Insights & Research
Data Engineering & Analytics
Data Contracts · Lineage · Lakehouse · Streaming
Practical data engineering insight: contracts between teams, lineage as a trust signal, and governed lakehouse design that survives contact with an LLM.
Synthetic Data Won't Save a Broken Pipeline — MNB Technology
Synthetic data solves specific problems well — privacy, scarcity, edge cases. It does not fix a pipeline whose real underlying issue is data quality.
Read article →Streaming vs Batch: A Decision Framework, Not a Preference — MNB Technology
Streaming is not automatically better than batch. A practical framework for choosing based on actual latency requirements, not architectural fashion.
Read article →The Real Cost of Reverse ETL You Didn't Budget For — MNB Technology
Reverse ETL solves a real problem — getting warehouse data back into operational tools — but its ongoing cost is usually underestimated at adoption time.
Read article →The Real Cost of 'Just Add a Dashboard' — MNB Technology
A dashboard request sounds like a small ask. The underlying pipeline, definition alignment and maintenance burden it actually requires rarely is.
Read article →Making Your Lakehouse Queryable by an LLM Without Leaking the Wrong Rows — MNB Technology
Connecting an LLM to your lakehouse for natural-language analytics is appealing and genuinely risky without row-level permission enforcement designed in from the start.
Read article →The Metrics Layer Is the Most Underrated Investment in Analytics — MNB Technology
Two dashboards showing a different number for 'revenue' is a metrics layer problem, not a dashboard problem, and it is one of the cheapest fixes available.
Read article →Lineage as a Sales Tool, Not Just a Compliance Artefact — MNB Technology
Data lineage is usually built to satisfy an auditor. Treated as a product feature instead, it becomes a genuine commercial differentiator.
Read article →Feature Stores Solve a Problem Most Teams Don't Have Yet — MNB Technology
A feature store is genuinely valuable once you have multiple models sharing features in production. Most teams adopt one long before they reach that point.
Read article →Why Data Quality Dashboards Get Ignored After Month Two — MNB Technology
Data quality dashboards launch with enthusiasm and get ignored within weeks. The reason is almost always alert design, not lack of interest.
Read article →Data Mesh Failed Because Governance Was an Afterthought — MNB Technology
Most data mesh implementations stall not because the architectural idea was wrong, but because federated governance was treated as a later phase.
Read article →Data Contracts as the Missing API Layer Between Teams — MNB Technology
Teams version and document their APIs carefully, then ship data with no contract at all. Data contracts close that gap and prevent silent breakage downstream.
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