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Data Lake vs Data Warehouse: What’s the Difference and Which Should You Choose?
Many organisations feel forced to choose between a data lake or a data warehouse due to cost, complexity, or skill constraints, often settling for suboptimal setups that limit agility and inflate costs. Leading data teams are now adopting hybrid lakehouse architectures and transition tools like Mirror Mode to unify storage, improve analytics speed, and cut spend, without the disruption of traditional migrations.
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Stop Scaling What You Don’t Understand – Data Platform Rebuild
Monzo’s data team, led by John Napoleon-Kuofie, chose to rebuild their data platform from first principles after inheriting over 1,000 inconsistent DBT models, prioritizing clarity and maintainability over scale. Their experience—shared on the Data Matas podcast—underscores a broader industry shift: sustainable innovation in data and AI begins with simplified models, clear ownership, and a culture that empowers individuals to drive meaningful change.
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Snowflake vs Databricks Pricing: Who’s Really Playing Fair?
Snowflake and Databricks take fundamentally different pricing approaches—Snowflake offers managed optimisation with less control, while Databricks provides flexibility with greater complexity. The real shift in value lies in adopting warehouse-agnostic, performance-based ETL pricing that aligns cost with actual infrastructure use, offering transparency and freedom from vendor lock-in.
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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.
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Snowflake Columnar Storage: Why This Architecture Could Cut Your Analytics Costs by 70%
Snowflake’s columnar storage architecture delivers faster analytics and lower costs by scanning only relevant data, compressing storage intelligently, and optimising queries automatically. This design enables significant performance gains and cost reductions across ETL, storage, and compute—transforming how businesses scale data operations and consume insights.
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