Charly Clairmont

OVHcloud shared our approach to the FAIR principles for building an ML platform. The four core concepts of F.A.I.R. are: - Findable - Accessible - Interoperable - Reusable Learn more about the principles and their meanings in this blog ⬇️

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Dive into the benefits, architecture, and practical implementation of a feature store at runtime in our latest article from Alexandre AUTRET, data scientist at Numberly: https://nubr.ly/LNvJxL You can also stay updated on all our latest posts by following our Numberly Tech Medium channel here: https://numberly.tech/ #MachineLearning #DataScience #FeatureStore

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SingleStore Announces Bi-Directional Integration with Apache Iceberg Learn more & get our take 👇 https://lnkd.in/gNTBCWAE "Our vision has always been to provide one single data store for all companies to be able to take advantage of speed, scale and simplicity. Our data platform is designed to unlock all types of enterprise data — including data that is frozen in data lakes." — Raj Verma, CEO at SingleStore "SingleStore’s data platform is indispensable for analyzing our data and delivering real-time insights to our customers. This latest update continues to push the boundaries of what we can accomplish by harnessing all of our enterprise data in real time." — Mauricio Aristizabal, Principal Data Architect and Founder at Giist Social "The integration with Iceberg, plus enhancing our platform’s scaling and processing capabilities will allow users to access and harness their data in real time, and build intelligent apps." — Nadeem Asghar, Head of Engineering at SingleStore #apache #product #SoTNews

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In this talk Sébastien Stormacq shows us how we can easily deploy an existing server-side Swift app to AWS Lambda with no code changes! Then you'll see how to deploy a native function to Lambda and finally how to use the SwiftPM plugin

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🚨Join us LIVE in ONE HOUR for the next YugabyteDB Community Open Hours.🚨 In this episode, Yugabyte experts Franck Pachot and Denis Magda will discuss and demonstrate their five top PostgreSQL extensions for application developers. Bring your questions to the chat and join the discussion.🚀 Register now!➡️https://hubs.la/Q02w7mph0 Join the YugabyteDB community!➡️ https://hubs.la/Q02w7p8f0 #YugabyteDB #cloudnative #distributedsql #Postgresql #community #opensource

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✨ Why you should consider using transform on Spark Article by Ange KOUAME ➡ https://lnkd.in/eJvb8YxZ - - - - - - - - - - - - - - - - - - - - - - - - - - - 💡 Follow for more great content! ✅ ✍️ Want to contribute? Connect with us. 🔗 Let's create together! ✅ - - - - - - - - - - - - - - - - - - - - - - - - - - - #dataengineering  #bigdata #dataanalysis #apachespark

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🔧 Fantastic session on Kubectl plugins by Aurélie Vache (OVHcloud) & 👩‍💻 Gaëlle Acas (Doctolib) at #KubeCon! Want to supercharge your kubectl experience? Here's how to create & share custom plugins in minutes: 🎯 Key Takeaways: • Create plugins in Bash or Go to simplify complex kubectl commands • Use Krew as your plugin package manager • Share plugins globally or within your organization • Implement help messages for better user experience • Leverage private indices for team-specific plugins 💡 Pro Tips: • Go is recommended for production plugins (easy binary generation & CI/CD) • Focus on solving real productivity pain points • Use meaningful naming conventions • Include clear documentation • Test across multiple platforms 🛠️ Tools Highlighted: • Krew - The kubectl plugin manager • Example plugins: view-secret, each • Private plugin indices for enterprise use Ready to build your first kubectl plugin? Watch the full demo: https://lnkd.in/gJrE7SRy #Kubernetes #DevOps #CloudNative #CNCF #K8s #CloudComputing

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If you missed the July Meetup, the video for “Check Your Privileges: The PostgreSQL Role System” with Christophe Pettus is now available on our YouTube Channel! https://buff.ly/3WesuZc @Xof @PostgreSQL #PostgreSQL #postgres

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⏰Join us at 11am ET TOMORROW for the next YugabyteDB Community Open Hours.⏰ In this episode, Yugabyte experts Franck Pachot and Denis Magda will participate in the recently launched Postgres App Century Challenge by performing a live migration of several existing Postgres applications to YugabyteDB. 👀 You’ll learn migration tips and tricks, plus how to avoid pitfalls. Join us to see how many apps Franck and Denis can migrate in one hour!💡 Learn more about the challenge here: https://hubs.la/Q02M_Mn_0 Bring your questions to the chat and join the discussion.🚀 Register now!➡️https://hubs.la/Q02M_NMS0 Join the YugabyteDB community!➡️ https://hubs.la/Q02M_GVT0 #YugabyteDB #cloudnative #distributedsql #Postgresql #community #opensource #migration

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Correlated IN Clause Transformation With PostgreSQL 17, the Correlated IN Clause is transformed into more efficient join options. This transformation optimizes the query by avoiding unoptimized nested access paths for non-indexed join columns. Posted by Deepak M. Exploring PostgreSQL 17: A Developer’s Guide to New Features – Part 5 : Correlated IN Clause Transformation https://lnkd.in/dNMmnJDu #postgresql #database

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⭐️ New video release 📺: Unleashing Confidence in SQL Development through Unit Testing Watch how Tobias Lampert explores the power of #SQL unit testing in ensuring data quality and boosting confidence in SQL development, offering insights into a robust framework tailored for BigQuery. 📺 Watch the video on YouTube: https://lnkd.in/dJG4B4_M Tobias Lampert, an Analytics Engineer with vast experience in Data Science and Data Engineering, delivers a compelling talk on "Unleashing Confidence in SQL Development through Unit Testing." In this enlightening session, he sheds light on the significance of SQL unit testing in ensuring data quality within Data Warehouses. By showcasing the challenges faced by data teams in maintaining complex SQL models, Tobias demonstrates how unit testing can validate modeling logic swiftly, preventing modifications from disrupting existing behavior. The talk emphasizes the importance of Lotum's SQL unit testing framework, meticulously designed in Python using pytest and tailored for BigQuery. By processing millions of events from mobile games daily, this robust framework facilitates efficient testing, guaranteeing the accuracy of SQL logic. Attendees gain valuable insights into defining test cases with small sets of mock data, enabling the identification of potential code errors with ease. Tobias underlines the necessity of a well-designed unit testing framework in collaborative data model development, highlighting its role in early error detection and maintaining data quality. He explains how SQL unit testing is particularly valuable during the refactoring of existing data models and integration of new data sources, ensuring the preservation of logic, even in edge cases. The practical implementation of a SQL unit testing framework is elucidated, focusing on Lotum's pytest-based framework. By delving into the internal workings of the framework, Tobias illustrates how mock data is injected into SQL statements for testing, allowing for case-by-case verification without manual query code modifications.

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🚨 As we continue to refine our integration of Ory Corp with NestJS, our latest blog post by Edouard Maleix, focuses on one crucial aspect: configuring external services to make our setup as straightforward and adaptable as possible across all environments— be it self-hosted or cloud-based. 🗃 Reusable Configuration Files: We've introduced a method to create separate template configuration files for Ory Kratos and Ory Keto, which can be easily extended with environment-specific values. 💼 Organized and Accessible: By storing these configurations in an 'infra' directory and using a consistent naming convention for environment variables, we ensure that our setup remains organized and easily manageable. 💡 Flexibility and Efficiency: This approach not only simplifies the initial configuration process but also enhances the flexibility to adjust settings as per the deployment environment, ensuring efficient management across the board. This part of our blog provides practical insights into simplifying complex configurations, helping developers streamline their setup without compromising functionality or security. 🔗 Explore the detailed guide to configuring external services here: https://lnkd.in/dXsvXGqS #NestJS #Ory #CloudComputing #WebDevelopment #DevOps

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In this first article of a new series on RAG, Josselin Thibault overviews the focus areas, including Technical Evolution and Core Components, Implementation Challenges, Advanced Topics and Future Directions. Directions.https://wix.to/X6dg5GC #newblogpost

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Headed to SciPy? Join Guen Prawiroatmodjo , Elena Felder, Alex Monahan, Mehdi Ouazza, and Nicholas Ursa from the MotherDuck team to learn more about DuckDB's highly efficient, in-memory engine. Tuesday, 7/9: All the SQL a Pythonista needs to know: Introduction to SQL & DataFrames with DuckDB https://lnkd.in/eaT9eKK4 Friday, 7/12: How to Bootstrap a Data Warehouse with DuckDB

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Thank you Daniel Beach for showcasing how fast Daft is compared to PySpark on Databricks, reading a Unity Catalog Delta Table and aggregating on 21+ million records — 7s (Daft) 👍 vs 20s (PySpark) 👎 “Daft can read large-ish tables no problem. (it crunched 22 million in 7 seconds including file IO) (so in theory it could scale up way way past that)” Keep reading to see how easy and painless it is to connect Daft to Unity Catalog and why he thinks Daft is the GOAT 🐐 #Daft #GOAT #UnityCatalog

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