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DLP(Data Loss Prevention)ã¨ã¯?
æ£è¦ã®IDã‚’æŒã¤ç¤¾å“¡ã«ã‚ˆã‚‹ç¤¾å†…æƒ…å ±ã®æŒã¡å‡ºã—ã‚„ã€æ”»æ’ƒè€…ãŒãƒ©ãƒ³ã‚µãƒ ã‚¦ã‚§ã‚¢ç‰ã‚’åˆ©ç”¨ã—ã¦æƒ…å ±ã‚’æŒã¡å‡ºãã†ã¨ã™ã‚‹è¡Œç‚ºã‹ã‚‰ã€æƒ…å ±æ¼ãˆã„ã‚’é˜²æ¢ã—ãŸã„ã€ã¨ã„ã†çŠ¶æ³ã®æ™‚ã«åˆ©ç”¨ã•ã‚Œã‚‹ã®ãŒDLP(Data Loss Prevention)ã¨å‘¼ã°ã‚Œã‚‹æŠ€è¡“ã§ã™ã€‚ DLPãã®ã‚‚ã®ã¯ã€10å¹´ä»¥ä¸Šå‰ã‹ã‚‰å˜åœ¨ã—ã¦ã„ãŸã‚‚ã®ã®ã€æ—¥æœ¬ã§ã¯å°Žå…¥ãŒé€²ã‚“ã§ã„ãªã‹ã£ãŸã“ã¨ã‚‚ã‚ã‚Šæœ€è¿‘å†ã³è„šå…‰ã‚’é›†ã‚ã¦ã„ã¾ã™ã€‚ æœ¬è¨˜äº‹ã§ã¯ã€DLPã«ã¤ã„ã¦è§£èª¬ã—ã¾ã™ã€‚ â– DLP(Data Loss Prevention)ã¨ã¯? DLPã¨ã¯ã€Data Loss Preventionã®ç•¥ã§ã‚ã‚Šã€æ©Ÿå¯†æƒ…å ±ã¨ã—ã¦åˆ†é¡žã—ãŸãƒ‡ãƒ¼ã‚¿ã®æ¼æ´©ã‚„æ¶ˆå¤±ã‚’é˜²ããŸã‚ã®æŠ€è¡“ã§ã™ã€‚æ£è¦ã®ãƒ¦ãƒ¼ã‚¶ã«ã‚ˆã‚‹ã€Œæ‚ªè³ªãªæƒ…å ±æ¼æ´©ã€ã ã‘ã§ãªãã€ãƒ’ãƒ¥ãƒ¼ãƒžãƒ³ã‚¨ãƒ©ãƒ¼ã«ã‚ˆã‚‹ã€Œã†ã£ã‹ã‚Šæƒ…å ±æ¼æ´©ã€ã‚’é˜²æ¢ã™ã‚‹ã®ã«ã‚‚æœ‰åŠ¹ã§ã™ã€‚ æ´å²ã¯å¤ãã€DLPã¨ã„ã†è¨€è‘‰ãŒå¸‚å ´ã«ç™»å ´ã—ãŸã®ãŒ2006å¹´ã€ãã—ã¦2007å¹´ã«åˆæœŸã®ãƒ–ãƒ¼ãƒ ãŒ
uokada 2025/01/12
data
ãƒªãƒ³ã‚¯
Databases in 2024: A Year in Review
Like a shot to your dome piece, I'm back to hit you with my annual roundup of what happened in the rumble-tumble game of databases. Yes, I used to write this article on the OtterTune blog, but the company is dead (RIP). I'm doing this joint on my professor blog. There is much to cover from the past year, from 10-figure acquisitions, vendors running wild in the streets with license changes, and the
uokada 2025/01/02
ã‚ã¨ã§èªã‚€

database

data

license
ãƒªãƒ³ã‚¯
å±¥æ´ãƒ†ãƒ¼ãƒ–ãƒ«ã‹ã‚‰æœ€æ–°ã®1ä»¶ã‚’å–ã£ã¦ãã‚‹æ–¹æ³• - ããƒ¼ã ã„ãªã‚‹ã‚‰ããŒãå¸³
ä¾‹ãˆã°æ¬¡ã®ã‚ˆã†ãªãƒ†ãƒ¼ãƒ–ãƒ«ãŒã‚ã£ãŸã¨ã™ã‚‹ã€‚ -- PostgreSQL CREATE TABLE history ( id SERIAL PRIMARY KEY, user_id INTEGER NOT NULL, data TEXT, created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP ); -- MySQL CREATE TABLE history ( id INT AUTO_INCREMENT PRIMARY KEY, user_id INT NOT NULL, data TEXT, created_at DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP ); INSERT INTO history (user_id, data, created_at) VALUES (1, 'First
uokada 2024/12/10
ã‚ã¨ã§èªã‚€

data
ãƒªãƒ³ã‚¯
GitHub - sdv-dev/SDV: Synthetic data generation for tabular data
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uokada 2024/12/01
data

machinelearning
ãƒªãƒ³ã‚¯
Back at my old job in ~2016, we built a cheap homegrown data warehouse via Postg... | Hacker News
Back at my old job in ~2016, we built a cheap homegrown data warehouse via Postgres, SQLite and Lambda.Basically, it worked like this: - All of our data lived in compressed SQLite DBs on S3. - Upon receiving a query, Postgres would use a custom foreign data wrapper we built. - This FDW would forward the query to a web service. - This web service would start one lambda per SQLite file. Each lambda
uokada 2023/10/24
data

serverless

database

lambda
ãƒªãƒ³ã‚¯
Exploring Notion's Data Model: A Block-Based Architecture | Notion
A generation of pioneers (Doug Engelbart, Ted Nelson, Alan Kay, and many more) saw the computer as tool to augment human probl em-solving by giving people power over information. Today, that information mostly rem ains siloed across tools. Take cloud-based document editors, where pages are their smallest atomic unit. Information is locked inside of pages and files and folders â€” thatâ€™s reminiscent of
uokada 2023/10/23
data

framework

architecture
ãƒªãƒ³ã‚¯
ãƒ‡ãƒ¼ã‚¿åŸºç›¤ã¯ã€Œãƒ‡ãƒ¼ã‚¿ã§ã€æ”¹å–„ã™ã‚‹ âˆ’ ä¸é–“ãƒ†ãƒ¼ãƒ–ãƒ«ä½œæˆã®äº‹ä¾‹ï½œMercari Analytics Blog
ãƒ¡ãƒ«ã‚«ãƒªAnalytics Infraãƒãƒ¼ãƒ ã®@__hiza__ã§ã™ã€‚ ã“ã®è¨˜äº‹ã§ã¯ã€ãƒ¡ãƒ«ã‚«ãƒªã«ãŠã‘ã‚‹ãƒ‡ãƒ¼ã‚¿åŸºç›¤ã®æ•´å‚™ã«ã¤ã„ã¦ç´¹ä»‹ã—ã¾ã™ã€‚ ä»Šå›žã¯ã€è†¨å¤§ãªç”Ÿã®ãƒ†ãƒ¼ãƒ–ãƒ«ã«ã¤ã„ã¦ã©ã®ãƒ†ãƒ¼ãƒ–ãƒ«ã‹ã‚‰ä¸é–“ãƒ†ãƒ¼ãƒ–ãƒ«åŒ–ã™ã‚‹ã¨åŠ¹æžœçš„ã‹ã€ãƒ‡ãƒ¼ã‚¿ã«ã‚‚ã¨ã¥ã„ã¦å„ªå…ˆé †ä½ã‚’ä»˜ã‘ãŸäº‹ä¾‹ã‚’èª¬æ˜Žã—ã¾ã™ã€‚ ã¾ãŸã€ã‚ã‚ã›ã¦å¤§è¦æ¨¡ãªãƒ‡ãƒ¼ã‚¿åŸºç›¤ã‚’æ”¹å–„ã™ã‚‹éš›ã«ã€Œãƒ‡ãƒ¼ã‚¿åŸºç›¤ã®åˆ©ç”¨çŠ¶æ³ã®ãƒã‚°ã€ãŒå½¹ç«‹ã¤ã“ã¨ã‚’ãŠè©±ã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚ ä¸é–“ãƒ†ãƒ¼ãƒ–ãƒ«ã‚’ä½œã‚‹æ„ç¾©ãƒ‡ãƒ¼ã‚¿åˆ†æžç”¨ã®ä¸é–“ãƒ†ãƒ¼ãƒ–ãƒ«ã‚’ä½œæˆã™ã‚‹æ„ç¾©ã‚’ç°¡å˜ã«ãŠã•ã‚‰ã„ã—ã¾ã™ã€‚ ä¾‹ãˆã°ã€Webã‚µãƒ¼ãƒ“ã‚¹ã®RDBã«å…¥ã£ã¦ã„ã‚‹ãƒ‡ãƒ¼ã‚¿ã§åˆ†æžã‚’è¡Œã†å ´åˆã«ä»¥ä¸‹ã®ã‚ˆã†ãªåŠ å·¥ã‚’ã—ãŸãƒ†ãƒ¼ãƒ–ãƒ«ã‚’ç”¨æ„ã™ã‚‹ã¨åˆ†æžãŒä¾¿åˆ©ã«ãªã‚Šã¾ã™ã€‚ ä¾‹) ç”Ÿãƒ†ãƒ¼ãƒ–ãƒ«ã‚’åˆ†æžã«ä½¿ã„ã‚„ã™ãã™ã‚‹åŠ å·¥ã®ä¾‹ ã‚ˆãjoinã—ã¦åˆ©ç”¨ã™ã‚‹è¤‡æ•°ã®ãƒ†ãƒ¼ãƒ–ãƒ«ã‚’ã‚ã‚‰ã‹ã˜ã‚joinã—ã¦ãŠã ã‚³ãƒ¼ãƒ‰åŒ–ã•ã‚ŒãŸå€¤ã‚’äººãŒè¦‹ã¦åˆ†ã‹ã‚‹å€¤ã«å¤‰æ›ã—ã¦ãŠã(éƒ½é“åºœçœŒã‚³ãƒ¼ãƒ‰01â†’åŒ—æµ·é“
uokada 2023/10/23
BigQuery

analytics

data
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Project RADAR: Intelligent Early Fraud Detection System with Humans in the Loop
Youâ€™re seeing information for Japan . To see local features and services for another location, select a different city. Show more Introduction Uber is a worldwide marketplace of services, processing thousands of monetary transactions every second. As a marketplace, Uber takes on all of the risks associated with payment processing. Uber partners who use the marketplace to provide services are paid
uokada 2023/08/10
data
ãƒªãƒ³ã‚¯
Upgrading Data Warehouse Infrastructure at Airbnb
uokada 2022/10/25
ã‚ã¨ã§èªã‚€

data

spark

framework

infrastructure
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GitHub - fatiando/pooch: A friend to fetch your data files
Just want to download a file without messing with requests and urllib? Trying to add sample datasets to your Python package? Pooch is here to help! Pooch is a Python library that can manage data by downloading files from a server (only when needed) and storing them locally in a data cache (a folder on your computer). Pure Python and minimal dependencies. Download files over HTTP, FTP, and from dat
uokada 2022/10/03
python

data
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Jsonnet - Jsonnet Configuration Language
A configuration language for app and tool developers Generate config data Side-effect free Organize, simplify, unify Manage sprawling config A simple extension of JSON Open source (Apache 2.0) Familiar syntax Reformatter, linter Editor & IDE integrations Formally specified
uokada 2021/10/26
data

json
ãƒªãƒ³ã‚¯
The Building Blocks of a Modern Data Platform | Towards Data Science
A beginnerâ€™s guide to the best of breed tools and capabilities for your Data Platform initiative Photo by Adam Nowakowski on Unsplash If you Google "modern data platform", youâ€™ll immediately be bombarded with advertisements and lots of companies professing that they are the one true data platform. Not so helpful, right? So what the heck is a modern data platform? What does that even mean, and ****
uokada 2021/03/05
data
ãƒªãƒ³ã‚¯
Nemo: Data discovery at Facebook
Large-scale companies serve millions or even billions of people who depend on the services these companies provide for their everyday needs. To keep these services running and delivering meaningful experiences, the teams behind them need to find the most relevant and accurate information quickly so that they can make informed decisions and take action. Finding the right information can be hard for
uokada 2020/10/10
data

engineering

facebook

architecture
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How to analyse 100s of GBs of data on your laptop with Python
Many organizations are trying to gather and utilise as much data as possible to improve on how they run their business, increase revenue, or how they impact the world around them. Therefore it is becoming increasingly common for data scientists to face 50GB or even 500GB sized datasets. Now, these kind of datasets are a bitâ€¦ uncomforta ble to use. They are small enough to fit into the hard-drive of
uokada 2020/01/04
data
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dataset 1.6.2 documentation
Although managing data in relational databases has plenty of benefits, theyâ€™re rarely used in day-to-day work with small to medium scale datasets. But why is that? Why do we see an awful lot of data stored in static files in CSV or JSON format, even though they are hard to query and update incrementally? The answer is that programmers are lazy, and thus they tend to prefer the easiest solution the
uokada 2020/01/01
data

python

sqlalchemy

orm
ãƒªãƒ³ã‚¯
DBLog: A Generic Change-Data-Capture Framework
Andreas Andreakis, Ioannis Papapanagiotou OverviewChange-Data-Capture (CDC) allows capturing committed changes from a database in real-time and propagating those changes to downstream consumers [1][2]. CDC is becoming increasingly popular for use cases that require keeping multiple heterogeneous datastores in sync (like MySQL and ElasticSearch) and addresses challenges that exist with traditional
uokada 2019/12/31
Netflix

log

framework

data
ãƒªãƒ³ã‚¯
Presto Infrastructure at Lyft
OverviewEarly in 2017 we started exploring Presto for OLAP use cases and we realized the potential of this amazing query engine. It started as an adhoc querying tool for data engineers and analysts to run SQL in a faster way to prototype their queries, when compared to Apache Hive. A lot of internal dashboards were powered by AWS-Redshift back then and it had data storage and compute coupled toget
uokada 2019/08/26
presto

Lyft

engineering

data
ãƒªãƒ³ã‚¯
Netflix - ãƒ‡ãƒ¼ã‚¿é‡è¦–ã®ã‚·ãƒªã‚³ãƒ³ãƒãƒ¬ãƒ¼æ–‡åŒ–ã¨äººé–“é–¢ä¿‚é‡è¦–ã®ãƒãƒªã‚¦ãƒƒãƒ‰æ–‡åŒ–ã®è¡çª
Do you want to subscribe to the notification em ail? Once subscribed, notification em ails will be sent to your registered em ail address when the content is republished or refreshed by the scheduling.
uokada 2019/07/30
Netflix

data
ãƒªãƒ³ã‚¯
Material Design
Build beautiful, usa ble products faster. Material Design is an adapta ble systemâ€”backed by open-source codeâ€”that helps teams build high quality digital experiences.
uokada 2019/07/13
visualization

data
ãƒªãƒ³ã‚¯
å›½ä¼šè°å“¡ç™½æ›¸â€•â€•æ”¿æ²»å®¶ã®ä»•äº‹ã‚’è¨˜éŒ²ãƒ»å…¬é–‹ã™ã‚‹ã‚¦ã‚§ãƒ–ã‚µã‚¤ãƒˆã§ã™
ç¾åœ¨ã€è¡†é™¢é¸ã«å‘ã‘ã¦æ›´æ–°ä¸ã§ã™ã€‚ãƒªãƒ³ã‚¯åˆ‡ã‚Œãªã©ãŒç”Ÿã˜ã¦ã„ã¾ã™ãŒã€ã”äº†æ‰¿ãã ã•ã„ã€‚ï¼ˆ2026/01/26ï¼‰ å°‚é–€å®¶å‘ã‘ãƒ¡ãƒ‡ã‚£ã‚¢ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ â€œtheLetterâ€ã§ãƒ‹ãƒ¥ãƒ¼ã‚¹ãƒ¬ã‚¿ãƒ¼ã®é…ä¿¡ã‚’ã—ã¦ã„ã¾ã™ã€‚æœ€è¿‘ã®è¨˜äº‹ã¯ã“ã¡ã‚‰ã§ã™ã€‚ãœã²ãŠèªã¿ã„ãŸã ã‘ã‚Œã°ã¨æ€ã„ã¾ã™ã€‚ å…¬æ˜Žå…šã¯ã€Œä¸‹é§„ã®é›ªã€ã§ã¯ãªãã€Œä¸‹é§„ã€ã§ã‚ã‚‹â€•â€•å»¶å‘½è£…ç½®ãŒå¤–ã‚ŒãŸè‡ªæ°‘å…šãŒç”Ÿãæ®‹ã‚‹é“ã¯ï¼Ÿ å†…é–£æ”¯æŒçŽ‡ã¯å¿…ãšä¸‹è½ã™ã‚‹â€•â€•ä¸–è«–èª¿æŸ»å ±é“ã¯ä½•ã‚’è¦‹èª¤ã£ã¦ã„ã‚‹ã®ã‹ é«˜ã„å†…é–£æ”¯æŒçŽ‡ã¨ã„ã†å¹»è¦šãŒå¼•ãèµ·ã“ã—ãŸãƒªã‚»ãƒžãƒ©è§£æ•£â€•â€•ä½Žè¿·ã™ã‚‹å…šæ”¯æŒçŽ‡ã®ä¸‹ã§é«˜å¸‚è‡ªæ°‘ã¯ã©ã“ã«å‘ã‹ã†ã®ã‹ï¼Ÿï¼ˆ2026/01/26ï¼‰ ã”æ¡ˆå†… å›½ä¼šè°å“¡ç™½æ›¸ã¯ã€è¡†å‚ä¸¡é™¢è°å“¡ã®å›½ä¼šã§ã®æ´»å‹•ã‚„é¸æŒ™çµæžœãªã©ã®æƒ…å ±ã‚’ã¾ã¨ã‚ãŸã‚¦ã‚§ãƒ–ã‚µã‚¤ãƒˆã§ã™ã€‚ æˆ¦å¾Œæ–°æ†²æ³•ä¸‹ã€1947å¹´ã‹ã‚‰è¡†è°é™¢è°å“¡ã¯2024å¹´10æœˆã¾ã§ã€å‚è°é™¢è°å“¡ã¯2025å¹´6æœˆã¾ã§ã«å½“é¸ã—ãŸå…¨è°å“¡ã«ã¤ã„ã¦ã€é¸æŒ™åŒºã¨é¸æŒ™çµæžœã€æœ¬ä¼šè°ã‚„å§”å“¡ä¼šã§ã®è³ªå•ãƒ»ç”
uokada 2019/06/01
data

gov
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