投稿時間:2022-01-23 17:19:09 RSSフィード2022-01-23 17:00 分まとめ(18件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
TECH	Engadget Japanese	ビジネスで使うファッション小物がAmazonセールでお買い得！ ネクタイ、ベルト、ソックスなど	https://japanese.engadget.com/fashion-time-sale-business-072326251.html	amazon	2022-01-23 07:23:26
TECH	Engadget Japanese	PS4用ゲームビルダー『Dreams Universe』でCG制作の映画、6月より撮影開始	https://japanese.engadget.com/sony-will-release-a-movie-made-using-ps-game-builder-072048094.html	awintersjourney	2022-01-23 07:20:48
python	Pythonタグが付けられた新着投稿 - Qiita	pythonのdequeに、append、pop、popleftしてみた際の備忘メモです。	https://qiita.com/seigot/items/4ad280bc2c021161b255	pythonのdequeに、append、pop、popleftしてみた際の備忘メモです。	2022-01-23 16:23:07
python	Pythonタグが付けられた新着投稿 - Qiita	pythonのlistに、先頭inseart、終端にinseart、classもinseartしてみた際の備忘メモ	https://qiita.com/seigot/items/9268ca84fec8b7a6ea33	pythonのlistに、先頭inseart、終端にinseart、classもinseartしてみた際の備忘メモpythonのlistに、先頭inseart、終端にinseart、classもinseartしてみた際の備忘メモです。	2022-01-23 16:17:06
Linux	Ubuntuタグが付けられた新着投稿 - Qiita	【Ubuntu 20.04】VirtualBox＋Vagrantを使用したインストール	https://qiita.com/kbys7367/items/5cc95c46941f25537d31	必要なツールおよびソフトウェア仮想化ソフトウェアインストール済みの環境を使用VirtualBoxvagrantLinuxゲストOSUbuntuLTSxbit作業ステップステップ①vagrantboxフィアルの検索ステップ②UbuntuLinuxゲストOSのインストールと初期設定ステップ①vagrantboxファイルの検索VagrantCloudのサイトから「Ubuntu」のvagrantboxファイルを検索します。	2022-01-23 16:25:41
Linux	Ubuntuタグが付けられた新着投稿 - Qiita	cloud-init で実施する SSH サーバー設定まとめ	https://qiita.com/SogoK/items/75f1ebbb636869d5fc82	cloudinitで実施するSSHサーバー設定まとめ今までUbuntuをヘッドレスインストールする時は、ネットワーク設定だけ済ませて、デフォルトのUbuntuユーザーでパスワード認証でいったんログインしてからetcsshsshdconfig書き換え……ということをやっていたのですが、RaspberryPiも増えてきてさすがに面倒になったので自動化してみました。	2022-01-23 16:19:43
Linux	Ubuntuタグが付けられた新着投稿 - Qiita	UbuntuにSAMBAインストールしてファイルサーバー環境構築！（WindowsとMac両方からアクセスできるやーつ）	https://qiita.com/waokitsune/items/27047b2ec3f2db8698c4	途中の出力は割愛SAMBAで公開するフォルダー作成と権限設定まずは下記コマンドでフォルダー作成mnttesterにしたのは何となくです。	2022-01-23 16:18:55
AWS	AWSタグが付けられた新着投稿 - Qiita	�既存の IPv4 CIDR ルールに 1 つの 参照先のグループ ID を指定することはできません。	https://qiita.com/himorishuhei/items/7426cab6cd83c3d8e4e3	ipvcidr	2022-01-23 16:38:03
Docker	dockerタグが付けられた新着投稿 - Qiita	GitHub PackagesでNode.jsのDockerイメージを管理する	https://qiita.com/bricolageart/items/8594e913545926f8de3a	最初に提案されたワークフロー「PublishDockerContainer」では、すでに確認したものと同様のDockerイメージ公開およびDockerイメージ構築ワークフローが設定されます。	2022-01-23 16:30:03
Docker	dockerタグが付けられた新着投稿 - Qiita	[めんどくさがりな人向け] docker + rails 即行資源	https://qiita.com/h9k2T6/items/f2817fc9ad26aedd6ab3	めんどくさがりな人向けdockerrails即行資源下記の方を対象に簡単な資源を用意しました。	2022-01-23 16:24:25
Git	Gitタグが付けられた新着投稿 - Qiita	Git user.nameとかuser.emailを設定する前にコミットしてしまった場合の回避策（未pushの場合）	https://qiita.com/miriwo/items/5cbc236ac619f2d1d1d8	Gitusernameとかuseremailを設定する前にコミットしてしまった場合の回避策未pushの場合概要Gitにてusernameとかuseremailを設定せずコミットしてしまった時の対処法をメモ的にまとめるご注意下記の方法はリモートリポジトリへ内容を反映する前だけ有効です。	2022-01-23 16:15:45
Ruby	Railsタグが付けられた新着投稿 - Qiita	[めんどくさがりな人向け] docker + rails 即行資源	https://qiita.com/h9k2T6/items/f2817fc9ad26aedd6ab3	めんどくさがりな人向けdockerrails即行資源下記の方を対象に簡単な資源を用意しました。	2022-01-23 16:24:25
海外TECH	DEV Community	Introduction to Modern Data Architecture (formerly Lake House)	https://dev.to/aws-builders/introduction-to-modern-data-architecture-formerly-lake-house-29g6	Introduction to Modern Data Architecture formerly Lake House Organizations have been building data lakes to analyze massive amounts of data for deeper insights into their data To do this they bring data from multiple silos into their data lake and then run analytics and AI ML directly on it It is common for these organizations to also have data stored in specialized data stores such as a NoSQL database a search service or a data warehouse to support different use cases To efficiently analyze all of the data spread across the data lake and other data stores businesses often move data in and out of data lake and between these data stores This data movement can get complex and messy as the data grows in these stores To address this businesses need a data architecture that not only allows building scalable cost effective data lakes but also supports simplified governance and data movement between various data stores We refer to this as a lake house architecture A lake house is a modern data architecture that integrates a data lake a data warehouse and other purpose built data stores while enabling unified governance and seamless data movement Architecture Options for Building an Analytics Application on AWS is a Series containing different articles that cover the key scenarios that are common in many analytics applications and how they influence the design and architecture of your analytics environment in AWS These series present the assumptions made for each of these scenarios the common drivers for the design and a reference architecture for how these scenarios should be implemented As shown in the following diagram with a lake house approach organizations can store their data in a data lake and also be able to use purpose built data stores that work with the data lake This approach allows access to all of their data to make better decisions with agility Modern data architectureIn a lake house design there are three different patterns for data movement They can be described as follows Inside out data movement A subset of data in a data lake is sometimes moved to a data store such as an Amazon OpenSearch Service cluster or an Amazon Neptune cluster to support specialized analytics such as search analytics building knowledge graphs or both This pattern is what we consider an inside out data movement For example enterprises send information from structured sources relational databases unstructured sources metadata media or spreadsheets and other assets first to a data lake that is moved to Amazon Neptune to build a knowledge graph Outside in data movement Organizations use data stores that best fit their applications and later move that data into a data lake for analytics For instance to maintain game state player data session history and leaderboards a gaming company right chooses Amazon DynamoDB as the data store This data can later be exported to a data lake for additional analytics to improve the gaming experience for its players We refer to this kind of data movement as outside in Around the perimeter In addition to the preceding two patterns there are scenarios where the data is moved from one specialized data store to another For example enterprises might copy customer profile data from their relational database to a NoSQL database to support their reporting dashboards This data movement is often considered as around the perimeter CharacteristicsScalable data lake A data lake is at the center of a well architected lake house design A data lake should be able to scale easily to petabytes and exabytes as data grows Use a scalable durable data store that provides the fastest performance at the lowest cost supports multiple ways to bring data in and has a good partner ecosystem Data diversity Applications generate data in many formats A data lake should support diverse data types ーstructured semi structured or unstructured Schema management A lake house design should support schema on read for a data lake with no strict source data requirement The choice of storage structure schema ingestion frequency and data quality should be left to the data producer A data lake should also be able to incorporate changes to the structure of the incoming data which is referred to as schema evolution In addition schema enforcement helps businesses ensure data quality by preventing writes that do not match the schema Metadata management Data should be self discoverable with the ability to track lineage as data flows through tiers within the data lake A comprehensive data catalog that captures the metadata and provides a query able interface for all data assets is recommended Unified governance A lake house design should have a robust mechanism for centralized authorization and auditing Configuring access policies in the data lake and across all the data stores can be extremely complex and error prone Having a centralized location to define the policies and enforce them is critical to a secure lake house architecture Transactional semantics In a data lake data is often ingested nearly continuously from multiple sources and is queried concurrently by multiple analytic engines Having atomic consistent isolated and durable ACID transactions is pivotal to keeping data consistent Reference architectureModern data architecture reference architecture Configuration notesTo organize data for efficient access and easy management The storage layer can store data in different states of consumption readiness including raw trusted conformed enriched and modeled It s important to segment your data lake into landing raw trusted and curated zones to store data depending on its consumption readiness Typically data is ingested and stored as is in the data lake without having to first define schema to accelerate ingestion and reduce time needed for preparation before data can be explored Partition data with keys that align to common query criteria Convert data to an open columnar file format and apply compression This will lower storage usage and increase query performance Choose the proper storage tier based on data temperature Establish a data lifecycle policy to automatically delete old data to meet your retention requirements Decide on a location for data lake ingestion that is an S bucket Select a frequency and isolation mechanism that meets your business needs Depending on your ingestion frequency or data mutation rate schedule file compaction to maintain optimal performance Use AWS Glue crawlers to discover new datasets track lineage and avoid a data swamp Manage access control and security using AWS Lake Formation IAM role setting AWS KMS and AWS CloudTrail No need to move data between a data lake and the data warehouse for the data warehouse to access it Amazon Redshift Spectrum can directly access the dataset in the data lake For more refer to the Derive Insights from AWS Lake House whitepaper Hope this guide gives you an Introduction to Modern data architecture formerly Lake House explains the Characteristics Reference Architecture and Configuration Notes for Modern data architecture Let me know your thoughts in the comment section And if you haven t yet make sure to follow me on below handles connect with me on LinkedInconnect with me on Twitter‍follow me on github️Do Checkout my blogs Like share and follow me for more content ltag user id follow action button background color important color fac important border color important Adit ModiFollow Cloud Engineer AWS Community Builder x AWS Certified x Azure Certified Author of Cloud Tech DailyDevOps amp BigDataJournal DEV moderator Reference Guide	2022-01-23 07:39:43
海外ニュース	Japan Times latest articles	Severely ill COVID patients in Japan up eightfold from start of ’22	https://www.japantimes.co.jp/news/2022/01/23/national/science-health/severely-ill-covid-patients-japan-eightfold-start-22/	Severely ill COVID patients in Japan up eightfold from start of The total number of severe patients announced by local governments nationwide stood at on Jan That figure topped on Friday for the	2022-01-23 16:19:12
ニュース	BBC News - Home	Nusrat Ghani: Muslimness a reason for my sacking, says ex-minister	https://www.bbc.co.uk/news/uk-politics-60100525?at_medium=RSS&at_campaign=KARANGA	ghani	2022-01-23 07:22:51
ニュース	BBC News - Home	Georgia Shooting: British man Matthew Willson shot through wall	https://www.bbc.co.uk/news/uk-england-surrey-60098840?at_medium=RSS&at_campaign=KARANGA	nearby	2022-01-23 07:44:07
ニュース	BBC News - Home	Nadal to face Shapovalov in Australian Open quarters	https://www.bbc.co.uk/sport/tennis/60097395?at_medium=RSS&at_campaign=KARANGA	Nadal to face Shapovalov in Australian Open quartersRafael Nadal continues his quest for a record breaking st major title at the Australian Open as Denis Shapovalov beats third seed Alexander Zverev	2022-01-23 07:31:41
北海道	北海道新聞	北京、区民２００万人全員にＰＣＲ　五輪開幕控え、感染封じ込めへ	https://www.hokkaido-np.co.jp/article/636783/	北京冬季五輪	2022-01-23 16:16:54