Posts Tagged ‘ETL Tools’
The Ticket Queue That Never Ends
Your analyst sends a Slack message at 9am.
“Hey, can you update the product-to-GL mapping table? Finance added three new categories last week and our revenue reports are off.”
It’s a five-minute job. You know exactly what needs doing. But you’re in the middle of debugging a pipeline issue, and context switching now means you’ll lose an hour getting back to where you were.
“I’ll get to it this afternoon,” you reply.
By 2pm, you’ve forgotten. The analyst follows up at 4pm. You finally do it at 5:30pm, right before you are about to leave. The analyst thanks you, clearly frustrated that a simple update took all day.
The next week, same analyst, different request. Update the territory assignments. Then the customer segment definitions. Then the exchange rates. Then back to the GL mappings because something changed again.
You’re not a data engineer anymore. You’re a ticket processor for data updates that shouldn’t require engineering at all.
If your week still includes exporting CSVs and uploading them into dashboards, you’re paying a “data tax” in delays, context switching, and stale numbers. Here are the top five use cases where teams should replace manual uploads with a real Google Sheets → warehouse pipeline plus a quick look at how Resident Advisor (RA) made this work in production.
Two weeks on from the DBT + Fivetran merger, and the dust hasn’t settled, it’s only just starting to reveal what comes next.
The deal didn’t just merge two companies. It merged two very different philosophies about how modern data should move, transform, and deliver value.
Introduction
Every organisation has lived this story. A new data platform or AI tool promises to fix everything. Budgets are approved, contracts are signed — and a few months later, the same problems remain. Dashboards don’t align, pipelines still break, and confidence in the numbers keeps slipping.
For Dylan Anderson, Head of Data Strategy at Perfusion, the real issue isn’t the technology itself — it’s where teams choose to begin.
“Vendors are great at selling the idea that their product will solve all your data needs,” he says. “But strategy isn’t about buying more tools. It’s about helping the business reach its goals using data, technology, and AI in the right way.”
In this episode of Data Matas, Dylan joins host Aaron Phethean to explore why strong data strategies always start with purpose, and how honest, outcome-driven technology earns its place in the stack.
How high-performing data teams align business goals, data strategy, and technology to build trust that lasts
Mixpanel gives you brilliant product analytics. Funnels, retention, user journeys. You can see exactly what users do in your application.
But as teams mature, they start asking questions Mixpanel can't answer on its own. Which users are most valuable? Which marketing campaigns drove high-value customers? How does feature usage correlate with revenue?
The most valuable insights come from connecting Mixpanel data to the rest of your business. User behaviour linked to revenue outcomes. Product engagement joined to marketing attribution. Feature usage correlated with customer lifetime value.
That's what becomes possible when you extend Mixpanel with a warehouse-first approach.
Most organisations are drowning in dashboards that no one trusts. In this Data Matas episode, former Worldpay and FIS data leader Phil Thirlwell explains why the key to better decisions isn’t building more it’s stopping first. He breaks down how dashboard sprawl, KPI overload, and service-desk habits create chaos, and how treating dashboards like products can rebuild trust. Phil shares practical ways to simplify metrics, prioritise outcomes, and run data teams with purpose. The takeaway: fewer dashboards, clearer decisions, stronger alignment between data teams and the business.
How to kill dashboard sprawl, service-desk habits, and KPI overload to rebuild trust in data.
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.
The term “zero-risk ETL transformation” may sound ambitious, but it’s real, proven, and achievable. With Matatika’s phased rollout, rigorous testing, transparent pricing, and post-deployment efficiency, it’s no longer a buzzword, it’s best practice.
ETL doesn’t have to be hard. It just has to be done right.
Data is as essential to manufacturing today as any raw material. Yet, while most manufacturers generate valuable data across their operations, fragmented and siloed systems often keep them from putting this information to effective use. Matatika’s ETL (Extract, Transform, Load) solution is designed specifically for manufacturing’s data challenges, enabling teams to unify, automate, and harness real-time insights across their operations.
SaaS ETL Tools pricing is broken. Too many businesses are stuck with platforms that charge by rows, gigabytes, or arbitrary metrics, pushing costs higher without delivering real value. It’s a model that inflates SaaS data costs, forcing companies to pay more for data that doesn’t always lead to better insights.
