Thumbnail for an article comparing YAML, XML, and JSON, showing the three formats on a scale from verbose to readable with the tagline "Right tool, right job."
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  • 10 minute read

YAML vs XML vs JSON: History, Trade-offs, and Where Each Wins in the Age of Agentic AI

XML, JSON, and YAML were built for different jobs in different eras. This post covers where each came from, how they compare, and where each one still wins, including why YAML borrows JSON Schema and how schemas became the contract layer for data governance and AI agents.
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Choosing an ERP for manufacturing: the vendor landscape from SAP and other Tier 1 platforms to mid-market specialists, with five AI disruption scenarios and the core message that fit beats default.
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  • 14 minute read

Choosing an ERP for Manufacturing: How AI Is Reshaping the Vendor Landscape

ERP vendor selection for manufacturing is not a product decision. It is a strategic bet on fit, total cost, and which AI future your vendor is building toward. Three German manufacturers chose SAP, ams.erp, and VlexPlus. All three were right. The lesson is that fit beats default, every time.
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  • 8 minute read

Beyond Enterprise Data Lineage: The Case for a Platform-Independent Data Catalog

Most organizations start their data governance journey by asking how to track where data comes from and where it goes. They quickly discover a harder question: why can none of their existing tools answer that across all systems? Vendor-specific lineage tools like Confluent, Snowflake Horizon, and Databricks Unity Catalog each do a good job within their platform boundary. The problem is the boundary. Enterprise-wide lineage requires a platform-independent catalog layer that integrates everything and is owned by none of the platforms it connects.
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Data Ownership in the Age of Agentic AI: SAP API Policy and the Data Integration Reckoning
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Data Ownership in the Age of Agentic AI: Why SAP’s API Policy Forces a Data Integration Reckoning for Every Enterprise

Every enterprise is being told to go agentic. Meanwhile, the platforms holding your most critical business data are tightening control over how AI agents can access it. SAP made that move explicitly. Other software vendors are doing the same thing, more quietly. This is not a SAP problem. It is an industry-wide structural shift. And it has a known solution. Enterprises solved this decades ago with mainframes. The pattern is the same: extract once, stage the data, let agents consume freely downstream. The trigger then was cost and scale. The trigger now is agentic AI. The architecture that solves it has been in production for years.
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  • 13 minute read

Flink CEP and Agentic AI: Real-Time Pattern Detection as the Foundation for Autonomous Decisions

AI agents fail in production when they are connected directly to raw event streams. Flink CEP is the missing layer between your data streams and your Agentic AI architecture: it detects meaningful event sequences in real time, reduces hundreds of thousands of raw events to a handful of grounded actionable signals, and makes every autonomous decision deterministic, auditable, and trustworthy enough for enterprise production.
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Flink CEP - When and how to use it
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Complex Event Processing (CEP) with Apache Flink: What It Is and When (Not) to Use It

Complex Event Processing is the most underused capability in Apache Flink. It detects meaningful event sequences in real time, fires only when a pattern is confirmed, and even catches events that never arrive. This guide covers what Flink CEP is, when to reach for it instead of stream processing, how to implement it, and what happened to the legacy CEP market.
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