Lukas Zainzinger
Platform SRE at willhaben
Vienna, Austria
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Lukas Zainzinger is a high-impact Platform Site Reliability Engineer (SRE) at willhaben.at, bringing over 8 years of experience in architecting resilient and scalable infrastructure. He currently leads a large-scale strategic migration from on-premise environments to AWS, specializing in high-availability systems, zero-downtime deployments, and Infrastructure as Code (IaC). Throughout his career, which includes DevOps roles at Siemens, shoepping.at, and Raiffeisen Informatik, Lukas has built a proven track record of automating complex environments and driving operational excellence.
He is currently completing his Master of Science in Cloud Computing Engineering at Hochschule Burgenland , where his research focuses on architecting vendor-agnostic, cloud-native predictive maintenance frameworks to prevent vendor lock-in. Lukas is deeply passionate about scaling systems, embracing open-source technologies, platform engineering and guiding teams through complex cloud transformations.
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Blueprint for Independence: Reclaiming Data Sovereignty with Open-Source
As organizations build increasingly complex, data-driven applications, the reliance on hyperscaler cloud platforms (like AWS, GCP, and Azure) has skyrocketed. While proprietary managed services lower the barrier to entry, they introduce a critical strategic risk: severe vendor lock-in. This dependency limits long-term flexibility, escalates costs, and ultimately compromises data sovereignty by tying critical data and machine learning pipelines to a single provider's ecosystem.
But can a fully standardized, open-source stack truly match the enterprise-grade performance and reliability of these proprietary giants?
To answer this, we need a stress test. In this talk, I will explore an empirically validated, cloud-native blueprint that challenges the necessity of managed cloud services. Using a highly demanding, high-throughput system as our proving ground (Predictive Maintenance for Industrial Use-Cases) — requiring massive data ingestion, sub-millisecond stream processing, and complex ML inference — I will demonstrate how to architect a completely vendor-agnostic data pipeline from the edge to the cloud.
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