| IT |
ITmedia 総合記事一覧 |
[ITmedia News] あおぞら銀、デビットカードで6％還元 コンビニ、Amazon、PayPayなどで |
https://www.itmedia.co.jp/news/articles/2302/02/news187.html
|
amazon |
2023-02-02 17:32:00 |
| IT |
ITmedia 総合記事一覧 |
[ITmedia Mobile] auとソフトバンクのデュアルSIMは「月数百円程度」を想定、eSIMにも対応 |
https://www.itmedia.co.jp/mobile/articles/2302/02/news185.html
|
itmediamobileau |
2023-02-02 17:09:00 |
| IT |
ITmedia 総合記事一覧 |
[ITmedia News] KDDIとソフトバンクのデュアルSIM、基本料金は「数百円程度」 ドコモ・楽天と協力の可能性は |
https://www.itmedia.co.jp/news/articles/2302/02/news184.html
|
itmedianewskddi |
2023-02-02 17:04:00 |
| TECH |
Techable（テッカブル） |
ノンアルコール飲料、意外に安くないのはなぜ？実はアルコールなしでも酒税がかかるケースも |
https://techable.jp/archives/195052
|
代表取締役 |
2023-02-02 08:00:49 |
| TECH |
Techable（テッカブル） |
enevoltの新シリーズ登場！スマートホーム対応IoT機器やデジカメに使えるリチウム電池 |
https://techable.jp/archives/194919
|
enevolt |
2023-02-02 08:00:09 |
| IT |
情報システムリーダーのためのIT情報専門サイト IT Leaders |
HPE、ストレージサーバー機の新製品「HPE Alletra 4000」、用途に合わせてSDSを使い分け | IT Leaders |
https://it.impress.co.jp/articles/-/24398
|
HPE、ストレージサーバー機の新製品「HPEAlletra」、用途に合わせてSDSを使い分けITLeadersヒューレット・パッカードエンタープライズHPE日本ヒューレット・パッカードは年月日、ストレージサーバー機「HPEAlletra」の受注を開始した。 |
2023-02-02 17:01:00 |
| python |
Pythonタグが付けられた新着投稿 - Qiita |
【Python】seleniumで親要素/子要素を取得する |
https://qiita.com/valusun/items/9ed118ad3c915eebfac0
|
fullxpath |
2023-02-02 17:15:46 |
| python |
Pythonタグが付けられた新着投稿 - Qiita |
Blenderでモーションデータの特定のボーンを一括でリネーム・削除するスクリプト |
https://qiita.com/phabyui/items/8c44653c7069ded3e899
|
blender |
2023-02-02 17:00:42 |
| AWS |
AWSタグが付けられた新着投稿 - Qiita |
Terraform + AWS EC2 / S3 - terraform init / apply のエラー例 |
https://qiita.com/YumaInaura/items/be1586a8c255b1e1b080
|
dersawssourcehashicorpaws |
2023-02-02 17:59:41 |
| AWS |
AWSタグが付けられた新着投稿 - Qiita |
PLCが出力した接点データを Amazon DynamoDB に格納する |
https://qiita.com/Sunelco_Support/items/a3bc3b0b4b4f6f8c0793
|
amazondynamodb |
2023-02-02 17:01:44 |
| golang |
Goタグが付けられた新着投稿 - Qiita |
[書籍レビュー] 実用Go言語 |
https://qiita.com/oba_atsushi/items/c7c48da3325ac1502656
|
通り |
2023-02-02 17:54:21 |
| 技術ブログ |
Developers.IO |
【地味に嬉しいアップデートかも】IoT デバイスのログを Amazon CloudWatch Logs にバッチで送信できるようになりました。 |
https://dev.classmethod.jp/articles/aws-iot-cores-rule-engine-device-logs-routing-amazon-cloudwatch-logs/
|
amazoncloudwatchlogs |
2023-02-02 08:41:18 |
| 技術ブログ |
Developers.IO |
Mixpanelのリテンション分析でユーザーの維持率を可視化する！ |
https://dev.classmethod.jp/articles/mixpanel-retention-howtouse/
|
mixpanel |
2023-02-02 08:18:30 |
| 技術ブログ |
Developers.IO |
AWS IoT GreengrassにDevice Defender コンポーネントをデプロイした際に発生したエラーを解消した |
https://dev.classmethod.jp/articles/resolved-error-deploy-device-defender-components-greengrass/
|
awsiotgreengrass |
2023-02-02 08:16:05 |
| 技術ブログ |
Hatena::Engineering |
Hatena Engineer Seminar #23 をオンラインで開催しました #hatenatech |
https://developer.hatenastaff.com/entry/engineer-seminar-23-report
|
HatenaEngineerSeminarをオンラインで開催しましたhatenatech年月日水に開催したHatenaEngineerSeminarのレポートです。 |
2023-02-02 18:00:00 |
| 海外TECH |
DEV Community |
22 Best DataOps Tools To Optimize Your Data Management and Observability In 2023 |
https://dev.to/chaos-genius/22-best-dataops-tools-to-optimize-your-data-management-and-observability-in-2023-1ooc
|
Best DataOps Tools To Optimize Your Data Management and Observability In The data landscape is rapidly evolving and the amount of data being produced and distributed on a daily basis is downright staggering According to the report by Statista currently there are approximately zettabytes of data in existence as of and this number is projected to reach zettabytes by As the volume of data continues to expand rapidly so does the demand for efficient data management and observability solutions and tools The actual value of data lies in how it is being utilized Collecting and storing data alone is not enough it must be leveraged and used correctly to get valuable insights These insights can range from demographics to consumer behavior and even future sales predictions providing an unparalleled resource for business decision making processes Also with real time data businesses can make quick and informed decisions adapt to the market and capitalize on live opportunities However this is only possible if data is of good quality outdated misleading or difficult to access which is precisely where DataOps comes to the rescue and plays a crucial role in optimizing and streamlining data management processes Unpacking the essence of DataOpsDataOps is a set of best practices and tools that aims to enhance the collaboration integration and automation of data management operations and tasks DataOps seeks to improve the quality speed and collaboration of data management through an integrated and process oriented approach utilizing automation and agile software engineering practices similar to that of DevOps to speed up and streamline the process of accurate data delivery It is designed to help businesses and 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 Now that we have a clear understanding of what DataOps means let s delve deeper into its key components The key components of a DataOps strategy include data integration data quality management and measurement data governance data orchestration and DataOps Observability Data integrationData integration involves integrating and testing code changes and promptly deploying them to production environments ensuring accuracy and consistency of data as it is integrated and delivered to appropriate teams Data quality managementData Quality Management involves identifying correcting and preventing errors or inconsistencies in data ensuring that the data being used is highly reliable and accurate Data governanceData governance ensures that data is collected stored and used consistently ethically and complies with regulations Data orchestrationData orchestration helps to manage and coordinates data processing in a pipeline specifying and scheduling tasks and dealing with errors to automate and optimize data flow through the data pipeline It is crucial for ensuring the smooth operation and performance of the data through the data pipeline DataOps observabilityDataOps observability refers to the ability to monitor and understand the various processes and systems involved in data management with the primary goal of ensuring the reliability trustworthiness and business value of the data It involves everything from monitoring and analyzing data pipelines to maintaining data quality and proving the data s business value through financial and operational efficiency metrics DataOps observability allows businesses and organizations to improve the efficiency of their data management processes and make better use of their data assets It aids in ensuring that data is always correct dependable and easily accessible which in turn helps businesses and organizations make data driven decisions optimize data related costs spend and generate more value from it Top DataOps and DataOps Observability tools to simplify data management cost amp collaboration processesOne of the most challenging aspects of DataOps is integrating data from various sources and ensuring data quality orchestration observability data cost management and governance DataOps aims to streamline these processes and improve collaboration among teams enabling businesses to make better data driven decisions and achieve improved performance and results In this article we will focus on DataOps observability and the top DataOps tools businesses can use to streamline their data management costs and collaboration processes A wide variety of DataOps tools are available on the market and choosing the right one can be a very daunting task To help businesses make an informed decision this article has compiled a list of the top DataOps tools that can be used to manage data driven processes Data Integration Tools FivetranFivetran is a very popular and widely adopted data integration platform that simplifies the process of connecting various data sources to a centralized data warehouse This enables users or businesses to easily analyze and visualize their data in one place eliminating the need to manually extract transform and load ETL data from multiple different sources Fivetran provides sets of pre built connectors for a wide range of data sources including popular databases cloud applications SaaS applicationsーand even flat files These connectors automate the process of data extraction ensuring that the data is always up to date fresh and accurate Once data is in the central data warehouse Fivetran performs schema discovery and data validation automatically creating tables and columns in the data warehouse based on the structure of the data source making it really very easy to set up and maintain data pipelines without the need for manually writing custom code Fivetran also offers features like data deduplication incremental data updates and real time data replication These features help make sure that the data is always complete fresh and accurate Talend Data FabricTalend Data Fabric solution is designed to help businesses and organizations make sure they have healthy data to stay in control mitigate risk and drive massive value The platform combines data integration integrity and governance to deliver reliable data that businesses and organizations can rely on for decision making processes Talend helps businesses build customer loyalty improve operational efficiency and modernize their IT infrastructure Talend s unique approach to data integration makes it easy for businesses and organizations to bring data together from multiple sources and power all their business decisions It can integrate virtually any data type from any data source to any data destination on premises or in the cloud The platform is flexible allowing businesses and organizations to build data pipelines once and run them anywhere with no vendor or platform lock in And the solution is an all in one unified solution bringing together data integration data quality and data sharing on an easy to use platform Talend s Data Fabric offers a multitude of best in class data integration capabilities such as Data Integration Pipeline Designer Data Inventory Data Preparation Change Data Capture and Data Stitching These tools make data integration data discovery search and data sharing more manageable enabling users to prepare and integrate data quickly visualize it keep it fresh and move it securely StreamSetsStreamSets is a powerful data integration platform that allows businesses to control and manage data flow from a variety of batch and streaming sources to modern analytics platforms You can deploy and scale your dataflows on edge on premises or in the cloud using its collaborative visual pipeline design while also mapping and monitoring them for end to end visibility The platform also allows for the enforcement of Data SLAs for high availability quality and privacy StreamSets enables businesses and organizations to quickly launch projects by eliminating the need for specialized coding skills through its visual pipeline design testing and deployment features all of which are accessible via an intuitive graphical user interface With StreamSets brittle pipelines and lost data will no longer be a concern as the platform can automatically handle unexpected changes The platform also includes a live map with metrics alerting and drill down functionality allowing businesses to efficiently integrate data in a breeze KViewKView provides enterprise level DataOps tools It offers a data fabric platform for real time data integration which enables businesses and organizations to deliver personalized experiences KView s enterprise level data integration tools integrate data from any kind of source and make it accessible to any consumer through various methods such as bulk ETL reverse ETL data streaming data virtualization log based CDC message based integration SQLーand APIs KView can ingest data from various sources and systems enhance it with real time insights convert it into its patented micro database and ensure performance scalability and security by compressing and encrypting the micro database individually It then applies data masking transformation enrichment and orchestration tools on the fly to make the data accessible to authorized consumers in any format while adhering to data privacy and security rules AlteryxAlteryx is a very powerful data integration platform that allows users to easily access manipulate analyze and output data The platform utilizes a drag and drop interface low code no code interface and includes a variety of tools and connectors for data blending predictive analytics and data visualization It can be used in a one off manner or more commonly as a recurring process called a workflow The way Alteryx builds workflows also serves as a form of process documentation allowing users to view collaborate support and enhance the process The platform can read and write data to files databases and APIs and it also includes functionality for predictive analytics and geospatial analysis Alteryx is currently being used in a variety of industries and functional areas and can be used to more quickly and efficiently automate data integration processes Some common use cases include combining and manipulating data within spreadsheets supplementing SQL development APIs cloud or hybrid access data science geospatial analysisーand creating reports and dashboards Note Alteryx is often compared to ETL tools but it is important to remember that its primary audience is data analysts Alteryx aims to empower business users by giving them the freedom to access manipulate and analyze data without relying on IT Data Quality Testing and Monitoring Tools Monte CarloMonte Carlo is a leading enterprise data monitoring and observability platform It provides an end to end solution for monitoring and alerting for data issues across the data warehouses data lakes ETL and business intelligence platforms It uses machine learning and AI to learn about the data and proactively identify data related issues assess their impact and notify those who need to know The platform s automatic and immediate identification of the root cause of issues allows teams to collaborate and resolve problems faster and it also provides automatic field level lineage data discovery and centralized data cataloging that allows teams to better understand the accessibility location health and ownership of their data assets The platform is designed with security in mind scales accordingly with the provided stack and includes a no code or low code code free onboarding feature for easy implementation with the existing data stack DatabandDataband is a data monitoring and observability platform recently acquired by IBM that helps organizations detect and resolve data issues before they impact the business It provides a fierce end to end view of data pipelines starting with source data which allows businesses and organizations to detect and resolve issues early reducing the mean time to detection MTTD and mean time to resolution MTTR from days and weeks to minutes One key features of Databand is its ability to automatically collect metadata from modern data stacks such as Airflow Spark Databricks Redshift dbt and Snowflake This metadata is used to build historical baselines of common data pipeline behavior which allows organizations to get visibility into every data flow from source to destination Databand also provides incident management end to end lineage data reliability monitoring data quality metrics anomaly detection and DataOps alerting and routing capabilities With this businesses and organizations can improve data reliability and quality and visualize how data incidents impact upstream and downstream components of the data stack Databand s combined capabilities provide a single solution for all data incidents allowing engineers to focus on building their modern data stack rather than fixing it Data FoldDatafold is a data reliability platform focused on proactive data quality management that helps businesses prevent data catastrophes It has the unique ability to detect evaluate and investigate data quality problems before they impact productivity The platform offers real time monitoring to identify issues quickly and prevent them from becoming data catastrophes Datafold harnesses the power of machine learning with AI to provide analytics with real time insights allowing data engineers to make top quality predictions from large amounts of data Some of the key features of Datafold include One Click Regression Testing for ETLData flow visibility Across all pipelines and BI reportsSQL Query Conversion Data Discovery and Multiple data source IntegrationsDatafold offers a simple yet intuitive user interface UI and navigation with powerful features The platform allows deep exploration of how tables and data assets relate The visualizations are really very easy to understand Data quality monitoring is also super flexible However the data integrations they support are relatively limited Query SurgeQuerySurge is a very powerful versatile tool for automating data quality testing and monitoring particularly for big data data warehouses BI reports and enterprise level applications It is particularly designed to integrate seamlessly allowing for continuous testing and validation of data as it flows Query Surge also provides the ability to create and run tests without needing to write SQL through smart query wizards This allows for column table and row level comparisons and automatic column matching Also users can create custom tests that can be modularized with reusable snippets of code set thresholds check data types and perform other advanced number of validation checks QuerySurge also has robust scheduling capabilities allowing users to run tests pronto at a specified date and time On top of that it also supports supported vendors and tech stacks so it can test across a wide variety of platforms including big data lakes data warehouses traditional databases NoSQL document stores BI reports flat files JSON filesーand a whole lot more One key benefits of QuerySurge is its ability to integrate with other solutions in the DataOps pipeline such as data integration ETL solutions build configuration solutions QA and test management solutions The tool also includes a Data Analytics Dashboard which allows users to monitor test execution progress in real time drill down into data to examine results and see stats for executed tests It also has an out of the box integration with plethora of services and any other solution with API access QuerySurge is available both on premises and in the cloud with support for AES bit encryption LDAP LDAPS TLS HTTPS SSL auto timeout and other security features In a nutshell QuerySurge is a very powerful and comprehensive solution for automating data monitoring and testing allowing businesses and organizations to improve their data quality at speed and reduce the risk of data related issues in the delivery pipeline Right DataRight Data s RDT is a powerful data testing and monitoring platform that helps businesses and organizations improve the reliability and trust of their data by providing an easy to use interface for data testing reconciliation and validation It allows users to quickly identify issues related to data consistency quality and completeness It also provides an efficient way to analyze design build execute and automate reconciliation and validation scenarios with little to no coding required which helps save time and resources Key features of RDT Ability to analyze DB It provides a full set of applications to analyze the source and target datasets Its top of the line Query Builder and Data Profiling features help users understand and analyze the data before they use the corresponding datasets in different scenarios Support of a wide range of data sources RDT supports a wide range of data sources such as ODBC or JDBC flat files cloud technologies SAP big data BI reportingーand various other sources This allows businesses and organizations to easily connect to and work with their existing data source Data reconciliation RDT has features like Compare Row Counts that let users compare the number of rows in the source dataset and the target dataset and find tables where the number of rows doesn t match It also provides a row level data compare feature that compares datasets between source target and identifies rows that do not match each other Data Validation RDT provides a user friendly interface for creating validation scenarios which enables users to establish one or more validation rules for target data sets identify exceptions and analyze and report on the results Admin amp CMS RDT has an admin console that allows the admin to manage and config the features of the tool The console provides the ability to create manage users roles and the mapping of roles to specific users Administrators can also create manage and test connection profiles which are used to create queries The tool also provides a Content Management Studio CMS that enables exporting of queries scenarios and connection profiles from one RightData instance to another This feature is useful for copying within the same instance from one folder to another and also for switching over the connection profile of queries DataOps Observability and Augmented FinOps Chaos GeniusChaos Genius is a powerful DataOps Observability tool that uses ML and AI to provide precise cost projections and enhanced metrics for monitoring and analyzing data and business metrics One of the main reasons the tool was built was to provide value to businesses by offering a powerful first in class DataOps observability tool that can help monitor and analyze data lower spending and improve business metrics The tool utilizes machine learning and artificial intelligence ML AI to sift through data and provide more precise cost projections and enhanced metrics Chaos Genius currently offers a service called Snowflake Observability as one of its main services Key features of Chaos Genius Snowflake Observability include Cost optimization and monitoring Chaos Genius is designed to help businesses and organizations optimize and monitor the cost of a Snowflake cloud data platform This includes finding places where costs can be cut and making suggestions for how to do so Enhanced query performance Chaos Genius can analyze query patterns to identify inefficient queries and make smart recommendations to improve their performance which can lead to faster and more efficient data retrieval and improve the overall performance of the data warehouse Reduced Spendings Chaos Genius enables businesses to better enhance the efficiency of their systems and reduce total spending by about Affordability Chaos Genius offers an affordable pricing model with three tiers The first tier is completely free while the other two are business oriented plans for companies that want to monitor more metrics This makes it accessible to businesses of all sizes and budgets UnravelUnravel is a DataOps observability platform that provides businesses and organizations with a thorough view of their entire data stack and helps them optimize performance automate troubleshooting and manage and monitor the cost of their entire data pipelines The platform is also designed to work with different cloud service providers for example Azure Amazon EMR GCP Cloudera and even on premises environments providing businesses with the flexibility to manage their data pipeline regardless of where their data resides Unravel uses the power of machine learning and AI to model data pipelines from end to end providing businesses with a detailed understanding of how data flows through their systems This enables businesses organizations to identify bottlenecks optimize resource allocation and improve the overall performance of their data pipelines The platform s data model enables businesses to explore correlate and analyze data across their entire environment providing deep insights into how apps services and resources are used and what works and what doesn t allowing businesses to quickly identify potential issues and take immediate action to resolve them Not only that but Unravel also has automatic troubleshooting features that can help businesses find the cause of a problem quickly and take steps to fix it saving them a huge amount of spending and making their data pipelines more reliable and efficient Data Orchestration Tools Apache AirflowApache Airflow is a fully open source DataOps workflow orchestration tool to author schedule and monitor workflows programmatically Airbnb first developed it but now it is under the Apache Software Foundation It is a tool for expressing and managing data pipelines and is often used in data engineering It allows users to define schedule and monitor workflows as directed acyclic graphs DAGs of tasks Airflow provides a simple and powerful way to manage data pipelines and it is simple to use allowing users to create and manage complex workflows quickly on top of that it has a large and active community that provides many plugins connectors and integrations with other tools that makes it very versatile Key features of Airflow include Dynamic pipeline generation Airflow s dynamic pipeline generation is one of its key features Airflow allows you to define and generate pipelines programmatically rather than manually creating and managing them This facilitates the creation and modification of complex workflows Extensibility Airflow allows using custom plugins operators and executors which means you can add new functionality to the platform to suit your specific needs and requirements this makes Airflow highly extensible and an excellent choice for businesses and organizations with unique requirements or working with complex data pipelines Scalability Airflow has built in support for scaling thousands of tasks making it very well suited for large scale organizations or running large scale data processing tasks ShipyardShipyard is a powerful data orchestration tool designed to help data teams streamline and simplify their workflows and deliver data at very high speed The tool is intended to be code agnostic allowing teams to deploy code in any language they prefer without the need for a steep learning curve It is cloud ready meaning it eliminates the need for teams to spend hours and hours spinning up and managing their servers Instead they can orchestrate their workflows in the cloud allowing them to focus on what they do bestーworking with data Shipyard can also run thousands of jobs at once making it ideal for scaling data processing tasks The tool can dynamically scale to meet the demand ensuring that workflows run smoothly and efficiently even when dealing with large amounts of data Shipyard comes with a very intuitive visual UI allowing users to construct workflows directly from the interface and make changes as needed by dragging and dropping The advanced scheduling webhooks and on demand triggers make automating workflows on any schedule easy It also allows for cross functional workflows meaning that the entire data process can be interconnected across the entire data lifecycle helping teams keep track of the entire data journey from data collection and processing to visualization and analysis Shipyard also provides instant notifications which help teams catch and fix critical breakages before anyone even notices It also has automatic retries and cutoffs which give workflows resilience so teams don t have to lift a finger Not only that it can isolate and address the root cause in real time so teams can get back up and running in seconds Also it allows teams to connect their entire data stack in minutes seamlessly moving data between the existing tools in the data stack regardless of the cloud provider With over integrations and low code templates to choose from data teams can begin connecting their existing tools in record speeed DagsterDagster is a next generation open source data orchestration platform for developing producing and observing data assets in real time Its primary focus is to provide a unified experience for data engineers data scientists and developers to manage the entire lifecycle of data assets from development and testing to production and monitoring Using Dagster users can manage their data assets with code and monitor runs across all jobs in one place with the run timeline view On the other hand the run details view allows users to zoom into a run and pin down issues with surgical precision Dagster also allows users to see each asset s context and update it all in one place including materializations lineage schema schedule partitionsーand a whole lot more Not only that but it also allows users to launch and monitor backfills over every partition of data Dagster is an enterprise level orchestration platform that prioritizes developer experience DX with fully serverless hybrid deployments native branching and provides out of the box CI CD configuration AWS glueAWS Glue is a data orchestration tool that makes it easy to discover prepare and combine data for analytics and machine learning workflows With Glue you can crawl data sources extract transform and load ETL data and create schedule data pipelines using a simple visual UI interface Glue can also be used for analytics and includes tools for authoring running jobs and implementing business workflows AWS Glue offers data discovery ETL cleansing and central cataloging and allows you to connect to over diverse data sources You can create run and monitor ETL pipelines to load data into data lakes and query cataloged data using Amazon Athena Amazon EMR and Redshift Spectrum It is serverless in nature meaning there s no infrastructure to manage and it supports all kinds of workloads like ETL ELT and streaming all packaged in one service AWS Glue is very user friendly and is suitable for all kinds of users including developers and business users Its ability to scale on demand allows users to focus on high value activities that extract maximum value from their data it can handle any data size and support all types of data and schema variations AWS Glue provides TONS of awesome features that can be used in a DataOps workflow such as Data Catalog A central repository to store structural and operational metadata for all data assets ETL Jobs The ability to define schedule and run ETL jobs to prepare data for analytics Data Crawlers Automated data discovery and classification that can connect to data sources extract metadata and create table definitions in the Data Catalog Data Classifiers The ability to recognize and classify specific types of data such as JSON CSV and Parquet Data Wrangler A visual data transformation tool that makes it easy to clean and prepare data for analytics Security Integrations with AWS Identity and Access Management IAM and Amazon Virtual Private Cloud VPC to help secure data in transit and at rest Scalability The ability to handle petabyte scale data and thousands of concurrent ETL jobs Data Governance Tools CollibraCollibra is an enterprise oriented data governance tool that helps businesses and organizations understand and manage their data assets It enables businesses and organizations to create an inventory of data assets capture metadata about em and govern these assets to ensure regulatory compliance The tool is primarily used by IT data owners and administrators who are in charge of data protection and compliance to inventory and track how data is used Collibra s main aim is to protect data ensure it is appropriately governed and used and eliminate potential fines and risks from a lack of regulatory compliance Collibra offers six key functional areas to aid in data governance Collibra Data Quality amp Observability Monitors data quality and pipeline reliability to aid in remedying anomalies Collibra Data Catalog A single solution for finding and understanding data from various sources Data Governance A location for finding understanding and creating a shared language around data for all individuals within an organization Data Lineage Automatically maps relationships between systems applications and reports to provide a comprehensive view of data across the enterprise Collibra Protect Allows for the discovery definition and protection of data from a unified platform Data Privacy Centralizes automates and guides workflows to encourage collaboration and address global regulatory requirements for data privacy AlationAlation is an enterprise level data catalog tool that serves as a single reference point for all of an organization s data It automatically crawls and indexes over different data sources including on premises databases cloud storage file systems and BI tools Using query log ingestion Alation parses queries to identify the most frequently used data and the individuals who use it the most forming the basis of the catalog Users can then collaborate and provide context for the data With the catalog in place data analysts and scientists can quickly and easily locate examine verify and reuse data hence boosting their productivity Alation can also be used for data governance allowing analytics teams to efficiently manage and enforce policies for data consumers Key benefits of using Alation Boost analyst productivityImprove data comprehensionFoster collaborationMinimize the risk of data misuseEliminate IT bottlenecksEasily expose and interpret data policiesAlation offers various solutions to improve productivity accuracy and data driven decision making These include Alation Data Catalog Improves the efficiency of analysts and the accuracy of analytics empowering all members of an organization to find understand and govern data efficiently Alation Connectors A wide range of native data sources that speed up the process of gaining insights and enable data intelligence throughout the enterprise Additional data sources can also be connected with the Open Connector Framework SDK Alation Platform An open and intelligent solution for various metadata management applications including search and discovery data governance and digital transformation Alation Data Governance App Simplifies secure access to the best data in hybrid and multi cloud environments Alation Cloud Service Offers businesses and organizations the option to manage their data catalog on their own or have it managed for them in the cloud Data Cloud and Data Lake Platforms DatabricksDatabricks is a cloud based lakehouse platform founded in by the creators of Apache Spark Delta Lake and MlFlow It unifies data warehousing and data lakes to provide an open and unified platform for data and AI The Databricks Lakehouse architecture is designed to manage all data types and is cloud agnostic allowing data to be governed wherever it is stored Teams can collaborate and access all the data they need to innovate and improve The platform includes the reliability and performance of Delta Lake as the data lake foundation fine grained governance and support for persona based use cases It also provides instant and serverless compute managed by Databricks The Lakehouse platform eliminates the challenges caused by traditional data environments such as data silos and complicated data structures It is simple open multi cloud and supports various data team workloads The platform allows for flexibility with existing infrastructure open source projects and the Databricks partner network SnowflakeSnowflake is a cloud data platform offering a software as a service model fo storing and analyzing LARGE amounts of data It is designed to support high levels of concurrency scalability and performance It allows customers to focus on getting value from their data rather than managing the infrastructure on which it s stored The company was founded in by three experts Benoit Dashville Thierry Cruanes and Marcin Zukowski Snowflake runs on top of cloud infrastructure such as AWS Microsoft Azure and Google s cloud platforms It allows customers to store and analyze their data using the elasticity of the cloud providing speed ease of use cost effectiveness and scalability It is widely used for data warehousing data lakes and data engineering It is designed to handle the complexities of modern data management processes Not only that but it also supports various data analytics applications such as BI tools ML AI and data science Snowflake also revolutionized the pricing model by utilizing a utilization model that focuses on a client s consumption based on whether they re computing or storing data making everything more flexible and elastic Key features of Snowflake include Scalability Snowflake offers scalability through its multi cluster shared data architecture allowing for easy scaling up and down of resources as needed Cloud Agnostic Snowflake is available on all major cloud providers AWS GCP AZURE while maintaining the same user experience allowing for easy integration with current cloud architecture Auto scaling Auto Suspend Snowflake automatically starts and stops clusters during resource intensive processing and stops virtual warehouses when idle for cost and performance optimization Concurrency and Workload Separation Snowflake s multi cluster architecture separates workloads to eliminate concurrency issues and ensures that queries from one virtual warehouse will not affect another Zero Hardware Software config Snowflake does not require software installation or hardware config or commissioning making it easy to set up and manage Semi Structured Data Snowflake s architecture allows for the storage of structured and semi structured data through the use of VARIANT data types Security Snowflake offers a wide range of security features including network policies authentication methods and access controls to ensure secure data access and storage Google BigqueryGoogle BigQuery is a fully managed and serverless data warehouse provided by Google Cloud that helps organizations manage and analyze large amounts of data with built in features such as machine learning geospatial analysis and business intelligence It allows businesses and organizations to easily store ingest store analyze and visualize large amounts of data Bigquery is designed to handle up to petabyte scale data and supports SQL queries for data analysis purposes The platform also includes BigQuery ML which allows businesses or users to train and execute machine learning models using their enterprise data without needing to move it around BigQuery integrates with various business intelligence tools and can be easily accessed through the cloud console a command line tool and even APIs It is also directly integrated with Google Cloud s Identity and Access Management Service so that one can securely share data and analytics insights across organizations With BigQuery businesses only have to pay for data storing querying and streaming inserts Loading and exporting data are absolutely free of charge Amazon RedshiftAmazon Redshift is a cloud based data warehouse service that allows for the storage and analysis of large data sets It is also useful for migrating LARGE databases The service is fully managed and offers scalability and cost effectiveness for storing and analyzing large amounts of data It utilizes SQL to analyze structured and semi structured data from a variety of sources including data warehouses operational databases and data lakes which are enabled by AWS designed hardware and powered by AI amp machine learning due to this it is able to deliver optimal cost performance at any scale The service also offers high speed performance and efficient querying capabilities to assist in making business decisions Key features of Amazon Redshift include High Scalability Redshift allows users to start with a very small amount of data and scale up to a petabyte or more as their data grows incrementally Query execution Performance Redshift uses columnar storage advanced compression and parallel query execution to deliver fast query performance on large data sets Pay as you go pricing model Redshift uses a pay as you go pricing model and allows users to choose from a range of node types and sizes to optimize cost and performance Robust Security Redshift integrates with AWS security services like AWS Identity and Access Management IAM and Amazon Virtual Private Cloud VPC ーand more learn more from here ーto keep data safe Integration Redshift can be easily integrated with varios other services such as Datacoral Etleap Fivetran SnapLogic Stitch Upsolver Matillionーand more Monitoring Management tools Redshift has various management and monitoring tools including the Redshift Management Console and Redshift Query Performance Insights to help users manage and monitor their clusters in their data warehouse ConclusionAs the amount of data continues to grow at an unprecedented rate the need for efficient data management and observability solutions has never been greater But simply collecting and storing data won t cut itーit s the insights and value it can provide that truly matter However this can only be achieved if the data is high quality up to date and easily accessible This is exactly where DataOps comes inーproviding a powerful set of best practices and tools to improve collaboration integration and automation allowing businesses to streamline their data pipelines reduce costs and workload and enhance data quality Hence by utilizing the tools mentioned above businesses can minimize data related expenses and extract maximum value from their data Don t let your data go to wasteーharness its power with DataOps References A Dyck R Penners and H Lichter Towards Definitions for Release Engineering and DevOps IEEE ACM rd International Workshop on Release Engineering Florence Italy pp doi RELENG Doyle Kerry “DataOps vs MLOps Streamline your data operations TechTarget February Accessed January Danise Amy and Bruce Rogers “Fivetran Innovates Data Integration Tools Market Forbes January Accessed January Basu Kirit “What Is StreamSets Data Engineering for DataOps StreamSets October Accessed January Chand Swatee “What is Talend Introduction to Talend ETL Tool Edureka November WhatIsTalend Accessed January “Delivering real time data products to accelerate digital business white paper KView Accessed January “Complete introduction to Alteryx GeeksforGeeks June Accessed January “Apache Airflow Use Cases Architecture and Best Practices Run AI Accessed January “What is AWS Glue AWS Glue AWS Documentation Accessed January “About Databricks founded by the original creators of Apache Spark Databricks Accessed January “You re never too old to excel How Snowflake thrives with dinosaur cofounders and a year old CEO LinkedIn September Accessed January “What is BigQuery Google Cloud Accessed January |
2023-02-02 08:30:04 |
| 海外TECH |
DEV Community |
10 things about AWS CDK |
https://dev.to/aws-builders/10-things-about-aws-cdk-3cg8
|
things about AWS CDKSome months ago I joined a new team they were on AWS and using cloudformation for some stuff and a lot of changes were made directly in the console After some discussion we ve decided to start fresh on a new infrastructure code with some constraint in mind We are not an OPS team but we are managing the infrastructure by our self We want no manual action in the AWS console The languages we are using in the team are GO for the backend and scala python for data We have considered to re work with Cloudformation and also with Terraform but since no one wanted to written thousands of lines of YAML we ve decided to use AWS CDK After all this journey of building our new stack I can give you these things to know before starting a CDK project but I can already tell you that this is awesome Write code in a real language not YAMLYou probably know that if you have ever looked to CDK but despite other infra as code technology you can write your stack in real language Why this is better You can keep your favorite tools to monitor your code such as your IDE your linters quality checks …Describe your infra using an object model You can do unit test your code This one is the awesome part CDK support multi language based on JSIIJSII is a framework developed by the technology that enables the CDK to deliver polyglot libraries from a single codebase Currently AWS CDK is supporting Typescript JavascriptJavaPython NETGOThis is awesome you can choose your favorite language in the list and start building your stack ️This is not that easy In my team since there is no GO support in CDK we ve decided to use Python because this is our second language To be honest this is not a bad choice everything works fine but it has some limits of not using Typescript Javascript Why typescript javascript is better All components are written with typescript at first so you are sure that they will work for you With other languages it works perfectly if you are using default CDK constructs but as soon as you are using rd party constructs it less certain that it will work And you can also have some weird documentation problems see issues below Example of an issue I had with a rd party library Weird documentation issue for python CDK hides complexityDespite cloudformation or terraform where you have to write everything you want to have CDK provides high level constructs that do the work for you For example if you want to create a VPC with a set of subnets and associate Nat Gateways it can end up with a thousand lines of code CF or TF With CDK most of the stuff is done automatically for you and you as you can see with the code below subnet configuration ec SubnetConfiguration name Public subnet type ec SubnetType PUBLIC cidr mask subnet configuration ec SubnetConfiguration name name subnet type ec SubnetType PRIVATE cidr mask vpc ec Vpc scope self id test vpc subnet configuration subnet configuration max azs cidr nat gateway subnets Public enable dns hostnames True enable dns support True CDK is based on cloud formationThis one is really important to understand CDK outputs some cloudformation code and apply it to your AWS account So if something is not available in cloudformation it means that there is no chance you will be able to do it with CDK But if like us you had a cloudformation stack before or if you want to import a rd party cloudformation script you can do it This code use a datadog CF template in a CDK stack cfn CfnStack scope self id PolicyMacroStack template url CDK has a fast release cycleI ve started my CDK project months ago using version v of CDK The actual version when I write this article is v which means that I have made version updates on my actual stack So be prepared to upgrade your dependencies all the time Experimental higher level constructsRelated to point be warned that if you are using Experimental constructs you can expect some breaking changes Experimental Higher level constructs in this module that are marked as experimental are under active development They are subject to non backward compatible changes or removal in any future version These are not subject to the Semantic Versioning model and breaking changes will be announced in the release notes This means that while you may use them you may need to update your source code when upgrading to a newer version of this package The good thing is that for every construct available even if they are experimental you still have access to CFN Resources which is basically a wrapper over cloudformation and these resources are always stable and safe to use Fine grained assertions are only available for TypescriptAs I said before you can unit test your infrastructure That being said if you want to use the full power of testing with CDK you are kind of stuck using javascript or typescript because you can use the fine grained assertions framework Fine grained assertions test specific aspects of the generated AWS CloudFormation template such as “this resource has this property with this value These tests help when you re developing new features since any code you add will cause your snapshot test to fail even if existing features still work When this happens your fine grained tests will reassure you that the existing functionality is unaffected These fine grained assertions are awesome because this makes it easy to test for real your expected stack Nested stack vs ConstructIf you have a lot of components in your stack at some point the question will be how to structure it You can use a lot of classes that extend Construct this the way most people are doing because before last week it was impossible to run a proper cdk diff to see what will be changed in your stack before deploying it The problem with using Construct is that you can reach the bytes limit from CloudFormation and you can reach this limit quickly because CDK is doing a lot of stuff for you behind the scene So in that case you can use some Nested Stacks the problem was that when you were using nested stacks you were totally blind before applying changes But last week Cloudformation has announced that changesets now support nested stacks So if you want to split your stack please use nested stacks now CDK is multicloudCDK is a project from AWS but now you can use it as a multi cloud tool since Hashicorp has released CDK for terraforming What it changes is that your CDK build will output some TF files so it can apply in any cloud provider because terraform supports most of them CDK for Terraform Enabling Python amp TypeScript SupportToday we are pleased to announce the community preview of the Cloud Development Kit for Terraform a collaboration…As you can see this project is really new but it gives hope in the future of CDK and fingers crossedthat it will become a standard even outside of AWS CDK everywhere CDKs projen … CDK is such a good idea that eladb the creator of CDK uses the CDK philosophy everywhere cdks is in beta and this is CDK for kubernetes it is AWS agnostic and can be used on any KS project Even if this is a beta you can already use it in production the only visible tradeoff is that all kubernetes features are not implemented yet eladb has also a side project called projen that allows you to create typescript javascript project with a init script that maintains your config files package json tsconfig json gitignore … ConclusionI don t regret to decide to use CDK instead of other technologies it makes the work WAY easier and all the team understands what we are doing not only the ops part of the team like before This is a work in progress tools but almost all major AWS component are already well covered so you don t have to worry when you start building your stack and with less than k lines of codes we have built a complete infra where we managed a lot of components VPC subnets security groups EKS Elasticsearch secrets IAM monitoring … My advice is to test CDK if you want a build a new stack you will be convinced by your self after a few hours of using it |
2023-02-02 08:20:36 |
| 海外TECH |
Engadget |
Sony has now shipped over 32.1 million PS5s following blockbuster holiday sales |
https://www.engadget.com/sonys-blockbuster-ps5-holiday-sales-show-its-supply-issues-appear-to-be-over-082455028.html?src=rss
|
Sony has now shipped over million PSs following blockbuster holiday salesSony s gaming business had a blockbuster holiday quarter as it sold million PSs from October to December compared to million in the same quarter last year That s a whopping percent increase so the company s supply issues appear to be largely solved ーmuch as the company has said as of late In other words you should be able to buy a PS now with little to no delay nbsp All of that resulted in a giant boost in revenue as its Game amp Network Services segment took in trillion yen billion up percent year on year That includes over double the revenue for hardware and healthy boosts in software percent network services percent and others including PSVR and first party software sales on other platforms percent nbsp To grasp the significance of all this Sony has now sold million PSs compared to million in November so total unit sales increased percent in just a single quarter It also means that Sony may hit its fiscal year PS sales forecast million units from March to March if it can ship million consoles something that seemed wildly optimistic last quarter If it does reach that goal it will hit PS sales of over million units Sony has fought Microsoft s acquisition of Activision though Microsoft itself recently pointed out that Sony has five times more exclusive games than Xbox In terms of first party titles God of War Ragnarök and Ghost of Tsushima Director s Cut were standouts on PS this quarter nbsp Sony s gaming business dwarfed its other segments though its imaging sensor business continues to rise as well with sales up percent year on year to billion yen billion Sony supplies the lion s share of camera sensors to both smartphone and mirrorless camera manufacturers nbsp nbsp |
2023-02-02 08:24:55 |
| 医療系 |
医療介護 CBnews |
東京都の新規陽性者数7日間平均が3週間連続減少-コロナ感染状況・医療提供体制分析 |
https://www.cbnews.jp/news/entry/20230202171621
|
新型コロナウイルス |
2023-02-02 17:30:00 |
| 海外ニュース |
Japan Times latest articles |
Sony to promote veteran CFO to role overseeing global operations |
https://www.japantimes.co.jp/news/2023/02/02/business/corporate-business/sony-new-president/
|
april |
2023-02-02 17:06:30 |
| ニュース |
BBC News - Home |
Shell reports highest profits in 115 years |
https://www.bbc.co.uk/news/uk-64489147?at_medium=RSS&at_campaign=KARANGA
|
profits |
2023-02-02 08:39:07 |
| ニュース |
BBC News - Home |
British Gas admits agents break into vulnerable homes |
https://www.bbc.co.uk/news/business-64491243?at_medium=RSS&at_campaign=KARANGA
|
meters |
2023-02-02 08:55:00 |
| ニュース |
BBC News - Home |
Children's care system plan focuses on early support |
https://www.bbc.co.uk/news/uk-64458311?at_medium=RSS&at_campaign=KARANGA
|
major |
2023-02-02 08:21:45 |
| ニュース |
BBC News - Home |
Six Nations 2023: Quickfire questions for Six Nations captains |
https://www.bbc.co.uk/sport/av/football/64451791?at_medium=RSS&at_campaign=KARANGA
|
Six Nations Quickfire questions for Six Nations captainsWatch as BBC Sport s Liam Loftus grills the captains of each Six Nations side on a series of burning issues including LeBron James chances of making it in rugby union and what their superpower would be |
2023-02-02 08:35:21 |
| ビジネス |
ダイヤモンド・オンライン - 新着記事 |
米企業に「群がる」アクティビスト、株安で勢い - WSJ発 |
https://diamond.jp/articles/-/317142
|
株安 |
2023-02-02 17:03:00 |
| ビジネス |
不景気.com |
日野自動車の23年3月期は550億円の最終赤字へ、不正認証で - 不景気com |
https://www.fukeiki.com/2023/02/hino-motors-2023-loss.html
|
日野自動車 |
2023-02-02 08:22:55 |
| ビジネス |
不景気.com |
プロルート丸光の23年3月期は10億円の最終赤字へ、無配継続 - 不景気com |
https://www.fukeiki.com/2023/02/proroute-marumitsu-2023-loss.html
|
最終赤字 |
2023-02-02 08:11:42 |
| GCP |
Google Cloud Platform Japan 公式ブログ |
Airflow DAG の改良による Cloud Composer の最適化 |
https://cloud.google.com/blog/ja/products/data-analytics/optimize-cloud-composer-via-better-airflow-dags/
|
aサブDAGは、DAG内に再利用可能なタスクのグループを作成するための機能として古いバージョンのAirflowで使用されていました。 |
2023-02-02 09:58:00 |
| ニュース |
Newsweek |
8歳の少年がサメに左胸を噛まれ、動画が途切れる |
https://www.newsweekjapan.jp/stories/world/2023/02/8-73.php
|
huntmasterioがTikTokに公開した映像は、歳の少年マニ君が獲った魚を披露するシーンを記録しようとしたものだ。 |
2023-02-02 17:45:00 |
| ニュース |
Newsweek |
【動画】ロシア軍兵士を殲滅した「殺人光線」の正体は？ |
https://www.newsweekjapan.jp/stories/world/2023/02/post-100764.php
|
【動画】ロシア軍兵士を殲滅した「殺人光線」の正体はウクライナの原野を移動中のロシア兵がどこからともなく発射された謎のビームで一瞬にして殲滅される。 |
2023-02-02 17:30:35 |
| ニュース |
Newsweek |
「誰！？」すっぴんのキム・カーダシアン ほぼ認識できず |
https://www.newsweekjapan.jp/stories/world/2023/02/post-100713.php
|
鼻が細くなったような、眉が丸くなったような、顔の印象が大きく変わったことも気になる。 |
2023-02-02 17:10:25 |
| ニュース |
Newsweek |
世界中のエグゼクティブが実践する超ストイックな25分間、「ポモドーロ・テクニック」とは？ |
https://www.newsweekjapan.jp/stories/lifestyle/2023/02/25-51.php
|
また休憩の間に、それまでしていた仕事のことを考えるべきでもない。 |
2023-02-02 17:05:31 |
| マーケティング |
MarkeZine |
スマドリとfermataの取り組みから考える、これからのマーケティングに必要な視点【参加無料】 |
http://markezine.jp/article/detail/41211
|
参加無料 |
2023-02-02 17:30:00 |
| IT |
週刊アスキー |
PC『SDガンダムオペレーションズ』に新★6ユニット「サイコ・ガンダム（MS）」＆「量産型キュベレイ」が実装！ |
https://weekly.ascii.jp/elem/000/004/123/4123251/
|
量産型 |
2023-02-02 17:55:00 |
| IT |
週刊アスキー |
HPE、第4世代インテルXeonスケーラブル・プロセッサー搭載のHPE ProLiant Gen11サーバー（4機種）を提供開始 |
https://weekly.ascii.jp/elem/000/004/123/4123159/
|
hpeproliantgen |
2023-02-02 17:30:00 |
| IT |
週刊アスキー |
桜を纏ったお花見スイーツをARで咲く桜の木と一緒に楽しもう！ 「キンプトン #Ohanami桜アフタヌーンティー」3月1日〜4月30日開催 |
https://weekly.ascii.jp/elem/000/004/123/4123182/
|
ohanami |
2023-02-02 17:30:00 |
| IT |
週刊アスキー |
サンワダイレクト、ディスプレーのふちなどに取り付けできるマルチフック「200-STN074」を発売 |
https://weekly.ascii.jp/elem/000/004/123/4123233/
|
ラック |
2023-02-02 17:30:00 |
| IT |
週刊アスキー |
丸亀製麺、富士通のAI需要予測サービス採用 |
https://weekly.ascii.jp/elem/000/004/123/4123235/
|
丸亀製麺 |
2023-02-02 17:30:00 |
| IT |
週刊アスキー |
都民限定&先着250人！ フォーティーズ、全国旅行支援「ただいま東京プラス」＋東京都「もっとTokyo」を併用したスペシャルプランを提供 |
https://weekly.ascii.jp/elem/000/004/123/4123200/
|
tokyo |
2023-02-02 17:15:00 |
| IT |
週刊アスキー |
カプコンがPS Store／ニンテンドーeショップ／Steamで「CAPCOM FEBRUARY SALE」を開催！ |
https://weekly.ascii.jp/elem/000/004/123/4123236/
|
capcomfebruarysale |
2023-02-02 17:15:00 |
| IT |
週刊アスキー |
あまりに緻密に制作されたクレイアニメーションの舞台裏が見られる！ 「ひつじのショーン展」第二弾 東京会場にて3月16日～27日開催 |
https://weekly.ascii.jp/elem/000/004/123/4123195/
|
京王百貨店 |
2023-02-02 17:10:00 |
| IT |
週刊アスキー |
Twitter、2月9日以降APIへの無料アクセスを停止。代わりに有償版を提供 |
https://weekly.ascii.jp/elem/000/004/123/4123240/
|
basictier |
2023-02-02 17:10:00 |
| 海外TECH |
reddit |
What's the sketchiest place in Japan you've been to? |
https://www.reddit.com/r/japanlife/comments/10rkhk3/whats_the_sketchiest_place_in_japan_youve_been_to/
|
What x s the sketchiest place in Japan you x ve been to I just watched some bozo Youtuber who quot braved the most dangerous neighborhood in Japan quot It was Nishinari ku in Osaka He was definitely playing up the danger like he was wandering around Gary Indiana at am On the other hand I can t think of anywhere I ve been in Japan that s more sketchy dodgy sleazy than Nishinari Where is the sketchiest place you ve been in this safety country submitted by u epicspeculation to r japanlife link comments |
2023-02-02 08:00:50 |
| GCP |
Cloud Blog JA |
Airflow DAG の改良による Cloud Composer の最適化 |
https://cloud.google.com/blog/ja/products/data-analytics/optimize-cloud-composer-via-better-airflow-dags/
|
aサブDAGは、DAG内に再利用可能なタスクのグループを作成するための機能として古いバージョンのAirflowで使用されていました。 |
2023-02-02 09:58:00 |
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