{"id":20781,"date":"2024-11-22T14:45:12","date_gmt":"2024-11-22T07:45:12","guid":{"rendered":"https:\/\/gcloudvn.com\/?p=20781"},"modified":"2024-11-25T08:39:19","modified_gmt":"2024-11-25T01:39:19","slug":"bigquerys-ai-assisted-data-preparation-is-now-in-preview","status":"publish","type":"post","link":"https:\/\/gcloudvn.com\/en\/kienthuc\/bigquerys-ai-assisted-data-preparation-is-now-in-preview\/","title":{"rendered":"BigQuery's AI-assisted data preparation is now in preview"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">In today's data-driven world, the ability to efficiently transform raw data into actionable insights is paramount. However, preparing and processing data is often a significant undertaking. And the advent of <strong>Google BigQuery&#8217;s AI-assisted data preparation<\/strong> has opened a new chapter that promises to revolutionize the way we work with data. By automating tedious tasks and boosting analytics, BigQuery is helping businesses get the most value from their data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Now in preview, <\/span><span style=\"font-weight: 400;\">BigQuery data preparation<\/span><span style=\"font-weight: 400;\"> provides a number of capabilities:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>AI-powered suggestions: <\/b>BigQuery&#8217;s AI-assisted data preparation<span style=\"font-weight: 400;\">\u00a0s\u1eed d\u1ee5ng Gemini trong BigQuery \u0111\u1ec3 ph\u00e2n t\u00edch d\u1eef li\u1ec7u v\u00e0 l\u01b0\u1ee3c \u0111\u1ed3 c\u1ee7a b\u1ea1n v\u00e0 \u0111\u01b0a ra c\u00e1c g\u1ee3i \u00fd th\u00f4ng minh \u0111\u1ec3 l\u00e0m s\u1ea1ch, chuy\u1ec3n \u0111\u1ed5i v\u00e0 l\u00e0m gi\u00e0u d\u1eef li\u1ec7u. \u0110i\u1ec1u n\u00e0y gi\u00fap gi\u1ea3m \u0111\u00e1ng k\u1ec3 th\u1eddi gian v\u00e0 c\u00f4ng s\u1ee9c c\u1ea7n thi\u1ebft cho c\u00e1c t\u00e1c v\u1ee5 chu\u1ea9n b\u1ecb d\u1eef li\u1ec7u th\u1ee7 c\u00f4ng.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data cleansing and standardization: <\/b><span style=\"font-weight: 400;\">Easily identify and rectify inconsistencies, missing values, and formatting errors in your data.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Visual data pipelines: <\/b><span style=\"font-weight: 400;\">The intuitive, low-code visual interface helps both technical and non-technical users easily design complex data pipelines, and leverage BigQuery's rich and extensible SQL capabilities.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data pipeline orchestration: <\/b><span style=\"font-weight: 400;\">Automate the execution and monitoring of your data pipelines. The SQL generated by BigQuery data preparation can become part of a <\/span><a href=\"https:\/\/cloud.google.com\/dataform\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Dataform<\/span><\/a><span style=\"font-weight: 400;\"> data engineering pipeline that you can deploy and orchestrate with CI\/CD, for a shared development experience.<\/span><\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-20785\" src=\"https:\/\/gcloudvn.com\/wp-content\/uploads\/2024\/11\/Thang-72024-2024-11-22T135353.777.jpg\" alt=\"\" width=\"600\" height=\"375\" srcset=\"https:\/\/gcloudvn.com\/wp-content\/uploads\/2024\/11\/Thang-72024-2024-11-22T135353.777.jpg 600w, https:\/\/gcloudvn.com\/wp-content\/uploads\/2024\/11\/Thang-72024-2024-11-22T135353.777-18x12.jpg 18w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">BigQuery data preparation helps you ensure the accuracy and reliability of your data, leading to more informed business decisions. BigQuery data preparation automates data quality checks and integrates with other Google Cloud services such as Dataform and Cloud Storage, providing a unified and scalable environment for your data needs.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_80 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/gcloudvn.com\/en\/kienthuc\/bigquerys-ai-assisted-data-preparation-is-now-in-preview\/#No_hoat_dong_the_nao\" >How does it work?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/gcloudvn.com\/en\/kienthuc\/bigquerys-ai-assisted-data-preparation-is-now-in-preview\/#Khach_hang_cua_BigQuery_dang_noi_gi\" >What BigQuery customers are saying\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/gcloudvn.com\/en\/kienthuc\/bigquerys-ai-assisted-data-preparation-is-now-in-preview\/#Bat_dau\" >Getting started<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"No_hoat_dong_the_nao\"><\/span><b>How does it work?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Getting started is easy. When you sample a BigQuery table in BigQuery data preparation, it uses state-of-the-art foundation models to evaluate the data and schema using Gemini in BigQuery to generate data preparation recommendations like filter and transformation suggestions. For example, it knows how to identify valid date formats by country and which columns can act as join keys, accelerating the data engineering process.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-20784\" src=\"https:\/\/gcloudvn.com\/wp-content\/uploads\/2024\/11\/Thang-72024-2024-11-22T135414.738.jpg\" alt=\"\" width=\"600\" height=\"375\" srcset=\"https:\/\/gcloudvn.com\/wp-content\/uploads\/2024\/11\/Thang-72024-2024-11-22T135414.738.jpg 600w, https:\/\/gcloudvn.com\/wp-content\/uploads\/2024\/11\/Thang-72024-2024-11-22T135414.738-18x12.jpg 18w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">In the above example (using synthetic data), the Birthdate column contains two different date formats and is of type STRING. BigQuery data preparation suggests to \u201c <\/span><i><span style=\"font-weight: 400;\">Convert column Birthdate from type string to date with the following format(s): '%Y-%m-%d','%m\/%d\/%Y <\/span><\/i><span style=\"font-weight: 400;\">\u201d. After you apply the suggestion card, you can verify the transformed preview data in a DATE format column.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-20783\" src=\"https:\/\/gcloudvn.com\/wp-content\/uploads\/2024\/11\/Thang-72024-2024-11-22T135429.744.jpg\" alt=\"\" width=\"600\" height=\"375\" srcset=\"https:\/\/gcloudvn.com\/wp-content\/uploads\/2024\/11\/Thang-72024-2024-11-22T135429.744.jpg 600w, https:\/\/gcloudvn.com\/wp-content\/uploads\/2024\/11\/Thang-72024-2024-11-22T135429.744-18x12.jpg 18w\" sizes=\"auto, (max-width: 600px) 100vw, 600px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">With <b>BigQuery&#8217;s AI-assisted data preparation<\/b>, you can<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Significantly reduce time spent discovering data quality issues and cleaning data by leveraging Gemini-assisted suggestion cards<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Customize your own suggestion cards by providing an example in the data grid<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Increase operational efficiency by deploying data preparation with incremental data processing<\/span><\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Khach_hang_cua_BigQuery_dang_noi_gi\"><\/span><b>What BigQuery customers are saying\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Customers are already solving numerous challenges with BigQuery data preparation.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">GAF is a major manufacturer of roofing materials in North America, and is adopting data preparation for creating data transformation pipelines on BigQuery.<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">\u201cGAF is looking to modernize the ETL infrastructure and adopt a BigQuery native, low-code solution. <\/span><\/i><span style=\"font-weight: 400;\">BigQuery data preparation<\/span><i><span style=\"font-weight: 400;\"> will help our skilled business users and the analytics team in the data preparation processes for the enablement of self-service analytics.\u201d <\/span><\/i><span style=\"font-weight: 400;\">- Puja Panchagnula, Management Director - Enterprise Data Management &amp; Analytics, GAF<\/span><\/p>\n<p><span style=\"font-weight: 400;\">mCloud Technologies helps businesses in sectors like energy, buildings, and manufacturing to optimize the performance, reliability, and sustainability of their assets.<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">\u201cWe receive data feeds from our partners. BigQuery data preparation allows our product managers to prepare and operate the file data feeds with little to no help from our data engineering team.\u201d <\/span><\/i><span style=\"font-weight: 400;\">- Jim Christian, Chief Product and Technology Officer, mCloud Technologies<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Public Value Technologies is a joint venture between two German public broadcasting organizations (ARD).<\/span><\/p>\n<p><i><span style=\"font-weight: 400;\">\u201cPublic Value Technologies receives data feeds from our media partners for our data mesh solution and AI applications. BigQuery data preparation allows our data analysts and scientists to rapidly integrate the data feeds that standardize and preprocess the data in a low code way.\u201d <\/span><\/i><span style=\"font-weight: 400;\">- Korbinian Schwinger, Team Lead Data Engineer, Public Value Technologies\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bat_dau\"><\/span><b>Getting started<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">With its powerful AI capabilities, intuitive interface, and tight integration with the Google Cloud ecosystem, <a href=\"https:\/\/gcloudvn.com\/en\/bigquery\/\">BigQuery<\/a> data preparation is set to revolutionize the way organizations manage and prepare their data. By automating tedious tasks, improving data quality, and empowering users, this innovative solution reduces the time you spend preparing data and improves your productivity.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To get started with BigQuery data preparation, explore the following resources:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/data-prep-introduction\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">See the user guides<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/www.youtube.com\/watch?v=KFHxW6x6uwM\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Watch the 2-minute demo video<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/cloud.google.com\/gemini\/docs\/bigquery\/overview\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Learn about Gemini in BigQuery<\/span><\/a><\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>Trong th\u1ebf gi\u1edbi d\u1eef li\u1ec7u ng\u00e0y nay, kh\u1ea3 n\u0103ng chuy\u1ec3n \u0111\u1ed5i d\u1eef li\u1ec7u th\u00f4 th\u00e0nh th\u00f4ng tin chi ti\u1ebft c\u00f3 th\u1ec3 h\u00e0nh \u0111\u1ed9ng m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3 l\u00e0 t\u1ed1i quan tr\u1ecdng. Tuy nhi\u00ean, vi\u1ec7c chu\u1ea9n b\u1ecb v\u00e0 d\u1ecdn d\u1eb9p d\u1eef&hellip;<\/p>","protected":false},"author":2,"featured_media":20782,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1,135],"tags":[],"class_list":["post-20781","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-kienthuc","category-google-cloud-platform","entry","has-media"],"_links":{"self":[{"href":"https:\/\/gcloudvn.com\/en\/wp-json\/wp\/v2\/posts\/20781","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gcloudvn.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gcloudvn.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gcloudvn.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/gcloudvn.com\/en\/wp-json\/wp\/v2\/comments?post=20781"}],"version-history":[{"count":0,"href":"https:\/\/gcloudvn.com\/en\/wp-json\/wp\/v2\/posts\/20781\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/gcloudvn.com\/en\/wp-json\/wp\/v2\/media\/20782"}],"wp:attachment":[{"href":"https:\/\/gcloudvn.com\/en\/wp-json\/wp\/v2\/media?parent=20781"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gcloudvn.com\/en\/wp-json\/wp\/v2\/categories?post=20781"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gcloudvn.com\/en\/wp-json\/wp\/v2\/tags?post=20781"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}