| python |
Pythonタグが付けられた新着投稿 - Qiita |
Arxivで公開されている論文を収集するCUIアプリケーション |
https://qiita.com/nomnomnonono/items/5375212dcd7084db006d
|
arxiv |
2023-06-05 08:57:29 |
| python |
Pythonタグが付けられた新着投稿 - Qiita |
Leetcode 217. Contains Duplicate |
https://qiita.com/takechanman1228/items/9808222c7ca95289dea9
|
eturntrueifanyvalueappear |
2023-06-05 08:18:50 |
| python |
Pythonタグが付けられた新着投稿 - Qiita |
Scikit-learnが実験的にGPU対応していたので調査してみた！ |
https://qiita.com/fujine/items/6c997a073fec5bcea512
|
fujine |
2023-06-05 08:14:44 |
| python |
Pythonタグが付けられた新着投稿 - Qiita |
【Python】初学者がハンズオンでKggleへ挑戦した |
https://qiita.com/dia_1/items/ad924a56333628822516
|
automlsonypredictionone |
2023-06-05 08:05:30 |
| js |
JavaScriptタグが付けられた新着投稿 - Qiita |
2023.5.13 プログラミングメモ slick |
https://qiita.com/nanakoshis/items/d693499887e7014c5f05
|
slick |
2023-06-05 08:25:46 |
| Ruby |
Rubyタグが付けられた新着投稿 - Qiita |
【学習】Ruby on Rails チュートリアル 第7版 - 第9章 高度なログイン機構 |
https://qiita.com/COYG_GINFF/items/0638dbfc5ed0ac97b0cd
|
rubyonrails |
2023-06-05 08:59:00 |
| Ruby |
Railsタグが付けられた新着投稿 - Qiita |
【学習】Ruby on Rails チュートリアル 第7版 - 第9章 高度なログイン機構 |
https://qiita.com/COYG_GINFF/items/0638dbfc5ed0ac97b0cd
|
rubyonrails |
2023-06-05 08:59:00 |
| 海外TECH |
DEV Community |
How to build your own data platform. Episode 2: authorization layer. Data Warehouse implementation. |
https://dev.to/adevintaspain/how-to-build-your-own-data-platform-episode-2-authorization-layer-data-warehouse-implementation-e0c
|
How to build your own data platform Episode authorization layer Data Warehouse implementation Introduction This article is the second part of the episode about building an authorization layer for your data platform You can find the whole list of articles following this link gu martinm list how to build your own data platform efeceIn the previous article we talked about how to implement the authorization layer in the Data Lake in this second part we will be talking about the same but in the Data Warehouse Authorization layer You can see in this diagram the Lakehouse with its metastore and the Data Warehouse We already talked about the authorization layer for the Lakehouse in the previous article Now it is the turn for the Data Warehouse Because we will be using Amazon Web Services with AWS Redshift we will be implementing this layer using Lake Formation Processing layer Human users and processes will be the ones accessing the stored data through the authorization layer Machines and processes like Zeppelin notebooks AWS Athena for SQL clusters of AWS EMR Databricks etc etc The problem with the authorization Data engineers data analysts and data scientists work in different and sometimes isolated teams They do not want their data to be deleted or changed by tools or people outside their teams Data owners are typically in charge of granting access to their data Owner ーconsumer relationship A data consumer requests access to some data owned by a different team in a different domain For example a table in a database The data owner grants access by approving the access request Upon the approval of an access request a new permission is added to the specific table Our authorization layer must be able to provide the above capability if we want to implement a data mesh with success Data Warehouse AWS Redshift The Data Warehouse is implemented on the top of AWS Redshift Not many years ago a new service was released by Amazon called AWS Redshift RA What makes RA different from the old Redshift is that in the new implementation computation and storage are separated Before having RA if users needed more storage capabilities more computation had also to be paid even if computation was not a problem And in the opposite way when users needed more computation capabilities more storage had to be paid So Redshift costs were typically high We will be using AWS Redshift RA Here you can find some useful links that explain further what are AWS Redshift and AWS Redshift RA Data Warehouse AWS Redshift RA Amazon Redshift data sharing allows you to securely and easily share data for read purposes across different Amazon Redshift clusters without the complexity and delays associated with data copies and data movement Data can be shared at many levels including schemas tables views and user defined functions providing fine grained access controls that can be tailored for different users and businesses that all need access to the data Lake Formation can be integrated with data sharing For further information visit the following links Authorization Federated Lake Formation Using Lake Formation with AWS Redshift RA we can manage the permissions across different accounts from only one central account in a federated way We are delegating permissions to other accounts but we keep the control of them Authorization implementation In order to implement federated authorization with AWS Redshift RA you can follow the next steps AWS Redshift RA producer account CREATE DATASHARE producer sharingGRANT USAGE ON DATASHARE producer sharing TO ACCOUNT FEDERATED GOVERNANCE ALTER DATASHARE producer sharing ADD SCHEMA producer schemaAWS Redshift RA consumer account CREATE DATASHARE consumer sharingGRANT USAGE ON DATASHARE consumer sharing TO ACCOUNT FEDERATED GOVERNANCE ALTER DATASHARE consumer sharing ADD SCHEMA consumer schemaAWS Redshift RA main federated account Through Lake formation console allow access from consumer account to producer sharing You can see a screenshot about this configuration down below With the above configuration the query from the consumer account will only see the column brand id Conclusion In this article we have explained how you can implement an authorization layer using AWS AWS Redshift RA and AWS Lake Formation With this authorization layer we will be able to resolve the following problems Producers and consumers from different domains must have the capability of working in an isolated way if they wish so if we want to implement a data mesh with success Producers must be able to decide how consumers can access their data They are the data owners and they decide how others use their data Fine grained permissions can be established At column and even if we want at row level This will be of great interest if we want to be GDPR compliant More information about how to implement the GDPR in your own data platform will be explained in future articles Stay tuned for the next article about how to implement your own Data Platform with success I hope this article was useful If you enjoy messing around with Big Data Microservices reverse engineering or any other computer stuff and want to share your experiences with me just follow me |
2023-06-04 23:05:20 |
| 海外科学 |
BBC News - Science & Environment |
Climate change: How is my country doing on tackling it? |
https://www.bbc.co.uk/news/science-environment-65754296?at_medium=RSS&at_campaign=KARANGA
|
economies |
2023-06-04 23:07:54 |
| 金融 |
日本銀行：RSS |
日銀当座預金増減要因（6月見込み） |
http://www.boj.or.jp/statistics/boj/fm/juqp/juqp2306.xlsx
|
当座預金 |
2023-06-05 08:50:00 |
| ニュース |
BBC News - Home |
Why is Prince Harry suing the Mirror Group? |
https://www.bbc.co.uk/news/uk-65768596?at_medium=RSS&at_campaign=KARANGA
|
british |
2023-06-04 23:01:50 |
| ニュース |
BBC News - Home |
Love Island 2023: Is the reality series past its prime? |
https://www.bbc.co.uk/news/entertainment-arts-65775237?at_medium=RSS&at_campaign=KARANGA
|
reality |
2023-06-04 23:06:39 |
| ニュース |
BBC News - Home |
Climate change: How is my country doing on tackling it? |
https://www.bbc.co.uk/news/science-environment-65754296?at_medium=RSS&at_campaign=KARANGA
|
economies |
2023-06-04 23:07:54 |
| マーケティング |
MarkeZine |
「顧客起点マーケティングの裏テーマは、数字と統計的処理の限界からの脱却」アソビュー宮本×西口一希対談 |
http://markezine.jp/article/detail/42202
|
|
2023-06-05 08:30:00 |
| マーケティング |
MarkeZine |
Googleアナリティクスを使いこなす！ワークを通してGA4操作と分析スキルを習得する3時間講座 |
http://markezine.jp/article/detail/42229
|
google |
2023-06-05 08:30:00 |
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