com/learnbydoingwithstevenGithub.iohttps://learnbydoingwithsteven.github.io/LinkedIn Newsletter(Business)https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7359504834644926464LinkedIn Newsletter(Tech)https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7283566848875384833Substackhttps://substack.com/@steven923044Bear Bloghttps://learnbydoingwithsteven.bearblog.dev/微信公众号世界模型的下一站：李飞飞团队World Labs如何定义“空间智能”新赛道？
📱 社交平台 | Social
平台链接小红书https://www.xiaohongshu.com/user/profile/5e0e450b0000000001001e26?xsec_token=YBolHet_ed8Npv1I7yr4lMGb6VRZWtbkE9QSkodxdCu0I=&xsec_source=app_share&xhsshare=CopyLink&appuid=5e0e450b0000000001001e26&apptime=1737132065&share_id=c4262bd995c34cebaab2e0d85e5a3875LinkedIn（独立项目）https://www.linkedin.com/in/steven-w-6828a31bb/LinkedIn（完整职业）https://www.linkedin.com/in/jian-w-83bb36440/X（Twitter）https://x.com/CatchingtidesFacebookhttps://www.facebook.com/profile.php?id=61571798756202Instagramlearnbydoingwithsteven
🤝 合作伙伴 | Collaborations
合作方链接Vanta Tech Labhttps://www.linkedin.com/company/vantatechlabDukeCEOhttps://www.linkedin.com/company/chinese-entrepreneurs-organization-at-duke-dukeceo创·见 Founder Visionhttps://www.xiaoyuzhoufm.com/podcast/66a322470736bb4045362844?s=eyJ1IjoiNjVkZDlhNDBlZGNlNjcxMDRhOThhNjZkIiwiZCI6MX0=我作为嘉宾的节目https://www.xiaoyuzhoufm.com/episode/690c8ad5d99642be96c4accc
🚗 兴趣爱好 | Hobby
内容链接Steven On The Road (YouTube)https://youtube.com/@stevenontheroad6129?si=pAUvAm0af6eFJrDnSteven On The Road（哔哩哔哩）https://space.bilibili.com/157133040
Learn by Doing with Steven 数能生智 | 播客矩阵
1) Steven Data Talk
“Steven Data Talk” — delivering the clearest conversations on cutting-edge AI, technology, innovation, business, and entrepreneurship.
Now available on Spotify, Apple Podcasts, YouTube, Amazon Music, Xiaoyuzhou, Ximalaya, and more.
Support the creator & explore all links:
https://linktr.ee/learnbydoingwithsteven
平台链接Apple Podcastshttps://podcasts.apple.com/gb/podcast/steven-data-talk/id1845702474Spotifyhttps://open.spotify.com/show/3qSV5WJBsHbivqdmIopEYR?si=Q7XxCzxsSTKXmKiW27hmAA喜马拉雅https://www.ximalaya.com/album/88884765小宇宙https://www.xiaoyuzhoufm.com/podcast/68ef81ce0a78e59c5c5c45e7YouTube Musichttps://music.youtube.com/playlist?list=PLfV0OO4XXVBk1oCeZg-xwdnYbNuSDqgmW&si=EQtgt96FfSZSwxvcAmazon Musichttps://music.amazon.com/podcasts/b31ecf00-32e8-41b5-96cd-13e86253d249/steven-data-talk
2) Steven 数据漫谈
《Steven数据漫谈》——用最清晰的方式，聊最前沿的人工智能、科技、创新、商业、创业思考。现已登陆 Spotify、Apple Podcasts、YouTube、Amazon Music、小宇宙、喜马拉雅 等平台。
所有链接，支持作者：https://linktr.ee/learnbydoingwithsteven
平台链接Apple Podcastshttps://podcasts.apple.com/gb/podcast/steven%E6%95%B0%E6%8D%AE%E6%BC%AB%E8%B0%88/id1845703144Spotifyhttps://open.spotify.com/show/4b8dqmQmVQiPPxuIZNR58w?si=QgCsksYYSV-jTqz1e4tFpw喜马拉雅https://www.ximalaya.com/album/89574928小宇宙https://www.xiaoyuzhoufm.com/podcast/68ef81d14ce3619b345a32b2YouTube Musichttps://music.youtube.com/playlist?list=PLfV0OO4XXVBmQOOLxMpZXn519_PW3uneG&si=rgCVgHICnqNK5rFXAmazon Musichttps://music.amazon.com/podcasts/6d68d8c4-d7bb-4c1d-8b6c-1e9b0946463d/steven%E6%95%B0%E6%8D%AE%E6%BC%AB%E8%B0%88
3) Steven AI Talk（多语）
“Steven AI Talk” — delivering the clearest conversations on cutting-edge AI, technology, innovation, business, and entrepreneurship with AI summarizations on various high quality source contents.
🔗 Support the Creator & Access All Links
https://linktr.ee/learnbydoingwithsteven
语言平台链接EnglishApple Podcastshttps://podcasts.apple.com/gb/podcast/steven-ai-talk-english/id1846320778EnglishSpotifyhttps://open.spotify.com/show/43CVIH13u3pvIyg9aTEHwY?si=w-gPlNheRmCTfvlIPOwL9wEnglishAmazon Musichttps://music.amazon.com/podcasts/7aaf0f86-7cb1-4f6f-bba3-6fd3cb9dcad2/steven-ai-talkenglishEnglish小宇宙https://www.xiaoyuzhoufm.com/podcast/68ef7ec2332567e348b6e57bEnglishYouTube Musichttps://music.youtube.com/playlist?list=PLfV0OO4XXVBk811V6mTVbL483S_56ZtF5&si=qqGM8Es0NTSFtflX中文Spotify（Steven AI 播客）https://open.spotify.com/show/7gLoHfOKO302yNcF7bzNOu?si=FU7xcKwUQU-jfTXxi0grBg中文喜马拉雅https://www.ximalaya.com/album/88276097ItalianoSpotify（Italiano）https://open.spotify.com/show/7D3BcWR5xGzGap8A1bSeoQ?si=ek8GNzrnT-OdiGJ91vi0rw
4) YC 斯坦福创业课 2015 CS183B 精讲
平台链接Apple Podcastshttps://podcasts.apple.com/us/podcast/yc%E6%96%AF%E5%9D%A6%E7%A6%8F%E5%88%9B%E4%B8%9A%E8%AF%BE2015cs183b%E7%B2%BE%E8%AE%B2/id1846320657Spotifyhttps://open.spotify.com/show/5dg2pUoVlwvWCu2RSRYUay?si=esrJ8TByS-Cqrw4aOqKDyw喜马拉雅https://m.ximalaya.com/album/109171033?from=pc小宇宙https://www.xiaoyuzhoufm.com/podcast/68ef7ec662e8bfe0dffdd116YouTube Musichttps://music.youtube.com/playlist?list=PLfV0OO4XXVBlRgHAHArWBcpbOYykZfMqI&si=KwnQAp25yoprI39HAmazon Musichttps://music.amazon.com/podcasts/97f55ca2-d30d-48d8-afa5-9d51362bf92c/yc%E6%96%AF%E5%9D%A6%E7%A6%8F%E5%88%9B%E4%B8%9A%E8%AF%BE2015cs183b%E7%B2%BE%E8%AE%B2
页:
[1]