Posts Tagged ‘Data Engineering’
How OLTP and OLAP Databases Differ and Why It Matters for Your Data Architecture
Most data teams misuse OLTP and OLAP systems by forcing mismatched workloads, leading to bottlenecks, high costs, and missed opportunities. Smart teams separate environments, optimise data flow with incremental syncing, and use safe migration tools like Mirror Mode to achieve both transactional efficiency and analytical power without disruption.
How to Optimise OLAP and OLTP Systems for Better Performance
Most data teams struggle because inefficient architectures force them to choose between fast transactions (OLTP) and powerful analytics (OLAP), creating delays, high costs, and frustrated users. Smart teams separate systems by purpose, use efficient syncing like Change Data Capture, and adopt performance-based pricing to achieve real-time insights, cost savings, and scalable architectures without disruption.
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%.
The Processing Paradox: Why Row-Based ETL Pricing Ignores How Analytics Actually Works
Modern ETL pricing models often charge based on row counts, which fundamentally misaligns with how analytical systems actually process data—via columnar methods focused on compute efficiency and performance. This disconnect not only creates technical debt and unpredictable costs but also diverts engineering resources away from optimisation and innovation toward managing arbitrary billing constraints.
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.
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.
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.
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.