Matatika

They say moving is one of the most stressful things you can do. Luckily, we're just moving bits and bytes! 📦 Matatika is officially rebranding to Meltano. It's the same powerful engine you trust, just with a much faster, sleeker coat of paint. Check out our new home: www.meltano.com Follow Meltano on LinkedIn to stay up to date with everything we are building next Same team. Same mission. New name on the door. Thanks for being part of it.

Unsupported connectors don’t usually break dashboards. They quietly become permanent infrastructure. When ETL vendors lack coverage, teams patch gaps with custom jobs, webhooks, and Airflow. What starts as a workaround becomes operational risk. We’ve broken this down in a new post, using MVF’s experience as a real example. 👉 Blog: https://lnkd.in/eCMMuTNx 👉 MVF case study: https://lnkd.in/e7cE5Hdq MVF replaced fragile pipelines, retired Airflow, closed critical connector gaps and cut ETL costs by over 70 percent, without downtime. If your ETL vendor can’t support the connectors your business relies on, the risk is already in your stack.

🚨 Data leaders: January was a reality check on the cost of complexity. This month’s Data Leaders Digest focuses on the Economics of the Modern Data Stack and why many teams are hitting a ceiling. Not because they lack tools, but because the stack has become too expensive, too fragmented, and too hard to operate with confidence. As the MDS matures, the conversation has shifted. It is no longer about what is possible. It is about what is sustainable. 📌 What’s inside: Jack Doherty — why treating data as a product requires external rigor, not “good enough” internal standards Maxime Beauchemin — how complexity, not capability, is now the biggest limiter in modern data stacks Taylor A Murphy — where open source went right, where economics broke, and what needs to change next A practitioner-led breakdown of why ingestion has become a commodity, while ops and pricing models quietly drain budgets 💡 The takeaway? The strongest data teams are not adding the 13th tool. They are simplifying ingestion, reducing operational drag, and aligning spend with real business value. Cost clarity is becoming a leadership skill. 👇 Read the full January digest and decide your next move, whether that is reassessing stack economics, pressure-testing vendor incentives, or shaping what comes next. #DataLeadership #ModernDataStack #AnalyticsEngineering #OpenSource #DataEconomics #ETL #Meltano

The modern data stack still works. But its economics are changing. We recently hosted a LinkedIn Live with Maxime Beauchemin, Taylor A Murphy, and Aaron Phethean to unpack how incentives, consolidation, and pricing power are reshaping architecture decisions across the stack. This wasn’t a tools discussion. It was about who captures value now, why migrations are so hard, and what that means for data leaders over the next 12–24 months. We’ve published a short blog capturing the key themes from the conversation. 👉 Read the blog: https://lnkd.in/eGUmxeGa 👉 Watch the full discussion on YouTube: https://lnkd.in/eC9MzFK6 More follow-ups from this session coming soon.

The modern data stack didn’t break. Its economics did. We’ve just released the full recording of our LinkedIn Live, The Economics of the Modern Data Stack, now available on YouTube. Over the last decade, open source and “best-of-breed” tooling reshaped how teams build analytics platforms. It worked. Adoption exploded. But as stacks scaled, costs crept up, complexity compounded, and leverage quietly shifted away from buyers. This session digs into what’s really happening beneath the tooling layer: • Why open source can create enormous value but only capture a fraction of it • What the dbt Labs–Fivetran merger signals about consolidation and pricing power • How managed open source (Airflow → Astronomer) became the dominant commercial model • Why data ingestion looks like a commodity but behaves like a moat • How connector sprawl inflates ETL spend without improving insight • What leaders can do to preserve optionality as the market consolidates This isn’t about picking tools. It’s about understanding incentives, designing for leverage, and avoiding stacks that slowly tax your budget year after year. 🎙️ Featuring: Maxime Beauchemin (Creator of Apache Superset) Taylor A Murphy (Arch) Aaron Phethean (Matatika) 📺 Watch the full discussion on YouTube: [https://lnkd.in/eC9MzFK6] If you’re accountable for data platform strategy or vendor spend, this conversation is worth your time.

When choosing an ETL tool, the real question isn’t what connectors it supports today. It’s what happens when you need one it doesn’t. For MVF, that connector was Baidu. A core marketing channel with no out-of-the-box support from their vendor. Instead of workarounds or internal builds, the connector was built properly and treated like first-class infrastructure. That flexibility mattered more than any single feature. It meant MVF no longer had to shape their data strategy around a vendor’s catalogue. If you’re evaluating ETL platforms, this is the capability to look for. 👉 We wrote a short blog on how MVF handled their Baidu ETL connector: https://lnkd.in/e_ezwYj6 #Baidu #ETL #ETLConnector #Data

How did MVF achieve a 7x reduction in ETL costs while migrating 1 Billion+ rows of data? It's a strategic escape from spiralling vendor lock-in. 📉 Cost Control: Direct cost was reduced to 1/3 vs. projected renewal. ⚡ Zero Downtime: 60+ sources migrated with zero downtime. ⏱️ Speed: 2 months to complete the entire, risk-free migration. "If something goes down with our previous vendor, it was four days of back and forth tickets. If something goes down with Matatika, it's a Slack message and it's fixed by the next day." - Andonis Pavlidis, Head of Data at MVF. The full story is a must-read for any CTO or Head of Data facing vendor renewal. Download the full MVF Case Study and see the blueprint for cost control. https://lnkd.in/e7cE5Hdq #DataPipelines #CustomConnectors #DataOps #EngineeringProductivity #Matatika

Marketing data feels harmless. Until it becomes your biggest ETL bill. Connector sprawl doesn’t announce itself. It just shows up in the invoice. Often through slow price creep at renewal rather than any single dramatic change. At MVF we ran into this exact problem. Paid media ingestion became the largest source of ETL cost, not because insight was lacking, but because small inefficiencies compounded over time. What we did was structural: We treated marketing connectors as infrastructure, not tools. We consolidated paid media ingestion across platforms. We removed duplicated pipelines and unnecessary API calls. The outcome: Costs dropped. Refresh times improved. Insight stayed intact. This wasn’t about squeezing vendors or cutting data. It was about fixing the design. 👉 Full case study: https://lnkd.in/e_54U2_P #ETL #Data #Casestudy

🚀 “Where is AI going to land?” Aaron Phethean asks the question that every data leader is thinking about. 👉 In 10 years, will AI sit inside the data team? 👉 Or will it evolve into an entirely new function? 👉 Or perhaps live closer to IT and infrastructure than analytics? Right now, many teams are being told to “go solve AI” without clarity on what that even means. And that’s the real challenge: AI isn’t analytics. It’s knowledge. Building knowledge systems requires a totally different mindset and skill set. Catch the full discussion with Julian Vecchio, Jake Barnett, and James Yerkess on YT - https://lnkd.in/ev2XwrDJ #AI #DataStrategy #Analytics #Infrastructure #Matatika #Leadership