Why ETL is Dying — and What Smart Firms Are Doing Instead

It’s time we all admit it: ETL—the proud workhorse of the last two decades—is slowly becoming obsolete. Not because it didn’t serve us well, but because business has changed. Expectations have changed. And most importantly, data architecture has changed. 

 The shift we’re seeing is more than just technical. It’s strategic. And if you’re still clinging to legacy ETL pipelines because “that’s how we’ve always done it,” you may already be falling behind. 

The Classic ETL Era: Built for a Different Time 

ETL (Extract, Transform, Load) was designed for an era when: 

• Data lived in a few predictable systems 

• Updates were nightly, maybe weekly 

• Reports were static, not interactive 

• Business questions changed quarterly, not hourly 

The old world assumed we could cleanse, model, and load everything into pristine enterprise data warehouses—like clockwork. But now, we live in a messier, faster world. 

Today’s Data Demands Have Outgrown ETL 

 Modern businesses need: 

• Real-time or near-real-time insights 

• Self-service analytics that don’t require IT bottlenecks 

• AI/ML pipelines that work off raw and semi-structured data 

• Composable data products, not brittle monoliths 

 Trying to shoehorn those needs into traditional ETL workflows is like trying to run a Formula 1 car on horse-and-buggy roads. 

Enter: ELT, Reverse ETL, and the Rise of the Data Platform 

 Over the last few years, the script has flipped. With platforms like Snowflake, Databricks, and BigQuery, raw data can land in cheap, scalable storage first. We then transform it later, closer to the time of need. That’s ELT (Extract, Load, Transform). 

 At the same time, the rise of reverse ETL lets us push enriched data back into operational systems—CRMs, finance apps, marketing platforms—to make data truly actionable. That’s the foundation of a “composable” data strategy—breaking big problems into reusable data products and serving the business where they already work. 

The Modern Data Stack: Still Too Complex? 

 Here’s the twist: while the modern data stack solved some problems, it created others. Now we have 10+ tools stitched together with duct tape and Slack channels. 

 The smart move in 2025 isn’t to jump on yet another tool—it’s to simplify intelligently: 

• Embrace cloud-native platforms that unify compute, storage, and governance 

• Move toward metadata-driven orchestration (OpenFlow, dbt, etc.) 

• Stop obsessing over pipelines. Start obsessing over products and value. 

What Winning Teams Are Doing Now 

We’re seeing a new model emerge—especially in mid-market and PE-backed firms: 

• Raw-first, schema-later data ingestion 

• AI-assisted modeling and pipeline generation (yes, it works now) 

• Operational feedback loops, where data products adapt based on usage 

• Cross-functional data product teams (not just IT’s problem anymore) 

At Continuus, we’re helping clients shift from pipeline builders to value orchestrators. We call it “Data Ops meets Product Thinking.” And it works. 

So… Is ETL Dead? 

Let’s be honest—ETL won’t vanish overnight. But it’s no longer the hero of the story. If your team is still building hand-coded ETL pipelines like it’s 2012, you’re not just wasting time—you’re setting up your future self for rework. 

The better question is: What value are your data flows delivering to the business today? 

 If the answer feels like “slow dashboards and brittle logic,” it might be time for a change.