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    • Why Fivetran’s Tobiko Data Acquisition Signals Trouble for Data Teams

      Fivetran’s acquisition of Tobiko Data signals a shift from open source innovation to commercial consolidation, creating what many see as a “platform prison” where Extract, Load, and Transform are locked into one vendor ecosystem. While this promises simplicity, the true cost emerges over time through rising fees, reduced flexibility, and strategic dependencies that make switching prohibitively expensive.

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    • 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%.

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    • Why DBT Optimisation Hits a Ceiling (And How SQL Mesh Breaks Through)

      DBT and Snowflake teams often reach a point where further optimisation delivers diminishing returns, with costs rising and engineering velocity slowing due to architectural limitations. This recap of our LinkedIn Live shows how SQL Mesh’s incremental, state-aware processing enables 50–70% cost savings, greater productivity, and sustainable growth by replacing DBT’s expensive full-rebuild approach.

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    • 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.

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    • 8 Hidden Costs in Row-Based ETL Pricing (And How to Eliminate Them)

      Row-based ETL pricing models conceal hidden costs such as duplicate processing, unchanged record syncing, and development retries, leading to inflated bills that often do not reflect actual data value. Shifting to performance-based pricing aligns costs with real infrastructure usage, enabling predictable budgeting, greater efficiency, and funding for innovation.

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