Default encryption at rest | Documentation | Google Cloud
Send feedback Default encryption at rest Stay organized with collections Save and categorize content based on your preferences. This content was last updated in May 2024 and represents the status quo as of the time that it was written. Google's security policies and systems may change going forward, as we continually improve protection for our customers. At Google, our comprehensive security strat
Default encryption at rest | Documentation | Google Cloud
Send feedback Default encryption at rest Stay organized with collections Save and categorize content based on your preferences. This content was last updated in May 2024 and represents the status quo as of the time that it was written. Google's security policies and systems may change going forward, as we continually improve protection for our customers. At Google, our comprehensive security strat
Observability for GKE | Google Kubernetes Engine (GKE) | Google Cloud
This page describes how you can understand the health of your applications and maintain application availability and reliability. Default observability features By default, GKE clusters are configured to do the following: Send system logs, audit logs, and application logs to Cloud Logging. Send system metrics to Cloud Monitoring. Use Google Cloud Managed Service for Prometheus to collect configure
Apache Kafka and Google Cloud Pub/Sub - Jesse Anderson
Blog Summary: (AI Summaries by Summarizes) Apache Kafka, Google Cloud Pub/Sub, and Amazon Kinesis are contenders for Big Data messaging systems.Pub/Sub is a cloud service provided by Google Cloud, while Kafka can be installed on-premises or in the cloud.Kafka can store as much data as needed and supports log compaction, while Pub/Sub stores messages for only seven days and cannot be configured to
Create custom cost controls | BigQuery | Google Cloud
Send feedback Stay organized with collections Save and categorize content based on your preferences. Create custom cost controls This document describes how to set or modify custom quotas to control costs. To learn how BigQuery analysts can estimate and control costs, see Control cost. If you have multiple BigQuery projects and users, you can manage costs by requesting a custom quota that specifie
BigQueryで150万円溶かした人の顔 - Qiita
※ かなり前の記事ですが、未だに引用されるので一応追記しておきます。タイトルと画像がキャッチーなのはちょっと反省していますが、これを見てBigQuery使うのを躊躇している人は多分あまり内容を読んでいないので気にする必要はないです。自分は当時の会社でも今の会社でも個人でも普通にBigQuery使っていて解析用データなどはBigQueryに入れる設計をよくしています。また、アドベントカレンダーだったのでネタっぽく書きましたが事前に想定できる金額です。 ※ 代役：プロ生ちゃん（暮井 慧） 巷のBigQueryの噂と言えば「とにかく安い」「数億行フルスキャンしても早い」などなど。とりわけ料金に関しては保存しておくだけであれば無視できるほど安く、SQLに不慣れなプロデューサーがクエリを実行しても月数ドルで済むなど、賞賛すべき事例は枚挙に暇がありません。 しかし、使い方によってはかなり大きな金額を使
Apache Spark - Wikipedia
Apache Sparkはオープンソースのクラスタコンピューティングフレームワークである。カリフォルニア大学バークレー校のAMPLabで開発されたコードが、管理元のApacheソフトウェア財団に寄贈された。Sparkのインタフェースを使うと、暗黙のデータ並列性と耐故障性を備えたクラスタ全体をプログラミングできる。 日経BP社が発表した「ITインフラテクノロジーAWARD 2015」において、SparkはDockerに次ぐ準グランプリとされた[2]。 概要[編集] フォールトトレラントシステムで管理され、複数マシンのクラスタに分散されたデータ項目の読み取り専用多重集合であるRDD（resilient distributed dataset）と呼ばれるデータ構造を中心とするアプリケーションプログラミングインターフェイスを備えている。MapReduceは、分散プログラム上で特定の線形データフロー
Overview | Transfer Appliance | Google Cloud
Send feedback Overview Stay organized with collections Save and categorize content based on your preferences. Transfer Appliance is a high-capacity storage device that enables you to transfer and securely ship your data to a Google upload facility, where we upload your data to Cloud Storage. For Transfer Appliance capacities and requirements, refer to the Specifications page.
Integrate Google BigQuery with Salesforce
If you research solutions that enable you to store and analyze big sets of data (and I mean REALLY big), you likely will come across BigQuery, a cloud-based data warehouse offered by our strategic partner Google. This blog post shows how to integrate transactional data from a Salesforce org and BigQuery using Apex and Lightning. Use case For this sample implementation we want to store changes on o
Google Prediction API — Google Developers
Learn how Gemini can help your business at our digital event, Gemini at Work, on Sept 24. Register now. Vertex AI is a fully-managed, unified AI development platform for building and using generative AI. Access and utilize Vertex AI Studio, Agent Builder, and 150+ foundation models. New customers get up to $300 in free credits to try Vertex AI and other Google Cloud products.
Work with arrays | BigQuery | Google Cloud
Send feedback Work with arrays Stay organized with collections Save and categorize content based on your preferences. In GoogleSQL for BigQuery, an array is an ordered list consisting of zero or more values of the same data type. You can construct arrays of simple data types, such as INT64, and complex data types, such as STRUCTs. The current exception to this is the ARRAY data type because arrays
The CREATE MODEL statement | BigQuery | Google Cloud
Send feedback Stay organized with collections Save and categorize content based on your preferences. The CREATE MODEL statement To create a model in BigQuery, use the BigQuery ML CREATE MODEL statement. This statement is similar to the CREATE TABLE DDL statement. When you run a query that contains a CREATE MODEL statement, a query job is generated for you that processes the query. For information
BigQuery documentation | Google Cloud
Stay organized with collections Save and categorize content based on your preferences. BigQuery is Google Cloud's fully managed, petabyte-scale, and cost-effective analytics data warehouse that lets you run analytics over vast amounts of data in near real time. With BigQuery, there's no infrastructure to set up or manage, letting you focus on finding meaningful insights using GoogleSQL and taking
「BigQueryML」でSQLを書いて機械学習モデルを構築&予測できる！ - Qiita
BigQuery上でpythonなどを使わずにSQLを用いてそのまま機械学習を回せるBigQueryMLという機能がベータ版で出ました！ 公式のチュートリアルを回してみて、かなり手軽に機械学習モデルを回せるのと、pythonよりも簡単にモデルの評価ができるので感動しました。 チュートリアルではBigQueryのサンプルデータを用いていたのですが、この記事ではKaggleのtitanicデータを対象にモデル構築、評価、KaggleへSubmitまでやってみようと思います。 All BigQuery ML Documentation Getting Started with BigQuery ML for Data Analysts 前処理 https://www.kaggle.com/startupsci/titanic-data-science-solutions このカーネル（公開されて
ユニオンページ | Google Cloud Dataprep のドキュメント | Google Cloud
This section provides a short overview of Dataprep by Trifacta, its key features, and how they interact with each other. Dataprep by Trifacta enables you to explore, combine, and transform diverse datasets for downstream analysis. Within an enterprise, data required for key decisions typically resides in various silos. It comes in different formats featuring different types. It is often inconsiste
Hacker News on BigQuery: Now with daily updates — So what are the top domains?
Author’s note: I left Google in 2020, hence these posts haven’t been updated to the latest features. But I’m still having fun with Hacker News’ data — this time with Snowflake. Thanks to the BigQuery Public Datasets Program, we now update Hacker News in BigQuery daily. Nothing better than having fresh data to analyze every day. To celebrate the occasion, I want to look at the top domains that Hack
Hacker News on BigQuery: Now with daily updates — So what are the top domains?
Author’s note: I left Google in 2020, hence these posts haven’t been updated to the latest features. But I’m still having fun with Hacker News’ data — this time with Snowflake. Thanks to the BigQuery Public Datasets Program, we now update Hacker News in BigQuery daily. Nothing better than having fresh data to analyze every day. To celebrate the occasion, I want to look at the top domains that Hack
BigQuery 活用術: UNNEST 関数
.app 1 .dev 1 #11WeeksOfAndroid 13 #11WeeksOfAndroid Android TV 1 #Android11 3 #DevFest16 1 #DevFest17 1 #DevFest18 1 #DevFest19 1 #DevFest20 1 #DevFest21 1 #DevFest22 1 #DevFest23 1 #hack4jp 3 11 weeks of Android 2 A MESSAGE FROM OUR CEO 1 A/B Testing 1 A4A 4 Accelerator 6 Accessibility 1 accuracy 1 Actions on Google 16 Activation Atlas 1 address validation API 1 Addy Osmani 1 ADK 2 AdMob 32 Ads
F-score - Wikipedia
Precision and recall In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not identified correctly, and th
麻酔科学 - Wikipedia
麻酔科学は、周術期を通じて患者の生命機能を安全に維持するための、麻酔の研究と臨床応用を中核とする専門分野である。19世紀以降、麻酔科学は、専門家ではない医師が未知の薬物や技術を使用する実験的な分野から、現在では高度に洗練され、安全で効果的な医療分野へと発展してきた。国によっては、麻酔科医は病院における最大の医師集団であり[8][9]、その役割は、手術室での麻酔治療という従来の役割をはるかに超えて、ペインクリニック、集中治療室の運営、術後鎮痛[10]、患者の手術への最適化を図るプレハビリテーション（英語版）などの分野にも及んでいる[8]。 全身麻酔手術中は、鎮静薬、鎮痛剤（麻酔薬、オピオイド(麻薬)等）、筋弛緩剤の使用などによって、呼吸を含む患者の生命維持機能の多くが減弱ないしは停止するため、必然的に麻酔のみでなく生命維持全般を受け持つ。術後の意識状態の確認も含み、救急医療での蘇生との学術的
周術期 - Wikipedia
周術期（しゅうじゅつき、英: perioperative period）または周手術期は、入院、麻酔、手術、回復といった、患者の術中だけでなく前後の期間を含めた一連の期間である。
Precision and recall - Wikipedia
Precision and recall In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written as a formula: Recall (also known
Feature learning - Wikipedia
Diagram of the feature learning paradigm in machine learning for application to downstream tasks, which can be applied to either raw data such as images or text, or to an initial set of features for the data. Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was inputted directly, compare transfer learning.[1] In machine lear
Feature engineering - Wikipedia
This article needs to be updated. Please help update this article to reflect recent events or newly available information. (February 2024) Feature engineering is a preprocessing step in supervised machine learning and statistical modeling[1] which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features. By providing models with relevant i
Digdag x Luigi x Beam が協力して賢者の石を取りに行くのをやめた - Qiita
TL;DR 前任者が闇の魔法使いだったという話 Workflow Engine は混ぜるな危険 ポエムなのでコードとかない 出てくる言葉については語らないのでいくらか前提知識が必要 Prologue 「ハリー・ポッターと賢者の石」でハリー・ポッター、ロン・ウィーズリー、ハーマイオニー・グレンジャーの三人はそれぞれが役割を分担してパスを繋ぎ賢者の石へと辿り着いていました。 Workflow Engine と呼ばれるようなものはようはそういう風にタスクで処理を独立させつつ順序に従って処理するための仕組みですね。 例えば YAML ベースの Digdag とか、 Python で記述する Luigi とか、Java や Python で Cloud Dataflow (Apache Beam) とか色々あります。 私は最近 これ を使ってログを分析して賢者の石に辿り着くためのデータ分析基盤の担当
Starship: Cross-Shell Prompt
Compatibility FirstWorks on the most common shells on the most common operating systems. Use it everywhere! Rust-PoweredBrings the best-in-class speed and safety of Rust, to make your prompt as quick and reliable as possible. CustomizableEvery little detail is customizable to your liking, to make this prompt as minimal or feature-rich as you'd like it to be.
GitHub - GoogleCloudPlatform/professional-services: Common solutions and tools developed by Google Cloud's Professional Services team. This repository and its contents are not an officially supported Google product.
The examples folder contains example solutions across a variety of Google Cloud Platform products. Use these solutions as a reference for your own or extend them to fit your particular use case. Anthos Service Mesh Multi-Cluster - Solution to federate two private GKE clusters using Anthos Service Mesh. Anthos CICD with Gitlab - A step-by-step guide to create an example CI/CD solution using Anthos
Managed Search Service - Amazon CloudSearch - AWS
Amazon CloudSearch is no longer available to new customers. Existing customers of Amazon CloudSearch can continue to use the service as normal. Learn more For more flexibility in search and analytics capabilities, try Amazon OpenSearch Service. Learn more here Amazon CloudSearch is a managed service in the AWS Cloud that makes it simple and cost-effective to set up, manage, and scale a search solu
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Identity and Access Management | Cloud Storage | Google Cloud
Managing models and jobs | AI Platform Training | Google Cloud
Google BigQuery でヒストリカルデータ保存の料金を半分に、 クエリの速度を 10 倍に
Query Bigtable data | BigQuery | Google Cloud
What is ANSI SQL, Oracle SQL and MySQL?
cbt CLI reference | Bigtable Documentation | Google Cloud
cbt CLI overview | Bigtable Documentation | Google Cloud
Cloud Storage
What is Storage Transfer Service? | Cloud Storage Transfer Service Documentation | Google Cloud
Google Prediction API — Google Developers
クイックスタート: BigQuery ML で ML モデルを作成する | Google Cloud
Getting started with Firebase Analytics, BigQuery - Firecasts
Domain engineering - Wikipedia
- ASF JIRA
beam/sdks/go at master · apache/beam
Beam Quickstart for Go
Google Cloud console
GitHub - apache/beam: Apache Beam is a unified programming model for Batch and Streaming data processing.
Cloud Developer Tools
Apache Beam - Wikipedia
Cloud Shell | Google Cloud
Cloud SDK - Libraries and Command Line Tools | Google Cloud
Quickstart: Create machine learning models in BigQuery ML | Google Cloud
Go by Example: Switch