投稿時間:2023-01-20 15:42:10 RSSフィード2023-01-20 15:00 分まとめ(44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
ROBOT	ロボスタ	【連載マンガ ロボクン vol.236】水玉ロボット登場！？ナノダ	https://robotstart.info/2023/01/20/robokun-236.html	firstappearedon	2023-01-20 05:25:05
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] ノルウェーのプレミアムフィッシュ「スクレイ」日本初上陸　イオン、イオンスタイルで限定販売	https://www.itmedia.co.jp/business/articles/2301/20/news133.html	itmedia	2023-01-20 14:42:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 「楽天モバイル 郵便局店」200店を閉店　チラシ設置で代替	https://www.itmedia.co.jp/business/articles/2301/20/news142.html	itmedia	2023-01-20 14:36:00
IT	ITmedia 総合記事一覧	[ITmedia PC USER] リンクス、Ryzen 7搭載6型ポータブルゲーミングPC「GPD WIN4」の取り扱いを開始	https://www.itmedia.co.jp/pcuser/articles/2301/20/news146.html	gpdtechnology	2023-01-20 14:25:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] アニメイトイオンモール橿原、3月24日グランドオープン　近畿圏有数のショッピングセンターに出店	https://www.itmedia.co.jp/business/articles/2301/20/news068.html	itmedia	2023-01-20 14:23:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] びっくりドンキー、新モーニングメニューを発表　ホテルのようなワンプレートで朝食を強化	https://www.itmedia.co.jp/business/articles/2301/20/news144.html	itmedia	2023-01-20 14:23:00
TECH	Techable（テッカブル）	近くにいるターゲットにだけスマホ広告を配信！ローカルビジネスにぴったりの「ロケポス」	https://techable.jp/archives/191878	位置情報	2023-01-20 05:53:37
TECH	Techable（テッカブル）	累計処方せん300万件超の調剤薬局経営支援SaaS「digicareアナリティクス」。売上データ収集・集計を自動化！	https://techable.jp/archives/191886	digicare	2023-01-20 05:51:34
TECH	Techable（テッカブル）	Shopifyで構築したECサイトから顧客情報を自動で取得。CRM施策を効率化するノーコードツール「テープス」	https://techable.jp/archives/191891	shopify	2023-01-20 05:35:46
TECH	Techable（テッカブル）	遠隔で玄関ドアの解施錠が可能に。三協アルミの電気錠とスマートホームの「SpaceCore」が連携	https://techable.jp/archives/191832	spacecore	2023-01-20 05:33:53
TECH	Techable（テッカブル）	テンタメ、東芝G「スマートレシート」と連携でレシートアップロードなしでポイ活可能に	https://techable.jp/archives/191858	東芝グループ	2023-01-20 05:32:21
TECH	Techable（テッカブル）	女性アスリートの生理・PMS事情に寄り添う。ピル処方サービス「スマルナ」がフットサルチームと連携	https://techable.jp/archives/191855	取り組み	2023-01-20 05:29:45
TECH	Techable（テッカブル）	案件紹介から給与通知までスマホアプリで完結。短期派遣向けキャスティングシステムが便利そう	https://techable.jp/archives/191842	人材派遣業	2023-01-20 05:27:48
TECH	Techable（テッカブル）	13の質問に答えるだけで、自分に合った食事が届く！カルビーの新サービス「OMA MESI」	https://techable.jp/archives/191837	omamesi	2023-01-20 05:26:21
TECH	Techable（テッカブル）	香港・シンガポールなどアジア主要都市の路線図を日本語表示。オフラインで使える乗換案内アプリ「NUUA METRO」	https://techable.jp/archives/191815	nuuametro	2023-01-20 05:23:56
IT	情報システムリーダーのためのIT情報専門サイト IT Leaders	NTTテクノクロス、同じ声で4カ国語を発話可能な音声合成ソフトウェア「FutureVoice Crayon」 | IT Leaders	https://it.impress.co.jp/articles/-/24337	NTTテクノクロス、同じ声でカ国語を発話可能な音声合成ソフトウェア「FutureVoiceCrayon」ITLeadersNTTテクノクロスは年月日、音声合成エンジン「FutureVoiceCrayon」に「クロスリンガル音声合成技術」を追加した。	2023-01-20 14:49:00
IT	情報システムリーダーのためのIT情報専門サイト IT Leaders	CTC、Microsoft 365の運用支援SaaS「AvePoint」を販売、データ移行/運用自動化などを支援 | IT Leaders	https://it.impress.co.jp/articles/-/24336	CTC、Microsoftの運用支援SaaS「AvePoint」を販売、データ移行運用自動化などを支援ITLeaders伊藤忠テクノソリューションズCTCは年月日、Microsoft運用支援クラウドサービス「AvePointOnlineServices」開発元米AvePoint、提供元AvePointJapan、AOSを販売開始した。	2023-01-20 14:03:00
python	Pythonタグが付けられた新着投稿 - Qiita	python Dataframe datetime to string	https://qiita.com/aizwellenstan/items/b6878f4bf2d5d1b894f1	astype	2023-01-20 14:53:56
python	Pythonタグが付けられた新着投稿 - Qiita	[FastAPI] 鬼の爆速実装！！！デコレータを使用したロギング	https://qiita.com/sotaheavymetal21/items/fda494485bdf2d3e7f95	fastapi	2023-01-20 14:43:47
js	JavaScriptタグが付けられた新着投稿 - Qiita	確実に動くGWT開発環境を作る	https://qiita.com/hprc/items/21e0b757df8ecf5682e9	googlewebtoolkit	2023-01-20 14:37:07
js	JavaScriptタグが付けられた新着投稿 - Qiita	Web音楽プレイヤー	https://qiita.com/nogizakapython/items/20f68230028d2596a25b	ltdoctypehtmlgtlt	2023-01-20 14:26:42
js	JavaScriptタグが付けられた新着投稿 - Qiita	&&の後に関数を書くと困る	https://qiita.com/ctk64/items/d2ce269ce8e8cd56aac8	dampampthisfunctionhogefi	2023-01-20 14:15:39
AWS	AWSタグが付けられた新着投稿 - Qiita	aws cli で組織子アカウントの課金をOU単位で振り分けした	https://qiita.com/asato-san/items/c6dac6ad600f17649c6a	awscli	2023-01-20 14:45:56
AWS	AWSタグが付けられた新着投稿 - Qiita	aws cli で組織子アカウントのサービス単位課金を抽出したい	https://qiita.com/asato-san/items/d610a7fcd0ddca7dde86	awscegetcosta	2023-01-20 14:23:00
AWS	AWSタグが付けられた新着投稿 - Qiita	CloudWAN④セグメント二つ、VPCアタッチメントによるマルチリージョン構成	https://qiita.com/tonkatsu_oishi/items/2169171d8590f699c43d	cloudwan	2023-01-20 14:12:00
AWS	AWSタグが付けられた新着投稿 - Qiita	CloudWAN③セグメント一つ、VPCアタッチメントによるマルチリージョン構成の構築	https://qiita.com/tonkatsu_oishi/items/cd3a389631782614cabc	cloudwan	2023-01-20 14:11:51
技術ブログ	Developers.IO	Systems Manager Quick Setup のパッチポリシーで AWS Organizations 配下のアカウントとリージョン全体に簡単にパッチ適用ができるようになりました	https://dev.classmethod.jp/articles/quick-setup-patch-policy/	awssystems	2023-01-20 05:33:07
海外TECH	DEV Community	Data Engineering and DataOps: A Beginner's Guide to Building Data Solutions and Solving Real-World Challenges	https://dev.to/chaos-genius/data-engineering-and-dataops-a-beginners-guide-to-building-data-solutions-and-solving-real-world-challenges-4p5j	Data Engineering and DataOps A Beginner x s Guide to Building Data Solutions and Solving Real World Challenges IntroductionData engineering is the process of designing building maintaining and running systems and infrastructure for storing processing and analyzing large complex datasets It is a field that has recently become much more important because of the growth of “big data and the growing reliance on business models that are driven by data In fact  according to a report by Gensigma demand for data engineers has grown so quickly that an organization needs at least data engineers for every three data scientists The global market for big data and data engineering services is also seeing significant growth with estimates ranging from a whopping to increase on a per year basis from to This shows how important it is to learn and improve data engineering skills since it can be a rewarding high paying and in demand field in the tech industry right now This particular innovation was primarily driven by the FAANG now MAANGO companies Facebook  Meta  Amazon  Apple  Netflix  Google and Oracle who have adopted data driven business models and built advanced data infrastructure to support them These companies have put a lot of money and time into hiring and developing data engineering talent and technologies They have also helped create new tools and ways to manage and analyze data at a large scale So nowadays companies and businesses rely heavily on data to improve their products and services by understanding user actions and behavior Because of this they “have to heavily rely on data engineers to design build maintain the infrastructure and systems that enable the collection storage and analysis of large and complex data sets Data engineering has therefore become a crucial field with skilled data engineers playing a key role in driving data driven innovations In this article we ll look into the different parts and processes involved in data engineering including DataOps and how they help companies and businesses use the power of data to make their products and services better Collecting and Storing DataIn today s digital world virtually every online action you perform generates information that is collected and held onto by businesses companies or corporations This includes visiting web apps and websites ordering products or merchandise using apps and more The MAIN question is where do these companies keep all of this data The answer is in a database management system DBMS There are two main types of DBMS Relational databases Relational databases store data in a way that looks like a spreadsheet format with rows columns These are often used to store structured data such as customer orders inventory A few perfect examples of a relational databases are MySQL PostgreSQL MariaDB  Microsoft SQL Server and Oracle Database To build a relational database we need to make a “data model that shows how the different tables work together This helps to understand the entire picture and makes it easier to analyze the data which would make the analysis a great deal and a whole lott less complicated and difficult to do so Non relational databases also referred to as NoSQL databases On the other hand NoSQL non relational databases store data in varied formats like key value pairs documents and graphs It is often used for handling large amounts of unstructured or semi structured data such as that generated by social media online giants They are also well suited for applications that require high levels of flexibility and scalability The type of database a company uses depends on its specific needs There are many different companies that make use of both relational and non relational db to store and manage their data For example Amazon uses both relational and non relational database like cassandra   DynamoDB to store customer product catalog and order and ads info Google also uses both types of databases with relational databases like MySQL and non relational databases like Bigtable and Cloud Datastore Facebook Twitter Netflix Uber Airbnb LinkedIn Indeed and Dropbox are also among the other companies that make use both relational and non relational databases to store and manage their data These databases are used to store and manage a wide variety of data including user data product and service data and business critical information Using SQL to Communicate with DatabasesWe can make use of a scripting language like Structured Query Language SQL  to extract all the necessary information from a database SQL allows us to communicate with the database easily and helps to retrieve the desired data by passing very simple commands For example as shown in the screenshot below we can use commands like SELECT FROM table name LIMIT This particular command retrieves the first five rows from a table of rows SQL also allows us to perform various different kinds of operations such as inserting updating and deleting data directly from the database itself Learn more about SQL form here Using Programming Languages with DatabasesIn addition to Structured Query Language SQL we can also use a variety of different programming languages such as Python  Java  JavaScript  R  Julia  Scala  or any other programming language as long as it supports a basic database connection and functions to perform all of those operations to connect to databases and perform more advanced query operations on the data This gives us greater flexibility and allows us to apply custom created logic to the data The Data Engineering ProcessOnce the data is stored in a database the next step is to use it to solve complex business problems This can be achieved by creating dashboard metrics machine learning models and various other types of solutions The process of going from raw data in a database to a final solution is known as “data engineering This “data engineering process also known as DataOps usually may consist of several steps and can be different from company to company depending on its specific needs as well as requirements Essential Role of OLTP and OLAP in Data EngineeringLet s skip ahead to the earlier section now that you understand what “data engineering is Relational databases are designed for faster reading writing and updating of data rather than in depth analysis This means that if you try to run a large analytics query on a relational database it may not be able to handle the workload and could potentially crash In order to gain insights from data we need a different type of system that is optimized for analytics work This is where OLAP Online Analytical Processing comes in But wait So what is OLTP and OLAP Online Transaction Processing OLTP Online Transaction Processing OLTP is a type of database system that is designed to support high concurrency data intensive transactions It is typically used to handle large volumes of data that are constantly being inserted updated deleted such as in a retail or financial application OLTP systems are typically implemented using a Relational Database Management System and use Structured Query Language SQL for data manipulation and query processing Learn more from here Online Analytical Processing OLAP On the other hand Online Analytical Processing OLAP is a type of database system that is designed for fast querying and analysis of data It is typically used to support business intelligence BI and decision making activities such as data mining data analysis statistical analysis and reporting OLAP systems are designed to support complex queries and calculations on large data sets often involving aggregations and roll ups of data across multiple dimensions Learn more from here Moving Data from OLTP to OLAP ETLTo analyze the data that is stored in an OLTP system such as a Postgres or MySQL database we need to transfer it to an OLAP system or a Data Warehouse like Snowflake This exact process is called ETL extract transform load ETL involves extracting data from one or multiple sources transforming it based on business logic or the data warehouse design and then loading it onto a one specific target location Learn more about ETL from here Traditional and Modern “ETL ApproachesTraditionally ETL pipelines were developed through the laborious process of writing them from absolutely scratch However newer approaches and tools are constantly being developed released and made easily available for purchase on the market So for instance rather than developing a complete ETL pipeline from scratch you can use a platform and tools like AWS Glue and Fivetran which provides a fully managed environment to Extract load and transform data in the data warehouse based on your specific requirements These particular tools are designed to save you the time and effort of having to manually write an entire ETL pipeline from absolute scratch There are numerous tools available on the market but it is important not to become TOO attached to any one of them because they may come and go However the fundamental concepts such as understanding query languages and data processing systems like OLTP and OLAP will remain the same forever The Data Processing Dilemma Batch vs Real Time ProcessingDifferent businesses companies and people have different requirements Some of them ー those businesses and companies ー want to view that data in real time while others want to view their data only once depending upon their use cases and requirements Therefore it is becoming increasingly important to carefully select the right processing system to manage and make use of that particular data So in general we have two processing techniques Batch processing Real time processingBatch processing involves persisting data as it comes in through events For example let s say A company named “Awesome operates a simple e commerce website that sells merchandise The company uses batch processing to periodically extract data from its transactional DB and load it into a data warehouse The data warehouse is used to perform data analysis and generate reports on customer behavior sales trends and other business metrics this is the perfect example of batch processing Whereas  Real time processing involves persistently storing data as it comes in through events in real time For example Companies like Uber and In Drive use GPS trackers in their fleets of vehicles Every vehicle s location speed and other data are constantly being sent to a centralized server by the GPS units installed in them So the real time processing system set up by these companies analyzes the data from the GPS units in near real time This information is used to give passengers up to date updates on things like vehicle locations and expected arrival times Processing Large Amounts of DataFor small amounts of data it is possible to process it on a single computer However when dealing with HUGE amounts of data multiple computers processing powerhouse are needed to divide and process the data in chunks and combine the final output There are several frameworks available for batch processing such as Hadoop  Apache Storm and DataTorrent RTS For real time streaming we have other frameworks and tools like Apache Kafka  ActiveMQ and AWS Kinesis Choosing the right processing system depends on the specific needs and requirements So by understanding the difference between OLTP and OLAP and the options for batch and real time processing you can select the right tools and technology to build a solution that meets your exact requirements Big data landscape and cloud computingThe big data landscape is filled with various tools technology for multiple different types of work they do and issues they solve However processing large amounts of data requires a powerful system such as big data crunching machines like supercomputers In the past companies and businesses would build their own servers and maintain them in a local data center This often resulted in multiple hardware failures and issues requiring maintenance and software upgrades Benefits of Moving to the CloudMany businesses and companies are moving and transitioning their entire operations to the cloud to escape headaches associated with hardware breakdowns and regular software updates as we mentioned earlier Because of this companies only have to pay for the resources that they really use and they can scale their servers to meet any demand Cloud service providers also provide several different kinds of services to manage large amounts of data and ease the process of storing and processing data making the entire process much more manageable According to a Gartner cloud computing infrastructure ranking the top three cloud platform providers are Amazon Web Services AWS  Google Cloud Platform GCP and Microsoft Azure Modern Data Stack and Data Engineering IndustryOnce a business or company has its architecture running on a cloud platform and has established ETL pipelines and a data warehouse they can use this data for analytics and machine learning applications After that data engineers AI ML engineers will be able to create and implement machine learning models in production allowing the company to develop and obtain deep insights Problems and Solutions in the Data Engineering Industry The Emergence of the Modern Data Stack The field of data engineering is growing rapidly and with it comes a wide range of MASSIVE challenges One common issue is the difficulty of migrating data from local on premise systems to cloud warehouses which can get very complex and time consuming Many businesses encounter problems during this process and try to create solutions for them When one company faces a problem it is likely that other companies might encounter the same kind of issues and difficulties This creates opportunities for companies businesses to identify gaps in the market and develop new tools to address these needs This is exactly what led to the development of the “Modern Data Stack ConclusionData engineering is a very important field that plays a VITAL role in helping out businesses companies startups and organizations break down valuable insights from the data they have By mastering the skills of data gathering storage and analysis skills data engineers can solve real world business challenges and drive business growth by an order of magnitude Whether you re just starting in data engineering or looking to advance your career it s important to continuously learn and improve your skills to stay competitive in this rapidly evolving field You can become a top performing engineer and make a meaningful contribution to the world if you have the correct tools resources and right mindset	2023-01-20 05:33:25
ニュース	BBC News - Home	Ukraine war: Allies to meet as Kyiv requests tank donations	https://www.bbc.co.uk/news/world-europe-64341337?at_medium=RSS&at_campaign=KARANGA	leopard	2023-01-20 05:17:37
ニュース	BBC News - Home	The Papers: 'Inflation corner turned' and levelling up anger	https://www.bbc.co.uk/news/blogs-the-papers-64341148?at_medium=RSS&at_campaign=KARANGA	chief	2023-01-20 05:37:45
ニュース	BBC News - Home	Australian Open 2023: Jessica Pegula through to fourth round in 65 minutes	https://www.bbc.co.uk/sport/tennis/64342585?at_medium=RSS&at_campaign=KARANGA	australian	2023-01-20 05:38:04
ビジネス	ダイヤモンド・オンライン - 新着記事	揺れるルーブル相場、対ロ制裁強化の影響は - WSJ発	https://diamond.jp/articles/-/316488	相場	2023-01-20 14:12:00
GCP	Google Cloud Platform Japan 公式ブログ	Cloud Storage 向けイベント ドリブン転送のリリースを発表	https://cloud.google.com/blog/ja/topics/developers-practitioners/announcing-launch-event-driven-transfer-cloud-storage/	GoogleCloudの分析機能と機械学習機能を活用するには、自動化されたプロセスでリアルタイムにAWSSからCloudStorageにデータを複製する必要があります。	2023-01-20 05:20:00
GCP	Google Cloud Platform Japan 公式ブログ	Vertex AI を使用した、業界をリードするピアグループ ベンチマーク ソリューションの構築	https://cloud.google.com/blog/ja/products/ai-machine-learning/using-vertex-ai-for-peer-group-benchmarking-in-capital-markets/	このパイプラインの最終結果が、次元削減、クラスタリング、説明可能性のための一連のトレーニング済みモデルであり、そのすべてがVertexAIModelRegistryに格納されます。	2023-01-20 05:10:00
ビジネス	東洋経済オンライン	｢自宅ミニマル化｣で人生の目標がクリアに見える 身の回りのものはあなたから時間を奪っている | 街･住まい | 東洋経済オンライン	https://toyokeizai.net/articles/-/645415?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2023-01-20 14:30:00
IT	週刊アスキー	AIイラスト特化型投稿サイト「chichi-pui」2ヵ月で会員数1万人を突破	https://weekly.ascii.jp/elem/000/004/121/4121336/	chichipui	2023-01-20 14:45:00
IT	週刊アスキー	クルマとコラボしたアフタヌーンティー！　新横浜プリンスホテル、「FIAT Collaboration Afternoon Tea」を販売	https://weekly.ascii.jp/elem/000/004/121/4121296/	collaborationafternoontea	2023-01-20 14:40:00
IT	週刊アスキー	IDC、2023年の国内IT市場を21兆3716億円と予測	https://weekly.ascii.jp/elem/000/004/121/4121297/	idcjapan	2023-01-20 14:40:00
IT	週刊アスキー	新作スマホゲーム『エンゲージ・キル』事前登録者数が20万人を突破	https://weekly.ascii.jp/elem/000/004/121/4121332/	engagekill	2023-01-20 14:30:00
IT	週刊アスキー	『ウマ娘』で★3育成ウマ娘「ダイタクヘリオス」が本日より出走！	https://weekly.ascii.jp/elem/000/004/121/4121329/	育成	2023-01-20 14:10:00
マーケティング	AdverTimes	ファミリーマート、サステナビリティ推進部をマーケ本部下に（23年3月1日付）	https://www.advertimes.com/20230120/article409433/	執行役員	2023-01-20 05:50:53
マーケティング	AdverTimes	マーケ戦略統括を新設　社労士支援のエムケイシステム	https://www.advertimes.com/20230120/article409429/	販売	2023-01-20 05:31:32
GCP	Cloud Blog JA	Cloud Storage 向けイベント ドリブン転送のリリースを発表	https://cloud.google.com/blog/ja/topics/developers-practitioners/announcing-launch-event-driven-transfer-cloud-storage/	GoogleCloudの分析機能と機械学習機能を活用するには、自動化されたプロセスでリアルタイムにAWSSからCloudStorageにデータを複製する必要があります。	2023-01-20 05:20:00
GCP	Cloud Blog JA	Vertex AI を使用した、業界をリードするピアグループ ベンチマーク ソリューションの構築	https://cloud.google.com/blog/ja/products/ai-machine-learning/using-vertex-ai-for-peer-group-benchmarking-in-capital-markets/	このパイプラインの最終結果が、次元削減、クラスタリング、説明可能性のための一連のトレーニング済みモデルであり、そのすべてがVertexAIModelRegistryに格納されます。	2023-01-20 05:10:00