Related posts for ‘#Blog’
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.
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.
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.
How Smart Data Teams Cut Costs Without Sacrificing Performance
This blog explores how data teams can strategically reduce costs without compromising performance, drawing insights from a recent LinkedIn Live featuring experts from Select.dev, Cube, and Matatika. It outlines five key strategies, from optimising human productivity to safely switching platforms, backed by real-world examples and practical implementation steps.
From Tool Mastery to Systems Design: How Data Engineers Actually Grow
Many data engineers plateau after mastering tools but struggle to scale because they haven't learned to think in systems. This blog explores how transitioning from query writing to system design is the key to sustainable growth, effective mentorship, and resilient analytics platforms.
Why Most SQL Server Data Tools Migrations Fail (And How to Build Better Ones
Many data teams avoid SQL Server Data Tools (SSDT) migrations due to cost, complexity, and risk, leaving them stuck with outdated systems and growing technical debt. Matatika’s Mirror Mode offers a safer, more cost-efficient alternative by enabling secure, isolated testing environments that mirror production without exposing sensitive data or inflating infrastructure costs.
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.
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.
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.
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.