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Discover how to revolutionize enterprise data infrastructure through strategic adoption of open-source technologies in this 17-minute conference talk that demonstrates achieving over $1 million in annual cost savings. Learn how to leverage Apache Hudi for building scalable data lakes with incremental processing capabilities that provide engineering and machine learning teams seamless access to data for analysis and model training. Explore the practical approach to replacing expensive SaaS solutions like Splunk and Segment with custom-built alternatives, including implementing an observability stack using OpenSearch and developing an in-house event processing platform while maintaining system reliability. Gain actionable insights into evaluating open-source alternatives against commercial solutions, designing scalable self-service data workflows, and accurately measuring return on investment when transitioning from vendor-dependent SaaS platforms to custom-built solutions. Master the strategic decision-making process for eliminating costly vendor dependencies without compromising operational excellence or data accessibility across your organization.
