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Column vs Row: Why It’s Time to Rethink How You Pay for ETL
Most data teams remain locked into outdated ETL platforms not out of satisfaction, but due to the perceived risk and disruption of switching, yet the real risk lies in doing nothing, especially under inefficient row-based pricing models that punish growth and hinder budgeting. This blog advocates for a shift to performance-based ETL pricing, highlighting how modern approaches reward efficiency, reduce costs by 30-90%, and can be safely trialed via parallel validation methods like Matatika’s Mirror Mode.
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Building Data Trust Through Effective ETL Staging Environments
Many teams avoid ETL staging due to cost and complexity, but this leads to production risks and data trust issues. Matatika offers secure, cost-efficient staging with parallel testing, obfuscated data, and performance-based pricing to catch issues early and deploy confidently.
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ETL Commodity – Why Are You Still Paying a Premium?
ETL is no longer a specialised function, it’s a commodity, yet many organisations are still paying inflated prices due to outdated, volume-based pricing models. This blog explores why ETL costs remain high, and how Matatika’s Mirror Mode offers a risk-free path to modern, performance-based pricing.
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Data Engineers Don’t Burn Out from Work They Burn Out from Pointless Work
This blog discusses key insights from a Data Matas podcast episode featuring Nik Walker, Head of Data Engineering at Co-op. It explores how data teams can reduce burnout, cut cloud costs, and build trust in their data without overhauling their entire stack. Key themes include eliminating low-value work, right-sizing syncs, prioritising discovery, and fostering psychological safety through structured leadership. The focus is on making smarter choices, not faster ones, to create scalable, resilient data delivery systems that serve both business needs and team wellbeing.
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How to Cut ETL Costs Without Disruption: Proven Strategies from Data Leaders
This blog summarises a LinkedIn Live session addressing how data teams can reduce ETL costs without compromising productivity or rushing into platform migrations. Drawing on insights from experienced industry leaders, it outlines strategies for improving cost visibility, minimising engineering friction, and approaching migration decisions with a structured, value-led plan rather than reactive urgency.
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