Posts Tagged ‘Cloud Cost Optimisation’
The FinOps Skills Every Data Engineer Needs in 2025
In 2025, data engineers are expected not only to deliver robust pipelines but also to integrate FinOps principles, ensuring systems scale economically as well as technically. Those who master cost attribution, pricing model evaluation, and cost-conscious architecture design are becoming business-critical, as financial awareness now defines engineering success.
How to Escape Your ETL Vendor Without Risk or Disruption
Most data teams stay locked into overpriced ETL contracts, overlooking hidden costs like wasted engineering hours, volume-based penalties, inefficiency, and auto-renewal traps. Matatika’s Mirror Mode eliminates migration risk by running old and new systems in parallel, proving savings before switching, and offering performance-based pricing that cuts ETL costs by 30–60%.
How To Reduce ETL Costs Without Sacrificing Performance
Cloud providers like AWS are introducing AI-powered cost transparency tools, while ETL vendors remain silent, continuing to profit from opaque, row-based pricing models that penalise efficiency and scale. By switching to performance-based pricing and auditing pipeline usage, data teams can cut ETL costs by up to 50% without sacrificing performance.
Understanding Today’s ETL Pricing Landscape: Column vs Row Approaches
Most ETL pricing models haven’t kept pace with the evolving data landscape, leaving many teams overpaying for row-based processing that penalises growth and efficiency. This blog advocates for a shift toward performance-based pricing aligned with column-oriented processing, offering scalable, transparent cost control that reflects actual infrastructure usage rather than arbitrary metrics.
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.