Charly Clairmont

⏰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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⏰ Check out our new blog post : “Chunking for RAG: best practices”. Chunking is a crucial data preprocessing step for RAG systems, but finding the optimal approach can be complex. In this post, we talk about the reasons for chunking data, explore common methods, and highlight Unstructured smart chunking strategies that preserve the semantic structure of your documents. These strategies ensure uninterrupted information flow and enhance retrieval precision, improving your RAG systems’ performance. Key takeaways: - Understanding the importance of chunking for RAG - Common chunking methods - Unstructured's smart chunking strategies - Practical tips for chunking optimization Get started for FREE with our Serverless API and bring your RAG system's performance to the next level. We can't wait to see what you build! Read the full blog post here: https://lnkd.in/eGNvyuJz

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Apache Hive is a data warehouse open source project to provide data querying and analysis capabilities supoorting SQL. The new release includes several features to leverage Iceberg: - Advanced Snapshot management - Branches & Tags support - DML (insert/update/delete/merge) - COW & MOR modes - Vectorised Reads & Writes - Table migration command - LOAD DATA statements support - Partition-level operations support - Improved statistics (column stats support)

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🚨 Looking for a powerful alternative to Kafka? ApachePulsar's unique architecture sets it apart in building event-driven applications. Watch Julien Jakubowski 🥑's #OSACon 2023 session and learn about its scalable processing power, exceptional message durability, and comprehensive use case coverage, rivaling #RabbitMQ and #Kafka with a single broker. #opensource https://lnkd.in/gMt72TUr

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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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Monday Morning ☕ calls for Orchestra's data roundup. It is also swiflty becoming a roundup of roundups too I guess as we like to include Christophe Blefari's newsletter in ours??? Check it out here ⚡ https://lnkd.in/ezV_wfVr 🧠 Stop being data-driven Cai Parry-Jones 🧠 Why did Databricks open-source Unity Catalog 🧠 7 surprising habits that make data engineers unstoppable Tim Webster 🧠 How to run Kafka on 100% object storage Vu Trinh 🎥 How to build a data infrastructure that lasts Benjamin Rogojan Seattle Data Guy and great content from Joe Reis 🤓 as always #dataengineering #dataroundup #datanews

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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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Data Engineers ! This one's for you: 🧠 The Economics of Generative AI Stephanie Kirmer 🧠 Connecting ML Models in Sagemaker to Snowflake and dbt Hugo Lu 🧠 Introduction to Interpretable Clustering NAKUL UPADHYAY 🧠 Snowflake automatic clustering cost estimation Tim Sander 🧠 Death to Tech Debt? Ari Joury, PhD Check it here; https://lnkd.in/ebdFKNZ5 #dataengineering #dataroundup #startup

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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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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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Watch Luigi Nardi's talk from #PosetteConf 2024 🌟 “Autotuning PostgreSQL on Azure Flexible Server” and Explore the world of #PostgreSQL parameter tuning & see a proof-of-concept for autotuning Azure Database for PostgreSQL Flexible Server 🌐

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Do you know how to ensure #data_integrity in your #database? Join us on July 10 to explore integrity features of relational databases with Federico Razzoli (Founder, Vettabase Ltd) and Tianzhou Chen (CEO, Bytebase) on a #webinar: https://lnkd.in/eWrCgnF6 #rdbms #dbms #data

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Great post enjoyed the read! I think Data Governance is very important to AI. The quality of the AI’s decisions are tied to the quality of the data presented to the tool. However, I think Data Governance can be taken a step further to even greater enhance AI capability. One of the great limitations around AI is the that it requires integrated data to be powerful, but integrating data is expensive and time consuming, mostly do the requirements of transforming the data. However, there is an approach that leverages data standards, around master data, which Data Governance can help create, that makes datasets directly interoperable at the source. These new standardized data sets can all share data through master data commonality, even though they are de-centralized. So, in this approach, Data Governance not only improves the quality of the data for AI but can be leveraged to increase the variety of enriched data the AI has access to. Enjoyed the post and look forward to more.

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📣📣📣 Today is the last chance to grab our #KubeCon 2024 Paris special offers! Level up your tech skills,  grab some of our offers:  🔹 Kubernetes Boot Camp with Exam voucher,  🔹 Kubernetes Certification Exam prep,  🔹 Kubeflow foundation course about AI & ML special Seize the opportunity to upgrade your tech skillset with a 50% off discount! We're here to help you boost your knowledge and tech skills. https://lnkd.in/eH_bqxAn now! Don't let this offer slip away! #techskills #cloudnative #AI #ML #offers

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Are you looking to enhance your version control skills and streamline your CI/CD pipeline? Don't miss out on this opportunity to learn from the experts! 🗓️ Date: June 26 🕒 Time: 5PM CET / 8AM PST In this webinar, we'll cover: 1️⃣ Understand how to set up CircleCI for CI/CD while using GitLab cloud as a VCS 2️⃣ Gain an understanding of CircleCI integrated with GitLab, including a feature overview 3️⃣ How to leverage CircleCI to deploy your project to a production ready environment 🔗 Register Now: https://circle.ci/4eFtQ7k

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“More Streamlined Data Exchanges” Isatis.neo’s latest version, 2024.04, introduces several major enhancements : You can now conveniently exchange any data or objects between Isatis.neo projects using the available explorers, including neighborhoods, geostatistical sets, color scales, chart files, scenes, batch files, and files in the public folder. Effortlessly import Fusion drillholes by connecting Isatis.neo to the Fusion database through a new interface. Export 3D grids and points to NetCDF files for easy reuse in QGis, ArcGISPro, or Paraview. Access the newest versions of pandas, numpy, and matplotlib with Python 3.11 and ensure compatibility with Vulcan’s latest version. Discover all here: 👉 https://lnkd.in/eh8CsDQC #isatisneo #software #geostatistics

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“More Streamlined Data Exchanges” Isatis.neo’s latest version, 2024.04, introduces several major enhancements : You can now conveniently exchange any data or objects between Isatis.neo projects using the available explorers, including neighborhoods, geostatistical sets, color scales, chart files, scenes, batch files, and files in the public folder. Effortlessly import Fusion drillholes by connecting Isatis.neo to the Fusion database through a new interface. Export 3D grids and points to NetCDF files for easy reuse in QGis, ArcGISPro, or Paraview. Access the newest versions of pandas, numpy, and matplotlib with Python 3.11 and ensure compatibility with Vulcan’s latest version. Discover all here: 👉 https://lnkd.in/eh8CsDQC #isatisneo #software #geostatistics

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Your contribution for the evening 🌟: Griptape👇 Co-founded by Kyle Roche and Vasily Vasinov, Griptape is a modular open source framework that allows developers to build and deploy LLM-based agents, pipelines, and workflows. Here’s why you should contribute to this repo: 1️⃣ With over 1,500 stars, contributing to Griptape means you can gain exposure and hands-on experience with LLMs and modular framework design, enhancing your skills in highly sought-after areas. You will also join the Griptape community, which allows you to connect with like-minded developers and experts in the field, expanding your professional network and potentially opening doors to new opportunities and collaborations! 💪 2️⃣ The repo is more accessible. At Quira, we rank Developers by their DevRank (more on this here: https://lnkd.in/eNtrs97F). We found that the median developer working on the repo belongs to the 90th percentile of GitHub developers. For reference, many of the open source companies we reported on have had a median contributor level of ~95th percentile. This means that you have a real opportunity to make an impact. ⚡️ 3️⃣ Returning contributors are a good proxy for the health of a repo and your chances to make a meaningful impact. Impressively, close to 60% of contributors merge a second pull request. This means that if you enjoyed contributing the first time, chances are you'll come back for more. 😉 You can check more of their open-source stats on Quira (https://lnkd.in/eguqtcWD) or access the GitHub page: https://lnkd.in/gPRRfAi2 📖

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