Striim

For years, democratization meant dashboards and self-service BI. That worked when insights could arrive tomorrow, but these days, batch pipelines and siloed systems are not longer enough. Modern data democratization starts with real-time access. Fresh, governed data that's usable the moment it's created. If your business depends on speed, this is no longer optional. It's a must. Read the full guide on how to unlock fresh data for the whole organizaton: https://lnkd.in/e4vnWqvy

280K+ claims paid annually. $236B in declared value. 690K shippers protected. UPS Capital operates at massive scale. And tackling porch theft on that scale demands fresh, instant data. With Striim and Google BigQuery, UPS Capital built DeliveryDefense Address Confidence, a flagship program designed to predict delivery risks and optimize logistics in real time. UPS has reshaped the shipping landscape with advanced AI and ML, powered by real-time data. See how they did it . 🔗 https://lnkd.in/gSPaXSC7

Real-time data processing enables systems to detect fraud as it happens, personalize customer experiences in the moment, and feed AI models with live, contextual information instead of yesterday’s data. In this post, we explain what real-time data processing really means for modern business, and how companies are re-architecting for it. Read the full guide: 🔗 https://lnkd.in/esBK4tMg

“Retrieval isn’t the end goal, it’s an enabler.” Clare Liguori from AWS dropped a gem in this clip from this What’s New in Data episode. If you’re working with agents or exploring orchestration, this mindset shift is a game-changer. We talked about:  • Why retrieval isn’t just about fetching documents  • How it supports reasoning in complex systems  • What builders should focus on before chasing multi-hop magic Watch the full episode here - https://lnkd.in/eTqmqM4T

Most Oracle to Fabric pipelines focus only on data movement. But Fabric isn't a single destination. It's three. A Data Warehouse for Power BI. A Lakehouse for ML and data engineering. A Mirror for native replication. Each destination needs data in a different form, yet most tools force teams to build separate pipelines for each format. That's three extractions from production, three pipelines to maintain, and three points of failure. Striim reads Oracle once and writes to all three simultaneously. One CDC stream feeding every Fabric workload in parallel, sub-second. Here's a full breakdown on the architecture. 🔗 https://lnkd.in/gXPSG_3M

When enterprises migrate databases, replicate across clouds, or feed AI systems, teams need to know whether their data arrived at the target intact. And most teams have no rigorous way to answer that. We built Validata to close that gap. Not with more scripts, but with a productized system for dataset equivalence that treats validation as a first-class engineering discipline. In the first post of our Validata Foundations series, we break down what validation actually means at enterprise scale, why row counts and checksums fall short, and how Validata approaches the problem differently. 🔗 https://lnkd.in/ewZ8Fj2c

It used be enough that data was "clean". But now that data fuels AI systems as context, the stakes, and the requirements of our data, are much greater. AI systems demand data that's fresh, continuously in sync, governed in-flight, and machine-actionable. In short, it needs to be useful the moment it's born. Allen Skees explains what it means to get data AI-ready, how to evaluate your current architecture, and the practical steps to build a real-time data foundation that delivers. 🔗 https://lnkd.in/g-_xxeTC

Batched data is simply too stale to prevent fraud. When UPS set out to prevent package losses, they discovered their batch pipelines couldn't feed the agentic systems making real-time decisions. Recently Benjamin Kennady, Cloud Solutions Architect at Striim, sat down with theCUBE at Google Cloud Next to talk through how UPS used Striim and Google Cloud to tackle package theft nationwide. 🎥 Watch the full conversation: 🔗 https://lnkd.in/e2dq4Uez

PostgreSQL is the database behind some of the largest production workloads in the world. When data in those systems changes, everything downstream needs to know immediately. Fraud models. Dashboards. AI agents. Supply chain optimizers. Change Data Capture is the engine that makes that possible. CDC reads PostgreSQL's Write-Ahead Log and streams every committed insert, update, and delete to downstream systems in near real time. In this guide, Srdan Dvanajscak walks through how CDC works in PostgreSQL, compares logical decoding vs. trigger-based approaches, and covers what it takes to run CDC reliably at scale. Discover CDC for Postgres for yourself in Srdan's post. 🔗 https://lnkd.in/gmmbkz8M

Too many enterprise teams rely on sprawling spreadsheets and ad hoc SQL scripts to validate data across migrations, pipelines, and AI systems. That doesn't scale or do hold up to regulatory pressure. That's where Validata comes in. By continuously monitoring data across legacy and cloud environments, surfacing real mismatches, and generating repair scripts so teams can actually fix them, Validata helps enterprises move from broken processes to a productized system for data confidence. Watch the video and read the overview to learn more. 🔗 https://lnkd.in/eVFrfyFV