Every data platform has a log list of things to improve, but only one area shapes everything around: workflow reliability and data quality. With predictable jobs and trustworthy data, teams can start making steady progress on the rest (scale, performance, costs, new use cases). Without it, even small changes can feel risky, and improvements take longer than they should. It’s not about perfecting everything at once, but about making the core stable enough that the platform can evolve without fear of breaking the next workload. #DataEngineering #DataPlatforms #DataQuality #DataOps #PlatformEngineering
The Scalable Way
Data Infrastructure and Analytics
Empowering analytics teams with scalable, automated, and secure data platforms that just work.
About us
We help IT teams build scalable, automated, and reliable data platforms while eliminating inefficiencies, reducing manual work, and improving security and governance. Our modular approach ensures seamless integration, long-term scalability, and a streamlined developer experience. With 30+ years of combined expertise in data platforms and infrastructure, we empower teams to move beyond fragmented workflows so their data just works.
- Website
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https://thescalableway.com/
External link for The Scalable Way
- Industry
- Data Infrastructure and Analytics
- Company size
- 11-50 employees
- Type
- Privately Held
- Founded
- 2025
- Specialties
- Data Engineering, Data Platforms, Modern Data Stack, Analytics Engineering, Data Strategy, and Data Infrastructure
Employees at The Scalable Way
Updates
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Built the lake? Cool. Now make sure people trust what comes out of it. 🌊
I help data teams move from firefighting to building | IaC, CI/CD & ELT-as-Code | Head of Data Engineering
The data is in the lake, but nobody's drinking from it. Had this exact conversation with a data analytics lead last week. His team built beautiful data marts, utilising advanced architectures. Yet business users still export to Excel and run their ancient stored procedures. His frustration: "They just don't get the benefits of our data platform." My question: "What's your pipeline success rate?" His answer: "Maybe 75% successful, 20% late arrivals." There's the problem. You can't sell a Ferrari when it only starts 3 out of 4 times. Business users aren't stubborn - they're practical. That Excel file might be outdated, but it's there when they need it. Most data leaders obsess over ROI while treating the "I" as silent. Yes, investment in infrastructure matters. But without investing in reliability and a service team that maintains it, you're just building expensive monuments to failed ambition. Trust isn't built on architecture diagrams or vendor promises. It's built on boring consistency: - Pipelines that complete on time, every time - Data quality checks that catch issues before users do - Monitoring that alerts before things break, not after - Rerun policies that handle failures gracefully - SLAs that actually mean something Your stored procedures might be ancient, but they've been running reliably for a decade. Until your data platform can match that track record, Excel will remain the shadow IT of choice. Stop building for tomorrow's use cases when you can't reliably serve today's. #DataEngineering #DataPlatform #DataReliability #TheScalableWay
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Not all workflows are created equal. In the world of modern data engineering, Prefect Flows bring structure to chaos, defining what runs, when it runs, and how it all connects. It’s our orchestration tool of choice, and for good reason. 🫡 #DataEngineering #Prefect #DataOrchestration #DataPlatform
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The Scalable Way reposted this
I help data teams move from firefighting to building | IaC, CI/CD & ELT-as-Code | Head of Data Engineering
Your pipelines weren't built for tomorrow's use cases - they were built for yesterday's. Every new dashboard, every "quick" analysis, every urgent executive request adds load to the same critical pipelines. What starts as a 2-hour refresh becomes 4, then 8, then fails during month-end when everyone needs data at once. Most analytics teams try to solve this with more compute power. But throwing resources at bottlenecks is like adding more lanes to a traffic jam - it just moves the problem downstream. Reliable data platforms handle load differently: - Workload isolation that prevents one heavy query from blocking everything else - Incremental processing instead of full refreshes - Priority queues for critical vs. exploratory workloads - Circuit breakers that fail fast instead of hanging forever - Horizontal scaling that actually scales with demand The real cost isn't the failed pipeline at 3 AM. It's the business decisions delayed because the data wasn't ready, the analysts stuck waiting instead of analyzing, the trust eroded every time someone asks "is this data fresh?" Stop patching bottlenecks. Start building platforms that scale with your ambitions. #DataEngineering #DataPlatform #DataReliability #TheScalableWay
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What if your Git repo wasn’t just for code, but for operations too? That’s the idea behind GitOps: treating your infrastructure like code and using Git as the single source of truth for everything you run. #DevOps #GitOps #Automation #InfrastructureAsCode #DataPlatform
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Pipelines that work for 10 runs often crumble at 1000. What used to be a clean setup turns into: ◾fragile DAGs that need manual restarts ◾slow orchestration ◾unclear ownership when things fail Scaling data operations isn't about ‘more compute’. It's all about a better design that allows observability, parallelization, and platform automation that actually scale. Will your pipelines still hold up when data volume or orchestration frequency increases? Do not wonder, take the test ➡️ https://lnkd.in/d9eSkTDP #DataEngineering #DataPlatform #DataOps #InfrastructureAsCode
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The full episode of the podcast is out! Mateusz walks through the realities of modern data platforms: clear, practical, and very relatable for anyone building them. You don't want to miss that one!
💥 New Podcast Alert! 🎙️🧱 Time to talk about #Data #Platforms without taboo: what works, what hurts, what to avoid. Tired of the marketing hype? In our latest episode of the "Między nami ekspertami" video podcast, we strip down Data Platforms to their core. We’re talking straight about what truly delivers value, what common pitfalls you must avoid, and how to build architecture that lasts. Zero sugarcoating, only practical insights from real-world deployments. ⚙️🧠 We dive into the tough questions: 🔹 build vs. buy: finding the sweet spot for your organization. 🔹 vendor lock-in: myth or a costly risk? How to protect your contracts and architecture. 🔹 the "Quick Wins" trap: structuring your roadmap to deliver fast results and lasting systems. 🔹 democratization limits: how much 'self-service' is too much? 🔹 TCO & hidden costs: the truth about multi-cloud and cloud provider migrations. Our guests guiding this no-fluff discussion are: ✨ Blazej Ksycki - Data Solutions Consultant ✨ Mateusz Paździor - Head of Data Engineering If you're a leader or practitioner looking to build a conscious, myth-free Data Platform focused on long-term business results, this episode is for you! 👇 Full episode link 👉 https://lnkd.in/ejjXwcJP Dorota Imiela Paweł Zieliński Magdalena Cebula Patryk Hetnał Renata Bratkowska 💡 Marta Eliza Grzybowska Katarzyna Z. Staroslawska Krzysztof Jędrzejewski Tomasz Tarczyński Monika Zawada Marta Mioduszewska Michał Choiński Filip Dzięcioł Wojciech Piątkowski Jakub Czerwonka Jan Bobak Natalia Traczewska Karolina Kociołek Kasia Syrówka Marek Czuma Michał Krzyżanowski Piotr Kalinowski Przemysław Sotowski Krzysztof Dzioba 📎 Krzysztof Osińskiński Krzysztof Heyda #DataEngineering #DataPlatform #BigData #CloudArchitecture #Podcast #TechTalk #BigData #DataExperts #BigDataBigChallengesCommunity
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When CI and CD come together, you get something powerful. CI/CD pipelines keep your delivery on autopilot, handling builds, tests, and deployments without breaking a sweat. Less manual work, more focus on building. #DevOps #CICD #Automation #SoftwareDelivery #DataPlatform
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When your pipeline runs fine… until it doesn’t. What’s missing? Here’s what “done” really means in data engineering. 👇 #DataEngineering #DataObservability #DataOps #AnalyticsEngineering
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Imagine every validated change going live automatically. That’s Continuous Deployment ‒ no waiting for approvals, no manual pushes, just a constant flow from commit to production. Fast, confident, and built on trust in your process. #DevOps #CD #ContinuousDeployment #Automation #DataPlatform
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