Qualdo™ - Monitor Data Reliability on Cloud’s Post

Data mesh offers a flexible approach to tackling today’s data complexities by decentralizing ownership and leveraging automation. Here are the four pillars that encompass the organizational and technical changes it espouses: ◾Domain Ownership: Domain teams own and manage their data products, ensuring relevance and accountability. ◾Data as a Product: Data is treated as a product, complete with metadata, quality standards, and APIs for seamless access. ◾Self-Serve Data Platform: Teams need autonomous tools for managing and accessing data products without creating new bottlenecks. ◾Federated Computational Governance: A shared governance framework enforces standards across domains, leveraging automation to ensure compliance and quality. Dive deeper with our white paper: https://lnkd.in/evd6F7cj Want to learn more live? Join experts Jay Piscioneri and Wayne Eckerson on October 31, where they’ll discuss how these frameworks support modern data strategies: https://lnkd.in/e_KG8Sc4 #DataMesh #DataFabric #DataStrategy #DataArchitecture #DataManagement

Top Practices in Data Engineering by Gartner Templatization for data engineering is crucial for several reasons: 1) Increase Automation and delivery speed 2) Standardization 3) Governance and compliance 4) Reduce Cognitive Load 5) Increase delivery quality 6) Freedom to operate 7) Future proof Witboost templates and blueprints are bringing these concepts to the next level: https://lnkd.in/e5rXxNJT Original post by Robert Thanaraj in the first comment #template #dataengineering #datamesh

Have a look at our website to learn more about Witboost Templates and Blueprints and how they can take you to the next level 🚀

📊 What is structured, semistructured, unstructured, and unmanaged data?🎥 I just released a video breaking down the key differences between structured, semi-structured, unstructured, and unmanaged data. 🏛️ #Structured data: Your well-organized library 🏷️ #Semi-structured: Flexible with tags 🌪️ #Unstructured: The chaotic room of social media 🎒 #Unmanaged: The messy backpack of data Boost your data management skills and learn how to apply these concepts in real-world scenarios. Watch now: https://lnkd.in/eXGji9xG #DataScience #DataManagement #TechEducation #Data #Information #Architecture, #Metadata #DataTypes #AZ900 #AzureStorage

Did you know that 90% of the world's data was created in the last two years? The challenge isn't just generating data, but making sense of it. At Roshdy Tech, we specialize in Data Engineering to turn your raw data into actionable insights. Whether it's simplifying complex pipelines, building scalable architectures, or delivering clean datasets for analysis, we help you unlock the true potential of your data. One of our clients reduced processing time by 70% after streamlining their data workflow. Ready to achieve the same? Reach out today. #DataEngineering #TechSolutions #DataDrivenInnovation #BusinessGrowth

🌟 Dive into the Boundless Potential of Data with RandomTrees Data Engineering! 🌐 1️⃣ Data Curation: Prepare your data seamlessly for Machine Learning. 2️⃣ Data Governance: Maximize value with optimization, management, and enhanced visibility. 3️⃣ Data Lakes & Warehouses: Discover deeper insights with top-performing solutions. 4️⃣ Data Management: Build a robust foundation for efficient data handling. Elevate your capabilities with RandomTrees! Contact us to explore further: 🌐 www.randomtrees.com #DataEngineering #RandomTrees #DataInsights #MachineLearning 🚀

Data Mesh is a modern approach to data architecture and organizational design, aimed at addressing the challenges of scaling data operations in large, complex organizations. It was introduced by Zhamak Dehghani in 2019 as a way to shift away from traditional, monolithic data architectures (like data lakes and data warehouses) and towards a more decentralized, domain-oriented approach. https://lnkd.in/gzTDyhyM

4 Principles for Robust Data Pipeline Design After years of building and optimizing data pipelines, here are my key principles: Idempotency: Design pipelines to produce the same results regardless of how many times they're run. Modularity: Break pipelines into reusable components for easier maintenance and scalability. Monitoring: Implement comprehensive logging and alerting. You can't improve what you can't measure. Error Handling: Plan for failure. Implement retry mechanisms and dead letter queues for data integrity. Remember: A well-designed pipeline is invisible; a poorly designed one becomes the center of attention. #DataEngineering #DataPipelines #ETL #BigData

🎉 Big News: Elementary + Atlan Integration 🎉 We're excited to announce a new integration that brings data quality insights directly into Atlan’s data catalog, helping bridge the gap between engineers and business teams. The Problem ❓ Business users often ask: Can I trust this data? 🤔 Engineers have answers, but those insights rarely reach the business side. The Solution 📊 Elementary pushes data health scores and open incident details straight into Atlan. ✅ How reliable is this data? (e.g., Health score: 95%) 🔧 What’s being fixed? (Active incidents: 2) Why It Matters ⚙️ For engineers: Seamless integration with dbt to track and validate data. 📂 For business users: Instant visibility into data quality directly in Atlan, right where they work. How It Works 1️⃣ Elementary monitors data pipelines for quality. 2️⃣ Insights are pushed to Atlan via a custom API. 3️⃣ Atlan users see health scores and incidents as part of their dataset metadata. Read the blog post here: https://lnkd.in/ddr_xZCT

Technology can be your ally when implementing Data Governance... Friction between policies and team priorities is a big challenge when implementing Data Governance. Imagine the following scenario: You are being told to implement Data Quality in your Data Products. In company A you need to come up with the architecture to calculate the metrics, publish and monitor the results and finally setup alerting in case something goes wrong. In company B your Data Platform has created an abstraction that allows you to run, publish, monitor and get alerted just by creating a YAML file or using a library. Which one do you think will be more likely to begin implementing Data Quality? #data #datagovernance #dataplatform #computationalgovernance