投稿時間:2022-01-18 23:30:35 RSSフィード2022-01-18 23:00 分まとめ(36件)

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python	Pythonタグが付けられた新着投稿 - Qiita	#Slack アプリ開発：モーダルでのバリデーションについて	https://qiita.com/geeorgey/items/40718b9fb5824c39a851	上記のようなモーダルを作っているのですが、URL欄にはhttpが含まれたものがほしい。	2022-01-18 22:54:51
AWS	AWSタグが付けられた新着投稿 - Qiita	ReactとNode.jsアプリをAWSにデプロイしたよ	https://qiita.com/natarisan/items/dfa403d14aac5c811606	⑫RouteのDNS設定で、取得したドメインとSのエンドポイントを紐づけて、ドメイン経由でアプリにアクセスできるようにした。	2022-01-18 22:32:46
AWS	AWSタグが付けられた新着投稿 - Qiita	EMRクラスタ作成~テーブル作成までのメモ	https://qiita.com/htj/items/f0233e7e9b2578c54e48	追記ポートフォワーディングしなくても、マスターノードのセキュリティグループgtインバウンドルールからを開ければさっとhueにアクセスできたかもしれない…sshiCpathtokeypemNLlocalhosthadoopecapnortheastcomputeamazonawscomこの後httplocalhostにアクセスするとhueに繋がった。	2022-01-18 22:03:53
Azure	Azureタグが付けられた新着投稿 - Qiita	Azure Function(C#)の動作環境のタイムゾーンとWEBSITE_TIME_ZONEで変更できるか調査	https://qiita.com/fukasawah/items/c2484b28b17c5fa20328	そういえば、ランタイム毎の違いはあるんでしょうか私はAzureFunctionではCで生きていくと決めたので調査はしません・・・蛇足CのDateTime型はめんどくさいCのDateTime型はtickと呼ばれる西暦年月日からのマイクロ秒単位の経過時間bitと、DateTimeKindbitで日時を表現します。	2022-01-18 22:32:49
Ruby	Railsタグが付けられた新着投稿 - Qiita	rails 7.0.1 + MySQL/SQLiteの構成だとrails6.0で作成したmigrateファイルの`references`/`belongs_to`定義が動かない	https://qiita.com/SoarTec-lab/items/69f9115d7e06a866c437	railsMySQLSQLiteの構成だとrailsで作成したmigrateファイルのreferencesbelongsto定義が動かないTLDRMySQLを使用しているrailsアプリケーションの場合、railsバージョンをにして、railsで生成したmigrateファイルを使ってdbmigrateを行うとエラーが発生します詳細発生するケース例えばusersテーブルが存在していて、usersに紐づく子テーブルpostsを追加しようとした場合で以下のようなmigrateファイルをrailsの作成したとします。	2022-01-18 22:46:02
技術ブログ	Mercari Engineering Blog	Introduction of Web Platform	https://engineering.mercari.com/blog/entry/20220118-introduction-of-web-platform/	hellip	2022-01-18 14:21:32
海外TECH	Ars Technica	After six decades, Russia will build its final Proton rocket this year	https://arstechnica.com/?p=1826461	falcon	2022-01-18 13:41:49
海外TECH	MakeUseOf	How to Show Yourself as Away on Asana	https://www.makeuseof.com/how-to-show-yourself-away-asana/	asana	2022-01-18 13:30:11
海外TECH	MakeUseOf	The Best Routers and Modems for Comcast Xfinity	https://www.makeuseof.com/tag/best-routers-modems/	internet	2022-01-18 13:18:03
海外TECH	DEV Community	tsParticles - Start confetti animation with just a click	https://dev.to/matteobruni/tsparticles-start-confetti-animation-with-just-a-click-2n37	tsParticles Start confetti animation with just a clicktsParticles Start confetti animation with just a clickRead more on GitHub Website matteobruni tsparticles tsParticles Easily create highly customizable particles animations and use them as animated backgrounds for your website Ready to use components available for React js Vue js x and x Angular Svelte jQuery Preact Inferno Solid Riot and Web Components tsParticles TypeScript ParticlesA lightweight TypeScript library for creating particles Dependency free browser ready and compatible withReact js Vue js x and x Angular Svelte jQuery Preact Inferno Riot js Solid js and Web Components Table of ContentsUse for your websiteLibrary installationOfficial components for some of the most used frameworksAngularInfernojQueryPreactReactJSRiotJSSolidJSSvelteVueJS xVueJS xWeb ComponentsWordPressPresetsBig CirclesBubblesConfettiFireFireflyFireworksFountainLinksSea AnemoneSnowStarsTrianglesTemplates and ResourcesDemo GeneratorCharacters as particlesMouse hover connectionsPolygon maskAnimated starsNyan cat flying on scrolling starsBackground Mask particlesMigrating from Particles jsPlugins CustomizationsDo you want to use it on your website Documentation and Development references here This library is available on the two most popular CDNs and it s easy and ready to use if you were using particles jsit s even… View on GitHub tsParticles Released Matteo Bruni・Jan ・ min read showdev javascript webdev html	2022-01-18 13:44:03
海外TECH	DEV Community	When to use the useRef hook ?	https://dev.to/thenerdydev/when-to-use-the-useref-hook--3iom	When to use the useRef hook Hey everyone In this article let us learn about a very special React Hook called as the useRef hook and understand how and when to use it This article was first published on Check the website for more interesting articles and tutorials on Web Development What is the useRef hook useRef hook is something that I briefly discussed in one of my articles where I described useRef hook as something equivalent to Removing the state updating function from the useState so basically just extracting the piece of state and take away the ability of state updation This is something that will be equivalent to the useRef hook I also discussed in that article that both the ref and state values persists across renders so it is not the case that on every subsequent re render they get set back to some default So do make a note of the above points useRef is a React Hook which accepts a single argument as the initial default value and gives us something called as a ref A ref is kind of an object that has a property called as current Let us see an example function SomeComponent const usernameRef React useRef const handleSomething gt console log usernameRef currrent usernameRef current someOtherValue Now the things to note here is that usernameRef current is used to access the value of the ref and if we want to update the ref value then we can simply do so by setting usernameRef current to a value of our choice To know more about refs check my existing article on useRef hook Here is the link for the same useRef Hook useRef vs useState The Nerdy Dev・Jul ・ min read html beginners react webdev In the above article I have also taken some examples where I have demonstrated the usage of the useRef hook we have discussed Ref Example An Interval Timer Ref Example Working with DOM using refs The third thing that we discussed in that article was useRef just like useState persists a value across renders but unlike useState it does not trigger a re render of the component So in essence useRef useState state updating functionIf you don t know about the useState no worries You can learn about the same in this article React Hooks Demystifying the useState hook in under minutes examples video The Nerdy Dev・Jun ・ min read daysofcode javascript programming react Let s sum up what we learnt A ref is a plain JavaScript Object current lt some value gt If you want to create a ref with the current value as null then you can also make use of the createRef method React createRef This gives us current null Next useRef someInitialValue also gives us a ref current someInitialValue The interesting thing about useRef is that it memoizes this ref so that it stays persistent across multiple renders of the component Another point to make a note of is that useRef null is basically equivalent to useState React createRef But this kind of abuses the functionality Lastly we learnt that useState causes a re render in contrast to useRef The state and the ref values both persist across re renders So this is it for this article Thanks for reading If you enjoy my articles consider following me on Twitter for more interesting stuff Twitter Don t forget to leave a like if you loved the article Also share it with your friends and colleagues PS If you are looking to learn Web Development I have curated a FREE course for you on my YouTube Channel check the below article Web Developer Full Course HTML CSS JavaScript Node js and MongoDB The Nerdy Dev・Apr ・ min read html css node javascript Looking to learn React js with one Full Project check this out Learn React with one BIG Project NOTES included Demo and Video Link The Nerdy Dev・Jun ・ min read daysofcode javascript react webdev	2022-01-18 13:38:56
海外TECH	DEV Community	Simple Quiz Project Using Vue	https://dev.to/medan_in_code/simple-quiz-project-using-vue-4hc9	Simple Quiz Project Using VueSimple Quiz app using vue js Link To CodeGithub Link Additional Resources InfoProject is demo on	2022-01-18 13:21:49
海外TECH	DEV Community	Lambda SQS Event Filters may delete your messages if you're not careful	https://dev.to/aws-builders/lambda-sqs-event-filters-may-delete-your-messages-if-youre-not-careful-ank	Lambda SQS Event Filters may delete your messages if you x re not carefulIn a recent post I wrote about Lambda Event Filters and their benefits That was a follow up to a talk I had given on the subject internally at tecRacer During that talk my colleague Sebastian Möhn asked an interesting question about the filters which I ll look into today He wanted to know what happens to SQS messages that don t match the filters We ll get to that in a bit but first let s take a step back and get a brief primer on Lambda Event Filters They were introduced in late and allow you to filter events from SQS Kinesis or DynamoDB before they trigger your Lambda function Previously all events from those sources would trigger Lambda and you had to filter in your own code which added complexity and cost because the function was invoked even for events it didn t care about With event filters that changes We can now offload the undifferentiated heavy lifting of event filtering to AWS at no additional cost It s an interesting question how the system responds to messages from SQS that don t match the filter because SQS is different from the other two event sources that support the filter DynamoDB Streams and Kinesis both deal with streaming data It s part of that paradigm that multiple parties can read the same datapoint from the stream That means it doesn t matter to other clients if one of the clients isn t interested in all the messages SQS on the other hand implements the producer consumer pattern That means one or more producers create messages that can be processed by one or more consumers Messages are usually only processed by one consumer in the pool of consumers so in most cases it behaves like one to one messaging Additionally consumers have no direct way of picking the messages they want to work on they have to process whatever SQS gives them This has implications for event filters between SQS Queues and Lambda functions If the filter rejects an incoming message from SQS one of two things could happen The message is considered processed and deleted from the Queue preventing other consumers from reading it The message is considered unprocessed and not deleted from the Queue It becomes available to other consumers again after the visibility timeout expires In the first case this has the implication that messages are removed from the queue even though other consumers may be able to process them That means one Lambda consumer on the Queue that doesn t want a message has side effects for all other consumers Depending on your architecture that may be a problem The second case could lead to internal problems at AWS if we assume that Lambda under the hood uses the same SQS API that s also publicly available To understand why you need to know how Lambda integrates with SQS When you set up a SQS trigger for a Lambda function the Lambda service will periodically poll the queue for work and invoke your code whenever there are messages to process That happens in the background and you don t really need to worry about it Assuming that Lambda uses the same APIs we do there is no way for the service to skip already seen and ignored messages So if there is only one Lambda consumer on the queue and discarded messages stay in the queue we may experience congestion over time That would be a less than ideal design so my guess is that in reality the Lambda service really deletes messages that don t match the filter Let s now see what the docs have to say on the matter You can define up to five different filters for a single event source If an event satisfies any one of these five filters Lambda sends the event to your function Otherwise Lambda discards the event ーLambda Event Filtering I m not a native speaker but to me discards is a bit ambiguous It could either mean that Lambda ignores the message and it s put back into the queue or that the service also removes the event from the queue I m not a big fan of ambiguity in technical documentation Let s see for ourselves what happens We re going to use the following test setup the code is available on Github A CDK App deploys a SQS Queue and a Lambda function that gets triggered by messages in the Queue The event source will be filtered to only accept messages that have a process with lambda attribute with the value The lambda will then increment a counter in a DynamoDB table that counts how many messages have been processed We ll also have a script that sends alternating messages to the Queue job generator py The first message will have the process with lambda attribute and the second won t This script will send messages of each kind to the queue and then we ll wait for a few seconds If the processing worked we should see a counter value of in the table Then we ll check how many messages are still in the queue If that number is we know that Lambda acts according to and discarded means the messages are deleted from the queue If there is a non zero number in the Queue Lambda sends messages back to the queue for further processing Let s run the code python job generator pyRemoving summary item from tablePurging QueuePurging queues can take up to seconds waiting Sending Message Group with messagesSending Message Group with messagesSending Message Group with messages Sending Message Group with messagesSending Message Group with messagesSending Message Group with messagesThe script first resets the counter in the table and purges the already existing messages from the Queue Since that can take up to s I ve added a sleep for one minute here Afterwards we send messages in total to the Queue Then it s time to wait for a few seconds to give Lambda time to process the messages asynchronously Afterwards we can run the script to fetch the results python get result py Lambda processed recordsThe Queue contained messagesAs expected the Lambda function received the messages according to the filter It turns out that option is what AWS implemented when it comes to dealing with messages that don t match the filter Messages that don t match it are simply deleted from the Queue ConclusionWe learned that the Lambda Event Filter deletes messages from the Queue when they don t match the filter criteria That has not been completely clear from the documentation but it s a sensible implementation given the constraints imposed by the SQS API This implementation has implications for queues that have multiple consumers of messages If you add a Lambda function with an event filter to the set of consumers there might be data loss if the other subscribers could process messages not intended for Lambda In that case you ll lose messages without a clear indication why As a workaround you could use a SNS topic in front of the Queue to fan out messages to multiple interested parties by setting up a Queue for each party This will come with additional costs though It would be great if AWS removed the ambiguity here and ideally added a warning to the console when you re adding filters If you know it you can plan for it if you don t you ll experience unexpected behaviour awswishlistI hope you enjoyed the post and for any feedback questions or concerns feel free to reach out via the social media channels in my bio	2022-01-18 13:19:58
海外TECH	DEV Community	Introduction to Machine Learning with AWS - Part 1	https://dev.to/aws-builders/introduction-to-machine-learning-with-aws-part-1-476m	Introduction to Machine Learning with AWS Part AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure putting machine learning in the hands of every developer data scientist and expert practitioner In this blog post we will discuss some of the most important AWS machine learning services that help you make accurate predictions get deeper insights from your data reduce operational overhead and improve customer experience AWS helps you at every stage of your ML adoption journey with the most comprehensive set of artificial intelligence AI and ML services infrastructure and implementation resources The Introduction to AWS is a Series containing different articles that provide a basic introduction to different aws topics categories Each article covers the detailed guide on how to work with particular topic category This series aims at providing A Getting Started Guide on Different aws topics categories AWS Machine Learning ServicesAWS helps streamline self service processes and reduce operational costs through chatbots and virtual assistants AWS compiles data from siloed and unstructured sources across your organization to drive business productivity and customer satisfaction AWS helps drive customer engagement and conversion with websites tailored to individual visitorsーand see your conversion rates soar Using AWS customers can instantly extract text and data from virtually any document such as loan applications and medical forms without manual effort SageMakerSageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build train and deploy machine learning models at any scale SageMaker removes all the barriers that typically slow down developers who want to use machine learning Machine learning often feels a lot harder than it should be to most developers because the process to build and train models and then deploy them into production is too complicated and too slow First you need to collect and prepare your training data to discover which elements of your data set are important Then you need to select which algorithm and framework you ll use After deciding on your approach you need to teach the model how to make predictions by training which requires a lot of compute Then you need to tune the model so it delivers the best possible predictions which is often a tedious and manual effort After you ve developed a fully trained model you need to integrate the model with your application and deploy this application on infrastructure that will scale All of this takes a lot of specialized expertise access to large amounts of compute and storage and a lot of time to experiment and optimize every part of the process In the end it s not a surprise that the whole thing feels out of reach for most developers SageMaker removes the complexity that holds back developer success with each of these steps SageMaker includes modules that can be used together or independently to build train and deploy your machine learning models SageMaker Ground TruthSageMaker Ground Truth helps you build highly accurate training datasets for machine learning quickly SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built in workflows and interfaces for common labeling tasks Additionally SageMaker Ground Truth can lower your labeling costs by up to using automatic labeling which works by training Ground Truth from data labeled by humans so that the service learns to label data independently Successful machine learning models are built on the shoulders of large volumes of high quality training data But the process to create the training data necessary to build these models is often expensive complicated and time consuming The majority of models created today require a human to manually label data in a way that allows the model to learn how to make correct decisions For example building a computer vision system that is reliable enough to identify objects such as traffic lights stop signs and pedestrians requires thousands of hours of video recordings that consist of hundreds of millions of video frames Each one of these frames needs all of the important elements like the road other cars and signage to be labeled by a human before any work can begin on the model you want to develop Amazon SageMaker Ground Truth significantly reduces the time and effort required to create datasets for training to reduce costs These savings are achieved by using machine learning to automatically label data The model is able to get progressively better over time by continuously learning from labels created by human labelers Where the labeling model has high confidence in its results based on what it has learned so far it will automatically apply labels to the raw data Where the labeling model has lower confidence in its results it will pass the data to humans to do the labeling The human generated labels are provided back to the labeling model for it to learn from and improve Over time SageMaker Ground Truth can label more and more data automatically and substantially speed up the creation of training datasets Amazon ComprehendAmazon Comprehend is a natural language processing NLP service that uses machine learning to find insights and relationships in text No machine learning experience required There is a treasure trove of potential sitting in your unstructured data Customer emails support tickets product reviews social media even advertising copy represents insights into customer sentiment that can be put to work for your business The question is how to get at it As it turns out Machine learning is particularly good at accurately identifying specific items of interest inside vast swathes of text such as finding company names in analyst reports and can learn the sentiment hidden inside language identifying negative reviews or positive customer interactions with customer service agents at almost limitless scale Amazon Comprehend uses machine learning to help you uncover the insights and relationships in your unstructured data The service identifies the language of the text extracts key phrases places people brands or events understands how positive or negative the text is analyzes text using tokenization and parts of speech and automatically organizes a collection of text files by topic You can also use AutoML capabilities in Amazon Comprehend to build a custom set of entities or text classification models that are tailored uniquely to your organization s needs For extracting complex medical information from unstructured text you can use Amazon Comprehend Medical The service can identify medical information such as medical conditions medications dosages strengths and frequencies from a variety of sources like doctor s notes clinical trial reports and patient health records Amazon Comprehend Medical also identifies the relationship among the extracted medication and test treatment and procedure information for easier analysis For example the service identifies a particular dosage strength and frequency related to a specific medication from unstructured clinical notes Amazon LexAmazon Lex is a service for building conversational interfaces into any application using voice and text Lex provides the advanced deep learning functionalities of automatic speech recognition ASR for converting speech to text and natural language understanding NLU to recognize the intent of the text to enable you to build applications with highly engaging user experiences and lifelike conversational interactions With Amazon Lex the same deep learning technologies that power Amazon Alexa are now available to any developer enabling you to quickly and easily build sophisticated natural language conversational bots “chatbots Speech recognition and natural language understanding are some of the most challenging problems to solve in computer science requiring sophisticated deep learning algorithms to be trained on massive amounts of data and infrastructure Amazon Lex democratizes these deep learning technologies by putting the power of Alexa within reach of all developers Harnessing these technologies Amazon Lex enables you to define entirely new categories of products made possible through conversational interfaces Amazon PollyAmazon Polly is a service that turns text into lifelike speech Polly lets you create applications that talk enabling you to build entirely new categories of speech enabled products Polly is an Amazon artificial intelligence AI service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice Polly includes lifelike voices spread across languages so you can select the ideal voice and build speech enabled applications that work in many different countries Amazon Polly delivers the consistently fast response times required to support real time interactive dialog You can cache and save Polly s speech audio to replay offline or redistribute And Polly is easy to use You simply send the text you want converted into speech to the Polly API and Polly immediately returns the audio stream to your application so your application can play it directly or store it in a standard audio file format such as MP With Polly you only pay for the number of characters you convert to speech and you can save and replay Polly s generated speech Polly s low cost per character converted and lack of restrictions on storage and reuse of voice output make it a cost effective way to enable Text to Speech everywhere Amazon RekognitionAmazon Rekognition is a service that makes it easy to add image analysis to your applications With Rekognition you can detect objects scenes and faces in images You can also search and compare faces The Amazon Rekognition API enables you to quickly add sophisticated deep learning based visual search and image classification to your applications Amazon Rekognition is based on the same proven highly scalable deep learning technology developed by Amazon s computer vision scientists to analyze billions of images daily for Prime Photos Amazon Rekognition uses deep neural network models to detect and label thousands of objects and scenes in your images and we are continually adding new labels and facial recognition features to the service The Amazon Rekognition API lets you easily build powerful visual search and discovery into your applications With Amazon Rekognition you only pay for the images you analyze and the face metadata you store There are no minimum fees and there are no upfront commitments Amazon TranslateAmazon Translate is a neural machine translation service that delivers fast high quality and affordable language translation Neural machine translation is a form of language translation automation that uses deep learning models to deliver more accurate and more natural sounding translation than traditional statistical and rule based translation algorithms Amazon Translate allows you to localize content such as websites and applications for international users and to easily translate large volumes of text efficiently Amazon TranscribeAmazon Transcribe is an automatic speech recognition ASR service that makes it easy for developers to add speech to text capability to their applications Using the Amazon Transcribe API you can analyze audio files stored in Amazon S and have the service return a text file of the transcribed speech You can also send a live audio stream to Amazon Transcribe and receive a stream of transcripts in real time Amazon Transcribe can be used for lots of common applications including the transcription of customer service calls and generating subtitles on audio and video content The service can transcribe audio files stored in common formats like WAV and MP with time stamps for every word so that you can easily locate the audio in the original source by searching for the text Amazon Transcribe is continually learning and improving to keep pace with the evolution of language Amazon ForecastAmazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts Companies today use everything from simple spreadsheets to complex financial planning software to attempt to accurately forecast future business outcomes such as product demand resource needs or financial performance These tools build forecasts by looking at a historical series of data which is called time series data For example such tools may try to predict the future sales of a raincoat by looking only at its previous sales data with the underlying assumption that the future is determined by the past This approach can struggle to produce accurate forecasts for large sets of data that have irregular trends Also it fails to easily combine data series that change over time such as price discounts web traffic and number of employees with relevant independent variables like product features and store locations Based on the same technology used at Amazon com Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts Amazon Forecast requires no machine learning experience to get started You only need to provide historical data plus any additional data that you believe may impact your forecasts For example the demand for a particular color of a shirt may change with the seasons and store location This complex relationship is hard to determine on its own but machine learning is ideally suited to recognize it Once you provide your data Amazon Forecast will automatically examine it identify what is meaningful and produce a forecasting model capable of making predictions that are up to more accurate than looking at time series data alone Amazon Forecast is a fully managed service so there are no servers to provision and no machine learning models to build train or deploy You pay only for what you use and there are no minimum fees and no upfront commitments Amazon TextractAmazon Textract is a service that automatically extracts text and data from scanned documents Amazon Textract goes beyond simple optical character recognition OCR to also identify the contents of fields in forms and information stored in tables Many companies today extract data from documents and forms through manual data entry that s slow and expensive or through simple optical character recognition OCR software that is difficult to customize Rules and workflows for each document and form often need to be hard coded and updated with each change to the form or when dealing with multiple forms If the form deviates from the rules the output is often scrambled and unusable Amazon Textract overcomes these challenges by using machine learning to instantly “read virtually any type of document to accurately extract text and data without the need for any manual effort or custom code With Textract you can quickly automate document workflows enabling you to process millions of document pages in hours Once the information is captured you can take action on it within your business applications to initiate next steps for a loan application or medical claims processing Additionally you can create smart search indexes build automated approval workflows and better maintain compliance with document archival rules by flagging data that may require redaction Amazon PersonalizeAmazon Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications Machine learning is being increasingly used to improve customer engagement by powering personalized product and content recommendations tailored search results and targeted marketing promotions However developing the machine learning capabilities necessary to produce these sophisticated recommendation systems has been beyond the reach of most organizations today due to the complexity of developing machine learning functionality Amazon Personalize allows developers with no prior machine learning experience to easily build sophisticated personalization capabilities into their applications using machine learning technology perfected from years of use on Amazon com With Amazon Personalize you provide an activity stream from your application page views signups purchases and so forth as well as an inventory of the items you want to recommend such as articles products videos or music You can also choose to provide Amazon Personalize with additional demographic information from your users such as age or geographic location Amazon Personalize will process and examine the data identify what is meaningful select the right algorithms and train and optimize a personalization model that is customized for your data All data analyzed by Amazon Personalize is kept private and secure and only used for your customized recommendations You can start serving your personalized predictions via a simple API call from inside the virtual private cloud that the service maintains You pay only for what you use and there are no minimum fees and no upfront commitments Amazon Personalize is like having your own Amazon com machine learning personalization team at your disposal hours a day Hope this guide helps you with the Introduction to Machine Learning with AWS Part In the Next Blog Post we will discuss more about different Machine learning Services that are available with AWS 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	2022-01-18 13:17:35
Apple	AppleInsider - Frontpage News	Microsoft acquiring Activision Blizzard in $68.7B gaming deal	https://appleinsider.com/articles/22/01/18/microsoft-acquiring-activision-blizzard-in-687b-gaming-deal?utm_medium=rss	Microsoft acquiring Activision Blizzard in B gaming dealSoftware giant Microsoft has agreed to purchase Activision Blizzard a major acquisition valued at billion that combines many major studios under one entity Announced on Tuesday the deal with see Microsoft acquire Activision Blizzard for per share in an all cash transaction The billion valuation is inclusive of Activision Blizzard s net cash Once completed the acquisition will turn Microsoft into the world s third largest gaming company by revenue behind Tencent and Sony Bobby Kotik will continue to operate as CEO of Activision Blizzard and will report to Microsoft Gaming CEO Phil Spencer Read more	2022-01-18 13:59:28
海外TECH	Engadget	Microsoft is buying Activision Blizzard for $68.7 billion	https://www.engadget.com/microsoft-activision-blizzard-acquisition-133637845.html?src=rss	Microsoft is buying Activision Blizzard for billionMicrosoft just made one of the largest ever bids for a game studio The company has announced plans to acquire Activision Blizzard for per share valuing the all cash deal at an enormous billion The deal would make the combined entity the quot third largest quot game company by revenue according to Microsoft and would put titles like Call of Duty World of Warcraft and Candy Crush under the company s wing Microsoft plans to add Activision Blizzard games to Game Pass as part of the deal Mobile gaming is also a significant factor in the acquisition Microsoft said On top of folding King s phone games into Microsoft s business the purchase promises to bring franchises like Halo and Warcraft to more devices The buyout is expected to close sometime in Microsoft s fiscal no later than June if regulators and Activision Blizzard shareholders greenlight the move The boards of directors for both companies have already approved the deal While news of the purchase comes as Activision Blizzard is still embroiled in a misconduct scandal you shouldn t expect significant leadership changes Bobby Kotick will remain Activision Blizzard s CEO despite calls for his resignation and will now report to Microsoft Gaming chief Phil Spencer In a company letter Kotick described Microsoft s move as a chance to quot further strengthen quot Activision Blizzard s workplace culture and quot set a new standard quot for inclusiveness Developing	2022-01-18 13:36:37
海外TECH	Engadget	UK government announces official crackdown on 'misleading' crypto ads	https://www.engadget.com/uk-announces-plan-to-set-stricter-rules-for-crypto-ads-132529935.html?src=rss	UK government announces official crackdown on x misleading x crypto adsThe UK government has revealed that it plans to update its laws in order quot to bring the promotion of cryptoassets within the scope of financial promotions legislation quot That will ensure any crypto promotion will be held to the same standards as promotion for stocks shares and insurance products It also has fall in line with the rules set by the Financial Conduct Authority FCA Governments have recently started cracking down on crypto ads in an effort to protect consumers from scams they may not be familiar with The Spanish government is currently establishing rules on how influencers and their sponsors promote cryptocurrencies Meanwhile Singapore s authorities asked crypto companies outright not to market their services to the public One type of scam is the quot pump and dump quot in which fraudsters find ways to artificially inflate the value of a cryptocurrency and to get people to invest money in it They then unload their shares while the value is high to make a lot of money thereby lowering the coin s value in the process nbsp According to the UK government approximately million people in the country are now thought to own a cryptoasset While cryptoassets popularity is rising the FCA s research suggests PDF that potential investors level of understanding about them is on the decline That means people may not be fully aware of the risks involved when it comes to investing in them which further supports quot the case for regulatory intervention to ensure that cryptoasset promotions are fair clear and not misleading quot	2022-01-18 13:25:29
海外TECH	The Apache Software Foundation Blog	The Apache Software Foundation Announces Open Source data orchestration platform Apache® Hop™ as a Top-Level Project	https://blogs.apache.org/foundation/entry/the-apache-software-foundation-announces80	The Apache Software Foundation Announces Open Source data orchestration platform ApacheHopas a Top Level ProjectWilmington DE ー January ー The Apache Software Foundation ASF the all volunteer developers stewards and incubators of more than Open Source projects and initiatives announced today ApacheHopas a Top Level Project TLP Apache Hop ーthe Hop Orchestration Platformーis a flexible metadata infused data orchestration engineering and integration platform The project originated more than two decades ago as the Extract Transform Load ETL platform Kettle Pentaho Data Integration was refactored over several years and entered the Apache Incubator in September nbsp quot We are pleased to successfully adopt the Apache Way and graduate from the Apache Incubator quot said Bart Maertens Vice President of Apache Hop quot Apache Hop enables people of all skill levels to build powerful and scalable data solutions without the need to write code As an Apache Top Level Project Hop is developed and used by people across the globe Hop s full project life cycle support helps these data teams to successfully build test and run their projects in ways that would otherwise be hard or impossible to do quot Using Apache Hop data professionals can rapidly and affordably facilitate all aspects of data and metadata orchestration whilst supporting DevOps best practices such as testing Apache Hop s Java based visual designer server and configuration tools are easy to set up deploy and maintain across numerous platforms Features include Lightweight “design once run anywhere architecture ーworkflows and pipelines can be designed in the Hop GUI and executed locally or remotely on the Hop native engine on Apache Flink Apache Kafka Apache Spark Google Dataflow or AWS EMR through Apache Beam runtimes Metadata driven ーevery object type in Hop describes how data is read manipulated or written or how workflows and pipelines need to be orchestrated In addition Hop itself is internally metadata driven using a kernel architecture with a robust engine nbsp Visual development environment ーintuitive drag and drop graphical user interface GUI enables developers to enjoy the ease and productivity of visual development rather than code Using Hop data engineers can focus on business logic and requirements rather than how it needs to be done Plug in integration ーmore than plugins make it easy to manage ecosystem complexity and add new functionality andBuilt in lifecycle management ーenables developers engineers and administrators to manage test deploy and switch between projects workflows pipelines environments purposes Git versions and more ーall from the Hop GUI Apache Hop has been designed to work in any scenario on premises on a cloud on a bare OS in containers IoT environments large datasets and more on Windows Linux and OSX Many of the thousands of organizations in finance retail supply chain and other sectors that use Kettle Pentaho Data Integration the precursor to Apache Hop have started to look into Hop or already are in the process of upgrading to Hop quot I m very happy that we can now safely collaborate with any company or person across the global community under the umbrella of the Apache Software Foundation on something as cool as Apache Hop quot said Matt Casters Chief Solution Architect at Neoj and member of the Apache Hop Project Management Committee quot We started adopting Apache Hop in our data integration projects in early because of its flexibility scalability and ease of use in various scenarios ranging from classical DWH ETL processes to highly critical real time processes quot said Sergio Ramazzina CEO and Chief Architect at Serasoft S r l and member of the Apache Hop Project Management Committee quot We are impressed by how responsive the community is in solving issues and helping users approaching the platform an important point to increase users adoption and trust We welcome everyone joining our Hop community and contributing to the project quot quot This graduation is just the beginning for Hop and is proof that great communities build great software The entire Hop community would like to thank the Apache Software Foundation for making this possible especially our mentors who guided us through the Incubator quot added Maertens quot We invite everyone to download and try Hop join our chat and become part of the Hop community quot Catch Apache Hop in action at a future Hop community event For more information and to register visit nbsp Availability and OversightApache Hop software is released under the Apache License v and is overseen by a self selected team of active contributors to the project A Project Management Committee PMC guides the Project s day to day operations including community development and product releases For downloads documentation and ways to become involved with Apache Hop visit and nbsp About the Apache IncubatorThe Apache Incubator is the primary entry path for projects and codebases wishing to become part of the efforts at The Apache Software Foundation All code donations from external organizations and existing external projects enter the ASF through the Incubator to ensure all donations are in accordance with the ASF legal standards and develop new communities that adhere to our guiding principles Incubation is required of all newly accepted projects until a further review indicates that the infrastructure communications and decision making process have stabilized in a manner consistent with other successful ASF projects While incubation status is not necessarily a reflection of the completeness or stability of the code it does indicate that the project has yet to be fully endorsed by the ASF For more information visit nbsp About The Apache Software Foundation ASF Established in The Apache Software Foundation is the world s largest Open Source foundation stewarding M lines of code and providing more than B worth of software to the public at no cost The ASF s all volunteer community grew from original founders overseeing the Apache HTTP Server to individual Members and Project Management Committees who successfully lead Apache projects and initiatives in collaboration with Committers through the ASF s meritocratic process known as quot The Apache Way quot Apache software is integral to nearly every end user computing device from laptops to tablets to mobile devices across enterprises and mission critical applications Apache projects power most of the Internet manage exabytes of data execute teraflops of operations and store billions of objects in virtually every industry The commercially friendly and permissive Apache License v is an Open Source industry standard helping launch billion dollar corporations and benefiting countless users worldwide The ASF is a US c not for profit charitable organization funded by individual donations and corporate sponsors that include Aetna Alibaba Cloud Computing Amazon Web Services Anonymous Baidu Bloomberg Capital One Cloudera Comcast Confluent Didi Chuxing Facebook Google Huawei IBM Indeed Microsoft Namebase Pineapple Fund Red Hat Replicated Talend Target Tencent Union Investment Workday and Yahoo For more information visit and nbsp The Apache Software Foundation quot Apache quot quot Hop quot quot Apache Hop quot and quot ApacheCon quot are registered trademarks or trademarks of the Apache Software Foundation in the United States and or other countries All other brands and trademarks are the property of their respective owners	2022-01-18 14:00:07
Cisco	Cisco Blog	Sustainability in Retail	https://blogs.cisco.com/retail/sustainability-in-retail	Sustainability in RetailRetail organizations are developing strategies for more sustainable products and operations reducing environmental impact by building more efficient stores and facilities using electric vehicles in supply chain and fulfillment operations choosing sustainable packaging and implementing more efficient processes across their operations By using technology to give better visibility into their operations retailers can monitor the impact these initiatives have on their sustainability goals Technology can also help them identify even more ways to reduce energy consumption and provide better service to customers	2022-01-18 13:55:07
Cisco	Cisco Blog	Why I Joined and Rejoined Cisco	https://blogs.cisco.com/wearecisco/why-i-joined-and-rejoined-cisco	Why I Joined and Rejoined CiscoKonstantin G is an Engineering Director Security Business Group and he shares why coming back to Cisco to make the world a better place just by going to work was an easy decision	2022-01-18 13:00:53
金融	金融庁ホームページ	高校生と先生のための金融教育シンポジウム@北海道を開催します！	https://www.fsa.go.jp/news/r3/sonota/20220118/20220118.html	金融教育	2022-01-18 15:00:00
金融	金融庁ホームページ	外貨建保険の販売会社における比較可能な共通KPIについて公表しました。	https://www.fsa.go.jp/news/r3/hoken/20220118/20220118.html	販売会社	2022-01-18 15:00:00
ニュース	BBC News - Home	Nobody warned me drinks event was against rules - PM	https://www.bbc.co.uk/news/uk-politics-60039868?at_medium=RSS&at_campaign=KARANGA	lockdown	2022-01-18 13:28:47
ニュース	BBC News - Home	Texas synagogue hostage-taker was known to MI5	https://www.bbc.co.uk/news/uk-60038207?at_medium=RSS&at_campaign=KARANGA	people	2022-01-18 13:28:54
ニュース	BBC News - Home	Australian Open: Emma Raducanu beats Sloane Stephens on Melbourne debut	https://www.bbc.co.uk/sport/tennis/60036998?at_medium=RSS&at_campaign=KARANGA	Australian Open Emma Raducanu beats Sloane Stephens on Melbourne debutBritain s Emma Raducanu shows quality and resilience to beat American Sloane Stephens in three sets for a spot in the Australian Open second round	2022-01-18 13:11:04
ニュース	BBC News - Home	Pay rises fail to keep up with the cost of living	https://www.bbc.co.uk/news/business-60035312?at_medium=RSS&at_campaign=KARANGA	record	2022-01-18 13:33:53
ニュース	BBC News - Home	Tonga tsunami: Health warnings over volcanic ash	https://www.bbc.co.uk/news/health-60036369?at_medium=RSS&at_campaign=KARANGA	dangerous	2022-01-18 13:00:36
ニュース	BBC News - Home	Covid contract to Dominic Cummings friends ruled lawful by Appeal Court	https://www.bbc.co.uk/news/uk-politics-60039356?at_medium=RSS&at_campaign=KARANGA	adviser	2022-01-18 13:20:12
ニュース	BBC News - Home	Crime bill: Lords defeats for government's protest clamp-down plans	https://www.bbc.co.uk/news/uk-politics-60032465?at_medium=RSS&at_campaign=KARANGA	noisy	2022-01-18 13:02:15
ニュース	BBC News - Home	Mourners line the streets for Ashling Murphy	https://www.bbc.co.uk/news/world-europe-60035626?at_medium=RSS&at_campaign=KARANGA	murphy	2022-01-18 13:49:41
ニュース	BBC News - Home	Giggs trial delayed until August over lack of court space	https://www.bbc.co.uk/news/uk-wales-60036711?at_medium=RSS&at_campaign=KARANGA	building	2022-01-18 13:50:36
LifeHuck	ライフハッカー［日本版］	会議調整の手間を最小化！ダブルブッキングも回避できる便利ツール【今日のライフハックツール】	https://www.lifehacker.jp/article/lht_schedule_management_tool_spir/	面倒	2022-01-18 13:05:00
北海道	北海道新聞	生乳生産抑制、長期化に危機感　酪農地帯の釧根、廃用牛として出荷も	https://www.hokkaido-np.co.jp/article/634896/	長期化	2022-01-18 22:16:25
北海道	北海道新聞	「期待してたのに」観光関係者落胆　さっぽろ雪まつり、２年連続オンライン　コロナ急拡大、判断に理解も	https://www.hokkaido-np.co.jp/article/634906/	実行委員会	2022-01-18 22:06:30
北海道	北海道新聞	新聞労連特別賞に道新「核のごみ報道」　大賞は毎日新聞「日米地位協定６０年」	https://www.hokkaido-np.co.jp/article/634882/	新聞労連	2022-01-18 22:05:54
ビジネス	東洋経済オンライン	中国非鉄金属大手の業績に｢相場上昇｣の追い風 五鉱集団の2021年の売上高は15兆円突破へ | 「財新」中国Biz＆Tech | 東洋経済オンライン	https://toyokeizai.net/articles/-/502815?utm_source=rss&utm_medium=http&utm_campaign=link_back	biztech	2022-01-18 22:30:00