Related posts for ‘#Blog’
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.
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.
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%.
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.
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.
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.
When Kiss Cams Go Wrong: What Astronomer’s PR Crisis Reveals About Vendor Culture
Astronomer’s PR mishap responding to a kiss cam controversy by hiring a celebrity, spotlights a deeper issue in vendor culture: misplaced priorities and poor judgment under pressure. For data leaders, it raises critical concerns about whether vendors invest in engineering excellence or opt for brand theatrics when things go wrong.
The Conversations Data Leaders Are Planning for Big Data LDN 2025 (But Won’t Admit Publicly)
The real value of Big Data LDN 2025 lies not in vendor pitches or keynote sessions, but in candid corridor conversations among data leaders grappling with vendor fatigue, renewal pressure, and cost consolidation. As budgets tighten and complexity rises, the smartest teams are shifting from reactive tool dependency to proactive strategies that prioritise flexibility, performance-based pricing, and long-term efficiency.