The Four Points in Your Medallion Architecture Where Data Testing Really Matters hubs.ly/Q04kw8CM0 #dataengineering #dataquality #opensource #dataobservability #dataops

The Four Points in Your Medallion Architecture Where Data Testing Really Matters hubs.ly/Q04l9GCm0 #dataengineering #dataquality #opensource #dataobservability #dataops

The Four Points in Your Medallion Architecture Where Data Testing Really Matters hubs.ly/Q04l9N6J0 #dataengineering #dataquality #opensource #dataobservability #dataops

New: The 2026 Data Observability Vendor Database 📊 Compare 20 platforms by founding year, funding, hosting (cloud/on‑prem), and pricing — so you can pick the right data reliability tool fast. #DataObservability #DataEngineering #ML bigdataanalyticsnews.com/dat…

Data observability tells you when data breaks. Data lineage tells you what it ruined downstream. 🚨🔍 Stop wasting days manually tracing broken columns across scripts. Build graph-based, automated data lineage to harden your AI compliance and speed up debugging. Own your data provenance: mcal.in #DataLineage #DataEngineering #DataObservability

The Four Points in Your Medallion Architecture Where Data Testing Really Mattershttps://datakitchen.io/blog/medallion-architecture-four-points-data-testing/ #dataengineering #dataquality #opensource #dataobservability #dataops

The Four Points in Your Medallion Architecture Where Data Testing Really Mattershttps://datakitchen.io/blog/medallion-architecture-four-points-data-testing/ #dataengineering #dataquality #opensource #dataobservability #dataops

The Four Points in Your Medallion Architecture Where Data Testing Really Mattershttps://datakitchen.io/blog/medallion-architecture-four-points-data-testing/ #dataengineering #dataquality #opensource #dataobservability #dataops

Listen to my conversation with Jordan Van Horn of @montecarlodata on Spotify: bit.ly/3v1R0Tu Apple: bit.ly/4bTCwpD Youtube: bit.ly/3uXthnv LinkedIn: bit.ly/3Xs8GQP Website: svppro.com ~~~~~~~~~~~~~ This episode is brought to you by Nebius — the ultimate cloud for AI innovators. Nebius provides AI infrastructure you can count on, combining reliability and speed with flexibility and engineering support unmatched by hyperscalers. AI leaders like Meta, Shopify, and Higgsfield already partner with Nebius to run their AI workloads. Plus, venture-backed startups can save up to $150,000 on compute costs when they apply for access. Visit nebius.com or nebius.com/startups to learn more ~~~~~~~~~~~~~ #DataObservability #MonteCarlo #AIGTM #DataAI #RevenueOperations

TestGen Data Quality Workshop. Join Us: Thursday, May 14, 3-4 PM ET hubs.ly/Q04g60F10 #dataquality #dataengineering #opensource #dataobservability #dataops

We’re heading into the final day at the @DISummit2030. This means it’s your last chance to see our Technology in Practice demos. Join our sessions on data intelligence and data observability. 💡 Stop by booth B24 or visit Hyperight’s YouTube and X channels before the Summit wraps. #DISummit2030 #DataIntelligence #DataObservability

Join us for Technology in Practice demo sessions at @DISummit2030 in booth B24 Attend in-person or catch it streaming on @Hyperight_AB’s X and YouTube channels. See how teams are building AI-ready data and enabling continuous observability. #DISummit2030 #DataObservability #DataIntelligence

DT DX CTX = 10x How to Make Data Analysis Ten Times Faster with AI and Large Language Models hubs.ly/Q04dW37h0 #dataengineering #dataquality #dataobservability #dataops

Tough times never last. Resilient systems do. We unpack fraud detection, privacy engineering, supply chain simulation & computational governance in the latest #AIoftheTiger. Check it out: tinyurl.com/2vebxzbd #SystemResilience #DataObservability #TigerAnalytics

Data Observability Tools are COOKED!!! youtube.com/watch?v=cJcBfzNi… You can just use Claude to build one. #dataobservability #ai #claude

If you’re not measuring data health, you’re guessing on data quality. And guessing with data is risky. Because when something breaks, it doesn’t stay small for long. That’s where data observability comes in 👇 It tracks signals like: freshness, quality, volume, schema, and lineage So you can catch issues early, fix them fast, and trust the data behind your analytics and AI. Spot data issues before they impact the business 👉 bit.ly/3NSj4T0 #DataObservability #DataTrust #DataReliability

The Modern Data Stack Explained (2025–2026 Edition) The Modern Data Stack (MDS) is a cloud-native, modular ecosystem designed to manage the entire data lifecycle—from extraction to insights to real-world action. Unlike legacy monolithic systems, MDS is flexible, composable, and scalable, allowing teams to swap tools as needs evolve. In this video, we break down each layer of the modern data stack and explain how organizations are building reliable, analytics-ready, and AI-powered data platforms in 2025–2026. 📌 What You’ll Learn: ✅ Why companies shifted from ETL to ELT ✅ How Lakehouse architecture is reshaping data storage ✅ The role of dbt, orchestration, and analytics engineering ✅ How Reverse ETL activates data in operational tools ✅ Why data observability & governance are critical ✅ How AI agents are transforming analytics and productivity 🧱 Modern Data Stack Layers Covered: 1️⃣ Data Ingestion & Integration – Fivetran, Airbyte 2️⃣ Storage & Lakehouse Architecture – Snowflake, BigQuery, Databricks 3️⃣ Transformation & Modeling – dbt, Dataform 4️⃣ Orchestration – Airflow, Dagster, Prefect 5️⃣ BI & Analytics – Tableau, Power BI, Looker Studio 6️⃣ Reverse ETL & Activation – Hightouch, Census, Weld 7️⃣ Observability & Monitoring – Monte Carlo, Bigeye, Soda 8️⃣ Governance & Discovery – Alation, Atlan, Collibra 9️⃣ Semantic Layer – dbt Semantic Layer, Cube, LookML 🔟 AI Agents & Automation – The future of analytics in 2026 🎯 Who This Video Is For: Data Analysts & Analytics Engineers Data Engineers & Platform Architects Product Managers & Founders Anyone learning modern data architecture 👍 If you found this helpful, like, subscribe, and share it with your data team! 💬 Drop a comment if you want deep dives on any specific layer. #ModernDataStack #DataEngineering #AnalyticsEngineering #DataArchitecture #ELT #Lakehouse #dbt #ReverseETL #DataObservability #AIinAnalytics #BigData #CloudData #Data2026 The Modern Data Ecosystem youtu.be/qODGMMo2luE?si=4WOS… via @YouTube

Have you ever spotted bad data and had no idea where in the pipeline it went wrong? If your pipeline isn't connected end-to-end, debugging means jumping between tools and manually tracing the issue. It's slow and error-prone. Instead of jumping between tools, Datadog Data Observability gives you one connected view from ingestion to analytics. It does this by: • Catching anomalies like missing rows or stale data automatically • Giving visibility into Spark and Airflow runs, including failures and cost • Tracing issues upstream to the source and downstream to every affected dashboard, model, and report In this example, Datadog follows the data lineage from a broken dashboard upstream to a failing Spark job, where you can see exactly what broke and why. 🚀 Try Datadog Data Observability: fandf.co/3PwX00J Thank you to Datadog for sponsoring this post. #DataEngineering #DataObservability #DataQuality #Sponsored

Schema drift is the #1 cause of silent data failures. A column gets renamed. Pipelines keep running. Dashboards show wrong numbers. Here we set up data observability in 30 seconds. #dataengineering #dataobservability #dataquality #schemadrift blog.anomalyarmor.ai/using-a…

Webinar - Sign Up today! The Four Points In Your Medallion Architecture Where Data Testing Really Matters Date: March 31st, 2026; 12 pm EST / 4 pm GMT hubs.ly/Q0485k_00 #dataengineering #dataquality #opensource #dataobservability #dataops