投稿時間:2021-11-21 22:19:46 RSSフィード2021-11-21 22:00 分まとめ(25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
python	Pythonタグが付けられた新着投稿 - Qiita	NumPy配列をシフトさせるnp.rollを使って単一画像のスクロールGIFを作ってみる	https://qiita.com/hkwsdgea_ttt2/items/23771884f14b76cabf4b	NumPy配列をシフトさせるnprollを使って単一画像のスクロールGIFを作ってみるはじめにNumPy配列ndarrayをシフトスクロールさせるnprollを用いて画像をスクロールさせて遊んでみます。	2021-11-21 21:05:29
python	Pythonタグが付けられた新着投稿 - Qiita	Python: 初心者がBランク問題を12問解いた際に必要だった知識のまとめ	https://qiita.com/baku2san/items/af2c6f7da8ab446b2261	Python初心者がBランク問題を問解いた際に必要だった知識のまとめ背景CC経験者が、話題になってたPython学んでおこうかと思い、paizaの問題を構文とか調べながらBランク問題を解いていった際の備忘録平均一問週程度で解いてると、忘れ去ってしまうので、たまにはまとめておかないと、というのが動機時点で、Bランク問ClearPythonだとこれが一番よい結果環境PythonBaseとするソースBaseのソースとしてまとめておいて、これを元に開始することで、解答時間内に調べなくてもよくなりつつある感じAランク・・・いつ挑戦しようかは悩み中。	2021-11-21 21:04:05
js	JavaScriptタグが付けられた新着投稿 - Qiita	dat.guiはlil-guiに移行中です	https://qiita.com/masato_makino/items/1e66b7e6b5bb69865ff4	lilguiは更新の途絶えたdatguiの代替品として開発されました。	2021-11-21 21:39:59
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	django aタグでの画面遷移先が、記述と異なるものになる	https://teratail.com/questions/370382?rss=all	djangoaタグでの画面遷移先が、記述と異なるものになる問題点現在、Djangoを用いて、Webアプリを開発しています。	2021-11-21 21:56:04
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Shellのfind文。１行目とファイル名の前の./を削除してファイル出力したい	https://teratail.com/questions/370381?rss=all	magiciitesttesttesttest	2021-11-21 21:44:46
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	JavaScript if文　背景色を変更する	https://teratail.com/questions/370380?rss=all	JavaScriptif文背景色を変更する前提・実現したいことボタンクリック時に入力されている値を取得した後に、if文を使用して背景色を変更する。	2021-11-21 21:44:15
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	AWSでのリポジトリ運用	https://teratail.com/questions/370379?rss=all	AWSでのリポジトリ運用AWSのCodecommitにリモートリポジトリを用意して、ローカルにそれをクローンして開発者が各々製作後、リモートリポジトリにpush→AWSのマネコンからプルリクエストを投げるという運用を行う際、ローカルでのgit操作クローンやリモートへのpushなどを行うツールとしてはtortoisegitがあると思います。	2021-11-21 21:32:14
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	詐欺サイトが表示される	https://teratail.com/questions/370378?rss=all	google	2021-11-21 21:09:04
AWS	AWSタグが付けられた新着投稿 - Qiita	'AmplifySignOut' is not exported from '@aws-amplify/ui-react'.を解消する	https://qiita.com/AkiSuika/items/265a08d0d58274af69c5	xAmplifySignOutxisnotexportedfromxawsamplifyuireactxを解消するはじめにこの記事は、AWSAmplifyハンズオンである「AWSでReactアプリケーションを構築する」を行っていた際に詰まった箇所のメモになります。	2021-11-21 21:36:51
技術ブログ	Developers.IO	Amazon SES のメール送信元をつきとめる	https://dev.classmethod.jp/articles/how-to-investigate-the-source-of-ses-email/	amazonses	2021-11-21 12:01:38
海外TECH	Ars Technica	Star Trek: Discovery is tearing the streaming world apart	https://arstechnica.com/?p=1814473	angers	2021-11-21 12:15:27
海外TECH	DEV Community	Backend Skillset Roadmap	https://dev.to/livesamarthgupta/backend-skillset-roadmap-4d5i	Backend Skillset Roadmap Hey There Are you a Java developer or a Python developer or maybe Golang developer Well whoever you are we all face the same set of problems in our backend just our language of choice is different in solving those problems But one may think how to grow not just in particular technology but as an engineer in a whole So I present the Backend Skillset RoadmapThis roadmap is language agnostic and could be thought of as a checklist instead of a roadmap You don t need to learn things serially one can progress through each parallelly P S Don t be Jack of all and Master of none	2021-11-21 12:43:13
海外TECH	DEV Community	How to use celery with flask	https://dev.to/itz_salemm/how-to-use-celery-with-flask-2k1m	How to use celery with flask IntroductionHave you ever come across programs or task that takes a lot of time to process or give an actual output Tasks such as sending emails and uploading data over the Internet takes time to process which can slow down your application s workflow These task should be run separately from other tasks Your application should be able to process with these task in the background and continue with other tasks After these task have processed and the result is ready then it can be served to the user I will be introducing you to setting up and configuration of celery and Redis in a flask project which handles async function or tasks like this We would also take a look at the application of celery in real time with build and email sender app Remember that these task takes time We would be making our email sender run as a background task PrerequisitesThis tutorial assumes that you already know about the basics of Python and Flask and also assumes that you have python and Flask framework set up on your system or machine You must also have the very least and basic understanding of HTML to build out email forms What is a Task Queue A task queue is a system that distributes task that needs to be complete as a background without interfering with the applications request and response cycle Task Queues makes Assigning work that slows down application processes while running easier Intensive application can be handle by software applications like this in the background while users still interacts with the website and carry on with other activities This ensures that the user s engagement is consistent timely and unaffected by the workload What is Celery Celery is a Python task queue that allows task to run asynchronously with web applications without disturbing the application s request response cycle Celery is highly scalable which is one of the several reasons why it is being used for background work and also allowing new workers to be dispatched on demand to handle increasing workload or traffic With Celery being a well supported project and also well documented Celery still has a thriving user community though it is still under development Celery is also easy to integrate into various web frameworks with most of them providing libraries to assist with this Celery can also interface with several other message brokers hence its thriving community What is an Asynchronous Task An asynchronous task is simply a function that runs behind every other process running on your app This kind of function when called does not affect the normal flow of the application With asynchronous operations you can switch to a new task before the previous one is complete By using asynchronous programming you can handle many requests at once accomplishing more in a shorter amount of time Installation and Configuration for Celery on FlaskRunning Celery requires the use of a broker Redis is the most well known of the brokers For sending and receiving messages Celery requires the use of message broker such as⦁RabbitMQ⦁Redis⦁Amazon SQSNote Message brokers are programs built to aids the communication between services when sending information from both ends We would be making use of the Redis server in the tutorial Creating a Flask serverCreating a Flask server is easy Navigate to the folder where you want your server created Create a new python file and give it a name in our case celeryapp pyAnd add this simple code to your python script from flask import Flask app Flask name app route def home return Hello World if name main app run debug True Now let s test our server to make sure it s working To start our server run the following commands in your terminal python celeryapp pyIf you have followed the article correctly your result should look simple to the image below Communicating between Celery and FlaskNow that we have created our server we need to connect Celery with our flask application To do this update your celeryapp py file to look like this below imports from flask import Flask from celery import Celery creates a Flask object app Flask name Configure the redis server app config CELERY BROKER URL redis localhost app config CELERY RESULT BACKEND redis localhost creates a Celery object celery Celery app name broker app config CELERY BROKER URL celery conf update app config Celery is initialized by creating an object of class Celery with the application name and the connection to the message broker URL which is set to CELERY BROKER URL as key in the app config If you run something other than Redis or have the broker on a different machine you will need to change the URL accordingly it s always best to add additional configuration through celery conf update for Celery Though it is not a requirement the CELERY RESULT BACKEND is only necessary to store status and results from tasks in Celery The function that would run as a background task is just a normal function with has the celery task decorator With just this decorator the function would always run in the back ground For example celery task def async function arg arg Async task return resultJust like any other function to execute our celery task it needs to be invoke To invoke the function add the following code to your celeryapp py file async function Sending an Asynchronous Email with Celery Now let s see how Celery works in the real world Let s apply Celery in sending an email with our flask application First we need to build out our email form to let users send emails Here is the HTML template to build the form lt html gt lt head gt lt title gt Flask and Celery lt title gt lt head gt lt body gt lt h gt Sending Asynchronous Email lt h gt for message in get flashed messages lt p style color red gt message lt p gt endfor lt form method POST gt lt p gt Send email to lt input type text name email value email gt lt p gt lt input type submit name submit value Send gt lt form gt lt body gt lt html gt This is just a regular HTML syntax with the ability to show flask messages from Flask Hopefully you should be able to get around with it Note The code above is the index html file To send Emails we would make use of the Flask Mail extension Flask Mail requires some configuration including information about the email server that it will use to send emails Add the following code to your celeryapp py app to configure your Email sender Flask Mail configuration app config MAIL SERVER smtp googlemail com app config MAIL PORT app config MAIL USE TLS True app config MAIL USERNAME os environ get MAIL USERNAME app config MAIL PASSWORD os environ get MAIL PASSWORD app config MAIL DEFAULT SENDER flask example com Note For this to work make sure you have saved your email and password to an environment variables For security reasons and easy accessibility my password and email are stored in an environment variables Since we have a single route in this app we created a route index alone to cater for it Update your celeryapp py file with the following code app route methods GET POST def index if request method GET return render template index html email session get email email request form email session email email sends this content email msg subject Testing Celery with Flask to email body Testing background task with Celery if request form submit Send sends the email content to the backgraound function send email delay email msg flash Sending email to format email else flash No Email sent return redirect url for index The code above shows a function which gets the input from our html form and saves it in a session for easier accessibility This function checks for events on the submit button and send email after the submit button is clicked The email msg contains the subject recipient s email address and the body of the message being sent To allow users know what is going in the background a flash message is displayed when the email is being submitted Note We saved the user s value in the text field in the session to remember it after the page reloads The last piece of this application is the asynchronous task that gets the job done when a user submits the email form celery task def send email email msg Async function to send an email with Flask Mail msg sub Message email msg subject email sender app config MAIL DEFAULT SENDER recipient email msg to msg sub body email msg body with app app context mail send msg sub As said earlier this task is decorated with celery task to make it run in the background The function creates a Message object from Flask Mail using the email data dictionary For Flask Mail to run it must build an application context before calling the send method ConclusionCelery more than a few extra steps beyond simply sending a job to a background thread but the benefits in terms of flexibility and scalability are hard to ignore Sending a scheduled task is easier done using celery than any other means of running scheduled task Imagine you want to perform a task daily In this case Celery can be used to run programs in the background with any necessarily human triggered Although Celery is used in most cases for long running task it can also be used to connect to third party APIs As soon as data is gotten back from the API in your celery task it is then served to the user	2021-11-21 12:30:08
海外TECH	DEV Community	AWS Serverless Data Analytics Pipeline | AWS White Paper Summary	https://dev.to/awsmenacommunity/aws-serverless-data-analytics-pipeline-aws-white-paper-summary-4h3f	AWS Serverless Data Analytics Pipeline AWS White Paper Summary IntroductionA serverless data lake architecture enables agile and self service data onboarding and analytics for all data consumer roles across a company By using AWS serverless technologies as building blocks you can rapidly and interactively build data lakes and data processing pipelines to ingest store transform and analyze petabytes of structured and unstructured data from batch and streaming sources without needing to manage any storage or compute infrastructure This architecture includes a data lake data processing pipelines and a consumption layer that enables several ways to analyze the data in the data lake without moving it including business intelligence BI dashboarding exploratory interactive SQL big data processing predictive analytics and ML Logical architecture of modern data lake centric analytics platformsArchitecture of a data lake centric analytics platformYou can think of a data lake centric analytics architecture as a stack of six logical layers where each layer is composed of multiple components A layered component oriented architecture promotes separation of concerns decoupling of tasks and flexibility This provides the agility needed to quickly integrate new data sources support new analytics methods and add tools required to keep up with the accelerating pace of changes in the analytics landscape In the following sections we look at the key responsibilities capabilities and integrations of each logical layer The ingestion layer is responsible for bringing data into the data lake It provides the ability to connect to internal and external data sources over a variety of protocols It can ingest batch and streaming data into the storage layer The storage layer is responsible for providing durable scalable secure and money saving components to store vast quantities of data The cataloging and search layer is responsible for storing business and technical metadata about datasets hosted in the storage layer The processing layer is responsible for transforming data into a consumable state through data validation cleanup normalization transformation and enrichment The consumption layer is responsible for providing scalable and performant tools to gain insights from the vast amount of data in the data lake The security and governance layer is responsible for protecting the data in the storage layer and processing resources in all other layers Serverless data lake centric analytics architectureTo compose the layers described in our logical architecture AWS introduces a reference architecture that uses AWS serverless and managed services In this approach AWS services provide the following capabilities Providing and managing scalable resilient secure and cost effective infrastructural componentsEnsuring infrastructural components natively integrate with each otherThis reference architecture enables you to focus more time on rapidly building data and analytics pipelines It significantly accelerates new data onboarding and driving insights from your data The AWS serverless and managed components enable self service across all data consumer roles by providing the following key benefits Easy configuration driven useFreedom from infrastructure managementPay per use pricing modelThe following diagram illustrates this architecture AWS Serverless Data Analytics Pipeline Reference Architecture Ingestion layerThe ingestion layer in the presented serverless architecture is composed of a set of purpose built AWS services to enable data ingestion from a variety of sources Each of these services enables simple self service data ingestion into the data lake landing zone and provides integration with other AWS services in the storage and security layers Individual purpose built AWS services match the unique connectivity data format data structure and data velocity requirements of operational database sources streaming data sources and file sources gt Operational database sourcesTypically organizations store their operational data in various relational and NoSQL databases AWS Data Migration Service AWS DMS can connect to a variety of operational RDBMS and NoSQL databases and ingest their data into Amazon Simple Storage Service Amazon S buckets in the data lake landing zone With AWS DMS you can first perform a one time import of the source data into the data lake and replicate ongoing changes happening in the source database AWS DMS encrypts S objects using AWS Key Management Service AWS KMS keys as it stores them in the data lake AWS DMS is a fully managed resilient service and provides a wide choice of instance sizes to host database replication tasks AWS Lake Formation provides a scalable serverless alternative called blueprints to ingest data from AWS native or on premises database sources into the landing zone in the data lake A Lake Formation blueprint is a predefined template that generates a data ingestion AWS Glue workflow based on input parameters such as source database target Amazon S location target dataset format target dataset partitioning columns and schedule A blueprint generated AWS Glue workflow implements an optimized and parallelized data ingestion pipeline consisting of crawlers multiple parallel jobs and triggers connecting them based on conditions gt Streaming data sourcesThe ingestion layer uses Amazon Kinesis Data Firehose to receive streaming data from internal and external sources With a few clicks you can configure a Kinesis Data Firehose API endpoint where sources can send streaming data This streaming data can be clickstreams application and infrastructure logs and monitoring metrics and IoT data such as devices telemetry and sensor readings Kinesis Data Firehose does the following Buffers incoming streamsBatches compresses transforms and encrypts the streamsStores the streams as S objects in the landing zone in the data lakeKinesis Data Firehose natively integrates with the security and storage layers and can deliver data to Amazon S Amazon Redshift and Amazon Elasticsearch Service Amazon ES for real time analytics use cases Kinesis Data Firehose is serverless requires no administration and has a cost model where you pay only for the volume of data you transmit and process through the service Kinesis Data Firehose automatically scales to adjust to the volume and throughput of incoming data gt File sourcesMany applications store structured and unstructured data in files that are hosted on Network Attached Storage NAS arrays Organizations also receive data files from partners and third party vendors Analyzing data from these file sources can provide valuable business insights Internal file sharesAWS DataSync can ingest hundreds of terabytes and millions of files from NFS and SMB enabled NAS devices into the data lake landing zone DataSync automatically handles scripting of copy jobs scheduling and monitoring transfers validating data integrity and optimizing network utilization DataSync can perform one time file transfers and monitor and sync changed files into the data lake DataSync is fully managed and can be set up in minutes Partner data filesFTP is most common method for exchanging data files with partners The AWS Transfer Family is a serverless highly available and scalable service that supports secure FTP endpoints and natively integrates with Amazon S gt Data APIsOrganizations today use SaaS and partner applications such as Salesforce Marketo and Google Analytics to support their business operations Analyzing SaaS and partner data in combination with internal operational application data is critical to gaining degree business insights Partner and SaaS applications often provide API endpoints to share data SaaS APIsThe ingestion layer uses Amazon AppFlow to easily ingest SaaS applications data into the data lake With a few clicks you can set up serverless data ingestion flows in Amazon AppFlow Your flows can connect to SaaS applications such as Salesforce Marketo and Google Analytics ingest data and store it in the data lake You can schedule Amazon AppFlow data ingestion flows or trigger them by events in the SaaS application Ingested data can be validated filtered mapped and masked before storing in the data lake Amazon AppFlow natively integrates with authentication authorization and encryption services in the security and governance layer Partner APIsTo ingest data from partner and third party APIs organizations build or purchase custom applications that connect to APIs fetch data and create S objects in the landing zone by using AWS SDKs These applications and their dependencies can be packaged into Docker containers and hosted on AWS Fargate AWS Glue Python shell jobs also provide serverless alternative to build and schedule data ingestion jobs that can interact with partner APIs by using native open source or partner provided Python libraries AWS Glue provides out of the box capabilities to schedule singular Python shell jobs or include them as part of a more complex data ingestion workflow built on AWS Glue workflows Third party data sourcesYour organization can gain a business edge by combining your internal data with third party datasets such as historical demographics weather data and consumer behavior data AWS Data Exchange provides a serverless way to find subscribe to and ingest third party data directly into Amazon S buckets in the data lake landing zone Storage layerAmazon S provides the foundation for the storage layer in our architecture Amazon S provides virtually unlimited scalability at low cost for our serverless data lake Data is stored as S objects organized into raw cleaned and curated zone buckets and prefixes Amazon S encrypts data using keys managed in AWS KMS IAM policies control granular zone level and dataset level access to various users and roles Amazon S provides of availability and of durability and charges only for the data it stores To significantly reduce costs Amazon S provides colder tier storage options called Amazon S Glacier amp S Glacier Deep Archive To automate cost optimizations Amazon S provides configurable lifecycle policies and S Intelligent Tiering options to automate moving older data to colder tiers AWS services in our ingestion cataloging processing and consumption layers can natively read and write S objects Cataloging and search layerA data lake typically hosts many datasets which have evolving schema and new data partitions A central data catalog that manages metadata for all the datasets in the data lake is crucial to enabling self service discovery of data in the data lake Additionally separating metadata from data into a central schema enables schema on read for the processing and consumption layer components In the presented architecture Lake Formation provides the central catalog to store and manage metadata for all datasets hosted in the data lake Organizations manage both technical metadata such as versioned table schemas partitioning information physical data location and update timestamps and business attributes such as data owner data steward column business definition and column information sensitivity of all their datasets in Lake Formation Services such as AWS Glue Amazon EMR and Amazon Athena natively integrate with Lake Formation and automate discovering and registering dataset metadata into the Lake Formation catalog Additionally Lake Formation provides APIs to enable metadata registration and management using custom scripts and third party products AWS Glue crawlers in the processing layer can track evolving schemas and newly added partitions of datasets in the data lake and add new versions of corresponding metadata in the Lake Formation catalog Lake Formation provides the data lake administrator a central place to set up granular table and column level permissions for databases and tables hosted in the data lake After Lake Formation permissions are set up users and groups can access only authorized tables and columns using multiple processing and consumption layer services such as Athena Amazon EMR AWS Glue and Amazon Redshift Spectrum Processing layerThe processing layer in our architecture is composed of two types of components Components used to create multi step data processing pipelines Components to orchestrate data processing pipelines on schedule or in response to event triggers such as ingestion of new data into the landing zone AWS Glue and AWS Step Functions provide serverless components to build orchestrate and run pipelines that can easily scale to process large data volumes Multi step workflows built using AWS Glue and Step Functions can catalog validate clean transform and enrich individual datasets and advance them from raw to cleaned and cleaned to curated zones in the storage layer AWS Glue is a serverless pay per use ETL service for building and running Python or Spark jobs written in Scala or Python without requiring you to deploy or manage clusters AWS Glue automatically generates the code to accelerate your data transformations and loading processes AWS Glue ETL builds on top of Apache Spark and provides commonly used out of the box data source connectors data structures and ETL transformations to validate clean transform and flatten data stored in many open source formats such as CSV JSON Parquet and Avro AWS Glue ETL also provides capabilities to incrementally process partitioned data Additionally you can use AWS Glue to define and run crawlers that can crawl folders in the data lake discover datasets and their partitions infer schema and define tables in the Lake Formation catalog AWS Glue provides more than a dozen built in classifiers that can parse a variety of data structures stored in open source formats AWS Glue also provides triggers and workflow capabilities that you can use to build multi step end to end data processing pipelines that include job dependencies and running parallel steps You can schedule AWS Glue jobs and workflows or run them on demand AWS Glue natively integrates with AWS services in storage catalog and security layers To make it easy to clean and normalize data Glue also provides a visual data preparation tool called Glue DataBrew which is an interactive point and click visual interface without requiring to write any code Step Functions is a serverless engine that you can use to build and orchestrate scheduled or event driven data processing workflows You use Step Functions to build complex data processing pipelines that involve orchestrating steps implemented by using multiple AWS services such as AWS Glue AWS Lambda Amazon Elastic Container Service Amazon ECS containers and more Consumption layerThe consumption layer in the presented architecture is composed using fully managed purpose built analytics services that enable interactive SQL BI dashboarding batch processing and ML Interactive SQLAmazon Athena is an interactive query service that enables you to run complex ANSI SQL against terabytes of data stored in Amazon S without needing to first load it into a database Athena queries can analyze structured semi structured and columnar data stored in open source formats such as CSV JSON XML Avro Parquet and ORC Athena uses table definitions from Lake Formation to apply schema on read to data read from Amazon S Data warehousing and batch analyticsAmazon Redshift is a fully managed data warehouse service that can host and process petabytes of data and run thousands highly performant queries in parallel Amazon Redshift uses a cluster of compute nodes to run very low latency queries to power interactive dashboards and high throughput batch analytics to drive business decisions You can run Amazon Redshift queries directly on the Amazon Redshift console or submit them using the JDBC ODBC endpoints provided by Amazon Redshift Business intelligenceAmazon QuickSight provides a serverless BI capability to easily create and publish rich interactive dashboards QuickSight enriches dashboards and visuals with out of the box automatically generated ML insights such as forecasting anomaly detection and narrative highlights QuickSight natively integrates with Amazon SageMaker to enable additional custom ML model based insights to your BI dashboards Predictive analytics and MLAmazon SageMaker is a fully managed service that provides components to build train and deploy ML models using an interactive development environment IDE called Amazon SageMaker Studio In Amazon SageMaker Studio you can upload data create new notebooks train and tune models move back and forth between steps to adjust experiments compare results and deploy models to production all in one place by using a unified visual interface Amazon SageMaker also provides managed Jupyter notebooks that you can spin up with just a few clicks Security and governance layerComponents across all layers of the presented architecture protect data identities and processing resources by natively using the following capabilities provided by the security and governance layer Authentication and authorizationAWS Identity and Access Management IAM provides user group and role level identity to users and the ability to configure fine grained access control for resources managed by AWS services in all layers of our architecture IAM supports multi factor authentication and single sign on through integrations with corporate directories and open identity providers such as Google Facebook and Amazon EncryptionAWS KMS provides the capability to create and manage symmetric and asymmetric customer managed encryption keys AWS services in all layers of our architecture natively integrate with AWS KMS to encrypt data in the data lake It supports both creating new keys and importing existing customer keys Access to the encryption keys is controlled using IAM and is monitored through detailed audit trails in CloudTrail Network protectionOur architecture uses Amazon Virtual Private Cloud Amazon VPC to provision a logically isolated section of the AWS Cloud called VPC that is isolated from the internet and other AWS customers Amazon VPC provides the ability to choose your own IP address range create subnets and configure route tables and network gateways AWS services from other layers in our architecture launch resources in this private VPC to protect all traffic to and from these resources Monitoring and loggingAWS services in all layers of our architecture store detailed logs and monitoring metrics in Amazon CloudWatch CloudWatch provides the ability to analyze logs visualize monitored metrics define monitoring thresholds and send alerts when thresholds are crossed All AWS services in our architecture also store extensive audit trails of user and service actions in CloudTrail CloudTrail provides event history of your AWS account activity including actions taken through the AWS Management Console AWS SDKs command line tools and other AWS services This event history simplifies security analysis resource change tracking and troubleshooting In addition you can use CloudTrail to detect unusual activity in your AWS accounts These capabilities help simplify operational analysis and troubleshooting ConclusionWith AWS serverless and managed services you can build a modern low cost data lake centric analytics architecture in days A decoupled component driven architecture enables you to start small and quickly add new purpose built components to one of six architecture layers to address new requirements and data sources ReferenceOriginal paper	2021-11-21 12:13:22
Apple	AppleInsider - Frontpage News	Crime blotter: iPhones, iPads, and a dog are stolen	https://appleinsider.com/articles/21/11/21/crime-blotter-iphones-ipads-and-a-dog-are-stolen?utm_medium=rss	Crime blotter iPhones iPads and a dog are stolenAn iPhone was stolen from a COVID clinic iPad taken from burned Bronx apartment and Apple devices are allegedly stolen by police ーand a pastor A year old man in Hong Kong was arrested in mid November for stealing an unknown number of iPhone Pro Max phones in seconds from a Mong Kok shop According to Coconuts Hong Kong the man was posing as an employee when he carried out the thefts Reportedly the man was able to load a backpack with the phones while the shopkeeper was distracted The store s own CCTV camera was not working but the shopkeeper checked footage from a neighboring store and saw the man Read more	2021-11-21 12:59:58
Apple	AppleInsider - Frontpage News	Apple recalls certain iPhone 12 models sold in UAE over sound fault	https://appleinsider.com/articles/21/11/21/apple-recalls-certain-iphone-12-models-sold-in-uae-over-sound-fault?utm_medium=rss	Apple recalls certain iPhone models sold in UAE over sound faultAn unknown number of iPhone and iPhone Pro models sold in the United Arab Emirates have been recalled by Apple because of an issue with an audio component iPhone and iPhone ProAccording to local publication Khaleej Times Apple has issued a recall in UAE for the iPhone and iPhone Pro ーthough specifically not the iPhone mini or iPhone Pro Max Read more	2021-11-21 12:25:27
ニュース	BBC News - Home	Covid: Netherlands and other parts of Europe see protests over new restrictions	https://www.bbc.co.uk/news/world-europe-59363256?at_medium=RSS&at_campaign=KARANGA	italy	2021-11-21 12:21:10
ニュース	BBC News - Home	Covid: Sajid Javid orders review of medical device racial bias	https://www.bbc.co.uk/news/uk-59363544?at_medium=RSS&at_campaign=KARANGA	oximeters	2021-11-21 12:41:43
ニュース	BBC News - Home	Sudan's military to reinstate ousted PM Hamdok	https://www.bbc.co.uk/news/world-africa-59364349?at_medium=RSS&at_campaign=KARANGA	protests	2021-11-21 12:06:08
ニュース	BBC News - Home	Peng Shuai: Video claims to show Chinese tennis player at tournament	https://www.bbc.co.uk/news/world-asia-china-59363156?at_medium=RSS&at_campaign=KARANGA	claims	2021-11-21 12:01:26
ニュース	BBC News - Home	Morikawa wins DP World event plus Race to Dubai title as McIlroy falls away	https://www.bbc.co.uk/sport/golf/59363112?at_medium=RSS&at_campaign=KARANGA	Morikawa wins DP World event plus Race to Dubai title as McIlroy falls awayCollin Morikawa wins the DP World Tour Championship in Dubai on under par and clinches the European Tour s season long Race to Dubai title	2021-11-21 12:55:58
ニュース	BBC News - Home	It's snow joke: 11-month-old hits the slopes	https://www.bbc.co.uk/news/world-59346367?at_medium=RSS&at_campaign=KARANGA	others	2021-11-21 12:02:37
北海道	北海道新聞	清水小と台湾・清水国民小、名前が縁でオンライン交流　クイズ形式で文化紹介	https://www.hokkaido-np.co.jp/article/614209/	紹介	2021-11-21 21:16:00
北海道	北海道新聞	米―１グランプリ、最高賞は岐阜県の曽我さん　蘭越で大会	https://www.hokkaido-np.co.jp/article/614203/	蘭越	2021-11-21 21:06:17
北海道	北海道新聞	旭川で９人感染　新型コロナ	https://www.hokkaido-np.co.jp/article/614200/	新型コロナウイルス	2021-11-21 21:01:00