Send feedback Stay organized with collections Save and categorize content based on your preferences. Introduction to column-level access control BigQuery provides fine-grained access to sensitive columns using policy tags, or type-based classification, of data. Using BigQuery column-level access control, you can create policies that check, at query time, whether a user has proper access. For examp
Principles and best practices for data governance in the cloud Today’s businesses both generate and consume data at unprecedented rates. Diversity of data types and sources means that organizations have to grapple with data access, security, governance, and let’s not forget—regulatory compliance. These concerns give some customers pause when they consider moving their sensitive data to the cloud.
こんにちは、みかみです。 これまで BigQuery のデータのアクセス制御を指定できる最下層のリソースはデータセットで、テーブルやカラム単位でのアクセス制御はできませんでしたが、Data Catalog のポリシータグを付与することで、カラムレベルのアクセス制御が指定できるようになったそうです。 BigQuery の列レベルのセキュリティで、きめ細かなアクセス制御を | GCP ブログ Introduction to BigQuery Column-level security | BigQuery ドキュメント Restricting access with BigQuery Column-level security | BigQuery ドキュメント やりたいこと BigQuery のカラムレベルのアクセス制御を行うにはどうすればいいのか知りたい BigQuery のカラムレベルの
Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In order to clarify the intended use cases of machine learning models and minimize their usage in contexts for which they are not well suited, we recommend that released models be accompanied by documentation detailing their performance character
Infrastructure Democratizing data analysis with Google BigQuery Every day, over a hundred million people come to Twitter to find out what’s happening in the world and talk about it. Every Tweet and user action generates an event that we make available for internal data analysis at Twitter. Hundreds of employees analyze and visualize this data, and improving their experience is a top priority for t
Summary. Although the ability to manage torrents of data has become crucial to companies’ success, most organizations remain badly behind the curve. More than 70% of employees have access to data they should not. Data breaches are common, rogue data sets propagate in silos, and companies’ data technology often isn’t up to the demands put on it. In this article, the authors describe a framework for
Series of lava rock pools situated just off the southern end of Lake Turkana, northern Kenya's Jade Sea (source: Thomas Kujawa on Wikimedia Commons) In the olden days of data science, one of the rallying cries was the democratization of data. No longer were data owners at the mercy of enterprise data warehouses (EDWs) and extract, transform, load (ETL) jobs, where data had to be transformed into a
I am an Assistant Professor with the University of Amsterdam, conducting research at the intersection of data management and machine learning. I am affiliated with the Intelligent Data Engineering Lab, manage the AI for Retail Lab and have a joint appointment as research fellow at Ahold Delhaize. My work addresses data-related problems that occur in the real world application of machine learning.
Big Data Governance: Hive Metastore Listener for Apache Atlas Use Cases At eBay, we are obsessed with data quality and governance. Because eBay's Hadoop platform hosts 500 PB of data running over 15,000 nodes, the focus on governance is of utmost importance. This article discusses our experiences handling data governance at scale. Data governance helps ensure that high data quality exists througho
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