投稿時間:2023-01-23 14:20:03 RSSフィード2023-01-23 14:00 分まとめ(24件)

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ROBOT	ロボスタ	「生産性向上を実現するロボットシステム導入のポイント」セミナーを2月21日に開催　東京都中小企業振興公社	https://robotstart.info/2023/01/23/iot-robot-seminar.html	「生産性向上を実現するロボットシステム導入のポイント」セミナーを月日に開催東京都中小企業振興公社シェアツイートはてブ東京都中小企業振興公社は、「生産性向上を実現するロボットシステム導入のポイント」にフォーカスしたセミナーを開催する。	2023-01-23 04:00:33
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 「スマホのトラブルに巻き込まれた」子どもの2割、内容は？	https://www.itmedia.co.jp/business/articles/2301/23/news108.html	itmedia	2023-01-23 13:37:00
IT	ITmedia 総合記事一覧	[ITmedia PC USER] プラネックス、コンセントに直刺しできる小型設計のトラベルルーター	https://www.itmedia.co.jp/pcuser/articles/2301/23/news110.html	ieeen	2023-01-23 13:31:00
IT	ITmedia 総合記事一覧	[ITmedia News] 2022年の買い物、スーパーの平均単価は4％アップ、増税以降の最高値に　東芝データ、100万人のレシートデータから分析	https://www.itmedia.co.jp/news/articles/2301/23/news106.html	itmedia	2023-01-23 13:06:00
AWS	AWS Japan Blog	週刊AWS – 2023/1/16週	https://aws.amazon.com/jp/blogs/news/aws-weekly-20230116/	週刊	2023-01-23 04:01:17
python	Pythonタグが付けられた新着投稿 - Qiita	【アニメーション付】できるだけ図解したいPID制御【Python，制御系ライブラリ不使用】	https://qiita.com/akinami/items/29a851c7051b1968b396	関係	2023-01-23 13:27:00
python	Pythonタグが付けられた新着投稿 - Qiita	ARC154回答メモ	https://qiita.com/Konini64/items/bbb11ef4c880ae0a910f	aswapdigit	2023-01-23 13:06:34
js	JavaScriptタグが付けられた新着投稿 - Qiita	文字列をspanタグで囲む	https://qiita.com/wataruNakai/items/3875b93e0366b996a736	texttexttexttexttextte	2023-01-23 13:06:37
AWS	AWSタグが付けられた新着投稿 - Qiita	[雑メモ]サブネットの利用可能なIPアドレス数を取得する	https://qiita.com/okadarhi/items/abf5f176d9c9ced9aab7	rnametagvaluevaluesokada	2023-01-23 13:44:46
golang	Goタグが付けられた新着投稿 - Qiita	“Preemptive interface Anti-Pattern in Go” とは？	https://qiita.com/_otakakot_/items/b0ece0f0995e83bee485	ceppreemptivei	2023-01-23 13:18:44
海外TECH	DEV Community	DataOps 101: An Introduction to the Essential Approach of Data Management Operations and Observability	https://dev.to/chaos-genius/dataops-101-an-introduction-to-the-essential-approach-of-data-management-operations-and-observability-2gea	DataOps An Introduction to the Essential Approach of Data Management Operations and ObservabilityIn today s day and age  data has become a crucial asset for organizations across all kinds of industries Industry after industryーfrom retail to e commerce to manufacturing to accounting to insurance to healthcare to financeーuses data to fuel innovation enhance operations and make informed decisions However managing and utilizing data effectively is no easy task That is exactly where the field of “DataOps comes in DataOps borrows concepts from DevOps and attempts to help organizations rapidly deliver the right data at a very fast pace The traditional process for delivering data to the business can be really very slow and really time consuming therefore DataOps aims to promote agility flexibility and the continuous delivery of fresh data In this article we ll provide a comprehensive introduction and guide to DataOps covering its essential key components the benefits it offers how it differs from DevOps and the best practices for implementing it We ll also go over some of the potential challenges in implementing DataOps and provide resources for further reading on this vital data management operations strategy But first let s define DataOps and explain why it s become such a crucial part of modern data management What is DataOps Data Operations or DataOps for short is used to describe a set of practices and processes that are designed to improve the collaboration integration and automation of data management operations and tasks These practices and processes include a focus on agile methodologies It is intended to help organizations better manage their data pipelines reduce the workload and time required to develop and deploy new data driven applications and improve the quality of the data being used DataOps is meant to eliminate barriers between data engineers data scientists and data business analystsーas well as other teams and departments within an organizationーenabling them to work together more efficiently and effectively to manage and analyze data Many businesses and organizations have already adopted DataOps principles to make better use of their data and increase productivity Let s take a look at Netflix as an example they have a large and very complex data environment with data coming from multiple different sources including subscriber accounts  viewing or streaming activity and customer support inquiries To manage this data effectively Netflix has implemented DataOps practices and tools such as automation collaboration and monitoring Netflix has automated the data ingestion automation and preparation processes allowing it to quickly and accurately integrate data from multiple different sources and prepare it for analysis which will help Netflix directly gain a better understanding of subscriber activity behaviour and preferences which in turn allows it to make better decisions about content movie show recommendations their own marketing campaigns and product development Why is DataOps important In today s fast paced modern business world DataOps plays a vital role in helping businesses and organizations stay ahead as the ability to analyze data rapidly and precisely can provide them with a competitive advantage over others DataOps simplifies and automates the complex process of collecting storing and analyzing data making it more efficient accurate and relevant to the business s needs requirements This enables businesses to make better use of their data assets and derive more value from them Overall DataOps plays a key part in any organization s data management and data management operations strategy because it lets them use their fresh data assets to drive business growth and fresh new innovations DataOps empowers businesses and organizations to make better faster decisions and get the most out of their data It helps them extract valuable insightsーand drive productivity as a result With the right tools and a well thought out plan businesses can make more informed timely decisions Nevertheless a significant obstacle to data driven initiatives is ensuring decision makers have access to important data and know how to use it effectively DataOps helps bridge this gap and fosters collaboration between teams thereby enabling organizations to deliver products faster and more effectively DataOps is a people driven practice meaning that it depends on the abilities and knowledge of the individuals It is not a tool or application that can be bought and implemented without the required human resources Instead it necessitates a team of proficient data experts that can collaborate effectively and efficiently Exploring the Key Differences Between DevOps and  DataOps DevOps is a set of practices that combines software development Dev and IT operations Ops to reduce system and application development lifecycles DevOps has been defined as an organizational approach aimed at creating empathy and cross functional collaboration It aims to establish an environment in which software development building testing and release can occur more quickly frequently and consistently img alt What is DevOps Source   gitlab com lt br gt height src dev to uploads s amazonaws com uploads articles yhvdcsazndpcas png width The main goal of DevOps is to improve the collaboration and communication between developers and operations teams and automate the build test and release service cycle and manage and monitor infrastructure and applications in production DevOps LifecycleDevOps lifecycle consists of several phases that are followed when developing and maintaining software applications Plan The plan phase involves identifying the goals objectives of the project and the resources that will be required to complete it Develop Develop phase where the software is actually developed This involves writing code building mockups prototypes and testing the software to ensure it is functional Test The test phase comes after the software has been developed it must be tested to ensure it is error free and will function as intended This may include unit testing integration testingーand other types of testing Deploy  Deploy phase is where the software or application is deployed to a production environment where end users can use it Maintain  Maintain phase is where the software will be maintained to guarantee it continues to work as expected This could involve patch fixes security upgrades and hotfixes to ensure the software runs smoothly indefinitely DataOps LifecycleThe DataOps lifecycle typically consists of the following stages Ingest  Ingest stage involves extracting data from multiple different raw data sources and storing it in a centralized location such as a data warehouse or data lake Prepare Prepare stage is where data engineers and data scientists prepare the data for analysis by extracting cleaning and transforming it This may involve tasks such as data deduplication  data integration mining and feature extraction Model The model  stage involves building AI ML models and other statistical models to analyze and make predictions based on the data Data scientists are typically responsible for this stage Visualize Visualize stage involves creating charts graphs and other visualizations to help others understand and interpret the data Deploy  Deploy stage is where the models and other data products developed in previous stages are deployed and made available to end users Observability The observability stage involves monitoring and analyzing the performance of the data quality and ensuring that it meets the needs of the end users This stage also involves collecting feedback and implementing improvements as needed To sum up now that you know what sets the DataOps and DevOps lifecycles apart DataOps seeks to optimize an organization s whole data lifecycle from data ingestion and preparation through analysis and visualization In contrast DevOps is focused on enhancing the agility of the software development process through automation and integration DataOps aims to improve the efficiency and effectiveness of data processing and utilization It can be thought of as the function within an organization that controls the data journey from source to value Collaboration Across Teams for Data DeliveryDataOps is a collaborative effort within an organization with many different teams of people working together to ensure that DataOps functions properly and delivers data value  So before the data is delivered to end users it is subjected to a number of treatments and refinements from multiple teams Data scientists first use their data science techniques such as machine learning and deep learning to build models using software stacks such as Python or R and tools such as Spark or Tensorflow among others and the models are then transferred to data engineers who collect and manage the data used to train and evaluate these models while data developers and data architects create complete applications that include the models The data governance team then implements data access controls for training and benchmarking purposes while the operations team Ops is in charge of putting everything together and making it available to end users Key Components of DataOpsDataOps involves several key components which work together to improve data management processes These includes Continuous integration and Continuous delivery CI CD Continuous integration and Continuous delivery CI CD  is a practice that involves frequently integrating and testing code changes and then quickly and efficiently pushing those changes to production environments In DataOps CI CD plays a crucial role in ensuring the accuracy and consistency of data as it is integrated and delivered to the appropriate people systems By constantly developing building automating and testing data changes and then quickly delivering them to production without any downtime DataOps teams can minimize the risk of errors and ensure that data is delivered in a timely and reliable manner Data governanceThe process of establishing policies procedures and standards for managing data assets as well as an organizational structure to support enterprise data management is known as data governance Data governance in DataOps helps to ensure that data is collected stored and used in a consistent and in ethical manner img alt Data Governance Source   graymatteranalytics com lt br gt height src dev to uploads s amazonaws com uploads articles wnshnzslcwxxhyypeb png width Data quality management and measurementData quality management and measurement involve identifying correcting and preventing any errors or inconsistencies in data It helps ensure that the data being used is fully reliableーand accurate This is critical because poor data quality can lead to incorrect or misleading insights and decisions which can have serious consequences img alt Data Quality Measurement Source   passionned com lt br gt height src dev to uploads s amazonaws com uploads articles ngvtecdxetxeq png width Data OrchestrationData orchestration refers to the management and coordination of data processing tasks in a data pipeline It involves specifying and scheduling how tasks will be completed as well as dealing with errors and how tasks interact with one another Data orchestration is critical in DataOps for automating and optimizing the flow of data through the pipeline This can include tasks such as extracting data from various sources transforming and cleaning the data and loading it into a target system for analysis or reporting purposes DataOps ObservabilityAs we ve already discussed what DataOps is let s briefly review it DataOps is a collection of best practices and technology used to manage and develop data products optimize data management processes improve quality speed and collaboration and promote continuous improvement DataOps is based on the same principles and practices as DevOps Still it has taken longer to become fully matured because data is constantly changing and can be more fragile than software applications infrastructures For example let s suppose that if a software application goes down it can be easily restored without significant impact but if data becomes corrupted it may have serious consequences This is the exact reason why DataOps has taken longer to get off the ground compared to DevOps To ensure that data performs optimally and meets desired standards for quality reliability and efficiency it is important to implement DataOps observability This involves regularly observing and monitoring data and using the insights gained to make informed decisions DataOps observability is a newer concept but the practice of observability itself has a long history in the DevOps world For example observability platforms solutions such as  AppDynamics and Splunk help software engineers improve application reliability and reduce site app downtime DataOps observability is not just limited to testing and monitoring data quality and the data pipeline It also includes monitoring the health of the machine learning models analyzing the application security measures to data infrastructure tracking KPI and business monitoring In other words it covers a wide range of areas beyond just monitoring the health of data quality and data pipeline DataOps observability is a somewhat ambiguous concept that is interpreted differently in the data community Still in essence it refers to an organization s businesses ability to fully understand the health of the data To sum it up DataOps observability must address a few key areas data quality and data pipeline reliability Data quality is important to business users who want high quality data they can trust Data pipeline reliability is critical to data engineers and scientists who need their data pipeline to run smoothly Also In addition to these two components DataOps observability includes BizOps which tracks monitors the health and KPI of the business as well as monitors the usage and the cost of the data This is exactly where Chaos Genius fits in Offering a complete observability solution it facilitates businesses and organizations in testing the resilience and reliability of data which can directly help businesses to improve their spending and boost their performance To create a successful data product businesses should focus on three key areas  data governance  data access and security and DataOps and quality Data governance involves understanding where the data comes from while data access and security ensure that the data is being used in an appropriate and secure manner Finally DataOps and Quality involve automation orchestration CI CD configuration management and observability to ensure that the data product is high quality The unification of these use cases is essential for the success of the data product In a nutshell DataOps Observability refers to the ability to monitor and understand the various processes and systems involved in data management with the main goal of ensuring the reliability trustworthiness and business value of the data It involves monitoring and analyzing data pipelines ensuring the quality of the data and demonstrating the business value of the data through metrics like financial and operational efficiency DataOps observability allows businesses to improve the efficiency of their data management processes and make better use of their data assets It helps to ensure that data is accurate reliable and easily accessible enabling businesses and organizations to make data driven decisions and drive business value Implementing DataOpsImplementing DataOps involves following a number of steps to ensure that data is collected stored and used in a way that supports business goals objectives This starts by identifying the data requirements and specifying the sources and types of data needed A data governance framework is then established to ensure that data is collected stored and used in a consistent and compliant manner Data pipelines are designed and implemented to extract transform and load data from various sources into a centralized repository and data quality checks and monitoring are put in place to ensure the accuracy completeness and consistency of the data To support a data driven culture it is crucial to build a collaborative and cross functional team and establish a focus on data literacy continuous improvement and data driven decision making Finally it is important to continuously monitor and optimize the DataOps processes to improve efficiency effectiveness and agility List of Top DataOps tools and platforms availableOne of the key components of DataOps is the use of specialized tools to manage and automate the flow of data Tools can help with tasks such as scheduling and monitoring the execution of data pipelines extracting transforming and cleaning data and integrating data from multiple different sources There are various different DataOps tools available on the market and the best choice will depend on your specific needs requirements Some tools are designed for general purpose data integration and transformation while others are more specialized for specific types of data or use cases Here are some of the TOP Trending and Popular DataOps tools currently available on the market Apache Airflow Apache airflow is an open source tool that is used for scheduling monitoring and managing the execution of data pipelines It provides a simple intuitive interface for defining and organizing tasks and it can be extended with custom plugins to support a wide range of data sources and destinations DatabricksDatabricks is a cloud based platform for data engineering data science and AI ML It is built on top of the Apache Spark big data processing engine and offers a variety of tools for working with large amounts of data Databricks collaborative workspace is a great way for groups to collaborate on data projects together in real time It provides a fully web based notebook like environment for writing and executing code as well as data exploration and visualization tools Databricks consist of connectors for common data sources and destinations a library of pre built transformations and functions and support for different programming languages SnowflakeSnowflake is not a DataOps tool per se it s a platform that can be used as a foundation for DataOps Snowflake is a cloud based data storage and analytics platform that is widely used for data warehousing data lakes and data engineering It is designed to handle the complexities of modern data management processes such as data integration data quality data security and data governance and to support a variety of data analytics applications such as BI tools ML and data science  Snowflake can also be used to manage the flow of data from various sources to the data warehouse where it can be transformed cleansed and optimized accordingly for analysis purposes Snowflake s architecture is designed to support high levels of concurrency scalability and performance making it well suited for handling large amounts of data in real time It also provides a number of features that supports data governance and security such as data lineage masking and auditing which can be a very important consideration in DataOps environments FivetranFivetran is also a cloud based service that simplifies the process of transferring data between various sources and destinations including Snowflake It includes a range of connectors for popular data sources and destinations including databases cloud storage SaaS applicationsーand more One of the main features of Fivetran is its ability to support real time synchronization and incremental updates which means that it can continuously transfer new and updated data This makes it ideal for use in scenarios where data needs to be kept up to date in near real time Fivetran has the ability to transfer data but it also has a number of tools for managing and monitoring data pipelines These tools include a web based dashboard for tracking the status of data transfers and alerts for detecting issues and fixing em TalendTalend is a commercial data integration platform that offers a wide range of tools for extracting transforming and loading ETL data It includes an awesome and highly interactive graphical user interface GUI for building data pipelines and a library of pre built connectors and transformations that can be used to integrate data from a wide range of sources and destinations One of the main key features of Talend is its support for a wide range of data sources and destinations including databases cloud storage SaaS applicationsーand more It also includes support for popular programming languages which allows users to write custom transformations and integrations Talend also provides a range of tools for data governance data quality and data management including support for tracking and managing data lineage Learn more about the Top DataOps tools available on the market in Future of DataOpsDataOps is constantly evolving in response to emerging technologies and changing business needs According to a report by MarketBiz the global DataOps platform market is expected to experience significant growth over the forecast period of with a projected value of   million This growth is driven by the increasing demand for real time data insights the adoption of cloud based solutions and the rising popularity of Agile and DevOps related practices The DataOps platform market is also anticipated to see growth in various regions including North America  Europe  Asia Pacific  Latin America the Middle Eastーand Africa The market is projected to reach a value of million up from million in with a compound annual growth rate of The future of DataOps looks VERY bright with the current adoption of automation and artificial intelligence AI Automating data related tasks and using AI ML to analyze data allows businesses to reduce the time and resources needed for data management leading to more efficient and accurate analysis Another main key factor that will contribute to the future success of DataOps is the growing importance of data governance As organizations collect and use more data it is crucial to have proper controls in place to ensure data privacy security DataOps practices can help businesses establish and maintain effective data governance Overall the future of DataOps is expected to see continued growth and evolution as businesses and organizations seek to optimize and leverage data driven insights to drive their success DataOps in Action Previously we discussed how Netflix uses DataOps to streamline its data management operations To have a complete understanding of how DataOps is used in practice let s examine a second case study Suppose a leading online store retailer decides to use DataOps to enhance their sales forecasting procedure Previously the retailer had difficulty making accurate sales forecasts due to the complexity of their data environment and the rigorous manual and laborious processes they had to go through to prepare and analyze data To address these challenges they formed a DataOps team that included data engineers data scientists and data business analysts The team then implemented an automated data ingestion and transformation pipeline utilizing a market leading data integration platform This allowed them to swiftly and efficiently gather sales data from multiple sources including online transactions in store purchases user product preferences user activity and market research The data was then cleaned transformed and validated using a series of predefined rules and procedures to ensure that it was ready for final analysis The team then collaborated with data scientists to create and deploy AI ML models that could predict future sales trends These models were trained on historical product sales data and were designed to learn and adapt over time becoming more accurate as more data was supplied to them And after that the team worked with data business analysts to integrate the sales forecasting technique into the retailer s overall decision making processes This included making dashboards and reports that showed the outputs of the forecasting models and how they worked as well as integrating the forecasts into the retailer s systems for managing product inventory and setting up product prices The results of the DataOps implementation were impressive The store was able to track sales more accurately which significantly improved managing the product inventory and aided in making smarter business decisions  Overall the DataOps approach helped the retailer store to make better understand and act on the data they had leading to improved efficiency accuracy and agility Resources for learning more about DataOpsTo learn more about DataOps there are a number of resources available including books articles online courses videos and events podcasts Some recommendations personal preference includes Books “Creating a Data Driven Enterprise with DataOps by Ashish Thusoo and Joydeep Sen Sarma Practical DataOps Delivering Agile Data Science at Scale by Harvinder Atwal“Data Teams A Unified Management Model for Successful Data Focused Teams by Jesse Anderson“Managing Data in Motion Data Integration Best Practice Techniques and Technologies  by April Reeve“The DataOps Cookbook by Christopher BerghArticles There are many articles available online that different cover aspects of DataOps Data Quality as a Crucial Part of DataOpsA Deep Dive Into Data QualityManaging Data in MotionWhat is DataOps Everything You Need to KnowWhat is DataOps DataOps is NOT Just DevOps for DataDataOps DevOps Plus Big DataVideos There are several videos online courses and training courses available for those interested in learning more about DataOps What is DataOps DataOps Why What How What is DataOps ConclusionDataOps is a crucial approach to data management operations that enables businesses to improve the speed quality and reliability of data processing and analysis It facilitates collaboration and communication and accelerates the delivery of insights and results at a rapid pace While implementing DataOps can present challenges following best practices and communicating the benefits to stakeholders can help ensure a successful adoption As emerging technologies continue to change the industry we may anticipate DataOps to evolve and potentially expand into more fields Above all DataOps is a people driven discipline meaning that it depends on the abilities and knowledge of the individuals It is not a tool or application that can be bought and implemented without the required human resources Instead it necessitates a team of proficient data experts that can collaborate effectively and efficiently References Swanson Brittany Marie “What is DataOps Everything You Need to Know  Oracle Blogs March   Accessed January DataOps and the future of data management  MIT Technology Review September   Accessed January Valentine Crystal and William Merchan “DataOps An Agile Methodology for Data Driven Organizations  Oracle   Accessed January Anderson C Creating a Data Driven Enterprise with DataOps O Reilly Media Inc Retrieved from  Accessed January A Dyck R Penners and H Lichter Towards Definitions for Release Engineering and DevOps IEEE ACM rd International Workshop on Release Engineering Saurabh Saket “What is DataOps Platform for the Machine Learning Age Nexla   Accessed January Heudecker Nick “Hyping DataOps Nick Heudecker Gartner Blog Network July   Accessed January	2023-01-23 04:49:26
海外TECH	DEV Community	Why you should be learning cloud in 2023 | AWS Community Builders Program	https://dev.to/aws-builders/why-you-should-be-learning-cloud-in-2023-aws-community-builders-program-4b37	Why you should be learning cloud in AWS Community Builders ProgramWhy should you learn cloud in ‍In this episode of the Tech Stack Playbook I m diving into how I built my career in tech and explain how the AWS Community Builders program allowed me to really dive deep into the tech ecosystem Today s episode focuses on AWS the community the core benefits of the program and how it has helped me level up as a software engineer and developer a few years ago For anyone who is looking to get a job searching for a new role looking to upskill and level up switching careers or pivoting career directions this episode will be a deep dive into what I did to get into tech and how the steps I took might be able to help you on your journey as well Applications for the AWS Community Builders Program close on Jan so please reach out if you have any questions about the program and be sure to apply by the deadline here Here s a glance at what you ll learn in this blog post Ways to increase your chances of getting hiredHow social media led me to the AWS Community Builders ProgramWhy learning cloud will future proof your skills careerThe core benefits of the AWS Community Builders ProgramHow you can future proof yourself and your skillsWhat to learn in Are you thinking of applying this year Let me know in the comments below ️ Check out the full recording below Let me know if you found this post helpful And if you haven t yet make sure to check out these free resources below Follow my Instagram for more BrianHHoughWatch my latest YouTube video for moreListen to my Podcast on Apple Podcasts and Spotify Join my FREE Tech Stack Playbook Discord ServerLet s digitize the world together Brian	2023-01-23 04:08:30
金融	ニッセイ基礎研究所	ドル円は2大テーマが激突へ～マーケット・カルテ２月号	https://www.nli-research.co.jp/topics_detail1/id=73692?site=nli	今月の長期金利は日銀の緩和修正観測を受けて一旦上昇し、中旬にかけてをたびたび突破したが、決定会合での現状維持を受けてやや低下し、足元では付近にある。	2023-01-23 13:42:05
ニュース	ジェトロ ビジネスニュース（通商弘報）	米ネクストディケード、伊藤忠商事とLNG売買契約、年間100万トンを15年間	https://www.jetro.go.jp/biznews/2023/01/4b2755eb5dce76d8.html	伊藤忠商事	2023-01-23 04:30:00
ニュース	ジェトロ ビジネスニュース（通商弘報）	米ニューヨーク州、光熱費延滞に6億7,200万ドルの財政支援発表	https://www.jetro.go.jp/biznews/2023/01/2f0b441f4a0ecfab.html	財政支援	2023-01-23 04:25:00
海外ニュース	Japan Times latest articles	New Zealand’s new prime minister signals policy overhaul to win back voters	https://www.japantimes.co.jp/news/2023/01/23/asia-pacific/politics-diplomacy-asia-pacific/new-zealand-chris-hipkins-economy-election/	New Zealand s new prime minister signals policy overhaul to win back votersChris Hipkins will prioritize the economy as a recession looms and may jettison some of Jacinda Ardern s policies ahead of an October election	2023-01-23 13:24:02
海外ニュース	Japan Times latest articles	California standoff ends after Lunar New Year mass shooting leaves 10 dead	https://www.japantimes.co.jp/news/2023/01/23/world/crime-legal-world/california-mass-shooting-lunar-new-year/	California standoff ends after Lunar New Year mass shooting leaves deadThe gunman year old Huu Can Tran was believed to have shot himself dead as police moved in to arrest him the local sheriff said	2023-01-23 13:18:56
海外ニュース	Japan Times latest articles	Much of Japan to see heavy snow from Tuesday as winter storm arrives	https://www.japantimes.co.jp/news/2023/01/23/national/snow-winter-storm/	Much of Japan to see heavy snow from Tuesday as winter storm arrivesAreas facing the Sea of Japan and much of the Pacific side of the archipelago are expected to see very strong winds and storms from	2023-01-23 13:07:22
ニュース	BBC News - Home	Monterey Park shooting: Suspect found dead after dance studio attack	https://www.bbc.co.uk/news/world-us-canada-64368796?at_medium=RSS&at_campaign=KARANGA	attackpolice	2023-01-23 04:05:08
IT	週刊アスキー	銀座コージーコーナー、ロングセラースイーツ「ジャンボシュークリーム」をリニューアル！	https://weekly.ascii.jp/elem/000/004/121/4121545/	銀座	2023-01-23 13:50:00
IT	週刊アスキー	新作アクションRPG『グラブル リリンク』のPVが公開！ストーリーの一端が明らかに!?	https://weekly.ascii.jp/elem/000/004/121/4121550/	発売予定	2023-01-23 13:50:00
IT	週刊アスキー	「出前一丁」と中華街の名店「招福門」コラボ ポップアップストア「出前坊やの香港屋台」期間限定オープン	https://weekly.ascii.jp/elem/000/004/121/4121548/	出前一丁	2023-01-23 13:40:00
IT	週刊アスキー	ビッグボーイ・ヴィクトリアステーション「冬の溢れシーフードグラタン＆直火焼きグリルフェア」	https://weekly.ascii.jp/elem/000/004/121/4121547/	直火焼き	2023-01-23 13:30:00
IT	週刊アスキー	進化した健康サポート機能と急速充電に対応した「OPPO Band 2」の予約受付開始、1月27日より順次販売	https://weekly.ascii.jp/elem/000/004/121/4121500/	oppoband	2023-01-23 13:15:00