Posts Tagged ‘Data Infrastructure’
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.
7 Hours of Firefighting: What Google Cloud’s June Outage Really Cost Data Teams
The June 12, 2025 Google Cloud outage revealed a harsh truth: modern data stacks often create more firefighting than innovation, as fragmented toolchains and so-called “managed” services increase maintenance burdens, costs, and risk. Matatika’s Mirror Mode offers a risk-free path out of this cycle by allowing teams to validate a more stable, antifragile infrastructure—enabling a shift from constant maintenance to strategic, high-impact data work.
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Most data teams remain locked into outdated ETL platforms not out of satisfaction, but due to the perceived risk and disruption of switching, yet the real risk lies in doing nothing, especially under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.
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is no longer a specialised function, it’s a commodity, yet many organisations are still paying inflated prices due to outdated, volume-based pricing models.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.
Why Data Migration Tools Fail? Here’s How to Make Them Work
This article explores why data migrations, despite good intentions, often fail to deliver real value, and how using the right data migration tools strategically can change the outcome. Drawing on real-world insights, it offers practical guidance for data leaders looking to break the cycle of reactive migrations and build more resilient, scalable systems.